Abstract:
A method of analyzing experimentally derived electrocardiograph (ECG) data, and system for practicing said method, which allow tracking of subject cardiac status change and which allow accurate catagorization of subjects into various abnormal and normal classifications is disclosed. The presently preferred embodiment applies an algorithm which compares representative parameter, (eg. root-mean-square (RMS) mean), values derived from analysis of a selected portion of a single cycle of an ECG PQRST waveform obtained from investigation of a subject, to similarly derived representative parameter, (eg. RMS mean and RMS standard deviation), values for a composite ECG waveform present in a compiled data bank derived from (ECG) investigation of numerous subjects who were documented as normals, typically in each of a plurality of frequency range bands. A highly diagnostic numerical “Score” is calculated by addition of “Score” components found to be acceptable under certain mathematical criteria, and provided by the algorithm. Visually interpretable time domain and power spectral density plots enhance the method. In addition, comparison of the calculated “Score” to subject cardiac ejection fraction provides indication of risk for sudden death as does the presence of “rhomboids” following a QRS complex in frequency domain plots. The present method is directly adapted to tracking subject cardiac status change by substituting a baseline subject data set for the normal population data set.normal population data set.

Description:
This is a Continuation-In-Part of application Ser. No. 08/908,543, filed Aug. 7, 1997, now U.S. Pat. No. 5,954,664 which in turn is a Continuation in part of U.S. application Ser. No. 08/418,175 filed Apr. 6, 1995, (now U.S. Pat. No. 5,655,540). 
    
    
     TECHNICAL FIELD 
     The present invention relates to safe noninvasive systems and methods of use thereof for application in identifying coronary disfunction, which methods and systems are suitable for application in investigation of human subjects. More particularly the present invention is primarily a method of processing data derived from application of an electrocardiograph (ECG) system which involves mathematical and statistical techniques, data filtering and windowing, and application of a unique algorithm to the end that highly predictive, easily interpreted numerically precise SEECADtm “Scores” and data patterns are determined. 
     BACKGROUND 
     It is generally accepted that approximately one-quarter of North Americans have some degree of Coronary Artery Disease (CAD). It is also generally accepted that approximately half thereof are not reliably detectable by conventionally applied diagnostic techniques, such as noting contour changes in the S-T segments of electrocardiograms (ECG&#39;s). the problematic nature the situation poses is perhaps most critically apparent when one considers that perioperative complications can be much more prevalent and serious in a patient with (CAD) than in a normal patient who does not have (CAD). That is, knowledge that a patient has (CAD), or any coronary disfunction, can be critical in fostering morbidity and mortality reduction procedure planning and scheduling on the part of medical professionals. As well, detection of (CAD) is, of course, important in everyday matters such as the planning and execution of a simple exercise routine. 
     It is noted that conventional (ECG) analysis provides time domain graphical results based primarily upon the low frequency (eg. 0 to 40 Hz), content of a subject&#39;s cardiac signals monitored by an (ECG) system. This is the case whether a Frank orthogonal X-Y-Z; a standard 12 Lead or a multi-Lead monitoring etc. (ECG) system is utilized. For instance, a Patent to Brown et al., U.S. Pat. No. 5,077,667 describes a method for measuring a clinically useful characteristic of a fibrillating heart related to the elapsed time since the onset of ventricular fibrillation, Their significant variable is the power in the frequency range of 7 to 8 Hz. The Brown et al. Patent describes the use of a transformation of sampled analog time domain signals into the frequency domain and subsequent analysis thereof as a step intermediate to applying corrective treatment to a fibrillating heart. 
     A Patent, U.S. Pat. No. 4,680,708 to Ambos et al., describes the use of a Fast Fourier Transform (FFT) applied to a portion of an (ECG) cycle. Mathematical analysis of the last forty (40) milliseconds of a waveform derived from the time domain (QRS) complex allows for calculation of a Figure Of Merit, (FOM), based upon the frequency content thereof. Said (FOM) is correlated to the likelihood of a patient experiencing ventricular tachycardia. While the Ambos et al. Patent mentions the presence of high frequency components in a signal derived from the (ECG), said Patent primarily focuses upon the analysis of frequencies between 20 and 50 Hertz in arriving at the (FOM). The Ambos et al. Patent further states that “Recent studies . . . have used a variety of low (25 to 100 Hz) and high (250 to 300 Hz) band pass filters. A major limitation . . . is a lack of a-priori knowledge of the frequency distribution of signals of interest and the inherent risk that filtering will exclude signals of particular interest.” 
     Other recent investigation has focused upon the diagnostic capability inherent in the presence of particular high frequency components present in an (ECG) signal. For instance, a very recent paper by Aversano et al., titled “High Frequency, QRS Electrocardiography In The Detection Of Reperfusion Following Thrombolytic Therapy”, (see Clinical Cardiology, (17, 175-182 April 1994)), states that the amplitude of the high frequency components, (eg. 150-250 Hz), of the (QRS) complexes decreases during cardiac ischemia, and returns to normal with resolution thereof. It is also stated that high frequency electrocardiography is a rapid and reliable bedside technique for discriminating between successful and failed reperfusion in patients treated with thrombolytic agents for myocardial infarction. The Aversano et al. paper also states that “Studies involving high-frequency QRS electrocardiography are few and modest.” 
     A paper by Moss and Benhorin titled “Prognosis and Management After a First Myocardial Infarction”, New England J. Medicine, Vol. 322, Nov. 11, 1990 points out the importance of being able to identify and distinguish patients with various types of (CAD) so that appropriate treatment can be prescribed. This paper, in conclusion acknowledges that noninvasive techniques currently available for detecting jeopardized ischemic myocardium are imperfect. 
     The above sampling of relevant prior reference materials shows that techniques such as direct morphologic analysis of conventional time domain signals, application of (FFT) to (ECG) time domain derived signals to provide frequency domain spectra for analysis, analysis of high frequency components of (ECG) signals and the focusing on specific portions of a QRS complex etc. are known. There remains, however, need for additional and more probative noninvasive methods of analyzing (ECG) derived data which allow incipient (CAD) in patients to be identified with improved certainty. In particular, there is a need for a method of accurately identifying subjects with (CAD), the validity of which has been shown to provide utility by actual clinical testing. 
     U.S. Pat. No. 5,655,540, to Seegobin is disclosed, and incorporated hereinto by reference, as it discloses a closely related invention. Said U.S. Pat. No. 5,655,540, and Allowed but Co-Pending, patent application Ser. No. 08/908,543, filed Aug. 7, 1997 are hereby incorporated hereinto by reference. 
     The present invention provides an improved method of analyzing (ECG) derived data to enable greatly improved ability to accurately and noninvasively separate abnormal from normal cardiac subjects. The method of the present invention, for instance, routinely allows identification of subjects with truely silent (CAD), and subjects who do not present with the tell-tale classic QRST and T wave changes. The method of the present invention also routinely allows identification of subjects with nonspecific S-T and T wave changes, and allows identification and separate classification of subjects with prior myocardial infarction, abnormal patients who present with normal (ECG), and simultaneously distinguishes the population of abnormal subjects who present with normal (ECG). The present invention also allows identification of subjects who are at risk for sudden death. 
     DISCLOSURE OF THE INVENTION 
     As generally disclosed in previous Patents by the Inventor, (eg. U.S. Pat. No. 5,655,540), the present invention, in its presently preferred embodiment, utilizes a Frank orthogonal X-Y-Z Lead electrocardiograph (ECG) system, but is primarily a method of analyzing and categorizing individual subject electrocardiogram (ECG) data obtained from any lead system. Said method has been shown to be capable of identifying and classifying subjects into cardiac categories such as: 
     1. Normal, and 
     2. Abnormal: 
     a. Presents with prior myocardial infarction, 
     b. Presents with nonspecific S-T and T wave changes, 
     c. Presents with normal (ECG) but known otherwise to be abnormal. 
     (Note, continued efforts are serving to greatly increase the specific classification capabilities of the present invention, and are even extending it to identification of specific anatomic abnornormality location(s), (eg. myocardium, cardiac artery etc.), which cause said specific abnormality). 
     As a starting point the present invention requires a substantial data base from which (ECG) data attributable to a “normal” population can be derived and used to form a “template” against which unknown subject (ECG&#39;s) can be compared. The present invention provides that by application of a discriminant Algorithm, (see supra), these unknown subjects may be appropriately classified. In the presently preferred embodiment of the present invention such a data base was developed by selection and testing of fit and healthy, relatively young subjects from families with a low prevalence of, and low risk factors for, coronary artery disease (CAD). Suitable subjects were required to fill out a questionnaire, analysis of which aided in determination of subject suitability as a “normal”. A total of two-hundred-fifty (250) normal subjects were identified and (ECG) data obtained from each thereof. A random sampling of data from one-hundred-forty (146) subjects from said group of two-hundred-fifty (250) normals was assembled and analyzed to provide relevant, (see supra), composite root-mean-square, (RMS), mean and standard deviation values. 
     To arrive at said relevant normal RMS mean and RMS standard deviation values, (ECS) signals for each normal were derived by acquiring a number of, (typically one-hundred (100)), full (ECG) cycles, (ie. full PQRST (ECG) cycles), for each normal subject, sampling each full cycle to provide six-hundred (600) data points over the extent thereof, and then selecting out the corresponding data points in each QRS complex in each of said full PQRST cardiac cycles. A single averaged (ECG) cardiac cycle was mathematically constructed from said number of QRS complexes and RMS mean and RMS standard deviation values calculated therefore. 
     In addition, similar RMS means and RMS standard deviation values were obtained from the same data, but which data had been subjected to digital filtering employing a Blackman-Harris window. The results are identified in the following table for each of the three Frank orthogonal (ECG) X-Y-Z system lead signals: 
     
       
         
               
               
               
               
             
               
             
               
               
               
             
               
             
               
               
               
             
               
             
               
               
               
             
           
               
                   
                   
               
               
                   
                 FREQUENCY RANGE 
                 DATA PROVIDED 
               
               
                   
                   
               
             
             
               
                   
               
             
          
           
               
                 FOR FRANK (ECG) SYSTEM LEAD XL (HORIZONTAL AXIS): 
               
             
          
           
               
                 (FULL-ALL FREQUENCIES) 
                 RMS MEAN 
                 RMS SD 
               
               
                 ((0) TO (10) HZ) 
                 RMS MEAN 
                 RMS SD 
               
               
                 ((10) TO (60) HZ) 
                 RMS MEAN 
                 RMS SD 
               
               
                 ((60) TO (150) HZ) 
                 RMS MEAN 
                 RMS SD 
               
               
                 ((150) TO (250) HZ) 
                 RMS MEAN 
                 RMS SD 
               
             
          
           
               
                 FOR FRANK (ECG) SYSTEM LEAD Y, (VERTICAL AXIS): 
               
             
          
           
               
                 (FULL-ALL FREQUENCIES) 
                 RMS MEAN 
                 RMS SD 
               
               
                 ((0) TO (10) HZ) 
                 RMS MEAN 
                 RMS SD 
               
               
                 ((10) TO (60) HZ) 
                 RMS MEAN 
                 RMS SD 
               
               
                 ((60) TO (150) HZ) 
                 RMS MEAN 
                 RMS SD 
               
               
                 ((150) TO (250) HZ) 
                 RMS MEAN 
                 RMS SD 
               
             
          
           
               
                 FOR FRANK (ECG) SYSTEM LEAD Z, FRONT TO BACK AXIS): 
               
             
          
           
               
                 (FULL-ALL FREQUENCIES) 
                 RMS MEAN 
                 RMS SD 
               
               
                 ((0) TO (10) HZ) 
                 RMS MEAN 
                 RMS SD 
               
               
                 ((10) TO (60) HZ) 
                 RMS MEAN 
                 RMS SD 
               
               
                 ((60) TO (150) HZ) 
                 RMS MEAN 
                 RMS SD 
               
               
                 ((150) TO (250) HZ) 
                 RMS MEAN 
                 RMS SD 
               
               
                   
               
               
                 where SD stands for Standard Deviation.  
               
