Patent Publication Number: US-2009228298-A1

Title: System and method of morphology feature analysis of physiological data

Description:
FIELD OF THE DISCLOSURE 
     The present disclosure relates to the field of physiological data analysis. More specifically the present disclosure relates to the detection and analysis of morphological features of physiological data. 
     BACKGROUND 
     Automatic and semi-automatic analysis of physiological data are important tools used in both medical and clinical research applications. In automatic analysis, one or more algorithms are applied to the physiological data to produce a computer generated interpretation and/or analysis of the physiological data. Semi-automatic analysis similarly applies one or more algorithms to the physiological data to produce a computer generated interpretation or analysis of the physiological data, but the computer generated interpretation is then presented to a clinician who reviews the interpretation and edits them on a computer screen according to the clinician&#39;s own review of the data and judgment in an interactive process. 
     Typically, analysis of physiological data can be performed by looking at either interval related features of the physiological data (i.e. the timing between features or events in the physiological data) or the morphology of the features in the physiological data (i.e. the shape or geometry of the features in the physiological data). Most automatic and semi-automatic physiological data analysis applications focus on interval related physiological data characteristics as these characteristics are easier to identify and quantify as opposed to the feature morphologies that are more subjective in detection and analysis. While algorithms exist for the detection and description of feature morphologies, these algorithms often produce outputs that consist of a prohibitively large number of parameters and typically express each of these parameters as a continuous value such as an integer or floating point value. 
     Therefore, the sheer number of morphological parameters and the continuous nature of the expression of each of these parameters make it difficult to use a semi-automatic physiological data analysis technique for the analysis of data feature morphology. 
     BRIEF DISCLOSURE 
     A system for the interactive analysis of morphological features of physiological data between computerized algorithms and review physicians is disclosed herein. In one embodiment, the system includes a morphological segment detection module that receives physiological data from a physiological data source and applies at least one morphological segment of the physiological data. The system further includes a segment feature rating module that applies at least one algorithm to the at least one identified morphological segment to identify at least one segment feature and produce a rating of the severity of the at least one segment feature. 
     Also disclosed herein is a method of analyzing physiological data morphology. The method includes the steps of receiving physiological data and identifying at least one morphological segment of the physiological data. The method further includes the steps of identifying at least one feature of each identified morphological segment and determining a feature rating for each identified feature. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  depicts an embodiment of a system for the analysis of morphological features of physiological data; 
         FIGS. 2   a  and  2   b  depict embodiments of the presentation of morphological features of a P-wave segment of electrocardiographic (ECG) data; 
         FIGS. 3   a  and  3   b  depict morphological features of a QRS segment of ECG data; 
         FIGS. 4   a  and  4   b  depict morphological features of a T-wave segment of ECG data; 
         FIG. 5   a  is a flow chart depicting the steps in an embodiment of a method of analyzing physiological data morphology; 
         FIG. 5   b  is a flow chart depicting the steps in an embodiment of a sub-method of analyzing physiological data morphology to allow clinician review and editing; 
         FIG. 5   c  is a flow chart depicting the steps in an embodiment of a sub-method of analyzing physiological data morphology to compare sets of ECG data; and 
         FIG. 5   d  is a flow chart depicting the steps in an embodiment of a sub-method of analyzing physiological data morphology to perform data mining analysis. 
     
    
    
     DETAILED DISCLOSURE 
     The detection and analysis of morphological features of physiological data is an important tool in both medical diagnosis and clinical research applications. One such application is the analysis of electrocardiographic data (ECG) which will herein be used in an exemplary manner; however, it should be understood that other types of physiological data such as, but not limited to, electromyography (EMG) and electroencephalography (EEG) may be aided by embodiments of the system and method as disclosed herein. 
       FIG. 1  depicts an embodiment of a system  10  for morphology feature analysis of physiological data. More specifically, the physiological data is ECG data. The ECG data is provided by an ECG data source  12 . The ECG data source  12  may be a cardiograph  14  that is connected to a patient (not depicted) and collects ECG data from the patient. Alternatively, the ECG data source  12  may be an ECG database  16 , the ECG database  16  being populated with historic ECG data that may have been collected at other times from one or more patients and stored in the database. 
     The ECG data from the ECG data source  12  is sent to a morphological segment detection module  18 . The morphological segment detection module  18  receives the ECG data and applies at least one algorithm to the ECG data. The results of the application of the at least one algorithm is to identify at least one morphological segment of the ECG data. The morphological segments that may be identified in the ECG data may include the P-wave, the QRS complex, the ST interval, the T-wave, or the U-wave. It is understood that alternative embodiments analyzing other physiological data may detect different morphological segments intrinsic to the physiological data being analyzed. The algorithms applied by the morphological segment detection module  18  may include a series of morphology descriptors that identify each of the ECG segments. These descriptors may be used in conjunction with pattern recognition techniques to identify each of the segments. 
