A new ECG classifier based on linear prediction techniques.
A longstanding tradition in automatic ECG classification has been the use of conventional features (amplitudes, duration, etc.) as waveform descriptors for pattern discrimination purposes. This paper presents an alternative approach in statistical ECG classification. It is based on the use of linear prediction coefficients, a sort of "abstract" features which, as waveform descriptors, enjoy the desirable property of whole-signal dependency, being rather insensitive to high-frequency noise. Experimental results obtained on 400 ECGs distributed by four clinical groups according to clinicopathological data (normal, myocardial infarction, right and left hypertrophies) show interesting potentialities of this new method, namely a classification error for equal class prevalences (30%) significantly lower than by using conventional features. Classification and cluster separability results are presented and discussed as well as the viability of the new method in a clinical environment.
D000293:Adolescent; D000328:Adult; D001185:Artificial Intelligence; D006332:Cardiomegaly / Q000175:diagnosis; D002648:Child; D003936:Diagnosis, Computer-Assisted; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D009203:Myocardial Infarction / Q000175:diagnosis; D013223:Statistics as Topic