             
          
         
       
     
     
       
         
               
             
               
               
               
               
               
             
               
               
             
           
               
                 TABLE D-1 
               
             
             
               
                   
               
               
                 EACH TABULAR CATEGORY IS PROVIDED AN RMS MEAN 
               
               
                 AND STANDARD DEVIATION 
               
             
          
           
               
                   
                 FREQUENCY (HZ) 
                 LEAD X 
                 LEAD Y 
                 LEAD Z 
               
               
                   
                   
               
             
          
           
               
                   
                 TOTAL SIGNAL 
               
               
                   
                  0-INFINITE Hz 
               
               
                   
                  0-10 Hz 
               
               
                   
                  10-60 Hz 
               
               
                   
                  60-150 Hz 
               
               
                   
                 150-250 Hz 
               
               
                   
                   
               
             
          
         
       
     
     Said resulting normal RMS mean and RMS standard deviation values for each of the frequency ranges and utilized Frank (ECG) system X-Y-Z lead serve to define assumed Gaussian “templates” for normals, against which similarly derived RMS means, acquired from individual subjects, are compared under the guidelines of the Algorithmic Method of the present invention, (see supra). 
     To date data from more than one-thousand (1000) abnormal subjects has been assembled by the inventor, and subject RMS mean values derived therefrom. It is noted that abnormal subjects tested provide representatives from each the three abnormal groups identified infra. 
     Continuing, to apply the Algorithm Method of the present invention many, (typically one-hundred (100)), of human subject derived full PQRST (ECG) cardiac cycles are obtained. Said full (ECG) cardiac cycles are each then sampled to provide six-hundred data points over the extent thereof and the sampled data points corresponding to the DRS complex in each full cycle are mathematically averaged to provide a composite QRS complex. As well, the digital filtering and application of the Blackman-Harris Window to allow calculation of the RMS means which correspond to each frequency range and Frank (ECG) system X-Y-Z lead utilized, are performed. The end result can be expressed as a table of data, (not shown), similar to the table presented above for the normal data but which contains only composite RMS mean values. 
     the Algorithm employed in the method of the present invention embodiment then provides for a maximum of thirty (30) calculations to be performed as follows: 
     a. Up to Fifteen of said calculations involve calculating the difference between the subject composite RMS mean and the corresponding normal composite RMS mean, and dividing the result by the corresponding normal RMS standard deviation, for each Frank X-Y-X (ECG) lead and each frequency range band, (i.e. the fifteen calculations break down as five (5) frequency range bands per each of the Frank X-Y-X (ECG) lead.) 
     b. Twelve of said calculations involve finding the RMS ratio for each frequency range band, (eg. 0-10 Hz, 10-60 Hz, 60-150 Hz and 150-250 Hz) to the total sum of all said frequency band RMS contributions for each appropriate said Frank (ECG) X-Y-Z lead, for a subject, and subtracting therefrom equivalent RMS ratio mean results derived based upon the same calculations as applied to normal data, and dividing the result by the RMS standard deviation for the corresponding frequency range, of said normal data. 
     c. Three of said calculations involve calculating the difference between ratios of the subject composite RMS means of Frank lead X/Y, Y/Z and X/Z ratios and the corresponding RMS means of normal X/Y, Y/Z and X/Z ratios and dividing by the RMS value of the normal RMS standard deviations of said ratios. 
     Each of the above identified thirty (30) calculations will result in a number (Pi). A “Score” component number (Si) is then derived based upon where a (Pi) number lies in an assumed Gaussian Distribution. This is calculated based upon normal data RMS Means and RMS Standard Deviations (X) as follows: 
     If −1X&lt;Pi&lt;1X then Si=0, 
     If −2X&lt;Pi&lt;−1X or 1X&lt;Pi&lt;2X then Si=1, 
     If −3X&lt;Pi&lt;−2X or 2X&lt;Pi&lt;3X then Si=2, 
     If −4X&lt;Pi&lt;−3X or 3X&lt;Pi&lt;4X then Si=3 etc. 
     Each of the resulting subject RMS mean values associated with a calculated Si value is then analyzed to determine if it is greater than or equal to ninety-five (95%) percent of the data points from which the normal RMS mean was calculated. If this is the case the associated Si is accepted. Otherwise it is rejected. That is, a ninety-five (95%) confidence interval, based upon normal data spread, is imposed, in determining whether to accept a calculated (SI) value. 
     Accepted Si values are then selected and added together to provide a final numerical “Score”. (Note, two Scores in the catagory a. above are commonly not selected as being redundant to other Scores. Said commonly unselected Scores are better identified in the Detailed Description Section). 
     (Note that the truncation involved in obtaining an Si value can be eliminated and the Pi Score utilized in determining said final numerical “Score”). 
     In either approach to “Score” calculation it has been found that if said final numerical “Score” is “low” (eg. approximately 0 to 7) then the subject is more likely to be normal. If the final numerical “Score” is high (eg. greater than about 8), then the subject is more likely to be abnormal. For instance, a “Score” of 7 provides a ninety (90%) percent confidence of normality, and a “Score” of 8.4 provides a ninety-five (95%) confidence of normality, (See FIG.  9 ). 
     As will be better presented in the Detailed Description Section of this Disclosure, the results of the application of the method of the present invention as described above can be presented in numerous ways. A particularly relevant approach is to present the results on a graph of (“Sensitivity” vs. “100—Specificity”). (The present invention provides that the “Score” value be plotted against the abscissa (100—Specificity) and that percentage of a group having said “Score” be plotted on the (Sensitivity) ordinate). Said approach to presentation is generally known as an ROC curve, (ROC stands for Receiver Operation Characteristic as the technique was originally derived for use in testing radio receiver quality). Said approach to presentation serves to visually demonstrate the success of the present invention method of analyzing (ECG) derived data. In particular, abnormal subjects which can not be identified by conventional (ECG) analysis techniques, are seen to be easily identified by application of the present invention algorithm. 
     in addition, the present invention provides that time domain data obtained from Frank X-Y-Z (ECG) leads should be subjected to a Fourier Transform and manipulated to provide Power Spectral Density (PDS) vs. Frequency plots. As will also be better presented in the Detailed description Section of this Disclosure, said (PDS) plots are typically easier than associated (ECG) data vs. time plots to visually interpret. Said plots complement the above described “Scoring” system approach to identifying coronary disfunction. 
     It should also be appreciated that if an initial patient specific data base is accumulated, it can serve as a baseline data base and be utilized as a replacement for the normal subject population data base. At later times, additional patient specific data can then be obtained, and compared to the patient baseline data base, in a tracking scenario. 
     It has further been found that, if dividing a “Score” for a patient as provided by practice of the present invention, by the ejection fraction of the patient, (as obtained by radionuclide imaging or other accurate technique), provides a result greater than one (1.0) then the subject patient involved is at high risk of sudden death. In addition, it has been found that if the S-T segement following a QRS complex has “Rhomboids” present therein, (eg. electrical signal activity on the order of three (3) standard deviations from a baseline signal, particulary in Time Domain plots in 60-150 and/or 150-250 Hz band(s)), then the patient involved is at high risk of sudden death. The present invention methodology can include as steps inclusion of said criteria. 
     A system for practicing the present invention method comprises (ECG) signal monitoring electrode means, (perhaps preferably such as described in an Allowed Strathbucker U.S. Pat. Nos. 5,678,545 and 5,760,449 which Claim a Bioelectric Interface with all necessary (ECG) Chest Mounted Electrodes present in a common electrode separation maintaining support material), and any necessary interface such that data monitored by said (ECG) electrodes is fed to a memory device, and possibly means for determining ejection fraction. In addition, computational means for performing necessary calculations and displaying results, and necessary interconnection and interfacing means are required. It should be appreciated that a system for practicing the present invention method can be fashioned from essentially any computer system with sufficient memory means and computational capability means, and it is the configuration thereof to carry out the method of the present invention which distinguishes said system over computer systems in general, rather than any specific system elements. 
     The present invention can be recited as a method of analyzing stored electrocardiography ECG data of a specific subject, comprising the steps of: 
     a. obtaining data from a selection from the group consisting of: 
     an ECG cycle from a subject, and 
     a plurality of ECG cycle(s) from said subject, followed by calculating an average selected ECG cycle portion data set by a procedure comprising combining corresponding ECG cycle portion data points for said selected ECG cycle portion for ECG cycle(s); 
     and obtaining data from a selection from the group consisting of: 
     an ECG cycle from a subject identified a normal, and 
     a plurality of ECG cycle(s) from subject(s) identified as normal, followed by calculating an average selected ECG cycle portion data set by a procedure comprising combining corresponding ECG cycle portion data points for said selected ECG cycle portion of ECG cycle(s); 
     then selecting frequency band(s), and separately applying necessary filtering techniques to said data obtained from said subject and to said data obtained from subject(s) identified as normal, to separate the data obtained from said subject into at least one frequency band(s), and said data obtained from said subject(s) identified as normal into essentially equivalent frequency band(s); 
     b. establishing criteria for, and in line therewith selecting some ECG cycle portion and arriving at representative parameter(s) for each selected frequency band for data obtained from each of the subject and the subject(s) identified as normal; 
     c. comparing said subject representative parameter(s) with corresponding subject(s) identified as normal representative parameter(s); and 
     d. combining selected differences between corresponding subject and subject(s) identified as normal representative parameter(s) to arrive at a score, said score being the result of differences in magnitudes of corresponding subject and normal representative parameter(s); 
     e. providing an output means and presenting said score by use thereof; and 
     f. utilizing said score as desired. 
     Another recitation of a present invention noninvasive method of investigating cardiac status of a subject and enabling classification of said subject into normal and abnormal cardiac categories utilizing electrocardiography ECG data obtained therefrom provides for, in a functional sequence, performance of at least the steps of: 
     a. obtaining data from ECG cycle(s) from each of a multiplicity of members of a population of subjects who have been documented as normal subjects, in that they do not show risk factors for, or demonstrate detectable cardiac abnormality, by providing, selecting and monitoring a lead of an ECG system; 
     b. establishing criteria for, and in line therewith selecting some ECG cycle portion and defining cycle portion data points therewithin, and calculating an average selected ECG cycle portion data set for said monitored ECG system lead by a procedure comprising combining corresponding ECG cycle portion data points for said selected ECG cycle portion for ECG cycle(s) obtained from each of a number of members of said multiplicity of members of a population of subjects who have been documented as normal subjects, each said calculated average selected ECG cycle portion data set being a composite data set of said selected ECG cycle portion for said population of normal subjects; 
     c. obtaining data from a selection from the group consisting of: 
     an ECG cycle from a subject, and 
     a plurality of ECG cycle(s) from said subject, followed by calculating an average selected ECG cycle portion data set for said monitored ECG system lead by a procedure comprising combining corresponding ECG cycle portion data points for said selected ECG cycle portion for ECG cycle(s); 
     by monitoring of said ECG system lead, said ECG system lead monitored being the same as the monitored ECG system lead utilized in step a. to obtain data utilized in step b.; 
     d. selecting some ECG cycle portion, which is essentially that selected in step b. for a monitored ECG system lead to provide a data set; 
     e. calculating corresponding representative parameter(s) from resulting data sets calculated in steps b. and d., for said monitored ECG system lead, for, respectively, said normal subject population and said subject; 
     f. comparing subject to corresponding normal subject population representative parameter(s), and combining results thereof to arrive at a “score”, the magnitude of which “score” results from difference(s) between magnitude(s) of corresponding normal subject population, and subject representative parameter(s), which “score”magnitude increases when said difference(s) in magnitude(s) between corresponding normal subject population, and subject, representative parameter(s) increase, the magnitude of which “score”provides an indication of the cardiac status of said subject, with a “score” near zero being indicative of a subject properly categorized as a cardiac normal in that the magnitude(s) of subject representative parameter(s) are generally more closely matched to the magnitude(s) of corresponding normal subject population representative parameter(s), and with a progressively higher “score” being indicative of a subject progressively more properly categorized as a cardiac abnormal in that the =magnitude(s) of subject representative parameter(s) are generally progressively less closely matched to the magnitude(s) of corresponding normal subject population representative parameter(s); then providing an output means for presenting said score and outputting said score. 
