Method and apparatus for detecting a condition associated with acute cardiac ischemia

A device (10) that detects and reports the presence of an acute cardiac ischemic condition in a patient includes sensing electrodes (12, 14, 16, 18, 20, 22, 24, 26, 28, and 30) placed on a patient for acquiring multiple (e.g., twelve) lead ECG data from the patient. The device evaluates the acquired ECG data by analyzing global features derived from the ECG data. Global features include projection coefficients calculated from projecting a concatenated vector of representative heartbeat data onto sets of basis vectors that define an acute cardiac ischemic ECG subspace and a non-ischemic ECG subspace. A classifier evaluates the global features to determine whether an acute cardiac ischemic condition is detected. In a further embodiment of the invention, one or more classifiers evaluate local features, such as local morphological features and patient clinical information, in addition to the global features to determine whether an acute cardiac ischemic condition is detected. The device (10) may be configured with an adjustable sensitivity/specificity operating point. The result of the evaluation is reported to the user of the device.

FIELD OF THE INVENTION
 The present invention relates generally to the analysis of cardiac
 electrical activity, and more specifically, to the evaluation of
 electrocardiogram data to detect and report cardiac abnormalities.
 BACKGROUND OF THE INVENTION
 A variety of physiological processes are electrically mediated and produce
 associated electrical signals. For example, the sinoatrial node in a human
 heart generates an electrical pulse that triggers the remainder of a
 heartbeat in a normally functioning heart. This pulse propagates through
 the heart's normal conduction pathways producing electrical signals that
 can be observed on the surface of a patient's body. Monitoring and
 analysis of such electrical signals have proved beneficial in evaluating
 the function of a patient's heart, including the detection of conditions
 associated with acute cardiac ischemia.
 Monitoring of a patient's cardiac electrical activity is conventionally
 performed using a 12-lead electrocardiogram (ECG) system that includes a
 monitor and ten electrodes attached to a patient. A conventional 12-lead
 ECG system monitors the voltages sensed by the ten electrodes and
 generates twelve combinations of these voltages to produce the "leads"
 required by the 12-lead ECG system. Of the ten electrodes in a 12-lead ECG
 system, four are "limb" electrodes typically placed on or near each of a
 patient's four limbs, and six are "precordial" electrodes positioned on
 the patient's chest over the heart. As an electrical impulse propagates
 through the heart, the monitor repetitively measures the voltages sensed
 by the electrodes. Although the electrodes collectively monitor the same
 heartbeats, the electrodes sense different voltages due to their placement
 with respect to the patient's heart. A time sequence of monitored voltages
 is used to produce ECG lead data. An ECG monitor typically plots this data
 to provide graphical waveforms representing the heart's electrical
 activity for each lead being monitored.
 An example of an ECG waveform is shown in FIG. 1. For purposes of analysis,
 an ECG waveform produced over a time interval corresponding to one cardiac
 cycle or "heartbeat" is divided into a number of waves. The portion of a
 waveform representing depolarization of the atrial muscle fibers is
 referred to as the "P" wave. Depolarization of the ventricular muscle
 fibers is collectively represented by the "Q," "R," and "S" waves of the
 waveform. The portion of the waveform representing repolarization of the
 ventricular muscle fibers is known as the "T" wave. Between heartbeats, an
 ECG waveform returns to an "isopotential" line. FIG. 1 also illustrates
 selected fiducial points labeled "q", "j", "t1", and "t2". Fiducial points
 define the boundaries of selected features and are used in measuring
 characteristics of an ECG waveform, such as the start and end of a
 heartbeat and the elevation of the ST portion of a heartbeat. The "q"
 point shown in FIG. 1 represents the start of the Q wave, the "j" point
 represents the end of the QRS complex, the "t1" point represents the start
 of the T wave, and the "t2" point represents the end of the T wave.
 As noted, an analysis of a patient's ECG may assist in detecting acute
 cardiac ischemia in the patient. As a matter of background, acute cardiac
 ischemia is a condition that arises from chronic or sudden onset of
 deprivation of blood, and hence oxygen, to muscles of the heart. If an
 ischemic condition is severe or prolonged, it can result in irreversible
 death or damage to myocardial cells (i.e., an infarction). A chronic
 cardiac ischemic condition, angina, is typically caused by narrowing of
 the coronary arteries due to spasms of the wall muscles or partial
 blockage by plaques. A sudden cardiac ischemic condition may be caused by
 a clot blocking the passage of blood in the coronary arteries. Symptoms of
 a cardiac ischemic event may include chest pain and pain radiating through
 the extremities, but not all such events present these symptoms. Current
 medical intervention for severe acute ischemic events includes the
 administration of a class of drugs called thrombolytics that dissolve
 clots in the occluded coronary artery, and emergent PTCA, a medical
 procedure that opens the artery by inflating a balloon inside the clot to
 make a passage for circulation.
 The amount of damage done to a heart by an ischemic event depends, in part,
 on the amount of time that lapses before treatment is provided. Therefore,
 ECG data should be evaluated as early as possible so that functional
 changes associated with cardiac ischemia can be detected and reported as
 early as possible. With early detection of acute cardiac ischemia,
 appropriate treatment can take place as early as possible and, thereby,
 maximize the preservation of myocardium. The American Heart Association
 recommends that a patient with suspected acute cardiac ischemia be
 evaluated by a physician using a 12-lead ECG within ten minutes of arrival
 at a hospital emergency department. Unfortunately, outside of a hospital,
 highly trained medical personnel are not always available to meet a
 patient's immediate needs. Quite often, a first responding caregiver to a
 patient in the field may not be competent in evaluating ECG waveforms to
 detect acute cardiac ischemic events. A need, therefore, exists for a
 device that not only obtains ECG data, but also quickly evaluates and
 automatically produces a preliminary diagnosis that an acute cardiac
 ischemic condition has been detected.
 Traditionally, acute cardiac ischemic conditions are detected by a
 physician's visual evaluation of 12-lead ECG waveforms. A physician
 typically selects one or more leads in the 12-lead ECG and makes an
 initial assessment by comparing selected features of the patient's ECG
 waveforms to equivalent features of other persons' ECG waveforms that are
 representative of various abnormal conditions. A physician may also look
 at the patient's ECG waveforms over time and evaluate any changes in
 waveform shape. A number of waveform features have been identified as
 useful in diagnosing acute cardiac ischemic conditions. Customarily, a
 physician observes the extent to which the ST portion of a waveform
 exceeds the isopotential line (i.e., the ST elevation) and uses this
 information to determine if an acute ischemic event has occurred.
 Nevertheless, because of the subtleties involved in evaluating ECG
 waveforms, even highly trained individuals often fail to correctly
 diagnose an acute cardiac ischemic event when evaluating an ECG using
 traditional features alone. More subtle, globally distributed ECG features
 remain undetected. A deed, therefore, exists for more accurate ways of
 detecting and reporting acute cardiac ischemic events.
 In recent years, there have been efforts to develop enhanced ECG waveform
 interpretation based on computer analysis. Conventional computer processes
 used for ECG waveform analysis are based on heuristics derived from the
 experience of expert physicians. Such processes implement rules that
 attempt to simulate an expert physician's reasoning but perform no better
 than the expert. In practice, many such processes perform more poorly than
 human expert evaluation.
 Furthermore, when a conventional heuristic process is used, it is difficult
 to choose an optimal operating point for the device in terms of
 sensitivity (i.e., detecting true positives) and specificity (i.e.,
 avoiding false positives). A device tuned to be more sensitive is
 typically less specific, while a device tuned to be more specific is
 typically less sensitive. A typical sensitivity/specificity tradeoff is
 illustrated by a receiver operating characteristics (ROC) curve, an
 example of which is shown in FIG. 15. Using a conventional heuristic
 process, it is difficult to make the sensitivity/specificity tradeoff
 explicit; thus, the selection of an operating point on the ROC curve is
 often made in a suboptimal, ad hoc manner. As such, there is a need for an
 apparatus and method that can provide better performance in terms of
 sensitivity and specificity and further provide for selecting a
 sensitivity/specificity tradeoff in a more systematic way.
 The present invention provides a method and apparatus having such features,
 as well as addressing other shortcomings in the prior art.
 SUMMARY OF THE INVENTION
 In accordance with the present invention, globally-distributed features in
 a patient's ECG are extracted and analyzed to detect an acute cardiac
 ischemic condition. First, representative heartbeat data for a patient is
 calculated from the patient's raw ECG data for each of the leads obtained.
