Abstract:
Computer method and apparatus of analyzing ECG signals of a subject include receiving a subject electrocardiogram signal and comparing it against signal patterns of known cardiac syndromes. A library of example predefined signals is employed. Distance measures indicating similarity of the subject signal to the example predefined signals are produced and form a sequence of vectors. The sequence of vectors are input into a classifier which determines existence of signal patterns indicative of any cardiac syndromes in the subject.

Description:
BACKGROUND OF THE INVENTION 
   Over the years cardiologists and electro-cardiologists have developed a body of knowledge pertaining to the analysis of electrocardiogram signals or ECG&#39;s. They have identified a number of basic “shapes” that correspond to basic heart syndromes. As of last count, more than 80 basic syndromes can be clearly linked with particular morphologies of the ECG signal (ABC of Clinical Electrocardiography by Francis Morrus, BMJ Publishing Group, 01-2003, ISBN 0727915363) (ECG&#39;s by Example, by Jenkings and Gerred, 1997, ISBN 0443058978). These syndromes include ischemic heart disease, hypertrophy patterns, atrioventricular blocks, bundle branch blocks, supraventricular rhythms and ventricular rhythms. 
   Previous work in analyzing ECG&#39;s has focused on building specific detectors for known syndromes. Typically a cardiologist provides a detailed morphological description of what to look for in the signal and this knowledge is encoded in a series of rules that codify an algorithm. This rule-based approach to detection/classification of ECG signals and the potential syndromes they encode has many drawbacks. Among others, clearly this is a time consuming approach that involves a trial and error method of algorithmic design. In addition, the algorithm designer is not exposed to large amounts of data and there is no guarantee that the rules encoding the algorithm are generic enough. Also, extracting the rules from the expert is difficult; sometimes experts don&#39;t know exactly how to distinguish one cardiac syndrome from another, they just “know” and cannot explain why they can make the distinction. 
   Other approaches use ECG data with annotations provided by a cardiologist. The expert assigns labels to regions of the ECG signal indicating whether the signal is normal or if a particular syndrome is present. Then pattern recognition techniques extract features from the ECG signal and using the labels try to build classifiers that minimize the error rate. While this application is better than the previous one and in general does not depend on a detailed understanding of the morphology of the signal (it only requires a label), it fails to take advantage of the extensive knowledge that experts have acquired over the years. Applicants have found that, in effect, too much is demanded from the pattern recognition algorithm that has to extract meaningful features from raw data and figure out on its own the rules that codify a particular syndrome. 
   SUMMARY OF THE INVENTION 
   The present invention overcomes the disadvantages of the prior art. In particular, the present invention approach combines the expertise of cardiologists (as encoded in ECG morphologies) with pattern recognition techniques. This effectively combines the best of both worlds, i.e., expert knowledge and automated classification techniques. 
   In one embodiment, the invention method and apparatus for analyzing ECG signals of a subject include (i) receiving a subject electrocardiogram signal to be analyzed; (ii) using a library of example predefined signals, comparing the subject signal against signal patterns of known cardiac syndromes; (iii) producing distance measures indicating similarity of the subject signal to the example predefined signals; and (iv) forming a sequence of vectors from the produced distance measures. The formed sequence of vectors is used as input into a classifier which determines existence of any cardiac syndromes in the subject (i.e., signal patterns indicative of syndromes). 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular description of preferred embodiments of the invention, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. 
       FIG. 1  is a block diagram of a training procedure utilized by the present invention. 
       FIG. 2  is a block diagram of ECG signal analysis of the present invention. 
       FIG. 3  is a schematic view of a digital processing environment in which the present invention may be practiced. 
   

   DETAILED DESCRIPTION OF THE INVENTION 
   A description of preferred embodiments of the invention follows. 
   The present invention provides a way of combining the expertise of cardiologists in diagnosing heart disease and syndromes with that of automatic machine learning systems that “learn” based on massive amounts of raw data. 
   Data-driven pattern classification techniques in which there is a concept of a distance include Support Vector Classifiers, Boosting classifiers and neural networks. At the core of these classification techniques is a distance function called a “kernel” which compares data points, represented as feature vectors, and produces a real number. In the present invention, the data points are segments of an ECG signal which are processed to produce a feature vector. A novel kernel and a collection of labeled training data (both based on cardiologists expertise) are used to learn a set of parameters that characterize the set of classes to be distinguished. This set of parameters, along with the kernel, is then used to classify new data points (ECG signals of unknown conditions). 
   The present invention works as follows: 
   A set of example ECG heartbeat patterns or shapes corresponding to the syndromes to be classified are extracted from the cardiology literature or developed in consultation with cardiologists. Each pattern is normalized in time and amplitude and synchronized with a prototypical heartbeat. The resulting patterns  13   a  . . . n are considered to be predefined example signals and are stored in a library  11  (implemented through a database, table or other data store) as illustrated in  FIG. 1 . 
   Next the patterns  13  of library  11  are used to construct a kernel function  15  that compares two data points and produces a distance. There are many ways this could be done as further detailed below. 
   The computed distance output by kernel function  15  is input to a pattern recognition engine  21  of a classifier  25 . The pattern recognition engine  21  and/or classifier  25  may be a neural network support vector machine or Boosting classifier or other type common in the art. Classifier  25  utilizes the pattern determinations made by pattern recognition engine  21  and determines class of (or otherwise classifies) the subject ECG signal. 
   In order to train the pattern recognition engine  21  and classifier  25 , a labeled or annotated training corpora  23  is employed. Training corpora  23  is a collection of known and previously analyzed ECG signals annotated with corresponding syndrome classes. A windowing member  17  segments each training ECG signal  23  into data chunks  27  typically at changes in signal pattern as illustrated by dotted vertical lies in  FIG. 1 . 