     Some embodiments herein disclosed may utilize one or more computers that apply one or more algorithms as disclosed herein to process data. The technical effect of these algorithms applied by at least one computer is to identify the morphological segments and segment features exhibited by the data and produce a rating of the identified segment features to simplify a clinician&#39;s review and editing of a computer determined analysis of a physiological signal. 
     The ECG data with the detected segments is then sent to a segment feature rating module  20 . The segment feature rating module  20  applies at least one algorithm to at least one identified morphological segment of the ECG data. The application of the at least one algorithm to the at least one identified morphological segment produces a rating of the severity at least one segment feature. Each of the identified morphological segments may be broken into a number of segment features which may be used to describe the morphological segment. Each of the features may reflect a potential segment morphology that may be clinically relevant. A fuzzy clustering technique may be used to quantify the existence of the features in the morphological segment. These feature ratings may be quantified into discrete severity levels such as to produce a rating of the severity of any detected segment features. 
     In an embodiment, the discrete severity levels may include four levels represented by the numbers 0, 1, 2, and 3. These severity level ratings may coincide with no, moderate, obvious, and severe ratings for the existence of a particular segment feature. The severity levels for each feature are generated from statistical analysis of the baseline distribution for these segment features. The baseline distribution may be acquired from a large pool of ECG data as a part of one or more databases. From this baseline distribution, clustering and/or fuzzy logic grouping techniques may be applied to generate the discrete severity levels. 
     Embodiments of the physiological data analysis system  10  disclosed herein may include specific elements directed towards particular applications facilitated by the identification of a discrete severity level for identified segment features performed by the morphological segment detection module  18  and the segment feature rating module  20 . 
     One embodiment of the system  10  may include a clinician review and editing sub-system  22  in which the ECG data with rated segment features is sent to an ECG display  24 . The ECG data and the identified discrete severity levels for each identified segment feature are presented to a clinician.  FIGS. 2   a - 4   b  are exemplary embodiments of the display of ECG data and the discrete segment feature severity levels. As illustrated in  FIG. 1 , an input device  26  is connected to the ECG display  24 . A clinician reviewing the display of ECG data and the rated segment features for each morphological segment may select a morphological segment and adjust the computer determined discrete severity level for any or all of the segment features. The adjustments to the rating level made by the clinician may include the identification of additional segment features or the removal of segment features as false positives. Once the segment feature rating levels have been modified by the clinician, the ECG data and the modified segment feature ratings may be stored in an ECG database  28 , or the newly modified morphology features can be used for a new analysis of interpretation and classification. The ECG database  28  may be connected to a larger hospital information network (not depicted) that connects various computer terminals and computing devices to one or more centralized servers and/or digital data storage within the hospital. 
       FIGS. 2   a  and  2   b  show an exemplary embodiment of the presentation of ECG data and the segment feature ratings as may be presented by the ECG display  24 .  FIGS. 2   a  and  2   b  may be embodied as graphical user interfaces  30  that are presented by ECG display  24 . Each of the GUIs  30  may have a plurality of tabs  32  which are associated with each of a plurality of morphological segment. As the “P” tab  32  is highlighted, this indicates that the P-wave morphological segment is of focus by the current presentation of the GUI  30 . The ECG data  34  is displayed as part of the GUI  30  and the P-wave morphological segment  36  is highlighted, indicating the morphological segment that is currently under review. 
     A segment feature rating region  38  of the GUI  30  includes indications of a plurality of segment features that may be identified within the morphological segment. An exemplary listing of the segment features may include, but is not limited to, Missing  40 , Biphasic  42 ; Sharp  44 ; Long PR; and Short PR  48 . The segment feature rating region  38  also includes a plurality of discrete levels  50  within which the segment features are rated. The discrete levels  50  may include “+” for moderate levels; “++” for obvious features; and “+++” for very severe features. In this fashion, each of the segment features may be indicated as being present or not present, and if they are present, then a discrete level of the severity of the feature is similarly presented. 