 The representative heartbeat data from each lead is concatenated to form a
 vector of representative heartbeat data for the set of measured leads. The
 concatenated vector of representative heartbeat data is then
 mathematically projected onto predetermined basis vectors that define
 signal subspaces of ECGs exhibiting acute cardiac ischemic and
 non-ischemic conditions. The resulting projection coefficients are "global
 features" that are evaluated by a classifier to determine whether the
 global features are indicative of an acute cardiac ischemic condition.
 Analysis of global features according to the present invention has been
 found to improve sensitivity while maintaining high specificity as
 compared to an analysis performed by conventional ECG analysis.
 In accordance with further aspects of the invention, local features may be
 analyzed in addition to global features to further enhance diagnostic
 accuracy. Local features include measures of morphological features
 defined for a local time interval less than the duration of a
 representative heartbeat. An example of a local morphological feature is a
 measure of ST elevation derived from individual lead data. Clinical
 information, such as a patient's age and sex, may also be included in the
 analysis as local features. The local features and global features are
 either separately evaluated with the results combined, or evaluated in
 combination to produce a single result, yielding a final decision of
 whether an acute cardiac ischemic condition is present in the patient.
 In accordance with other aspects of this invention, a device incorporating
 the present invention preferably has an adjustable sensitivity/specificity
 operating point. The sensitivity/specificity operating point is selected
 by adjusting the threshold in the classifier that determines the
 probability of detection of an acute cardiac ischemic condition. The
 sensitivity/specificity operating point may be set by the manufacturer of
 the device, by the purchaser of the device, or by a user of the device.
 For example, if the sensitivity/specificity operating point of the device
 is set for a high level of sensitivity for detection of an acute cardiac
 ischemic event, the device functions as a screening tool that identifies
 candidates at risk. In a setting where a decision to treat a patient is
 being made, the operating point of the device can be readjusted for a high
 level of specificity to confirm the diagnosis of acute cardiac ischemia in
 the patient.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
 In regard to the present invention, acute cardiac ischemia includes
 conditions, both chronic and sudden, that result in a deprivation of
 blood, and hence oxygen, to the muscles of the heart, thus requiring
 urgent treatment if the long-term viability of the cardiac muscle cells is
 to be maintained. While these conditions are reversible at the time of
 detection, these conditions will result in permanent damage to the cardiac
 muscle cells if sufficient blood flow is not restored. FIG. 2 depicts a
 device 10 that detects and reports cardiac abnormalities associated with
 acute cardiac ischemia in accordance with the present invention. When the
 device 10 is attached to a patient via a plurality of sensing electrodes
 (e.g., ten electrodes), the device 10 obtains ECG data from the patient,
 automatically evaluates the data, and reports whether the data indicates
 an acute cardiac ischemic condition in the patient.
 The ten electrodes shown in FIG. 2 are attached to the skin of the patient
 40 at a variety of locations. First and second sensing electrodes 12 and
 14 are shown attached to the right and left shoulder areas of the patient
 40, respectively. Third and fourth sensing electrodes 16 and 18 are shown
 attached to the left and right side areas of the patient's torso,
 respectively, near the patient's legs. A fifth sensing electrode 20 is
 shown attached to the patient's chest area over the heart. Sixth through
 tenth sensing electrodes 22, 24, 26, 28, and 30 are also spread across the
 chest of the patient 40.
 The ten electrodes shown in FIG. 2 are placed in the standard positions for
 a 12-lead ECG. The first through fourth sensing electrodes 12, 14, 16, and
 18 are known as "limb" electrodes. The fifth through tenth sensing
 electrodes 20, 22, 24, 26, 28, and 30 are known as "precordial"
 electrodes. The signals received from the ten electrodes are used to
 produce 12-lead ECG data in a manner well-known to those skilled in ECG
 technology. Since the techniques for producing 12-lead ECG data from ten
 sensing electrodes is well-known and does not form part of this invention,
 how 12-lead ECG data is produced is not described here. As is
 conventional, the twelve leads are identified as the I, II, III, aVR, aVL,
 aVF, V1, V2, V3, V4, V5, and V6 leads.
 For a user's convenience, the precordial electrodes may be embodied in a
 single patch 42. The patch 42 is designed for quick and easy placement of
 the precordial electrodes on the patient's chest and may be used to reduce
 the amount of cabling needed to connect the precordial electrodes to the
 device 10. In the event a patch 42 is not used, each of the precordial
 electrodes may be individually attached to the patient 40.
 Although FIG. 2 shows electrode placement in accordance with one actual
 embodiment of the invention, it will be appreciated that the electrodes
 may be placed at alternative locations. For example, the sensing
 electrodes 12, 14, 16, and 18 may be placed at the ends of the limbs of
 the patient 40 as indicated by the open circles 32, 34, 36, and 38,
 respectively. Moreover, it will be appreciated from the discussion below
 that a device formed according to the present invention may use an
 alternative number of electrodes other than ten sensing electrodes (i.e.,
 more or less sensing electrodes than shown in FIG. 2). Regardless of the
 number of sensing electrodes chosen, one or more leads of ECG data are
 derived from the signals sensed by the electrodes. These leads, which may
 be a subset selected from a larger set of leads potentially derived from
 the ECG signals sensed by the electrodes, are referred to herein as the
 "available leads."
 FIG. 3 is a block diagram illustrating the major components of the device
 10 shown in FIG. 2. ECG signals sensed by the sensing electrodes described
 above (not shown in FIG. 3) are communicated to a preamplifier 50 via
 lines 52. The preamplifier 50 both amplifies and filters the ECG signals.
 Amplification is required because the strength of the signals sensed by
 the electrodes is generally too low (i.e., in millivolts) to be analyzed
 by the circuitry of the device. The preamplifier 50 may amplify the ECG
 signals on lines 52 by a factor of 1,000 or more.
 The amplified ECG signals are filtered to eliminate noise and other signal
 contaminants. In one actual embodiment of the invention, the filtering
 includes a high-pass filter 54 that attenuates low frequency signals
 (e.g., frequencies below 0.05 Hertz), a low-pass filter 56 that attenuates
 high frequency signals (e.g., frequencies above 150 Hertz), and a notch
 filter 58 configured to attenuate signals at a particular frequency (e.g.,
 50 or 60 Hertz, depending on the local line power frequency). Alternative
 embodiments of the present invention may include further signal filtering
 to adapt the device for use in a particular environment.
 The amplified and filtered ECG signals are converted into digital ECG data
 by an analog-to-digital (A/D) converter 60. In the embodiment shown in
 FIG. 3, the ECG signals are multiplexed by the preamplifier 50 and
 serially communicated to the A/D converter 60. Alternatively, the ECG
 signals may be communicated in parallel from the preamplifier 50 to the
 A/D converter 60 via separate lines for each of the signals (not shown)
 and multiplexed by the A/D converter. Still further, a separate A/D
 converter can be provided for each amplified and filtered ECG signal and
 the output of the multiple A/D converters multiplexed.
 The digital ECG data produced by the A/D converter(s) is applied to a
 processing unit 62 for further processing and evaluation. A memory 64 in
 communication with the processing unit 62 stores the digital ECG data and
 other data subsequently generated by the acute cardiac ischemia detection
 process described in more detail below. The memory 64 also stores an acute
 cardiac ischemia detection process 66 in the form of computer program
 instructions that, when executed by the processing unit 62, evaluates the
 digital ECG data and detects the occurrence of acute cardiac ischemia. The
 memory 64 also stores preprocessed parameters 67 derived earlier from
 patients in a training population during a training phase (described
 below). The preprocessed parameters 67 are used by the acute cardiac
 ischemia detection process 66 to evaluate the ECG of a current patient for
 acute cardiac ischemia.
 Although analog signal filters 54, 56, and 58 are shown in FIG. 3 and
 described above, those skilled in the art will appreciate that,
 alternatively, digital filtering after the ECG signals are converted from
 analog form to digital form can be used, if desired. Furthermore,
 filtering of the ECG data can be performed after the data is stored in the
 memory 64, rather than being performed before storage, as shown.
 FIG. 3 also depicts an input device 68 and a display 70 in communication
 with the processing unit 62 for exchanging information with a user of the
 device. The input device 68 allows a user to input information and
 selectively adjust the operation of the device while the display 70 allows
 the device to report the results of an ECG evaluation to the user. The
 display 70 also permits the device to communicate instructions to the
 user.