     In addition, said noninvasive method of investigating cardiac status of a subject and enabling classification of said subject into normal and abnormal cardiac categories utilizing electrocardiography ECG data obtained therefrom, as just recited can further comprise performance of the additional steps of calculating, and comparing ratio(s) of subject, to corresponding ratio(s) of normal subject population, representative parameters, and combining results thereof with those from comparing subject to corresponding normal subject population, representative parameter(s), in arriving at said “score”, the magnitude of which “score” then further results from difference(s) between magnitude(s) between corresponding normal subject population and subject ratio(s) of representative parameters, which “score” magnitude increases when difference(s) in magnitude(s) between corresponding normal subject population, and subject, ratio(s) of representative parameters increase. 
     Said noninvasive methodology of investigating cardiac status of a subject and enabling classification of said subject into normal and abnormal cardiac categories utilizing electrocardiography ECG data obtained therefrom as recited infra can optionally further comprise, as additional steps a grouping of steps selected from the group consisting of: 
     g., h. and i; 
     j., k, and l; and 
     m. and n.; 
     said steps g., h., and i., being: 
     g. determining the subject&#39;s cardiac ejection fraction, (in percent); 
     h. dividing said “score” by said cardiac ejection fraction, (in percent); 
     i. providing an output means and presenting the result provided in step h. therewith, and if said result is determined to be greater than one (1.0), considering said subject as at high risk for sudden death; 
     and said steps j., k., and l. being: 
     j. providing at least a coordinate system consisting of magnitude vs. time, and optionally a coordinate system consisting of magnitude vs. frequency, and in step b. selecting a portion of the ECG cycle including the region beyond the QRS complex and before the T wave; 
     k. for said ECG cycle portion, performing calculations necessary to plot and display normal subject population and subject ECG data as a function of at least time and optionally frequency, to respectively provide as desired, visually interpretable plots of ECG magnitude and power spectral density data, observation of which provides an indication of the cardiac status of said subject; and 
     l. providing an output display means and visually plotting and displaying therewith at least a magnitude vs. time plot for said ECG cycle portion beyond the QRS complex and before the T wave, then noting if Rhomboids are therewithin, and if present, considering said subject as high risk for sudden death; and 
     said steps m. and n. being: 
     m. determining realtive magnitude pattern(s) amongst at least one selection from the group consisting of: 
     subject representative parameter values; and 
     ratios of subject representative parameter values; and 
     n. providing an output means, and via said output means obtaining and utilizing said relative magnitude pattern(s) as additional basis for investigating the cardiac status of said subject. 
     Another recitation of a present invention noninvasive method of tracking cardiac status of a subject utilizing electrocardiography ECG data obtained therefrom, provides for performing at least the steps of, in a functional sequence: 
     a. obtaining data from a selection from the group consisting of: 
     an ECG cycle from a subject, and 
     a plurality of ECG cycle(s) from said subject, followed by calculating an average selected ECG cycle portion data set for said monitored ECG system lead by a procedure comprising combining corresponding ECG cycle portion data points for said selected ECG cycle portion for ECG cycle(s); 
     from said subject, by monitoring a lead of said ECG system; 
     b. establishing criteria for, and in line therewith selecting some ECG cycle portion and defining cycle portion data points therewithin, and calculating an average selected ECG cycle portion data set for said monitored ECG system lead by a procedure comprising combining corresponding ECG cycle portion data points for said selected ECG cycle portion for ECG cycle(s) obtained from said subject, and selecting a plurality of frequency bands and applying filtering techniques, to provide a plurality of data sets for said monitored ECG system lead monitored, each said data set being an initial composite data set of said selected ECG cycle portion for said subjects in a monitored lead and selected frequency band range; 
     c. obtaining follow-on data from a selection from the group consisting of: 
     an ECG cycle from said subject, and 
     a plurality of ECG cycle(s) from said subject, followed by calculating an average selected ECG cycle portion data set for said monitored ECG system lead by a procedure comprising combining corresponding ECG cycle portion data points for said selected ECG cycle portion for ECG cycle(s); 
     from said subject at a later time, by monitoring said lead of said ECG system, said ECG system lead monitored being the same as the monitored ECG system lead utilized in step a. to obtain data utilized in step b.; 
     d. selecting some ECG cycle portion, said ECG cycle portion being essentially that selected in step b. for said initial subject data for a monitored ECG system lead, and applying filtering techniques which are essentially those applied in step b. for said initial subject data, to provide a plurality of data sets for said monitored ECG system lead; 
     e. calculating corresponding representative parameter(s) from resulting composite data sets calculated in steps b. and d., in said selected frequency band ranges for said monitored ECG system lead, for respectively, said initial subject data and said follow-on subject data; 
     f. comparing values for at least one member of the group consisting of: 
     initial subject to corresponding follow-on subject representative parameter(s), and 
     specific ratio(s) of initial subject to corresponding specific ratio(s) of follow-on subject representative parameters, 
     and combining results thereof to arrive at a “score”; the magnitude of which “score” results from difference(s) between magnitude(s) of corresponding initial subject, and follow-on subject representative parameter(s) and/or ratio(s) of initial subject representative parameters, and follow-on subject representative parameters; which “score” magnitude increases when said difference(s) in magnitude (s) between corresponding initial subject, and follow-on subject, representative parameter(s) and/or ratio(s) of initial subject representative parameters, and follow-on subject representative parameters increase, the magnitude of which “score” provides an indication of a change in cardiac status of said subject, with a “score” near zero being indicative of a subject properly categorized as having undergone no cardiac change, and with a progressively higher “score” being indicative of a subject progressively more properly categorized as a subject who has undergone cardiac change; and 
     g. providing an output means and presenting said score therewith; 
     said noninvasive method of tracking cardiac status of a subject utilizing electrocardiography ECG data obtained therefrom optionally further comprising as additional step(s) groupings of steps selected from the group consisting of: 
     h., i. and j; 
     k., l, and m; and 
     n and o.; 
     said steps h., i., and j., being: 
     h. determining the subject&#39;s cardiac ejection fraction, (in percent); 
     i. dividing said “score” determined in step f. by said cardiac ejection fraction, (in percent); 
     j. providing an output means and presenting the result provided in step i. therewith, and if said result is determined to be greater than one (1.0), considering said subject as at high risk for sudden death; 
     and said steps k., l. and m. being: 
     k. providing at least a coordinate system consisting of magnitude vs. time, and optionally a coordinate system consisting of magnitude vs. frequency, and in step b. selecting a portion of the ECG cycle including the region beyond the QRS complex and before the T wave; 
     l. for said ECG cycle portion, performing calculations necessary to plot and display initial subject and follow-on subject ECG data as a function of at least time and optionally frequency, to respectively provide as desired, visually interpretable plots of ECG magnitude and power spectral density data, observation of which provides an indication of the cardiac status of said subject; and 
     m. providing an output display means and visually plotting and displaying therewith at least a magnitude vs. time plot for said ECG cycle portion beyond the QRS complex and before the T wave, then noting if Rhomboids are therewithin, and if present, considering said subject as at high risk for sudden death; and 
     said steps n. and o. being: 
     n. determining realtive magnitude pattern(s) amongst at least one selection from the group consisting of: 
     subject representative parameter values; and 
     ratios of subject representative parameter values; and 
     o. providing an output means, and via said output means obtaining and utilizing said relative magnitude pattern(s) as additional basis for tracking said subject cardiac status. 
     Said noninvasive method of tracking the cardiac status of a subject utilizing electrocardiography ECG data obtained therefrom as just recited can further comprise performance of the step of applying a confidence level acceptance test to results of comparing corresponding specific initial subject to specific follow-on subject representative parameters, and/or corresponding specific ratios of said representative parameters of follow-on subject, to specific ratios of initial normal subject, representative parameters prior to combining results thereof to arrive at a “score”, said confidence level test comprising: 
     a. the step of determining mean and standard deviation acceptance parameters for specific initial subject representative parameter(s) and/or specific ratio(s) of initial subject representative parameter(s) based upon the ECG data from which said composite data set of said selected ECG cycle portion for said initial subject data was calculated; 
     b. the step of determining an acceptance parameter for each specific corresponding follow-on subject representative parameter and/or each corresponding specific ratio of follow-on subject representative parameters based upon the ECG data from which said composite data set of said selected ECG cycle portion for said follow-on subject data was calculated; and 
     c. the step of accepting the results of comparing a specific follow-on subject representative parameter to a corresponding specific initial subject representative parameter in arriving at said “score”, only if the acceptance parameter for said specific follow-on subject representative parameter is set off by at least one associated initial subject acceptance standard deviation from the acceptance mean of the corresponding specific initial subject representative parameter; and/or accepting the results of comparing a specific ratio of follow-on subject representative parameters to a corresponding specific ratio of representative parameters for said initial subject population, in arriving at said “score”, only if the acceptance parameter of said specific ratio of said follow-on subject representative parameters is set off by at least one associated initial subject acceptance standard deviation from the acceptance mean of said corresponding specific ratio of initial subject representative parameters. 
     Additionally, said noninvasive method of tracking cardiac status change in a subject utilizing electrocardiography ECG data obtained therefrom as just described, can provide that said follow-on data is obtained at a time after acquisition of said initial data selected from the group consisting of: 
     immediately thereafter as in a continuous monitoring scenario; and 
     after application of a suitable stress test; and 
     after intervention; and 
     after medical therapy; 
     the benefit being identification of a subject who has undergone cardiac change. 
     Further, said noninvasive method of tracking cardiac status of a subject utilizing electrocardiography ECG data obtained therefrom as just recited, can further comprise determining if said subject is at high risk for sudden death by the additional steps of: 
     a. determining the subject&#39;s cardiac ejection fraction, (in percent); 
     b. dividing said “score” by said cardiac ejection fraction, (in percent); and 
     c. utilizing said output means, providing the result determined in step b. by use thereof, and if said result is greater observed than one (1.0), considering said subject as at high risk for sudden death. 
     Another recitation of a present invention noninvasive method of investigating cardiac status of a subject utilizing electrocardiography ECG data obtained therefrom, said method enabling classification of said subject into normal and abnormal cardiac categories and determining if said subject is at high risk for sudden death, said method comprising, in a functional sequence, performance of the steps of: 
     a. obtaining data from ECG cycle(s) from each of a multiplicity of members of a population of subjects who have been documented as normal subjects, in that they do not show risk factors for, or demonstrate detectable cardiac abnormality, by providing, selecting and monitoring of an ECG system; 
     b. establishing criteria for, and in line therewith selecting some ECG cycle portion and defining cycle portion data points therewithin, and calculating an average selected ECG cycle portion data set for said a monitored ECG system lead, by a procedure comprising combining corresponding ECG cycle portion data points for said selected ECG cycle portion for ECG cycle(s) obtained from each of a number of said multiplicity of members of a population of subjects who have been documented as normal subjects; and selecting a plurality of frequency bands and applying filtering techniques, to provide a plurality of data sets for said a monitored lead, each said data set being a composite data set of said selected ECG cycle portion for said population of normal subjects in a monitored lead and selected frequency band range; 
     c. obtaining data from an ECG cycle from a selection from the group consisting of: 
     an ECG cycle from a subject, and 
     a plurality of ECG cycle(s) from said subject, 
     followed by calculating an average selected ECG cycle portion data set for said monitored ECG system lead by a procedure comprising combining corresponding ECG cycle portion data points for said selected ECG cycle portion for ECG cycle(s); 
     by monitoring an ECG system lead, said monitored ECG system lead being the same as the monitored ECG system lead utilized in step a. to obtain data utilized in step b.; 
     d. selecting some ECG cycle portion, said ECG cycle portion being essentially that selected in step b. for said normal subject population, selecting a plurality of frequency bands, said selected frequency bands being essentially those selected in step b. for said normal subject population, and applying filtering techniques which are essentially those applied in step b. for said normal subject population, to provide a plurality of data sets for said monitored ECG system lead; 