   The resulting ECG Signal  23  segments or data chunks  27  are input into a feature extraction module  19 . For each segment  27 , feature extraction module  19  (i) extracts the signal pattern of interest from the segment/data chunk  27 , and (ii) produces ECG segment data points  29  representative of the extracted feature (interesting signal pattern). The feature extraction module  19  outputs these data points  29  for input to kernel function  15 . 
   In one embodiment, in kernel function  15  an internal distance function computes a respective distance between given data points (of an ECG segment)  29  and each of the patterns,  13   a  . . . n in library  11 . To that end, the kernel function  15  computes: 
   (a) for each pattern  13  (in library  11 ) a vector of distances from data points  29  in the given ECG segment  27  to data points in the pattern  13  and then computes 
   (b) for each ECG segment  27 , the distance between the vectors of (a) using a classical metric (Euclidian distance, Mahalanobis distance, etc.) as its final output. 
   In another embodiment, kernel function  15  computes a vector of distances for each ECG segment  27  as follows. For a given ECG segment  27 , the respective vector has as many components as there are patterns  13  in library  11 . That is, each component corresponds to a different pattern  13 . Further each component has a similarity value defined as the probability or likelihood of sameness between the data points  29  (of ECG given segment  27 ) and the data points of the component&#39;s associated library pattern  13 . From the resulting multi-component vector, kernel function  15  computes and outputs a score for the corresponding ECG segment  27  according to techniques disclosed in U.S. patent application Ser. No. 09/724,269, filed 28 Nov. 2000 herein incorporated by reference. The score represents a measured likeliness (or distance of sorts) between the given ECG segment  27  and the library patterns  13 . 
   The end result of the learning and training of  FIG. 1  is a set of parameters  31  ( FIG. 2 ) that further characterize specific conditions for the syndromes being classified. The set of parameters  31  is employed during run time of the present invention as discussed with reference to  FIG. 2 . 
   During analysis or testing of ECG signals  33  of a patient (subject) with unknown cardiac conditions, the windowing member  17  and feature extraction module  19  operate as described previously but on test corpora  33 . The subject ECG signals  33  (being analyzed) are thus segmented and ultimately represented at the output of feature extraction module  19  as ECG data chunks  27  to being analyzed for indications of possible cardiac syndromes. 
   The kernel function  15 , as constructed according to one of the embodiments or the like described above, receives the subject ECG segments  27  and the library  11  of patterns  13  as input. The kernel function  15  computes distance measures or other quantitative indications of similarity between the subject ECG segments  27  and the library patterns  13 . Preferably kernel function  15  produces such a quantitative measure for each subject ECG segment  27  in sequence of the test signal  33 . Ultimately from the computed distance measures, kernel function  15  produces a sequence of distance vectors for input to classifier  25 . The classifier  25 , as trained above in  FIG. 1 , and supported by learned parameters  31 , is responsive to the sequence of distance vectors from kernel function  15  and classifies (or categorizes according to classes) the subject ECG segments  27  of test signal  33 . To that end, classifier  25  outputs an annotated version  35  of test signal  33  labeled with specific cardiac syndromes, confidence scores, etc. 
   In summary, the present invention uses cardiologist-designed kernels  15  based on well known and characterized patterns  13  of cardiac disease as an internal component of a classification algorithm that learns additional parameters  31  from annotated training data  23 . The present invention thus incorporates cardiologist expertise in two ways. First in a completely novel way via cardiologist-designed kernels  15 , and then in a more traditional way via their annotations on ECG training data  23 . 
   Illustrated in  FIG. 3  is a computer system (environment)  100  in which the present invention may be implemented. That is, training routine/program  40  of  FIG. 1  may be executed in such a computer system  100 . Testing/analysis routine or program  50  may be executed by the same or a different computer system  100 . Each computer system  100  has a working memory  90  for running (executing) routine/programs  40 ,  50  and is coupled to supporting data stores  94  holding library  11 , classifier parameters  31  and the like. 
   In particular, each computer  100  contains system bus  79 , where a bus is a set of hardware lines used for data transfer among the components of a computer. Bus  79  is essentially a shared conduit that connects different elements of a computer system (e.g., processor, disk storage, memory, input/output ports, network ports, etc.) that enables the transfer of information between the elements. Attached to system bus  79  is I/O device interface  82  for connecting various input and output devices (e.g., displays, printers, speakers, etc.) to the computer. Network interface  86  allows the computer to connect to various other devices attached to a network. Memory  90  provides volatile storage for computer software instructions (e.g., Program Routines  92  and Data  94 ) used to implement an embodiment of the present invention. Program routines  92  include invention procedures  40 ,  50  of  FIGS. 1 and 2 . Disk storage  95  provides non-volatile storage for computer software instructions and data used to implement an embodiment of the present invention. Central processor unit  84  is also attached to system bus  79  and provides for the execution of computer instructions. 
   Network interface  86  enables procedures  40 ,  50  to be downloaded or uploaded across a network (e.g., local area network, wide area network or global network). I/O device interface  82  enables procedures  40 ,  50  to be ported between computers  100  on diskette (CD-ROM, etc.). Other transmission of procedures  40 ,  50  in whole or in part between computers  100  is in the purview of one skilled in the art. Accordingly, procedures  40 ,  50  may be run on a standalone computer  100 , distributed across computer systems  100 , or executed in a client-server fashion or other arrangement. 
   While this invention has been particularly shown and described with references to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims. 
   For example, the patient may be human or animal.