 Those of ordinary skill in the art will appreciate that various devices may
 be used to implement the function of the components shown in FIG. 3. For
 example, the processing unit 62 may be a microprocessor controlled by
 computer program instructions stored in the memory 64. The memory 64 may
 include nonvolatile memory in the form of read-only memory (e.g., EPROMs),
 storage memory (e.g., a hard drive), and volatile memory in the form of a
 random access memory (RAM). The input device 68 may include keys, dials,
 or switches. Similarly, the display 70 may be a combination of lights or a
 text display screen, e.g., AMLCD, LCD, or printer. Audible alerts may also
 be provided. The construction of suitable signal amplifiers, filters, and
 analog-to-digital converters are well-known to those with ordinary skill
 in the art and in many cases are readily available in off-the-shelf
 devices.
 As part of an analog-to-digital conversion process, ECG signals sensed by
 the electrodes are sampled to obtain discrete voltage values at discrete
 time intervals. The rate at which the ECG signals are sampled depends on
 the configuration of the analog-to-digital converter 60. In one actual
 embodiment of the present invention, ECG signals are sampled at a rate of
 500 samples per second. Those of ordinary skill in the art will appreciate
 that other sampling rates may be used.
 As will be more readily understood from the discussion below, the device 10
 not only acquires ECG data but also evaluates the data and reports when
 conditions associated with acute cardiac ischemia are detected, including
 conditions that would lead to an acute myocardial infarction or unstable
 angina. The device 10 extracts and evaluates subtle, globally-distributed
 features from a patient's ECG. The extracted global features are then
 classified to determine whether an acute cardiac ischemic condition has
 been detected. In a further embodiment of the present invention, the
 device 10 also derives local features from a patient and classifies both
 the global and local features to determine whether an acute cardiac
 ischemic condition has been detected.
 FIG. 4 is a flow diagram illustrating a version of an acute cardiac
 ischemia detection process 66a that determines detects acute cardiac
 ischemia based on classifying global features. The detection process
 includes both the extraction of global features in blocks 100-114 and the
 classification of the global features in block 116. The detection process
 begins in a block 100 with the acquisition of ECG data from a patient. As
 illustrated more fully in FIG. 5, ECG data acquisition begins in a block
 122 with attaching electrodes, such as sensing electrodes 12, 14, 16, 18,
 20, 22, 24, 26, 28, and 30 to a patient 40. Once the electrodes are
 attached to a patient, the user of the device 10 initiates ECG data
 acquisition in a block 124 by initiating an input device 68, e.g., by
 pressing an ANALYZE button on the device. Alternatively, the device 10
 could automatically initiate ECG data acquisition, e.g., upon expiration
 of a predetermined period of time counted by a timer that is started upon
 deployment or activation of the device 10. For a period of time
 thereafter, the voltage signals sensed by the electrodes are amplified in
 a block 126, filtered in a block 128, and converted to digital ECG data in
 a block 130 as discussed earlier in reference to the preamplifier 50 and
 the A/D converter 60 shown in FIG. 3. The ECG data is subsequently stored
 in a block 132 in the memory 64 of the device 10.
 The period of time in which ECG data is acquired in block 100 of FIG. 4 is
 long enough to obtain sufficient, high-quality data representative of one
 or more heartbeats. In one actual embodiment of the present invention,
 about ten seconds of ECG data is acquired. This period of time may be
 increased or decreased depending on factors that affect the quality of the
 data acquired, including the quality of the connection between the
 electrodes and the patient, whether the patient is moving, and whether
 significant electromagnetic noise is present. Accordingly, depending on
 various factors, the period time for ECG acquisition may be increased or
 decreased to, for example, twenty seconds or five seconds, and still be
 considered an equivalent of about ten seconds of data for purposes of the
 subsequent processing discussed below.
 After acquiring a patient's ECG data in block 100, the device 10 analyzes
 the ECG data and generates representative heartbeat data for each
 available lead in a block 102. Preferably, the representative heartbeat
 data represents an atrially stimulated heartbeat that is common for the
 lead and is typically characterized by a high signal-to-noise ratio. If
 the acquired ECG data is of sufficient high quality, a single beat may be
 used for the representative heartbeat data. In most cases, however, it is
 preferable to combine two or more heartbeats on each lead to generate
 representative heartbeat data for the lead.
 FIG. 6 illustrates in more detail a process for generating representative
 heartbeat data suitable for use in the present invention. In the process
 shown, multiple heartbeats on a lead are used to generate the
 representative heartbeat data for the lead. The device 10 first locates
 the heartbeats on each lead in a block 134. In that regard, heartbeats may
 be located by detecting the dominant feature of the QRS complexes, i.e.,
 the R waves (see FIG. 1), of each heartbeat. Because the quality of the
 representative heartbeat data is improved by selecting and using only high
 quality ECG data, the raw ECG data is analyzed in a block 136 to exclude
 low quality heartbeat data. For example, heartbeat data that exhibits
 significant noise content (e.g., data with energy values greater than one
 standard deviation away from the mean of the QRS energy values measured,
 for example, by a sum of squared values) is excluded in block 136 from
 further consideration for generating a representative heartbeat. Of the
 retained heartbeat data, the middle three heartbeats (middle in a temporal
 sense) are analyzed and two of the three heartbeats that have a highest
 pairwise cross-correlation are selected in a block 138 for further
 consideration. The non-selected third heartbeat is excluded from further
 consideration.
 The retained heartbeats are then individually compared with the first
 heartbeat of the pair selected above. Specifically, a cross-correlation
 value is determined for each of the QRS complexes of the remaining
 heartbeats with respect to the first beat of the selected pair. Those
 heartbeats having a correlation value greater than a specified threshold
 value (e.g., 0.9 out of a perfect 1.0) are retained. The heartbeats having
 a lower correlation value are excluded in a block 140 from further
 consideration for generating a representative heartbeat. The heartbeats
 retained are used in generating a representative heartbeat for the lead
 being analyzed.
 To generate a representative heartbeat for the lead, the waveforms of the
 retained heartbeats are aligned in a block 142 by matching up waveform
 features such as the R wave peak, preferably according to the maximum
 cross-correlation of data samples in the retained heartbeats. Further, a
 baseline offset calculated for each beat is subtracted in a block 144 from
 the data samples in each retained heartbeat. The baseline offset is the
 amount that the data samples in a heartbeat exceed a selected reference
 level. In an actual embodiment of the invention, the baseline offset is
 estimated as the mean of 48 milliseconds of ECG data taken from an 88
 millisecond mark to a 40 millisecond mark prior to the QRS peak sample
 (e.g., if the QRS peak was located at the 100 millisecond mark, the
 estimated baseline offset was the mean of the samples from the 12
 millisecond mark to the 60 millisecond mark). It is appreciated that more
 or less ECG data may be used, or the timing of the ECG segment used may be
 shifted, in calculating a baseline offset.
 Next, in a block 146, a set of values representative of an average
 heartbeat for each lead is computed from the retained heartbeat data. In
 an actual embodiment of the invention, the representative heartbeat data
 is generated by calculating a sample-by-sample average of the retained
 heartbeats. Nevertheless, it is understood that a wide variety of
 mathematical operations may be used instead of averaging. For example,
 representative heartbeat data may be calculated from a sample-by-sample
 mean, mode, weighted mean (using weighing coefficients), or trimmed mean
 (i.e., calculate a mean from a set of values that excludes values that are
 too large or too small), or median.
 Before the resulting representative heartbeat data is subjected to further
 evaluation, it may be advantageous to reduce the amount of data being
 evaluated. In that regard, data samples in a segment of the representative
 heartbeat are selected in a block 148 to represent the ECG information of
 particular interest. In one actual embodiment of the present invention, a
 480 millisecond segment of representative heartbeat data for each
 available lead is selected beginning at an 8 millisecond mark before each
 representative beat's QRS peak. Data outside the selected segment is
 excluded from the further processing and evaluation performed. In reducing
 the amount of data being evaluated, it is appreciated that a segment of
 ECG data longer or shorter than 480 milliseconds may be selected. The
 beginning mark may also be adjusted earlier or later in the sequence of
 ECG data.
 While the process shown in FIG. 6 is used to generate representative
 heartbeat data in one actual embodiment of the invention, it should be
 understood that other ones of the many known procedures for generating
 representative heartbeats can be used. For example, the methods of
 representative heartbeat generation used in the LIFEPAK.RTM. 11 or
 LIFEPAK.RTM. 12 defibrillators manufactured by Physio-Control Corporation
 of Redmond, Wash., may be used, if desired.