     e. calculating corresponding representative parameter(s) and corresponding ratio(s) involving representative parameters from resulting composite data sets calculated in steps b. and d., in said selected frequency band ranges for said monitored ECG system lead, for respectively, said normal subject population and said subject; 
     f. comparing specific subject and corresponding specific normal subject population representative parameter(s), and combining results thereof with the results of comparing specific ratio(s) of subject to corresponding specific ratio(s) of normal subject population representative parameters, to arrive at a “score”, the magnitude of which “score” results from difference(s) in magnitude(s) between corresponding subject and normal subject population representative parameter(s) and difference(s) between magnitude(s) of corresponding ratio(s) of normal subject population, and ratio(s) of subject representative parameters, which “score” magnitude increases when difference(s) in magnitude(s) between corresponding subject and normal subject population representative parameter(s) increase and difference(s) in magnitude(s) between ratio(s) of corresponding normal subject population, and ratio(s) of subject representative parameters increase, the magnitude of which “score” provides an indication of the cardiac status of said subject, with a “score” near zero being indicative of a subject properly categorized as a cardiac normal in that magnitude(s) of subject representative parameter(s) are generally more closely matched to the magnitude(s) of corresponding normal subject population representative parameter(s) and magnitude(s) of ratio(s) of subject representatives parameters are generally more closely matched to the magnitude(s) of corresponding ratio(s) of normal subject population representative parameters, and with a progressively higher “score” being indicative of a subject progressively more properly categorized as a cardiac abnormal in that magnitude(s) of subject representative parameter(s) are generally progressively less closely matched to the magnitude(s) of corresponding normal subject population representative parameter(s) and magnitude(s) of ratio(s) of subject representative parameter(s) are generally progressively less closely matched to the magnitude(s) of ratio(s) of corresponding normal subject population representative parameters; 
     said method further optionally comprising as additional steps at least one grouping of steps selected from the group consisting of: 
     g., h. and i; 
     j., k, and l; and 
     m. and n.; 
     said steps g., h., and i., being: 
     g. determining the subject&#39;s cardiac ejection fraction, (in percent); 
     h. dividing said “score” by said cardiac ejection fraction, (in percent); 
     i. providing an output means and presenting the result provided in step h. therewith, and if said result is determined to be greater than one (1.0), considering said subject as at high risk for sudden death; 
     and said steps j., k., and l. being: 
     j. providing at least a coordinate system consisting of magnitude vs. time, and optionally a coordinate system consisting of magnitude vs. frequency, and in step b. selecting a portion of the ECG cycle including the region beyond the QRS complex and before the T wave; 
     k. for said ECG cycle portion, performing calculations necessary to plot and display normal subject population and subject ECG data as a function of at least time and optionally frequency, to respectively provide as desired, visually interpretable plots of ECG magnitude and power spectral density data, observation of which provides an indication of the cardiac status of said subject; and 
     l. providing an output display means and visually plotting and displaying therewith at least a magnitude vs. time plot for said ECG cycle portion beyond the QRS complex and before the T wave, the noting if Rhomboids are therewithin, and if present, considering said subject as at high risk for sudden death; and 
     said steps m. and n. being: 
     m. determining realtive magnitude pattern(s) amongst at least one selection from the group consisting of: 
     subject representative parameter values; and 
     ratios of subject representative parameter values; and 
     n. providing an output means, and via said output means obtaining and utilizing said relative magnitude pattern(s) as additional basis for investigating the cardiac status of said subject. 
     Said present invention noninvasive method of investigating cardiac status of a subject and enabling classification of a subject into normal and abnormal cardiac categories utilizing electrocardiography (ECG) data obtained therefrom as just recited, can further comprise performance of the step of applying a confidence level acceptance test to results of comparing subject, to corresponding normal subject population representative parameter(s), and the results of comparing ratios of subject representative parameters to corresponding ratios of normal subject population representative parameters, prior to combining results thereof to arrive at a “score”, said confidence level test comprising: 
     a. the step of determining means and standard deviation acceptance parameters for specific normal subject population representative parameter(s) and specific ratio(s) of normal subject population representative parameters based upon the (ECG) data from which said composite data set of said selected (ECG) cycle portion for said population of normal subjects was calculated; 
     b. the step of determining an acceptance parameter for each corresponding specific subject representative parameter and each corresponding specific ratio of subject representative parameters based upon the (ECG) data from which said composite data set of said selected (ECG) cycle portion for said subject was calculated; and 
     c. the step of accepting the results of comparing a specific subject representative parameter to a corresponding specific normal subject population representative parameter in arriving at said “score”, only if the acceptance parameter for said specific subject representative parameter is set off by at least one associated normal subject population acceptance standard deviation from the acceptance mean of the corresponding specific normal subject population representative parameter; and accepting the results of comparing a specific ratio of subject representative parameters to a corresponding specific ratio of representative parameters for said normal subject population, in arriving at said “score”, only if the acceptance parameter of said specific ratio of said subject representative parameters is set off by at least one associated normal subject population acceptance standard deviation from the acceptance mean of said corresponding specific ratio of normal subject population representative parameters. 
     The present invention will be better understood by reference to the to the Detailed Description Section of this Disclosure in conjunction with the Drawings. 
     SUMMARY OF THE INVENTION 
     It is therefore a primary objective and/or purpose of the present invention to teach a non-invasive method of investigating cardiac status of a subject and a method of analyzing stored electrocardiography ECG data of a specific subject, comprising, in any functional order, the steps of: 
     a. obtaining data from a selection from the group consisting of: 
     an ECG cycle from a subject, and 
     a plurality of ECG cycle(s) from said subject, followed by calculating an average selected ECG cycle portion data set by a procedure comprising combining corresponding ECG cycle portion data points for said selected ECG cycle portion for ECG cycle(s); 
     and obtaining data from a selection from the group consisting of: 
     an ECG cycle from a subject identified as normal, and 
     a plurality of ECG cycle(s) from said subject(s) identified as normal, followed by calculating an average selected ECG cycle portion data set by a procedure comprising combining corresponding ECG cycle portion data points for said selected ECG cycle portion for ECG cycle(s); 
     then selecting frequency band(s), and separately applying necessary filtering techniques to said data obtained from said subject and to said data obtained from subject(s) identified as normal, to separate the data obtained from said subject into at least one frequency band(s), and said data obtained from said subject(s) identified as normal into essentally equivalent frequency band(s); 
     b. establishing criteria for, and in line therewith selecting some ECG cycle portion and arriving at representative parameter(s) for each selected frequency band for data obtained from each of the subject and the subject(s) identified as normal; 
     c. comparing said subject representative parameter(s) with corresponding subject(s) identified as normal representative parameter(s); 
     d. combining selected differences between corresponding subject and subject(s) identified as normal representative parameter(s) to arrive at a score, said score being the result of differences in magnitudes of corresponding subject and normal representative parameter(s); and 
     e. providing an output means and presenting said score by use thereof; and 
     f. utilizing said presented score as desired. 
     It is a further objective and/or purpose of the present invention to teach a noninvasive method of investigating cardiac status of a subject and enabling classification of said subject into normal and abnormal cardiac categories utilizing electrocardiography ECG data obtained therefrom which optionally further comprises as additional steps, a grouping of steps selected from the group consisting of: 
     g., h. and i; 
     j., k, and l; and 
     m. and n.; 
     said steps g., h., and i., being: 
     g. determining the subject&#39;s cardiac ejection fraction, (in percent); 
     h. dividing said “score” by said cardiac ejection fraction, (in percent); 
     i. providing an output means and presenting the result provided in step h. therewith, and if said result is determined to be greater than one (1.0), considering said subject as at high risk for sudden death; 
     and said steps j., k., and l. being: 
     j. providing at least a coordinate system consisting of magnitude vs. time, and optionally a coordinate system consisting of magnitude vs. frequency, and in step b. selecting a portion of the ECG cycle including the region beyond the QRS complex and before the T wave; 
     k. for said ECG cycle portion, performing calculations necessary to plot and display normal subject population and subject ECG data as a function of at least time and optionally frequency, to respectively provide as desired, visually interpretable plots of ECG magnitude and power spectral density data, observation of which provides an indication of the cardiac status of said subject; and 
     l. providing an output display means and visually plotting and displaying therewith at least a magnitude vs. time plot for said ECG cycle portion beyond the QRS complex and before the T wave, then noting if Rhomboids are therewithin, and if present, considering said subject as at high risk for sudden death; and 
     said steps m. and n. being: 
     m. determining relative magnitude pattern(s) amongst at least one selection from the group consisting of: 
     subject representative parameter values; and 
     ratios of subject representative parameter values; and 
     n. providing an output means, and via said output means obtaining and utilizing said relative magnitude pattern(s) as additional basis for investigating the cardiac status of said subject. 
     It is a further objective and/or purpose of the present invention to teach that subject initial data can be substituted for data obtained from subject(s) identified as normal, to provide a subject tracking methodology. 
     It is a further objective and/or purpose of the present invention to teach that a present invention method can be practiced utilizing one or more leads of an ECG System. 
     Additional objectives and/or purposes of the present invention will become clear from the Specification. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 a  shows a representation of a human torso with Frank X and Y (ECG) system leads attached thereto. 
     FIG. 1 b  shows a cross section taken at a—a in FIG. 1 a , with Frank Z (ECG) system leads attached thereto. 
     FIG. 2 shows a demonstrative “PQRST” (ECG) waveform. 
     FIG. 3 demonstrates a table for recording data necessary for practice of the present invention. 
     FIG. 4 shows an assumed gaussian distribution for use in assigning “Score” component numbers during practice of the present algorithm. 
     FIG. 5 shows sample results as provided by practice of the present invention plotted on an ROC curve in which a (100-Specificity), and Sensitivity, appear on the abscissa and ordinate respectively. 
     FIG. ( 6 X 1 ,  6 X 2 ,  6 X 3 ,  6 X 4 ), ( 6 Y 1 ,  6 Y 2 ,  6 Y 3 ,  6 Y 4 ), ( 6 Z 1 ,  6 Z 2 ,  6 Z 3 ,  6 Z 4 ), show twelve related plots of subject time domain sample data recorded from Frank X-Y and Z (ECG) system leads for a plurality of frequency range bands. Higher frequency frequency bands are presented as one progresses from FIG.  6 X 1  to  6 X 4 , and from  6 Y 1  to  6 Y 4  and from  6 Z 1  to  6 Z 4 . 
     FIGS. ( 7   a X 1 ,  7   a X 2 ,  7   a X 3 ,  7   a X 4 ,  7   a X 5 ), ( 7   a Y 1 ,  7   a Y 2 ,  7   a Y 3 ,  7   a Y 4 ,  7   a Y 5 ), ( 7   a Z 1 ,  7   a Z 2 ,  7   a Z 3 ,  7   a Z 4 ,  7   a Z 5 ), show fifteen related plots of subject frequency domain power spectral density. Shown in ( 7   a X 1 ,  7   a Y 1  and  7   a Z 1 ) are transforms of full frequency band requisite data. Transforms of data from progressively higher frequency frequency bands are presented as one progresses from FIG. 7 a X 1  to  7   a X 5 , from  7   a Y 1  to  7   a Y 5  and from  7   a Z 1  to  7   a Z 5 . 
     FIGS. ( 7   b X 1 ,  7   b X 2 ,  7   b X 3 ,  7   b X 4 ,  7   b X 5 ), ( 7   b Y 1 ,  7   b Y 2 ,  7   b Y 3 ,  7   b Y 4 ,  7   b Y 5 ), ( 7   b Z 1 ,  7   b Z 2 ,  7   b Z 3 ,  7   b Z 4 ,  7   b Z 5 ), show fifteen plots of related subject time domain data. Shown in ( 7   b X 1 ,  7   b Y 1  and  7   b Z 1 ) are full frequency band requisite data. Data from higher frequency frequency bands are presented as one progresses progresses from FIG.  7 BX 1  to  7   a Y 5 , from  7   a Y 1  to  7   a Y 5  and from  7   a Z 1  to  7   a Z 5 . 
     FIG. 8 shows actual data obtained from various subject groups by practice of the present invention, on a ROC curve 
     FIG. 9 shows actual data obtained from various subject groups by practice of the present invention, on a graph in which the abcsissa is scaled linearly with the present invention “Score”. 
     FIG. 10 shows a flow chart of the method of the present invention. 
     FIGS. 11 a  and  11   b  show respectively, presentation of data for subjects at risk of sudden death and subjects not at risk of sudden death produced by present invention methodology. 
     FIG. 12 shows a presentation of data developed by present invention methodology and indicates that patterns of subject abnormality can be observed in such a presentation. 
     FIG. 13 shows a system for practicing the present invention method. 
    