     In  FIG. 2   a  the P-wave  36  of the ECG data  34  exhibits a Long PR feature. As this feature falls into the obvious category by the computer implemented algorithms, such is noted by the highlighted “++” circle under the Long PR segment feature. If the reviewing clinician reviews this ECG data and determines that the P-wave only exhibits a moderately Long PR then the clinician may select the P-wave tab and then select the “moderate” severity level for the Long PR  46  segment feature. This modification, along with any additional modifications, may be stored as a new morphological segment feature analysis in conjunction with the ECG data. Similarly,  FIG. 2   b  depicts different ECG data  52 , however the P-wave  36  is still highlighted on the ECG data  52 . Since the highlighted P-wave  36  is nonexistent, the “Missing” segment feature includes a highlighted circle at the “very severe” or “+++” level. 
     The clinician is able to review each of the identified morphological segments for the ECG data. In one embodiment, this is performed by selecting a variety of tabs  32  that are each associated with a different morphological segment.  FIGS. 3   a  and  3   b  each depict GUIs  30  within which the “QRS” tab  32  has been selected. Different segment features are associated with the QRS complex as the depolarization process in the heart cycle; therefore, the segment feature rating region  38  displays a variety of new segment features, each of these associated with the QRS complex. These segment features may include the Q-wave  54 ; delta  56 ; rSR′  58 ; notch  60 ; flat  62 ; and wide QRS  64 . 
     In  FIG. 3   a  the ECG data  66  displayed in the GUI  30  has the QRS complex  68  highlighted. The QRS complex  68  exhibits both a “moderate” (“+”) notch feature  60  and a “very severe” (“+++” ) wide feature  64 . These are indicated by highlighting the proper circles associated with the discrete feature rating levels. 
       FIG. 3   b  depicts still further ECG data  70  with the QRS complex  72  highlighted. In this example, the QRS complex  72  exhibits a “very severe” Q-wave feature. It is indicated as such in the segment feature rating region  38  by highlighting the circle associated with the “very severe” segment feature rating. As described with respect to  FIG. 2 , a clinician may review a presentation of ECG data and computer identified rating levels as in  FIGS. 3   a  or  3   b  and modify the segment feature rating levels displayed in the segment feature rating region  38  in order to adjust the output of the previous application of the algorithms to the ECG data. Any clinician modifications may be saved to the ECG database  28  such that they may be available at a later time and at a remote location to a later reviewing clinician. 
     Additionally,  FIGS. 4   a  and  4   b  each depict GUI&#39;s  30  within which the “T-U wave” tab  32  has been selected, which together with ST segment cover whole repolarization process in the heart cycle. Different segment features are associated with the T-wave as opposed to the QRS complex or the P-wave; therefore, the segment feature rating region  38  displays a variety of new segment features, each of these associated with the T-wave. The segment features associated with the T-wave may include a notch  82 ; flatness  84 ; unsymmetrical  86 ; U  88 ; inverse  90 ; and biphasic  92 . 
     In  FIG. 4   a  the ECG data  94  displayed in the GUI  30  has the T-wave  95  highlighted. The T-wave  95  only exhibits a “moderate” (“+”) U feature  88 . The proper discrete feature level is indicated by highlighting the (“+”) circle under the U feature  88 . There are no other abnormal morphology features identified for this segment of the ECG data  94 . 
       FIG. 4   b  depicts still further ECG data  98  with the T-wave  96  highlighted. In this example, the T-wave  96  has been identified by the morphological feature analysis algorithm to exhibit “obvious” notch  82 , flatness  84 , and unsymmetrical  86  features as well as the same “moderate” U feature  88  found in ECG data  94 . However, a comparison of the ECG data  94  and the ECG data  98  yields that the T-wave  95  appears to be very different from T-wave  96 . In fact, the clinician, upon viewing the ECG  98  as presented by the GUI  30 , may determine that the T-wave  96  of the ECG data  98  exhibits a “very severe” unsymmetrical feature as opposed to the computer determined “obvious” level of the unsymmetrical feature  86 . The clinician may then select the T-wave segment  96  and change the unsymmetrical feature  86  rating level to that which the clinician determines to be more proper. 
     Similarly, upon a review of the ECG data  94  in comparison to the ECG data  98 , the clinician may decide that T-wave  95  only presents a “moderate” unsymmetrical feature  86 . The clinician may at that time choose to select the T-wave  95  segment and change the segment feature rating for the unsymmetrical feature  86  to identify that feature as being only “moderate”. Any clinician modifications that have been made may be saved to the ECG database  28  such that they may be available at a later time and add a remote location to a later reviewing clinician. 