 Returning again to FIG. 4, once representative heartbeat data is generated
 for each of the available leads in block 102, in accordance with the
 present invention, the data is subjected to a global classification
 evaluation in blocks 104-116. As will be better understood from the
 discussion below, a global classification evaluation involves
 concatenating the representative heartbeat data produced on the available
 leads, extracting global features from the concatenated representative
 heartbeat data, and determining whether the global features are in a class
 indicative of acute cardiac ischemia. More specifically, as will be
 discussed more fully below, a global classification evaluation involves
 mathematically projecting a concatenated vector of representative
 heartbeat data onto predetermined basis vectors that define an acute
 cardiac ischemic ECG subspace and onto predetermined basis vectors that
 define a non-ischemic ECG subspace. The resulting projection coefficients
 are the global features that are classified to determine whether an acute
 cardiac ischemic condition is detected.
 While a 480 millisecond segment of representative heartbeat data is
 selected in an actual embodiment of the invention in block 148 (FIG. 6),
 it is again noted that any segment length (preferably containing, at
 least, the QRST portions of the ECG) may be used. Nevertheless, as will be
 better understood from the discussion below, a fixed number of samples in
 a representative heartbeat must be set a priori during a preprocessing
 training phase for the later performed global classification evaluation of
 the present invention. The length of the basis vectors determined during
 the training phase and later used in the global classification evaluation
 depends on both the number of data samples in each representative
 heartbeat and the number of available leads used from the ECGs of a
 training population of patients in the preprocessing training phase.
 Prior to discussing the global classification evaluation illustrated in
 blocks 104-116 of FIG. 4, a brief discussion of some concepts underlying
 the evaluation is provided. A description of the preprocessing training
 phase required to derive the basis vectors is also provided.
 The global classification evaluation shown in FIG. 4 is based on the
 concept that a series of numbers can be viewed as coefficients of a vector
 that defines a point in a multidimensional signal space. In the case of
 the present invention, this means that a series of ECG data points in a
 patient's representative heartbeat may be viewed as coefficients of a
 vector defining a point in a multidimensional ECG signal space. The number
 of dimensions in the ECG signal space is determined by the number of data
 points in the patient's representative heartbeat data. In a similar
 manner, a series of global features derived from a patient's ECG according
 to the present invention may be viewed as coefficients of a vector
 defining a point in a multidimensional feature space. The number of
 dimensions in the feature space is determined by the number of global
 features derived from the patient's ECG. It should be understood that the
 ECG signal space and the feature space are completely different conceptual
 spaces, though in regard to this invention they are related by virtue of
 the projecting operation described later in reference to blocks 110 and
 112 of FIG. 4.
 For purposes of discussion herein, patients in a training population of
 patients known to have acute cardiac ischemia are referred to as ischemic
 training patients or an ischemic training population. Likewise, patients
 in a training population of patients known to not have acute cardiac
 ischemia are referred to as non-ischemic training patients or a
 non-ischemic training population. Sets of global features derived from the
 ECG data of ischemic training patients may be plotted as points defining
 an "ischemic" region of a feature space. In a similar fashion, sets of
 global features derived from the ECG data of non-ischemic training
 patients may be plotted as points defining a "non-ischemic" region of the
 feature space. According to the present invention, if a set of global
 features derived from the ECG data of a patient under current evaluation
 defines a point closer to the ischemic region than the non-ischemic
 region, a diagnosis of acute cardiac ischemia is reported. The threshold
 of "closeness" for making such a diagnosis is adjustable, thus providing
 the device with an adjustable sensitivity/specificity tradeoff, as will be
 discussed in more detail below.
 Within the ECG signal space (which, as noted, is different than the feature
 space), ECG data obtained from ischemic training patients is viewed as
 defining an ischemic ECG subspace. Likewise, ECG data obtained from
 non-ischemic training patients is viewed as defining a non-ischemic ECG
 subspace. The ischemic and non-ischemic ECG subspaces may be succinctly
 characterized by mathematical basis vectors derived in a training phase
 (discussed below). The basis vectors are stored as preprocessed parameters
 67 in memory 64 of the device 10. As will be discussed later in greater
 detail, the basis vectors are used in blocks 110 and 112 of FIG. 4 to
 derive the projection coefficients (i.e., global features) of the patient
 under current evaluation.
 Before deriving a set of basis vectors for characterizing each of the
 ischemic and non-ischemic ECG subspaces, the quality of this
 characterization may be enhanced by dividing the ischemic and non-ischemic
 training populations into smaller groups according to a selected
 characteristic. Sets of basis vectors (ischemic and non-ischemic) are then
 derived for each group from the ECG data of the training patients in the
 group. However, dividing the training populations into groups is not
 required.
 In one embodiment of the invention, the training populations are divided
 into groups according to locations of ischemic conditions (e.g., anterior,
 inferior, and other). The selected characteristic used to divide the
 training populations is the identity of the lead with the greatest ST
 elevation. It has been found that the identity of the lead with the
 greatest ST elevation is generally indicative of the location of an acute
 cardiac ischemic condition. Accordingly, each patient in the ischemic and
 non-ischemic training populations is assigned to an "inferior,"
 "anterior," or "other" group based on the patient's ST elevation measures.
 While in this regard the ST elevation on each individual lead of a patient
 may be separately evaluated, in an actual embodiment of the present
 invention, three composite ST elevation measures {u.sub.1, u.sub.2,
 u.sub.3 } are calculated and used to group the patients in the training
 populations. The measure u.sub.1 is the mean of the ST elevation on leads
 II and aVF. The measure u.sub.2 is the mean of the ST elevation on leads
 I, aVL, V6. Lastly, the measure u.sub.3 is the mean of the ST elevation on
 leads V2, V3, and V4. If the measure u.sub.1 for a given patient is the
 greatest of the three composite measures, the patient is assigned to the
 "inferior" group. If the measure u.sub.2 is greatest, the patient is
 assigned to the "other" group (which includes lateral and septal ischemic
 locations). If the measure u.sub.3 is greatest, the patient is assigned to
 the "anterior" group.
 It is appreciated that alternative groups may be defined according to other
 selected characteristics. For example, local features other than ST
 elevation, such as T wave amplitude, QRS area measures, etc., and patient
 clinical information (e.g., age, sex, etc.), may be used to divide the
 training populations into groups. Alternatively, selected local features
 may be classified first to produce a preliminary determination of ischemia
 that is used as a basis for dividing the training populations. Local
 features and classification methods suitable for producing preliminary
 determinations in this regard are described in more detail below in
 reference to FIGS. 13 and 14. It is further appreciated that basis vectors
 may be derived for each of the training populations as a whole, without
 dividing the training populations into groups.
 For the sets of ischemic and non-ischemic training patients in each group
 (or for each training population as a whole if grouping is not performed),
 a set of basis vectors is derived. FIG. 7 illustrates in more detail a
 process for deriving basis vectors. In a block 150 in FIG. 7, ECG data is
 acquired from each of the patients in the ischemic and non-ischemic
 training populations. The ECG data may be acquired using a conventional
 12-lead ECG device or a device 10 such as that shown in FIGS. 2 and 3, in
 the manner earlier described in reference to FIG. 5. After acquiring ECG
 data from each of the training patients, representative heartbeat data is
 generated from the ECG data in a manner as earlier described in reference
 to FIG. 6. More specifically, for each patient in the training
 populations, representative heartbeat data is generated from ECG data
 collected, for example, on each of twelve leads labeled I, II, III, aVR,
 aVL, aVF, V1, V2, V3, V4, V5 and V6. These leads are depicted generally in
 FIG. 9 in which dots are used to indicate a series of numbers that, in
 this regard, forms the representative heartbeat data.
 Assuming for the sake of discussion that the training populations are
 divided into groups according to locations of acute cardiac ischemia as
 described above, the ECG of each patient in the training populations is
 categorized into a group in a block 154 according to the ST elevation
 measures calculated for each patient. Next, in a block 156 the
 representative heartbeat data generated for each patient in each group is
 concatenated to form a representative heartbeat vector "x". A concatenated
 representative heartbeat vector x includes the representative heartbeat
 data of a first lead (e.g., lead I), immediately followed by the
 representative heartbeat data of a second lead (e.g., lead II), a third
 lead (e.g., lead III), a fourth lead (e.g., lead aVR), and so on. A
 concatenated representative heartbeat vector x is depicted generally in
 FIG. 10, with vertical dashed partitions between the lead data shown for
 illustrative purposes only. It is to be understood that alternative
 embodiments of the invention may use a different (and/or smaller)
 combination of leads than that shown in FIGS. 9 and 10.