    
     DETAILED DESCRIPTION 
     The present invention is similar to that disclosed in Parant U.S. Pat. No. 5,655,540, (from which priority derives), with the exception that a single subject ECG cycle is utilized in analysis rather than a composite subject ECG cycle. In the following a specific embodiment of the present invention is presented much as it was in the earlier Parent Patent, and previous examples are provided. Said specific embodiment assumes the use of an (ECG) system which utilizes Frank (ECG) orthogonal X-Y-Z leads. It is to be understood, however, that the present invention is not limited to such and can be practiced with (ECG) systems in which any number of leads, (eg. standard twelve (12), sixteen (16), or mapping arrays of twenty-four (24) or more etc.), are present, and in which only some of the present leads are utilized. The following specific embodiment is presented as it is well documented and is presently the preferred embodiment. 
     Turning now to the drawings, there is shown in FIG.  1 ( a ) a frontal view of a torso of a human, with (ECG) Frank X and Y leads properly affixed thereto. FIG.  1 ( b ) shows a cross section taken at a—a in FIG.  1 ( a ) with (ECG) Frank Z leads properly attached thereto. In use said (ECG) Frank X-Y-Z leads are attached to an (ECG) system and serve to effect orthogonal monitoring of (ECG) full cardiac cycle PQRST signals which are essentially shaped as shown in FIG.  2 . 
     The present invention requires as a starting point that a significant data base be available, which significant data base contains representative composite (ECG) data for all, or some portion of full (ECG) PQRST cycles for each (ECG) lead, for a normal population. (Note, a normal population is defined as one in which the subjects have no detectable coronary artery disease (CAD) by history and multiple conventional diagnostic tests, and are not at risk therefore based upon age, family history etc.) Such a significant data base for normals was acquired by obtaining a number of full (ECG) PQRST cycles from each of the Frank X-Y-Z (ECG) leads present in the presently discussed embodiment of the invention, from each of two-hundred-fifty (250) normals. (It is noted that the present invention is not limited to cases in which all leads present in an (ECG) system are monitored but that a preferred embodiment does utilize all available information). Next, a random sample of one-hundred-forty-six (146) of said two-hundred-fifty (250) normals was selected and a representative number of the full (ECG) PQRST cycles from each, (typically one-hundred (100)), for each Frank X-Y-Z (ECG) lead, were then selected and each subjected to a sampling procedure which provided some number of data points for each, (six-hundred (600) was chosen in the presently discussed embodiment). Next, the sampled data points corresponding to the QRS depolarization complexes in each selected full (ECG) PQRST cycle were selected and a representative composite QRS complex for each Frank (ECG) X-Y and Z leads formed therefrom by mathematical averaging thereof. Said representative composite was then subjected to filtering and windowing techniques to provide a number of data sets for each of the Frank (ECG) X-Y-Z leads. Said data sets in the presently preferred embodiment of the present invention provide information present in said representative composite in the frequency bands: 
     a. All frequencies; 
     b. Between zero (0) and ten (10) Hz; 
     c. Between ten (10) and sixty (60) Hz; 
     d. Between sixty (60) and one-hundred-fifty (150) Hz; 
     e. Between one-hundred-fifty (150) and two-hundred-fifty (250) Hz. 
     For each of the Frank (ECG) X-Y-Z leads then, five (5) sets of data were derived as described, and from each of said sets of data a Root-Mean-Square (RMS) mean and (RMS) standard deviation were calculated. This, it will be appreciated, resulted in fifteen (15) RMS representative composite means and standard deviations being available, (five for each Frank (ECG) X-Y-Z lead). 
     In view of the described RMS mean and RMS standard deviations (SD&#39;s) available clinical application of the present invention can be practiced. 
     In prior practice data were obtained from a subject in a manner essentially the same as described infra for normals, but the present embodiment provides that a single ECG cycle be obtained and utilized. That is, a number of full (ECG) PQRST full cardiac cycles from each Frank (ECG) X-Y-Z lead are obtained and a representative one selected and subjected to a sampling procedure. Some portion of a selected full PQRST waveform is selected, (eg. the QRS depolarization complex is utilized in the presently preferred embodiment of the present invention), for each Frank (ECG) system X-Y-Z lead. For said representative (ECG) cycle a RMS mean is then calculated so that a table equivalent to that shown in FIG. 3, but containing subject RMS mean data, is formed. 
     With the described normal RMS mean and RMS standard deviation data, and subject RMS mean data then available, the algorithm of the method of the present invention can be applied to arrive at a diagnostic mathematical “Score”. 
     The algorithm of the present invention involves mathematical comparison of: 
     a. Normal and subject RMS means in view of normal RMS standard deviation; 
     b. Ratios of normal and subject RMS frequency range and means to the summation of RMS means for all Frequency range bands for each Frank (ECG) X-Y-Z lead in view of normal standard deviation for the numerator frequency band. 
     c. Ratios of normal and subject Frank (ECG) X-Y-Z lead RMS means in view of normal standard deviations of said ratios. 
     Briefly, application of each of the identified steps provides a numerical result (Pi), which in general is typically not a whole integer. The next step is to process said numerical result (Pi) by comparison to an assumed Gaussian Distribution derived from the normal population data to arrive at a whole number integer which represents how many RMS standard deviations the subject RMS mean is away from the normal RMS mean, and assign a whole integer “Score” component number (Si) based thereupon. The algorithm then requires that a ninety-five (95%) confidence interval, based upon normal RMS standard deviation data be applied to determine if a “Score” component should be accepted and included in calculation of a final “Score”, said final “Score” being arrived at by an addition of accepted “Score” components. Said algorithm will now be described in detail. 
     The first step in applying the algorithm of the presently described presently preferred embodiment of the present invention is perform up to fifteen (15) calculations comprising subtracting the Subject RMS mean from a corresponding Normal RMS mean and dividing the result by a corresponding normal RMS standard deviation for each Frank (ECG) X-Y-Z lead in each frequency range identified infra, to provide numbers (Pi). 
     For the Frank (ECG) X lead, (IE. HORIZONTAL AXIS): 
     For all frequencies:              (        Subject                 RMS                 mean        -        Normal                 RMS                 mean        )         Normal                 RMS                 Standard                 Deviation       =     PX1   =   P1                            
     For the frequency range band zero (0) to ten (10 Hz):              (        Subject                 RMS                 mean        -        Normal                 RMS                 mean        )         Normal                 RMS                 Standard                 Deviation       =     PX2   =   P2                            
     For the frequency range band ten (10) to sixty (60 Hz):              (        Subject                 RMS                 mean        -        Normal                 RMS                 mean        )         Normal                 RMS                 Standard                 Deviation       =     PX3   =   P3                            
     For the frequency range band sixty (60) to one-hundred-fifty (150 Hz):              (        Subject                 RMS                 mean        -        Normal                 RMS                 mean        )         Normal                 RMS                 Standard                 Deviation       =     PX4   =   P4                            
     For the frequency range band one-hundred-fifty (150 Hz) to two-hundred-fifty (250 Hz):              (        Subject                 RMS                 mean        -        Normal                 RMS                 mean        )         Normal                 RMS                 Standard                 Deviation       =     PX5   =   P5                            
     For the Frank (ECG) Y lead, (IE. VERTICAL AXIS): 
     For all frequencies:              (        Subject                 RMS                 mean        -        Normal                 RMS                 mean        )         Normal                 RMS                 Standard                 Deviation       =     PY1   =   P6                            
     For the frequency range band zero (0) to ten (10 Hz):              (        Subject                 RMS                 mean        -        Normal                 RMS                 mean        )         Normal                 RMS                 Standard                 Deviation       =     PY2   =   P7                            
     For the frequency range band ten (10) to sixty (60 Hz):              (        Subject                 RMS                 mean        -        Normal                 RMS                 mean        )         Normal                 RMS                 Standard                 Deviation       =     PY3   =   P8                            
     For the frequency range band sixty (60) to one-hundred-fifty (150 Hz):              (        Subject                 RMS                 mean        -        Normal                 RMS                 mean        )         Normal                 RMS                 Standard                 Deviation       =     PY4   =   P9                            
     For the frequency range band one-hundred-fifty (150 Hz) to two-hundred-fifty (250 Hz):              (        Subject                 RMS                 mean        -        Normal                 RMS                 mean        )         Normal                 RMS                 Standard                 Deviation       =     PY5   =   P10                            
     For the Frank (ECG) Z lead, (IE. FRONT TO BACK AXIS): ps For all frequencies:            (       Subject                 RMS                 mean     -     Normal                 RMS                 mean       )       Normal                 RMS                 Standard                 Deviation       =     PZ1   =   P11                            
     For the frequency range band zero (0) to ten (10 Hz):            (       Subject                 RMS                 mean     -     Normal                 RMS                 mean       )       Normal                 RMS                 Standard                 Deviation       =     PZ2   =   P12                            
     For the frequency range band ten (10) to sixty (60 Hz):            (       Subject                 RMS                 mean     -     Normal                 RMS                 mean       )       Normal                 RMS                 Standard                 Deviation       =     PZ3   =   P13                            
     For the frequency range band sixty (60) to one-hundred-fifty (150 Hz):            (       Subject                 RMS                 mean     -     Normal                 RMS                 mean       )       Normal                 RMS                 Standard                 Deviation       =     PZ4   =   P14                            
     For the frequency range band one-hundred-fifty (150 Hz) to two-hundred-fifty (250 Hz):            (       Subject                 RMS                 mean     -     Normal                 RMS                 mean       )       Normal                 RMS                 Standard                 Deviation       =     PZ5   =   P15                            
     Twelve (12) additional groups of calculations are then performed in which the relative RMS mean content of each frequency range band identified infra is determined as a percentage of the RMS mean of the sum of the filter derived frequency range bands for each Frank (ECG) system X-Y-Z system lead, for both Subject and Normal data, the differences therebetween being divided by the corresponding normal RMS Standard Deviation to provide additional numbers (Pi): 
     For Frank (ECG) X lead, (IE. HORIZONTAL AXIS: 
     Define:            (     100   ×   Subject                 RMS                   (     0      –10                 Hz     )                   mean     )             (       Subject                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   SX0               (     100   ×   Normal                 RMS                   (     0      –10                 Hz     )                   mean     )             (       Normal                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   NX0                          