     By presenting the morphological feature analysis as a plurality of discrete levels for each of the predetermined clinically relevant morphological features, the clinician&#39;s review of the ECG data is focused on those features. This helps the clinician to distill the multitudes of morphological feature data that may be produced by an automated system; and, therefore, enable the clinician to effectively interject his or her own clinical opinion into the automated morphological feature analysis result. This combination of both automated and clinician analysis of the ECG data thus yields a more accurate morphological feature analysis, capitalizing on the strengths of automated systems as well as clinician review and modification of those results. 
     It is understood that the clinician may select any of the tabs  32  of the GUI  30  to navigate to each of the other morphological segments, including the ST segment and the T-U segment. Upon selection of these alternative segment tabs, a similar segment feature rating region  38  would be brought up that includes segment features that are associated within or particular to the selected morphological segment. Also, the selected morphological segment would be highlighted on the display of ECG data below the segment feature rating region  38 . 
     The clinician review and editing sub-system  22  of the physiological data analysis system  10  gives the reviewing clinician the ability to review and modify an analysis or interpretation of ECG data performed by the application of algorithms to the ECG data, similar to that which is already available with respect to interval based physiological data analysis. This promotes improved quality in the final analysis of the ECG data, as the clinician is assisted by the algorithm analysis, but can adjust the output to account for algorithm identified false positives and modifications. 
     Referring back to  FIG. 1 , the ECG comparator sub-system  67  of the segment feature rating module  20  provides ECG data with the rated segment features to an ECG comparison module  69 . The ECG comparison module  69  is connected to an ECG database  71 . The ECG database  71  provides second ECG data that includes rated features to the ECG comparison module  69 . The ECG comparison module  68  produces a comparison output  73  that is an indication of the similarities and differences between the first ECG data and the second ECG data. 
     In one embodiment of the ECG comparison module  69 , the ECG comparison module  69  compares each of the feature ratings between the first ECG data and the second ECG data to determine the similarity and differences between the first ECG data and the second ECG data. 
     In a still further embodiment, the comparison between the first ECG data and the second ECG data may be performed by using a distance measure method wherein a numerical value is given to each of the discrete segment feature levels and the difference between the levels for each of the segment features is found. In one simple distance measure method, each of the differences are squared and summed. The square root of this summation is indicative of the overall difference between the two ECG signals and may be easily implemented by the application of this algorithm. It is also understood that other methods and/or algorithms may be used to provide a comparison between the first ECG data and the second ECG data as well. These alternative methods and/or techniques are considered within the scope of the present disclosure. 
     The data mining sub-system  74  of the physiological data analysis system  10  uses the ECG data with the rated segment features from the segment feature rating module  20  to create an improved data mining system  74 . An ECG database populator  76  receives the ECG data with the rated segment features from the segment feature rating module  20 . The ECG database populator  76  sorts the ECG data by the segment feature and the rated level for each segment feature. This sorted ECG data is then stored in an ECG database  78  wherein the sorted ECG data may be stored as a lookup table wherein the ECG data is tabulated by each segment feature and its rating severity level. A morphology feature based database search engine can be built by first generating a morphology index server. A data mining module  80  may access the index sever to search a specific segment feature and/or segment feature level very fast. This can easily and quickly allow the retrieval of a very specific data set comprising all of the ECG data that exhibits a specified segment feature and/or specified feature level. 
     Thus, the data mining system  74  can improve upon previous data mining systems in that sets of morphology based segregated ECG data may be easily acquired to enhance the application of data mining techniques that may be applied to the obtained data sets. 
     It should be understood that in the present disclosure the term module has been used to describe components of the physiological data analysis system  10 . In the present disclosure, the term module is used to refer to a logical component of a system that is implemented in either hardware, software, or firmware that receives an input and produces an output. 
     Also disclosed herein is a method of analyzing physiological data morphology, as depicted in  FIGS. 5   a - d.  The method begins in  FIG. 5   a  with the step of receiving first physiological data, step  100 . As described above, the first physiological data may come from a database of physiological data or may be recorded from a patient using a patient monitoring device. Next, at step  102 , the morphological segments in the first physiological data are identified. This may be accomplished by the application of one or more algorithms to the first physiological data such as to identify morphological segments particular to the type of physiological data being analyzed. At step  104  each morphological segment is analyzed to identify at least one segment feature from the identified morphological segments. Segment features may be common or characteristic features that may occur in one or more morphological segments. These segment features may be indicative of or correlated to particular physiological risks or conditions. 
     After at least one segment feature has been identified in step  104 , a severity level for the identified segment features is determined at step  106 . The severity level for the identified segment features may be represented by a discrete number of levels upon which the severity of the identified segment features are rated. The severity level for each of the identified segment features may be determined by the degree in which the identified segment feature deviates from a specified baseline norm for that particular segment feature. The baseline may be calculated from an analysis of exemplary physiological data. 