 In an embodiment of the invention where twelve leads of ECG data are
 acquired at a sampling rate of 500 samples per second, and a 480
 millisecond segment of data per representative heartbeat is used, each
 lead (e.g., the leads shown in FIG. 9) includes 240 samples of heartbeat
 data, thus producing a concatenated representative heartbeat vector x (as
 shown in FIG. 10) having 2,880 samples of heartbeat data. A concatenated
 representative heartbeat vector x is thus obtained for each of the
 patients in the respective training populations.
 For the ischemic and non-ischemic sets of patients in each group, the
 patients' concatenated heartbeat vectors x are combined together in a
 block 158 in FIG. 7 to calculate correlation matrices R.sub.isc and
 R.sub.non using the following general equation:
 ##EQU1##
 The values in matrix R of Equation (1) are normalized according to the
 total number of patients "N" whose concatenated heartbeat vectors x were
 used. Thus, using Equation (1), a correlation matrix R.sub.isc is
 calculated for the ischemic patients in each group, and a correlation
 matrix R.sub.non is calculated for the non-ischemic patients in each
 group.
 For each correlation matrix R (regardless of whether the training
 populations are divided into groups), a Karhunen-Loeve (KL) transformation
 is performed in a block 160 to derive a matrix V and a matrix .LAMBDA.
 that satisfies the following general equation:
EQU R=V.LAMBDA.V.sup.T (2)
 A matrix V and a matrix .LAMBDA. are illustrated generally in FIGS. 11A and
 11B, respectively. The columns of the matrix V are mutually orthogonal
 basis vectors that collectively define the ECG subspace of the
 concatenated heartbeat vectors x used in Equation (1) to form the
 correlation matrix R. The matrix .LAMBDA. in FIG. 11B is a matrix whose
 diagonal consists of eigenvalues corresponding to the correlation matrix R
 and are ordered from largest to smallest in value along the diagonal.
 Likewise, the basis vectors (i.e., the columns in matrix V) are
 eigenvectors that correspond to the eigenvalues of the correlation matrix
 R. If the concatenated heartbeat vectors x have 2,880 samples each, then
 the dimension of both matrix V and matrix .LAMBDA., as well as correlation
 matrix R, is 2880.times.2880.
 Because the eigenvalues in matrix .LAMBDA. are ordered from largest to
 smallest value along the diagonal, the initial columns of matrix V, which
 correspond with the eigenvalues of greatest value, are more significant in
 terms of signal synthesis than the latter columns of matrix V, which
 correspond with smaller eigenvalues. A set of basis vectors is selected in
 a block 162 of FIG. 7 (e.g., the first ten columns of matrix V labeled
 BV1, BV2, BV3, BV4, . . . , BV10 in FIG. 11A) for later use as
 preprocessed parameters 67 in computing the current patient's global
 features. Although signal representation error theoretically decreases
 with the inclusion of additional basis vectors, experience thus far has
 indicated that using more than ten basis vectors does not markedly improve
 classification performance in the present invention. Moreover, the basis
 vectors corresponding to smaller eigenvalues (i.e., the latter columns of
 matrix V) that are not used are more likely to be affected by noise.
 It will be appreciated that other sets of columns, or basis vectors, may be
 selected from matrix V. For example, instead of using the first ten
 columns of matrix V, a set of basis vectors including the first, third,
 fifth, and seventh columns may be used. Basis vectors beyond the first ten
 columns may also be selected An optimal set of basis vectors may be
 determined empirically for the ischemic and non-ischemic training patients
 in each group. If the training populations are not divided into groups, a
 single paired set of basis vectors (i.e., a set of ischemic basis vectors
 and a set of non-ischemic basis vectors) may be used to represent the ECG
 subspaces of the ischemic population and the non-ischemic population as a
 whole. The selected vectors are then stored in a block 163 as preprocessed
 parameters 67 in memory 64 for later use in deriving global features from
 the ECG of a patient undergoing evaluation.
 Returning now to FIG. 4, given a defined number of groups and a paired set
 of ischemic and non-ischemic basis vectors associated with each group, the
 ECG of a patient under current evaluation is categorized in a block 104
 into the group to which it best pertains. As part of categorizing the
 patient's ECG, composite ST elevation measures u.sub.1, u.sub.2, and
 u.sub.3 are calculated for the current patient as they are described above
 for the training populations. The current patient's ECG is categorized
 into the "inferior" group if the measure u.sub.1 is greatest. If the
 measure u.sub.3 is greatest, the patient's ECG is categorized into the
 "anterior" group. If the measure u.sub.2 is greatest, the patient's ECG is
 categorized into the "other" group.
 Once a patient's ECG is categorized into a particular group in block 104,
 the paired set of basis vectors associated with the group are selected in
 a block 106 from the preprocessed parameters 67 in memory 64 for use in
 evaluating the patient's ECG. The patient's representative heartbeat data
 for each of the available leads is then concatenated in a block 108 to
 form a concatenated heartbeat vector "x", in a manner as described earlier
 for the patients in the training populations. Next, in blocks 110 and 112
 of FIG. 4, the patient's concatenated heartbeat vector x is mathematically
 projected onto the basis vectors selected in block 106. Broadly stated,
 the projecting operation (described in more detail below) results in a
 number of projection coefficients that are used as global features of the
 patient's ECG. The number of global features extracted from the patient's
 ECG corresponds to the number of basis vectors used in the projecting
 operation.
 More specifically, the projecting operation involves projecting the
 patient's concatenated heartbeat vector x onto the basis vectors defining
 an acute cardiac ischemic ECG subspace in block 110 by computing an inner
 product of the vector x with each of the ischemic basis vectors. The
 patient's concatenated heartbeat vector x is also projected onto the basis
 vectors defining the corresponding non-ischemic ECG subspace in block 112
 by computing an inner product of the vector x with each of the
 non-ischemic basis vectors. If, for example, ten basis vectors are used to
 characterize each of the ischemic and non-ischemic ECG subspaces, the
 projecting operation results in a total of twenty scalar projection
 coefficients that are used as global features, as generally depicted in
 FIG. 12A. Once the patient's concatenated heartbeat vector x is projected
 onto the ischemic and non-ischemic basis vectors in blocks 110 and 112 of
 FIG. 4 (i.e., once the global features are calculated), the global
 features are concatenated in a block 114 into a single global feature
 vector "f", as generally depicted in FIG. 12B.
 Next, in a block 116, the global features derived from the current
 patient's ECG data are classified to determine whether acute cardiac
 ischemia is detected. The classifier in block 116 evaluates the global
 features of the current patient relative to representative global features
 previously derived during a training phase from patients in the ischemic
 and non-ischemic training populations. In one actual embodiment of the
 present invention, a Gaussian classifier is used to compare the current
 patient's global features with a set of mean global features normalized by
 covariances previously derived from the training populations. If the
 current patient's global features are "closer" to the normalized mean
 global features of the ischemic population than the non-ischemic
 population (hence, in a graphical sense, define a point "closer" to the
 ischemic region than the non-ischemic region of the feature space), a
 report of acute cardiac ischemia is produced.
 In order to understand the classifier in block 116, it is necessary to
 understand the preprocessing performed to train the classifier. During the
 training phase in which the ischemic and non-ischemic basis vectors are
 derived, a vector of mean global features "m" and a covariance matrix "C"
 are calculated for each set of ischemic training patients and non-ischemic
 training patients. In other words, a vector m.sub.isc and matrix
 C.sub.isc, and a vector m.sub.non and matrix C.sub.non, are calculated for
 each group (as described below). The vectors of mean global features
 m.sub.isc and m.sub.non, and covariance matrices C.sub.isc and C.sub.non,
 are used in classifying the current patient's global features. The
 preprocessing performed during the training phase to train the classifier
 in this regard is shown in more detail in FIG. 8.
 In blocks 164 and 165 of FIG. 8, global features for each patient in each
 group of training patients are calculated using the basis vectors derived
 for the group. More specifically, the concatenated representative
 heartbeat vector x produced in block 156 (FIG. 7) for each training
 patient is mathematically projected onto the ischemic and non-ischemic
 basis vectors for the training patient's group in the manner as described
 earlier in reference to blocks 110 and 112 of FIG. 4. Next, in a block
 166, the resulting ischemic and non-ischemic projection coefficients for
 each training patient are concatenated to form a global feature vector f
 in the manner as earlier described and generally depicted in FIG. 12B.