     Normal RMS Standard Deviation (0-10 Hz)=NXSD 0   
     Then:            SX0   -   NX0     NXSD0     =     PX6   =   P16                            
     Define:            (     100   ×   Subject                 RMS                   (     10      –60                 Hz     )                   mean     )             (       Subject                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   SX1               (     100   ×   Normal                 RMS                   (     10      –60                 Hz     )                   mean     )             (       Normal                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   NX1                          
     Normal RMS Standard Deviation (10-60 Hz)=NXSD 1   
     Then:            SX1   -   NX1     NXSD1     =     PX7   =   P17                            
     Define:            (     100   ×   Subject                 RMS                   (     60      –150                 Hz     )                   mean     )             (       Subject                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   SX2               (     100   ×   Normal                 RMS                   (     60      –150                 Hz     )                   mean     )             (       Normal                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   NX2                          
     Normal RMS Standard Deviation (60-150 Hz)=NXSD 2   
     Then:            SX2   -   NX2     NXSD2     =     PX8   =   P18                            
     Define:            (     100   ×   Subject                 RMS                   (     150      –250                 Hz     )                   mean     )             (       Subject                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   SX3               (     100   ×   Normal                 RMS                   (     150      –250                 Hz     )                   mean     )             (       Normal                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   NX3                          
     Normal RMS Standard Deviation (150-250 Hz)=NXSD 3   
     Then:            SX3   -   NX3     NXSD3     =     PX9   =   P19                            
     For Frank (ECG) Y lead, (IE. VERTICAL AXIS): 
     Define:            (     100   ×   Subject                 RMS                   (     0      –10                 Hz     )                   mean     )             (       Subject                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +       (     60      –150                 Hz     )          (     150      –250                 Hz     )     )       =   SY0             =   SY0               (     100   ×   Normal                 RMS                   (     0      –10                 Hz     )                   mean     )             (       Normal                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   NY0                          
     Normal RMS Standard Deviation (0-10 Hz)=NYSD 0   
     Then:            SY0   -   NY0     NYSD0     =     PY6   =   P20                            
     Define:            (     100   ×   Subject                 RMS                   (     10      –60                 Hz     )                   mean     )             (       Subject                 RMS                 mean                   (     0      –10                 Hz     )       +                   (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +       (     150      –250                 Hz     )     )               =   SY1               (     100   ×   Normal                 RMS                   (     10      –60                 Hz     )                   mean     )             (       Normal                 RMS                 mean                   (     0      –10                 Hz     )       +                   (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +       (     150      –250                 Hz     )     )               =   NY1                          
     Normal RMS Standard Deviation (10-60 Hz)=NYSD 1   
     Then:            SY1   -   NY1     NYSD1     =     PY7   =   P21                            
     Define:            (     100   ×   Subject                 RMS                   (     60      –150                 Hz     )                   mean     )             (       Subject                 RMS                 mean                   (     0      –10                 Hz     )       +                   (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +       (     150      –250                 Hz     )     )               =   SY2               (     100   ×   Normal                 RMS                   (     60      –150                 Hz     )                   mean     )             (       Normal                 RMS                 mean                   (     0      –10                 Hz     )       +                   (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +       (     150      –250                 Hz     )     )               =   NY2                          
     Normal RMS Standard Deviation (60-150 Hz)=NYSD 2   
     Then:            SY2   -   NY2     NYSD2     =     PY8   =   P22                            
     Define:            (     100   ×   Subject                 RMS                   (     150      –250                 Hz     )                   mean     )             (       Subject                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   SY3               (     100   ×   Normal                 RMS                   (     150      –250                 Hz     )                   mean     )             (       Normal                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   NY3                          
     Normal RMS Standard Deviation (150-250 Hz)=NYSD 3   
     Then:            SY3   -   NY3     NYSD3     =     PY9   =   P23                            
     For Frank (ECG) Z lead, (IE. FRONT TO BACK AXIS): 
     Define:            (     100   ×   Subject                 RMS                   (     0      –10                 Hz     )                   mean     )             (       Subject                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   SZ0               (     100   ×   Normal                 RMS                   (     0      –10                 Hz     )                   mean     )             (       Normal                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   NZ0                          
     Normal RMS Standard Deviation (0-10 Hz)=NZSD 0   
     Then:            SZ0   -   NZ0     NZSD0     =     PZ6   =   P24                            
     Define:            (     100   ×   Subject                 RMS                   (     10      –60                 Hz     )                   mean     )             (       Subject                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   SZ1               (     100   ×   Normal                 RMS                   (     10      –60                 Hz     )                   mean     )             (       Normal                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   NZ1                          
     Normal RMS Standard Deviation (10-60 Hz)=NZSD 1   
     Then:            SZ1   -   NZ1     NZSD1     =     PZ7   =   P25                            
     Define:            (     100   ×   Subject                 RMS                   (     60      –150                 Hz     )                   mean     )             (       Subject                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   SZ2               (     100   ×   Normal                 RMS                   (     60      –150                 Hz     )                   mean     )             (       Normal                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   NZ2                          
     Normal RMS Standard Deviation (60-150 Hz)=NZSD 2   
     Then:            SZ2   -   NZ2     NZSD2     =     PZ8   =   P26                            
     Define:            (     100   ×   Subject                 RMS                   (     150      –250                 Hz     )                   mean     )             (       Subject                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   SZ3               (     100   ×   Normal                 RMS                   (     150      –250                 Hz     )                   mean     )             (       Normal                 RMS                 mean                   (     0      –10                 Hz     )       +                     (     10      –60                 Hz     )     +     (     60      –150                 Hz     )     +     (     150      –250                 Hz     )       )             =   NZ3                          
     Normal RMS Standard Deviation (150-250 Hz)=NZSD 3   
     Then:            SZ3   -   NZ3     NZSD3     =     PZ9   =   P27                            
     Three (3) additional calculations are then performed in which Subject RMS means of ratios of RMS means obtained from the Frank (ECG) X-Y-Z leads, are subtracted from corresponding RMS means of ratios obtained similarly from normals, the results of which subtraction are then divided by RMS Standard Deviations of normal corresponding RMS ratios to provide additional numbers (Pi): 
     For the Frank (ECG) X-Y-Z leads:              Subject                 RMS                   (     X   /   Y     )                   mean     -     Normal                 RMS                   (     X   /   Y     )                   mean         Normal                 Standard                 Deviation                   (     X   /   Y     )         =       P        (     X   /   Y     )       =   P28                   Subject                 RMS                   (     Y   /   Z     )                   mean     -     Normal                 RMS                   (     Z   /   Y     )                   mean         Normal                 Standard                 Deviation                   (     Y   /   Z     )         =       P        (     Y   /   Z     )       =   P29                   Subject                 RMS                   (     X   /   Z     )                   mean     -     Normal                 RMS                   (     X   /   Z     )                   mean         Normal                 Standard                 Deviation                   (     X   /   Z     )         =       P        (     X   /   Z     )       =   P30                            
     Continuing, each of the above up to thirty (30) calculated numbers: 
     (PX 1 -P 1 , PX 2 -P 2 , PX 3 -P 3 , PX 4 -P 4 , PX 5 -P 5 , PX 6 -P 6  PX 7 -P 7 , PX 8 -P 8 , PX 9 -P 9 ), (PY 1 -P 10 , PY 2 -P 11 , PY 2 -P 11 , PY 3 -P 12 , PY 4 -P 14 , PY 6 -P 15 , PY 7 -P 16 , PY 8 -P 17 , PY 9 -P 18 ), (PZ 1 -P 19 , PZ 2 -P 20 , PZ 3 -P 21 , PZ 4 -P 22 , PZ 5 -P 23 , PZ 6 -P 24 , PZ 7 -P 25 , PZ 8 -P 26 , PZ 9 -P 27 ), P(X/Y-P 28 ), P(Y/Z)-P 29  and P(X/Z)-P 30 ), (generally identified as (Pi)), 
     can optionally be compared to a corresponding assumed Gaussian distribution of normal data to arrive at a “Score” component number. If a number (Pi) is within some +/− “X” RMS Standard Deviation range of the RMS mean as shown below, a “Score” component number (Si) is taken to be:                If              -     1      X       &lt;     (   Pi   )     &lt;     1      X                       then           Si   =   0     ;                 If              -     2      X       &lt;     (   Pi   )     &lt;       -   1        X           or                                       If                 1      X     &lt;     (   Pi   )     &lt;     2      X                       then           Si   =   1     ;                 If              -     3      X       &lt;     (   Pi   )     &lt;       -   2        X           or                                       If                 2      X     &lt;     (   Pi   )     &lt;     3      X                       then           Si   =   2     ;                 If              -     4      X       &lt;     (   Pi   )     &lt;       -   3        X           or                                       If                 3      X     &lt;     (   Pi   )     &lt;     4      X                       then         Si   =     3                   etc   .                                    
     FIG. 4 demonstrates this graphically. 
     Continuing, each of this resulting “Score” component (PI) or (Si) calculated as just described is then subjected to a final test to determine if it should be accepted or rejected. Said final test involves comparing the Subject RMS mean to the data from which the Normal RMS mean was calculated. If less than or equal to ninety-five (95%) percent of the data points from which the Normal RMS mean was calculated are more than the subject&#39;s RMS mean the associated “Score” component (Si) is accepted, otherwise it is rejected. Accepted “Score” component numbers are then added to provide a final numerical “Score”. 
     It has been found that if said final numerical “Score” is “low”, (eg. approximately 0 to 7), then the Subject involved is more likely to be normal. If the final numerical “Score” is “high”, (eg. greater than about 8), then the Subject is more likely to be abnormal. 