     The determined severity levels for the identified segment features of step  106  in combination with the first physiological data may be utilized in a variety of alternative sub-method applications as represented by reference point  200 . These sub-methods may include clinician review and modification of the physiological data  210 ; serial comparison between the first physiological data and other physiological data  220 ; and data mining applications  230 . 
     Referring to  FIG. 5   b,  an embodiment of a clinician review and modification sub-method  210  for analysis and interpretation of the physiological data is depicted. The physiological data and the determined segment feature levels, at reference  200  (from step  106 ), are presented to the clinician at step  108 . Next, the clinician reviews the physiological data and the determined segment feature levels. Upon reviewing the physiological data and the segment feature levels, if the clinician feels that one or more of the determined segment feature levels are an incorrect characterization of the physiological data, then the clinician may input, and the system receive, a modification to at least one segment feature level at step  110 . By modification of the determined segment feature levels in step  110 , the clinician provides increased accuracy in any forthcoming physiological analysis from the segment feature levels. The modified segment feature levels are saved at step  114  for retrieval and use by other clinicians with access to the media upon which the saved modified segment feature levels are stored. 
     Referring to  FIG. 5   c,  an alterative embodiment of a data comparison sub-method  220  is depicted. The determined severity level for the identified segment features at reference  200  (from step  106 ) and the first physiological data are compared at step  118  to second physiological data with identified segment features and levels received at step  116 . The comparison of the first and second physiological data at step  118  may include techniques that compare the first and second physiological data based upon the determined levels for each of the identified segment features alone and in combination with the other levels for the other segment features of the physiological data. More specifically, the comparison of step  118  may be performed using a sum of squares technique to compute the “distance” between the discrete levels the segment features. Finally, the result of the comparison in step  118  produces an output indicative of the comparison between the first and second physiological data at step  120 . The output produced in step  120  may provide a quantitative comparison of the similarities between the first and second physiological data. 
     Lastly,  FIG. 5   d  depicts a data mining sub-method  230 . The physiological data and the determined severity levels for the identified segment features, at reference  200  (from step  106 ), are sorted in step  122  by the identified segment features and segment feature levels. Then, at step  124 , the sorted physiological data is used to create a database with the physiological data stored as it was sorted according to the segment feature and level. This may create a database in which physiological data is grouped and organized not only by the morphological segment features that are identified, but of the relative severity level of each of the identified segment features. 
     At step  126 , a data set is retrieved from the database created in step  124  that includes physiological data of a specified segment feature and level. The organization and grouping of the physiological data in the database created in step  124  facilitates the retrieval of these highly specified data sets in step  126 . The data set retrieved in step  126  may then be used in step  128  to build a morphology feature based index server. The morphology based index server may be constructed in the form of a look-up table that allows for the selection and/or sequential ordering of sets of ECG data based on any of the identified morphological segment features stored with each of the sets of ECG data. It is understood, however, that other strategies for data organization and index server structure may be utilized in connection with the morphology feature based index server. 
     Finally, data mining techniques are applied in step  130  using the index server created in step  128 . The application of data mining techniques may be facilitated by the specialized data sets that may be easily retrieved from the index server built in step  128  due to the organization and the grouping of the physiological data by the identified segment feature and the segment feature levels in the index server. Thus, the data mining techniques applied in step  130  may result in faster and more accurate results due to the efficiencies gained through the use of the morphology feature based index server. 
     One particular field in which the system and method as disclosed herein may be of particular relevance may be in the field of pharmaceutical cardiac safety testing. As pharmaceutical cardiac safety testing requirements increase, these tests may require more sophisticated analysis techniques that look not only at ECG data interval timing but also at ECG morphology changes, since the inclusion of ECG morphology analysis may yield a higher correlation with severe drug induced arrhythmia than simply ECG interval measurements alone. Therefore, a technique wherein clinicians are able to review ECG data and a series of computer determined segment features, segment feature severity levels, check the computer determined levels for accuracy, and modify the determined levels with the clinician&#39;s own interpretation of the ECG data would be beneficial in that the resulting ECG data with human annotated computer derived segment feature levels would be more accurate then that determined by the computer or the clinician alone. 
     This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to make and use the invention. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent elements with insubstantial differences form the literal languages of the claims. 
     Various alternatives and embodiments are contemplated as being with in the scope of the following claims, particularly pointing out and distinctly claiming the subject matter regarded as the invention.