 Mean feature vectors m.sub.isc and m.sub.non are then calculated in a block
 167 for each group by computing a feature-by-feature mean of the
 concatenated global feature vectors f produced from the respective
 ischemic and non-ischemic patients in each group. In a configuration where
 ten basis vectors are used to characterize each of the ischemic and
 non-ischemic ECG subspaces, the concatenated global feature vector f for
 the patients in the training populations, and thus the mean feature
 vectors m.sub.isc and m.sub.non for each group, will include twenty
 values.
 In addition to calculating mean feature vectors m.sub.ics and m.sub.non in
 block 167, covariance matrices C.sub.isc and C.sub.non are calculated in
 block 168 for the respective ischemic and non-ischemic training patients
 in each group. Generally speaking, a covariance matrix C contains the
 statistical covariance of the global feature vectors f of a set of N
 number of patients, and is calculated as follows:
 ##EQU2##
 The covariance matrix C provides an indication of how widely dispersed the
 global features of the set of patients are from the calculated mean global
 features for the same set of patients. The mean feature vector m and the
 covariance matrix C thus calculated for each of the ischemic and
 non-ischemic sets of patients in each group are stored in a block 169 as
 preprocessed parameters 67 in memory 64 (FIG. 3) for later use in
 classifying the current patient's global features.
 Returning to FIG. 4, according to one implementation of the invention, the
 Gaussian classifier in block 116 evaluates the current patient's global
 feature vector f with respect to the mean global feature vectors m.sub.ics
 and m.sub.non, and covariance matrices C.sub.isc and C.sub.non according
 to the following equation:
EQU d=(f-m).sup.T C.sup.-1 (f-m) (4)
 The quantity "d" computed from Equation (4) reflects the distance between
 the patient's global feature vector f and a mean global feature vector m
 weighted by a covariance matrix C. Accordingly, a quantity d.sub.isc
 reflects the distance between a patient's global feature vector f and the
 mean global feature vector m.sub.isc weighted by a covariance matrix
 C.sub.isc for the acute cardiac ischemia ECG subspace. Similarly, a
 quantity d.sub.non reflects the distance between a patient's global
 feature vector f and the mean global feature vector m.sub.non weighted by
 the covariance matrix C.sub.non for the non-ischemia ECG subspace. The
 Gaussian classifier in block 116 then compares the quantities disc and
 d.sub.non, and if disc is less than d.sub.non, an acute cardiac ischemic
 condition is detected and reported. It is appreciated that alternative
 distance metrics may be used for comparing a patient's global feature
 vector f with a mean global feature vector m to produce other quantities
 for "d."
 The covariance matrix C is inverted in Equation (4) so that if a mean
 global feature has a corresponding high covariance, any deviation of the
 patient's respective global feature from the mean global feature is not
 weighted as greatly as a situation wherein the respective mean global
 feature has a low covariance. Thus, if a particular global feature of the
 patient deviates significantly from a corresponding mean global feature
 having a low covariance, greater attention is drawn to the patient's
 deviation on that feature by giving greater weight to the resultant value.
 For purposes of discussion herein, it is presumed that the Gaussian
 classifier in block 116 uses a distance metric that reports the detection
 of acute cardiac ischemia if d.sub.isc is less than d.sub.non. In other
 words, if d.sub.non &gt;d.sub.isc, a detection of acute cardiac ischemia is
 reported. From the foregoing, it follows that if d.sub.non -d.sub.isc &gt;0,
 a detection of acute cardiac ischemia is reported. The computed quantity
 on the left side of the latter inequality is called a classification
 statistic. The value "0" on the right side of the latter inequality is a
 decision threshold against which the classification statistic is compared.
 More generally stated, if d.sub.non -d.sub.isc &gt;t, where "t" is the
 decision threshold, acute cardiac ischemia is reported. As will be
 discussed below, the sensitivity and specificity of the device 10 may be
 adjusted by varying the threshold "t".
 While a Gaussian classifier has been described above, it is appreciated
 that alternative statistical classifiers may be used to evaluate a
 patient's global features. Such alternative statistical classifiers may
 use, for example, the techniques of logistic regression, k-nearest
 neighbor procedures, discriminate analysis, as well as neural network
 approaches. The output of a statistical classifier is a quantity that is
 typically compared to a numerical threshold to arrive at a final binary
 decision, e.g., whether or not acute cardiac ischemia is present. For an
 expanded description of suitable alternative statistical classifiers, see
 R. Duda and P. Hart, Pattern Classification and Scene Analysis (1973),
 published by John Wiley & Sons, New York, which is incorporated herein by
 reference.
 As noted, the outcome of the evaluation made by the classifier in block 116
 is reported to the user of the device in a block 118. As a further aspect
 of the invention, if the training populations are divided into groups
 according to ischemia location, the reported outcome may also identify the
 location of the ischemic condition (if acute cardiac ischemia is detected)
 based on the group into which the patient's ECG was categorized. Knowing
 whether a detected ischemic condition is at an inferior, anterior, or
 other location may assist a caregiver in treating the ischemic condition.
 Although the acute cardiac ischemia detection process 66a described above
 involves projecting a patient's concatenated heartbeat vector x onto basis
 vectors that collectively define both an acute cardiac ischemic ECG
 subspace and a non-ischemic ECG subspace, it should be understood that,
 alternatively, the patient's concatenated heartbeat vector x may be
 projected onto one or more basis vectors that define only an acute cardiac
 ischemic ECG subspace, i.e., without projection onto any basis vectors
 that define a non-ischemic ECG subspace. In that regard, only "ischemic"
 projection coefficients (i.e., ischemic global features) are produced and
 classified.
 A classifier for classifying only ischemic global features does not need to
 be any different in structure than a classifier that classifies both
 ischemic and non-ischemic global features, as described above in reference
 to block 116 (FIG. 4). The only difference is the number of global
 features used in the classification and the training of the classifier
 performed beforehand in a training phase.
 In the training phase, the basis vectors defining an acute cardiac ischemic
 ECG subspace are derived from the ischemic training population as
 described earlier in reference to FIG. 7. Then, for each training patient
 in both the ischemic and non-ischemic training populations, ischemic
 global features are calculated using the derived ischemic basis vectors in
 a manner as described earlier in reference to block 164 in FIG. 8. The
 ischemic global features for each patient (ischemic and non-ischemic) are
 concatenated into a global feature vector f as described earlier in
 reference to block 166.
 Mean feature vectors m.sub.isc and m.sub.non and covariance matrices
 C.sub.isc and C.sub.non are then calculated in a manner as described in
 reference to blocks 167 and 168. The vectors m.sub.isc and m.sub.non and
 matrices C.sub.isc and C.sub.non, are stored as preprocessed parameters 67
 in the memory 64 for later use in classifying the ischemic global features
 of the current patient as discussed in reference to block 116.
 Alternatively, the patient's concatenated representative heartbeat vector x
 may be projected onto one or more basis vectors that define only a
 non-ischemic ECG subspace, i.e., without projection onto any basis vectors
 that define an acute cardiac ischemic ECG subspace. In that regard, only
 "non-ischemic" projection coefficients (i.e., non-ischemic global
 features) are produced and classified. The same procedures discussed above
 for training the classifier are used, except the basis vectors that define
 a non-ischemic ECG subspace are used instead of the ischemic basis
 vectors. Furthermore, as discussed earlier, if the training populations
 are divided into groups according to ischemia location, the reported
 outcome (if ischemia is detected) may also identify the location of the
 ischemic condition.
 FIG. 13 is a flow diagram illustrating another version of an acute cardiac
 ischemia detection process 66b formed in accordance with the present
 invention. In this version, local features are derived from a patient and
 classified along with global features to diagnose acute cardiac ischemia.
 Local features derived from a patient include local morphological measures
 taken from individual heartbeats of a patient, e.g., ST elevation, T wave
 amplitude, and QRS area measures. Local features may also include patient
 clinical information input into the device, such as the patient's age and
 sex. Global features, on the other hand, are comprised of projection
 coefficients that are calculated as earlier described.
 In the manner earlier described in reference to FIGS. 4-6, in a block 170,
 the device 10 acquires about ten seconds of ECG data that is amplified and
 filtered. In a block 172, the device 10 then generates representative
 heartbeat data for each of the available leads. At that point, in either a
 parallel or sequential fashion, a set of global features are determined in
 blocks 176, 178, and 180, and a set of local features are determined in
 blocks 184 and 186 by the device 10.