     A particularly relevant approach to presenting the results of applying the disclosed method of the present invention is demonstrated by FIG.  5 . FIG. 5 shows a plot in which the abscissa is (100-specificity) and the ordinate is (sensitivity). These terms are well known and mean:        Specificity   =       Normals                 with                 Negative                 Test       All                 Normals                 Tested               Sensitivity   =         Abnormal                 with                 Positive                 Test       All                 Abnormals                 Tested       .                            
     The curve in FIG. 5 is demonstrative of those which populations of subjects provide in an (ROC) format. The present invention method provides that (ROC) curves be prepared by associating a “Score” value with the abscissa, in a nonlinear manner, and the percentage of a group having said “Score” value with the ordinate of such a plot. The success of the present invention in identifying and distinguishing abnormal subjects has been demonstrated to be quite striking. FIGS. 8 and 9, discussed supra, better serve to demonstrate this with actual empirically derived data. 
     FIGS.  6 X 1  through  6 Z 4  show twelve (12) diagrams,  6 X 1 ,  6 X 2 ,  6 X 3 ,  6 X 4 ,  6 Y 1 ,  6 Y 2 ,  6 Y 3 ,  6 Y 4 ,  6 Z 1 ,  6 Z 2 ,  6 Z 3  and  6 Z 4 , of subject time domain sample data recorded from Frank X, Y and Z leads. Higher frequency band data are presented as one progresses from FIG.  6 X 1  to  6 X 4 , and from FIG.  6 Y 1  to  6 Y 4 , and from FIG.  6 Z 1  to  6 Z 4 . 
     More particularly, FIGS.  6 X 1 ,  6 Y 1  and  6 Z 1  show filtered composite subject (ECG) data set time domain waveforms obtained from X, Y and Z leads, respectively, of a Frank ECG system in frequency band range of 0.0-10 HZ. FIGS.  6 X 2 ,  6 Y 2  and  6 Z 2  show filtered composite subject (ECG) data set time domain waveforms obtained from X, Y and Z leads, respectively, of a Frank ECG system for the frequency band of 10-60 HZ. FIGS.  6 X 3 ,  6 Y 3  and  6 Z 3  show filtered composite subject (ECG) data set time domain waveforms obtained from X, Y and Z leads, respectively, of a Frank ECG system for the frequency band of 60-150 HZ. FIGS.  6 X 4 ,  6 Y 4  and  6 Z 4  show filtered composite subject (ECG) data set time domain waveforms obtained from X, Y and Z leads, respectively, of a Frank ECG system for the frequency band of 150-250 HZ. It is noted that the plots for the 60-150 and 150-250 HZ bands present as “envelopes” as signals go positive to negative and vice versa in very short time periods, (ie. over a few “Sample Numbers”). All plots in FIGS.  6 X 1 - 6 Z 4  have the ordinate marked in micro-volts, and the abscissa is marked in digital filter data points 0 to 600, taken at progressive times during an (ECG) cycle. 
     It is noted that FIGS.  6 Y 2  and  6 Y 4  show “Rhomboids” present in the segment past the QRS complex region, (ie. in channels 375-600). The Rhomboids are shown as dashed-line to indicate that they were added to the actual patient data graph. This was done in preference to cluttering the Disclosure with an additional page of Drawings, however, the point to be made is that the presence of said “Rhomboids” in at least one time domain, frequency band plot, and especially said presence in more than one such frequency band plot, is very indicative of a patient in danger of Sudden Death. 
     FIGS. 7 a X 1 - 7   a Z 5  show fifteen (15) diagrams of typical subject data in frequency domain Power Spectral Density form, (with Magnitude on ordinate), plotted as a function of Frequency, (on abscissa). FIGS. 7 b X 1 - 7   b Z 5  show fifteen (15) diagrams of typical subject data in Time Domain form, (Magnitude on ordinate), plotted as a function of Time, (on abscissa). All said identified plots provide Magnitude, on the ordinate, in microvolts. 
     More particularly, it is noted that FIGS. 7 a X 1 ,  7   a Y 1  and  7   a Z 1  show subject composite (ECG) data set frequency domain power spectral density plots derived from X, Y and Z leads, respectively, of a Frank ECG system, over a frequency band of 0.0 to 100 HZ. FIGS. 7 a X 2 ,  7   a Y 2  and  7   a Z 2  show subject composite (ECG) data set frequency domain power spectral density plots derived from X, Y and Z leads, respectively, of a Frank ECG system, over a frequency band of 0.0 to 15 HZ. FIGS. 7 a X 3 ,  7   a Y 3  and  7   a Z 3  show subject composite (ECG) data set frequency domain power spectral density plots derived from X, Y and Z leads, respectively, of a Frank ECG system, over a frequency band of 0.0 to 80 HZ. FIGS. 7 a X 4 ,  7   a Y 4  and  7   a Z 4  show subject composite (ECG) data set frequency domain power spectral density plots derived from X, Y and Z leads, respectively, of a Frank ECG system, over a frequency band of 50 to 200 HZ. FIGS. 7 a X 5 ,  7   a Y 5  and  7   a Z 5  show subject composite (ECG) data set frequency domain power spectral density plots derived from X, Y and Z leads, respectively, of a Frank ECG system, over a frequency band of 100 to 300 HZ. All plots in FIGS. 7 a X 1 - 7   a Z 4  have the ordinate marked in micro-volts, and the abscissa is marked in HZ, (ie. cycles per second). 
     As well, FIGS. 7 b X 1 ,  7   b Y 1  and  7   b Z 1  show subject composite (ECG) data set time domain waveforms obtained from X, Y and Z leads, respectively, of a Frank ECG system in an unfiltered full requisite frequency band. FIGS. 7 b X 2 ,  7   b Y 2  and  7   b Z 2  show subject composite (ECG) data set time domain waveforms obtained from X, Y and Z leads, respectively, of a Frank ECG system in a filtered frequency band range of 0.0-10 HZ. FIGS. 7 b X 3 ,  7   b Y 3  and  7   b Z 3  show subject composite (ECG) data set time domain waveforms obtained from X, Y and Z leads, respectively, of a Frank ECG system in a filtered frequency band range of 10-60 HZ. FIGS. 7 b X 4 ,  7   b Y 4  and  7   b Z 4  show subject composite (ECG) data set time domain waveforms obtained from X, Y and Z leads, respectively, of a Frank ECG system in a filtered frequency band range of 60-150 HZ. FIGS. 7 b X 5 ,  7   b Y 5  and  7   b Z 5  show subject composite (ECG) data set time domain waveforms obtained from X, Y and Z leads, respectively, of a Frank ECG system in a filtered frequency band range of 150-250 HZ. It is noted that the plots for the 60-150 and 150-250 HZ bands present as “envelopes” as signals go positive to negative and vice versa in very short time periods, (ie. over a few “Sample Numbers”). All plots in FIGS. 7 b X 1 - 7   b Z 5  have the ordinate marked in micro-volts, and the abscissa is marked in digital filter Sample Number data points, taken at progressive times during a (ECG) cycle. The present invention the makes use of such visual aids as an added feature. The three curves in each plot represent normal mean and plus/minus one standard deviations, and subject data. It is also to be understood that the above described approach to diagnosis can be applied to tracking patients over time and can be applied before and after various stress tests which attempt to provoke otherwise indolent or silent coronary artery abnormalities. Stress tests can, for example, involve treadmill exertion or a cold pressor test in which a subject simply places an arm into cold water for a few minutes. Changes in “Score” results combined with changes in the appearance of Power Spectral Density (PSD) and Amplitude Plots over time or before and after stress tests can provide insight as to a subject&#39;s coronary health not made available by less vigerous testing. Multiple mean curves can be simultaneously presented on a single plot to allow easy visual comparison of changes in Power Spectral Density as a function of time or stress. Observation of changes in (PSD) plots in the various frequency bands is a correlated part of the method of the present invention. Of particular interest, the inventor has noted that plots of (PSD) in the frequency ranges of sixty (60) to one-hundred-fifty (150) HZ and one-hundred-fifty (150) HZ to two-hundred-fifty (250) HZ show the greatest change in visually observable shape when a cold pressor test is administered. This is considered a significant observation. 
     Note also that as shown in FIG. 7 a X 1 , it is common to include numerical representation in frequency as well as the time domain plots. Four numbers can be present. Using the Power Spectral Density plot as an example, when present said numbers are representations of: 
     Upper left—the number of Standard Deviations a Subject Power Spectral Density Value is away from a corresponding Normal Power Spectral Density Value for the Frequency Band in the Plot. 
     Lower Left—the Percentage of Normals which are below the Subject Power Spectral Density Value for the Frequency Band in the Plot. 
     Upper Right—the number of Normalized, (ie. Subject Power Spectral Density Value in the Frequency Band of the Plot divided by the Sum of Power Spectral Density Values for all Frequency Bands), Standard Deviations of Subject Power Spectral Value is away from a corresponding Normalized Subject Power Spectral Density Value for Normals for the Frequency Band in the Plot. 
     Lower Right—the Percentage of Normals which are below the Normalized Subject Power Spectral Density Value for the Frequency band in the Plot. 
     (Note that (RMS) values can be substituted for Power Spectral Density). Said numbers and visual Plots aid in interpretation of a Subject&#39;s “Score”. 
     FIGS. 8 and 9 show (ROC) plots for actual data arrived at using the present invention method. Again, (ROC) curves typically plot Sensitivity vs. (100-Specificity) on ordinate and abscissa respectively, presented as percentages. Said Plots in FIGS. 8 and 9 were generated by associating the present invention “Score” with the abscissa (100-Specificity), but with the zero (0) thereof being at the right side so that the “Score” increases to the left. As the “Score” increases the percentage of each group of subjects associated therewith is plotted on the ordinate. By observation of FIGS. 8 and 9 it will be appreciated that as the “Score” increases the percentage of normals in a group of known normals having said “Score” value drops off rapidly, but the percentage of known abnormals in a group of known abnormals drops off much more slowly. For instance, at a “Score” of zero (0) all members of all groups are present. At a “Score” of five (5) approximately eighty (80%) percent of all members of an abnormal group will be present, but only approximately eleven (11%) percent of normals are present. 
     It is noted that a “Score” scale along the abscissa will be nonlinear, when compared to the (100-Specificity) scale. 
     FIG. 8 shows data presented in (ROC) format for Abnormals in various categories: 
     For subjects known to have had a myocardial infarction (MI) shown by twelve (12) lead (ECG), identified as (BEMI); 
     For subjects with non-specific ST-T wave abnormality on twelve (12) lead (ECG), identified as (BEST); 
     For a subjects with normal twelve (12) lead (ECG) but awaiting surgery, identified as (BNOB). 
     For a test set of patients who have (CAD), identified as (BTEST) and (BGENSIA). 
     FIG. 9 shows data plotted in FIG. 8 plotted in a different format in which the abcissa is scaled in terms of the “Score” developed by the present invention method. 
     Present is also a curve for Normals data, identified as (NORM). 
     Also included are curves for two groups additional groups of volunteer subjects which contain patients who have known risk factors for (CAD) identified as (BMAQ) and (BTNR). These constitute a “real-world” population of what are considered normals, in that both normals and abnormals are present. As would be expected, the data for the (BMAQ) and (BTNR) groups is generally positioned between the data for the known abnormal (BTEST) and normal groups. 
     The important thing to note is that the method of the present invention very definitely separates the various groups whether presented in the format of FIG. 8 or FIG.  9 . 
     FIG. 10 provides a Flow Chart representation of the primary focus of the preferred embodiment of the Method of the present invention, said method comprising a noninvasive approach to investigating cardiac status of a subject, and enabling classification of a subject into normal and abnormal cardiac categories utilizing electrocardiography (ECG) data obtained therefrom. 
     It has further been found by investigation of CAMI/ 11  data base data for Subjects known to be at risk for Sudden Death, that if dividing a present invention “Score” for a patient, (as provided by the described practice of the present invention), by the ejection fraction, (in Percent), of the patient, provides a result greater than one (1.0), then the patient involved is at high risk of Sudden Death. 
     A visual presentation of the just described phenomona is quite striking, as is shown by in FIGS. 11 a  and  11   b  which show scatter-graphs demonstrating the relationship between said present invention: 
     
       
         (“Score”/Ejection Fraction)  
       
     
     plotted against the present invention “Score”, (termed “SEECAD”™ Score. (“SEECAD” is a Trademark owned by R &amp; S Incorporated, a Canadian Corporation). Note, as shown in FIG. 11 b,  that a Population of Subjects not at risk for Sudden Death present with results wherein Subject data “scatter” is closely confined about the line which begins at (0.0, 0.0) and ends at (50, 1.5); whereas a population which demonstrated Sudden Death presents with data which demonstrate a much larger range of scatter. In addition, and most importantly on an individual patient basis, note that no Subject data in FIG. 11 b  exceed that value of 1.0 on the Abscissa, whereas a large number of Subjects shown in FIG. 11 a  provide data points above 1.0. The bottom line conclusion to be appreciated is that should a Subject present with a: 
     
       
         (“Score”/Ejection Fraction)  
       
     
     value greater than 1.0, said Subject should be considered to definitely be a risk for Sudden Death. If said ratio is coupled with the presence of previously described “Rhomboids” present following a QRS complex in Time Domain Plots, (see for instance demonstration in FIGS.  6 Y 3  and  6 Y 4 ), then the patient involved should be considered to be at very high risk for sudden death. This combination of present invention “SEECAD” Score with other typically obtained Cardiac Data provides insight to the potential scope of application of the present invention. The definition of and availability of the described “SEECAD” Score provided by practice of the present invention, has opened a whole new and very promising avenue in the area of Subject evaluation. 
     FIG. 12 shows a three-dimensional presentation of Data Components utilized in computing a “SEECAD”™ Score. FIG. 12 is included to show that such a presentation indicates that Patterns of: 
     Data components Standard Deviation from Normality, 
     which data components were derived utilizing present invention methodology, can identify specific Subject Abnormality Data Patterns. It is emphasized that known efforts of previous researchers have had as a focus the diagnosis of Subject abnormality by the comparison of: 
     Subject Data to Abnormal Subject Population Data, and looking for a match. 
     The present invention then has a new focus, emphasis added. Again, the present invention focus is on comparing Subject Data to Normal Subject Population Data, and Patterns of Data Components which naturally arise thereform are found to be indicative of Specific Categories of Abnormality. The fact that the present invention approach, based in comparing Subject Data to Normal Subject Population Data, results in Data Patterns which serve to idicate a Specific Subject Abnormality is a distinguishing factor of the present invention, and provides an extremely exciting area of continued development. It is projected that further work utilizing present invention non-invasively obtained “SEECAD” Score data and methodology will provide the ability to not only separate Abnormal from Normal Subjects, (already possible), but to further identify the most likely anatomical location of the source of identified Abnormality, (eg. specific myocardium, specific coronary-arteries etc). 
     FIG. 13 shows a Diagram of the basic components of a system which can be utilized to practice the present invention method. A partial human torso is shown with a Chest mounted Bioelectric Interface (BI) thereon. (Note that equivalent limb electrodes (RA), (LA) (LL) are present therein). Conventional individual Limb and Precordial (ie. (v 1 ), (v 2 ), (v 3 ), (v 4 ), (v 5 ) and (v 6 )) leads can, of course, be utilized as well. It has been found, however, that use of a chest mountable Bioelectric Interface (BI), as shown, provides better (ECG) signals by maintaining relatively better electrode contact to a subject and relatively constant electrode spacing, in use. A Cable (C) provides electrical signals from said electrodes (RA), (LA), (LL), (v 1 ), (v 2 ), (v 3 ), (v 4 ), (v 5 ) and (v 6 ) to an (ECG) monitor (ECG), which feeds to a Computational Means (COMPUTER), which in turn provides SEECAD™ data to a (VISUAL DISPLAY) and to a (PRINTER/PLOTTER). Of course FIG. 13 is only demonstrative and the present invention system is not limited to the configuration shown. 
     It is to be understood that throughout this Disclosure the RMS Mean values are cited. It is possible to utilize other calculated values, such as Averages, in the method of the present invention. The term “Mean” should be interpreted broadly to include such alternatives. 
     The terms “Assumed Gaussian” have also been used throughtout this Disclosure when refering to Data Distribution RMS Means and RMS Standard Deviations. It is noted that in fact, analysis of empirically obtained data has proven the assumption to be valid. 
     It is also to be understood that the Term “Rhomboid” is used herein only to generally identify the presence of (ECG) activity beyond the QRS complex as shown by dashed lines in FIGS.  6 Y 3  and  6 Y 4 , and does not impose any plot locus shape limitations. 
     A print-out of major portions of the computer program utilized in the practice of the present invention was included in U.S. Pat. No. 5,655,540, said patent being incorporated herein by reference. The present invention practice is similar but does not require the combining of numerous subject ECG waveforms, in that it operates on a single ECG cycle. 
     It is also disclosed that Tracking of a subject can be continuous, and can utilize data obtained before and after, for instance: a suitable stress test; intervention (angioplasty etc.); and/or medical therapy. Of interest is the fact that signal magnitude in Frequency Domain Plots, (eg.  7   a X 1 - 7   a Z 5 ), particularly in 60-150 and 150-250 HZ ranges has routinely been noted to drop by thirty (30%) percent or more upon subjecting patients who are prone to ischemia, to a cold-pressor test. 
     It is also to be understood that the terminology “Coronary Artery Disease” is used throughout this Disclosure, the present invention serves to identify Coronary Disfunction generally, which can include myocardial poblems separate from Coronary Artery disease per se. 
     Finally, it is generally described herein that, for instance, as differences between Normal Subject Population, and Subject Representative Parameters increase, the “Score” of the present invention increases. It would be a simple matter indeed to place a negative sign on the “Score” and declare that it “decreases” when differences between Normal Subject Population and Subject Representative Parameters increase. It would further be a simple matter to utilize slightly different but substantially the same Normal Subject Population, and Subject Data Frequency Bands, or select slightly different but substantially the same Normal Subject Population, and Subject Data (ECG) cycle portions. As to attempt to draft definite claim language to overcome all such possibilities would be an impossible task in view of the complexity of the present invention subject matter, it is therefore to be understood that the Doctrine of Equivalent applies to, and the claims are to be interpreted to include all such contrived and substantially indifferent functional equivalents in the practice of the recited method of the present invention, emphasis added. 
     Having hereby disclosed the subject matter of the present invention, it should be obvious that many modifications, substitutions and variations of the present invention are possible in light of the teachings. It is therefore to be understood that the invention may be practiced other than as specifically described, and should be limited in breadth and scope only by the claims.