 To determine the set of global features, the device 10 categorizes a
 patient's ECG into a group (e.g., inferior, anterior, or other, as earlier
 described) and selects the applicable sets of basis vectors in block 176.
 The patient's representative heartbeat data is concatenated in block 178
 into a concatenated heartbeat vector x. Global features are calculated in
 block 180 by projecting the concatenated representative heartbeat vector x
 onto the selected basis vectors for the acute cardiac ischemic and
 non-ischemic ECG subspaces. The global features are then concatenated and
 input into a classifier in block 182 to compute a global classification
 statistic. As discussed earlier, a classifier suitable for use with this
 aspect of the present invention is a multidimensional Gaussian classifier
 that evaluates the global features relative to mean global features
 weighted by corresponding covariance matrices. At this time, however, the
 resulting global classification statistic is not compared with a decision
 threshold but instead is provided to a combiner in a block 190.
 To determine the set of local features, one or more local morphological
 measures are calculated from the representative heartbeats in block 184.
 Patient clinical information, such as the age and sex of the patient, may
 also be obtained in block 186 from the user of the device 10. With regard
 to sex information, a "1" may be used for males and a "0" may be used for
 females. If clinical information is not entered or available at this time,
 default values may be used. While clinical information may improve
 accuracy in the detection of acute cardiac ischemia, the ECG
 classification of the present invention can be performed without this
 information. The values of the one or more local morphological measures
 and clinical information are concatenated to form a local feature vector
 that is input into a local feature classifier in a block 188 that computes
 a local classification statistic. The local classification statistic is
 not compared with a decision threshold at this time, but instead is also
 provided to the combiner in block 190.
 The local feature classifier in block 188 may be either a statistical
 classifier or a heuristic classifier. A suitable statistical classifier
 for this aspect of the invention may be one similar in form to the
 Gaussian classifier used for classifying global features and described
 above. In that regard, the local feature classifier in block 188 is
 trained in a similar fashion using selected local morphological measures
 and clinical information as features derived from both ischemic and
 non-ischemic training populations. The selected local features for each
 training population, or group within a population, are concatenated to
 form local feature vectors from which a mean local feature vector is
 calculated. The corresponding local features of the patient are evaluated,
 using Equation (4), against the mean local feature vectors. The result of
 this evaluation is a local classification statistic that is provided to
 the combiner in block 190 as noted above.
 Alternatively, a heuristic classifier may be used. A heuristic classifier
 tries to mimic procedures used by an expert (i.e., a cardiologist) to
 evaluate an ECG and report a diagnosis. A heuristic classifier compares
 the local features to expert-determined thresholds. For an expanded
 description of a suitable heuristic classifier, see G. Wagner, Marriott's
 Practical Electrophysiology, 9th Ed. (1994), published by Williams &
 Wilkins, Baltimore, which is incorporated herein by reference. The result
 of the evaluation is a local classification statistic that is supplied to
 the combiner in block 190.
 As another alternative, or in addition to directly inputting a patient's
 local morphological measures and clinical information as local features
 into a local feature classifier, one or more composite local features
 derived from the local morphological measures and clinical information may
 be calculated and input into the local feature classifier. In one actual
 embodiment of the invention, two different procedures are used for
 calculating composite local features. One procedure involves using a
 logistic regression equation to produce a preliminary prediction of
 whether acute cardiac ischemia is present. The logistic regression
 equation is derived during a preprocessing training phase (e.g., the
 training phase described earlier in which the basis vectors and classifier
 parameters, i.e., the preprocessed parameters 67, are derived) according
 to a logistic regression model defined by the following equation.
 ##EQU3##
 In Equation (5), "p" denotes the probability of detected acute cardiac
 ischemia, a.sub.o is a calculated constant, a.sub.i denotes the calculated
 i.sup.th regression coefficient, and x.sub.i denotes the i.sup.th
 explanatory variable (in this case, the local features of a patient).
 During the training phase, the probability of detected acute cardiac
 ischemia is known (i.e., the probability of ischemia is 1 for the ischemic
 training population and the probability of ischemia is 0 for the
 non-ischemic training population). Using the known probability information
 and the local features of the patients in the respective training
 populations, the regression coefficients of the logistic regression
 equation are determined in accordance with Equation (5). The regression
 coefficients are stored as preprocessed parameters 67 in the memory 64
 (FIG. 3) for later use in producing a probability of detected acute
 cardiac ischemia in the patient under current evaluation.
 If the training populations are divided into groups (e.g., anterior,
 inferior, and other) as described earlier, a logistic regression equation
 is derived for each of the groups. In that regard, ECG features and leads
 known to be associated with anterior acute cardiac ischemic events (e.g.,
 the leads used in calculating the composite ST elevation measure u.sub.3
 discussed above) are selected for use in deriving the regression
 coefficients of an "anterior" logistic regression equation. Likewise, the
 ECG features and leads known to be associated with acute cardiac ischemia
 in an inferior or other location (e.g., the leads used in calculating the
 composite ST elevation measures u.sub.1 or u.sub.2 discussed above) are
 selected for use in deriving an "inferior" and an "other" logistic
 regression equation. The regression coefficients derived for each of the
 groups is stored as preprocessed parameters 67 in the memory 64 for later
 use in calculating a probability of detected acute cardiac ischemia in the
 patient under current evaluation.
 For the patient under current evaluation, the patient's local features are
 input into the logistic regression equation derived during the training
 phase described above. More specifically, the patient's local features are
 weighted by the derived regression coefficients and combined as shown in
 Equation (5) to produce an output "p." The output "p" (i.e., the
 probability of detected acute cardiac ischemia) is used as a composite
 local feature to be classified in the local feature classifier in block
 188. Where a logistic regression equation is derived for each of an
 anterior, inferior, and other group, a probability of detected acute
 cardiac ischemia in the current patient (i.e., a composite local feature)
 is calculated with respect to each of the groups. Thus, in that regard, an
 "anterior" composite local feature, and "inferior" composite local
 feature, and an "other" composite local feature are calculated for the
 current patient and input into the local feature classifier.
 Alternatively, rather than directly inputting the composite local features
 into the local feature classifier in block 188, it may be advantageous to
 first dichotomize the composite local features. For instance, if the
 composite local feature sensitive to anterior acute cardiac ischemia
 exceeds a preselected threshold, the composite local feature is assigned a
 value of "1" (indicating predicted anterior acute cardiac ischemia). If
 the composite local feature does not exceed the threshold, it is assigned
 a value of "0" (indicating predicted non-ischemia). Similarly,
 dichotomized outputs "1" and "0" may be determined for the composite local
 features sensitive to inferior and other locations of acute cardiac
 ischemia. The thresholds used to dichotomize the composite local features
 are selected based on prediction patterns observed in the training
 populations during the training phase. The dichotomized composite local
 features are then input into the local feature classifier in block 188.
 For additional description of constructing and implementing logistic
 regression models, see D. Hosmer and S. Lemeshow, Applied Logistic
 Regression (1989), John Wiley & Sons, New York, incorporated by reference
 herein.
 Another procedure for creating a composite local feature involves
 calculating a Mahalanobis distance. A Mahalanobis distance "d" is
 calculated according to Equation (4) and measures the distance between the
 patient's set of local features and a representative set of local features
 derived from a training population. A Mahalanobis distance d.sub.isc
 and/or d.sub.non, calculated using representative local features from
 ischemic and/or non-ischemic training populations, may be provided as
 composite local features to the local feature classifier in block 188.
 In implementations of the invention where ECGs are categorized into groups
 (e.g., anterior, inferior, and other), a Mahalanobis distance may be
 calculated for a patient's local features with respect to each of the
 anterior, inferior, and other groups. Using a nearest-neighbor approach,
 the patient's local features are then identified with a group (either
 anterior, inferior, other, or non-ischemia) according to the closest
 calculated distance. This group identification is provided as a composite
 local feature to the local feature classifier in block 188. Preferably,
 the local feature classifier in block 188 receives more than one composite
 local feature, including one or more dichotomized composite local features
 derived by logistic regression and one or more composite local features
 derived from calculating a Mahalanobis distance.
 The local feature classifier in block 188 receiving the composite local
 features may use a logistic regression model such as that described above
 in reference to Equation (5) to determine acute cardiac ischemia from
 non-ischemia based on the composite local features. In a manner as
 described above, regression coefficients for a logistic regression
 equation are derived during the training phase by applying the composite
 local features derived from patients in the training populations with the
 known outcome of ischemia or non-ischemia in the populations. The
 regression coefficients are stored as preprocessed parameters 67 in the
 memory 64 (FIG. 3) for later use in determining the probability of acute
 cardiac ischemia in the patient under current evaluation. The probability
 of acute cardiac ischemia produced from the local feature classifier in
 block 188 is a local classification statistic that is supplied to the
 combiner in block 190 for evaluation in combination with the global
 classification statistic.
 The combiner in block 190 is a classifier that receives the classification
 statistics from both the global and local feature classifiers (i.e., from
 blocks 182 and 188, respectively). The combiner is preferably a
 statistical classifier that uses a simple statistical model, such as a
 linear discriminate classifier or logistic discriminate classifier. The
 combiner evaluates the global and local classification statistics to
 produce a combined classification statistic that is compared against a
 threshold "t". If the combined classification statistic exceeds the
 threshold "t", the local and global classification statistics are
 classified as belonging to a class of patients experiencing acute cardiac
 ischemia. On the other hand, if the combined classification statistic is
 less than the threshold "t", the local and global classification
 statistics are classified as belonging to a non-ischemic class of
 patients. Suitable classifiers for use in the combiner in block 190 are
 discussed by R. Duda and P. Hart in Pattern Classification and Scene
 Analysis, referenced above. The result of the classification made by the
 combiner (i.e., whether or not acute cardiac ischemia is present) is then
 reported to the user in a block 192.
 FIG. 14 is a flow diagram that illustrates another alternative version of
 an acute cardiac ischemia detection process 66c formed in accordance with
 the present invention in which both global and local features are
 evaluated. In FIG. 14, a single classifier is used in a block 196 in place
 of the separate global and local classifiers (blocks 182 and 188,
 respectively) and the combiner (block 190) described in reference to FIG.
 13. The single classifier in block 196 receives both the global features
 computed in block 180 and the local features calculated in block 184 as
 features to be jointly classified. Patient clinical information may also
 be input into the classifier in block 196 as additional local features
 (though not shown in FIG. 14). Alternatively, or in addition to the local
 features discussed above, composite local features may be calculated and
 provided to the classifier in block 196 along with the global features.
 The classifier in block 196 may be a statistical classifier having a form
 similar to the classifiers described above. For example, the classifier
 may be a Gaussian classifier previously trained during a preprocessing
 training phase using corresponding sets of global and local features
 derived from patients in the ischemic and non-ischemic training
 populations. The current patient's global features and local features are
 concatenated into a single global/local feature vector. The classifier
 then evaluates the patient's combined global/local feature vector with
 respect to calculated representative global/local feature vectors derived
 from the ischemic and non-ischemic populations to produce a classification
 global/local statistic. The global/local classification statistic is then
 compared with a selected threshold to produce a diagnosis of whether an
 acute cardiac ischemic condition is present. In an actual embodiment of
 the present invention, a logistic regression classifier is used to
 evaluate the combined global/local feature set. For additional detail on a
 logistic regression classifier, see D. Hosmer and S. Lemeshow, Applied
 Logistic Regression (1989), referenced above. Depending on the outcome
 ofthe evaluation performed by the classifier in block 196, a report
 indicating detection of acute cardiac ischemia is produced in block 192.
 It should be understood that a classifier is typically implemented as a
 computer software routine. In reference to FIG. 3, a classifier executed
 by the processing unit 62 forms part of a computer program that carries
 out the functions of the acute cardiac ischemia detection process 66.
 Alternatively, a classifier executed by the processing unit 62 may
 comprise a separate software routine implemented by a separate processor
 or circuit in communication with the processing unit 62.
 The ability to identify and correctly diagnose acute cardiac ischemic
 events is indicated by the sensitivity and specificity of the device 10.
 As noted earlier, a typical sensitivity/specificity tradeoff is
 illustrated by a receiver operating characteristics (ROC) curve 200 as
 shown in FIG. 15. The y-axis in FIG. 15 represents sensitivity, or
 fraction of true positives detected, and the x-axis represents the
 quantity of "1-specificity", or fraction of false positives detected.
 Values on both axes are expressed as percentages. Thus, as shown in FIG.
 15, a detection device 10 tuned to be more sensitive in its analysis is
 typically less specific, and a device tuned to be more specific is
 typically less sensitive. If the device 10 could correctly diagnose all
 cases, the analysis would have a specificity of one and a sensitivity of
 one. In the present invention, the point at which a detection device 10
 operates on its ROC curve may be adjusted by varying the decision
 threshold "t" against which the calculated classification statistic
 (described earlier) is compared.
 In one embodiment of the invention, the decision threshold, and hence the
 sensitivity/specificity operating point of the detection device 10, is set
 at the time of manufacture in the software that carries out the acute
 cardiac ischemia detection process 66. Alternatively, the threshold may be
 adjusted at the point of sale of the device according to the purchaser's
 needs by adjusting appropriate variables in the software or by setting an
 internal dial or switch that is read by the software. The device 10 may
 further be configured to receive a user input (e.g., an external dial,
 switch, or key input) that selectively adjusts the threshold used by the
 software, and thus adjusts the sensitivity/specificity operating point of
 the device.
 A device 10 constructed with an adjustable sensitivity/specificity tradeoff
 according to the present invention may be used for screening patients with
 possible acute cardiac ischemia as well as confirming a detected ischemic
 condition for treatment purposes. FIG. 16 is a flow diagram illustrating
 an acute cardiac ischemia detection process 220 for screening and
 confirming a detected acute cardiac ischemic condition. In a block 222, a
 desired sensitivity/specificity operating point for the device is selected
 either by the manufacturer, purchaser, or user. In that regard, to screen
 patients for possible acute cardiac ischemia, it is generally preferred
 that the sensitivity/specificity operating point of the device be set for
 a high level of sensitivity.
 In a block 224, the device 10 acquires ECG data and, in a block 226, the
 device evaluates the ECG data to detect acute cardiac ischemia as
 described by the acute cardiac ischemia detection processes 66a, 66b, or
 66c shown in FIGS. 4, 13, or 14. In a block 228, the device 10 reports the
 results of the acute cardiac ischemia detection process via a user
 display. In a block 230, if acute cardiac ischemia is detected, the user
 of the device is given opportunity to adjust the sensitivity/specificity
 operating point to a higher level of specificity for a second evaluation
 of the patient.
 After adjusting the sensitivity/specificity operating point in block 230,
 the device 10 performs a second evaluation of the patient by returning to
 block 224 to acquire ECG data from the patient. The patient's ECG data is
 analyzed and evaluated in block 226 using the adjusted
 sensitivity/specificity operating point to confirm whether an acute
 cardiac ischemic condition has been detected. The result of the second
 evaluation is reported in block 228. It is appreciated that additional
 evaluations of the patient may be performed as desired.
 Although FIG. 16 illustrates a second evaluation being performed on newly
 acquired ECG data (i.e., by returning to block 224 from block 230), it
 should be understood that a second and additional evaluations may be
 performed on the original set of ECG data acquired from the patient in the
 first evaluation. In that regard, rather than returning to block 224 from
 block 230, the device 10 would return to block 226 to reevaluate the
 original ECG data using the adjusted sensitivity/specificity operating
 point.
 While various embodiments of the invention have been illustrated and
 described herein, it is appreciated that changes may be made without
 departing from the spirit and scope of the invention. For example, rather
 than concatenating the representative heartbeat data on each of the leads
 and projecting the concatenated heartbeat vector onto the basis vectors,
 each of the particular leads may be individually projected onto a set of
 basis vectors derived for the particular lead and the resulting projection
 coefficients may be used as global features. It should also be understood
 that with sufficient human expert evaluation of global features,
 classifying the set of global features may be performed heuristically.
 Furthermore, when reporting whether an acute cardiac ischemic condition is
 detected, a device constructed according to the invention may report an
 outcome in a range of outcomes (e.g., the likelihood of acute cardiac
 ischemia on a scale of 1-10) instead of reporting a binary "yes/no"
 result. In that regard, rather than comparing a calculated classification
 statistic to a single threshold (to produce a binary result), the
 classification statistic may be quantized into a range of values and the
 closest value in the range of values is reported to the user. It is
 intended, therefore, that the scope of the invention be determined from
 the claims that follow and equivalents thereto.