ECG waveform feature extraction and its application to automated prognosis

Automated correlation of ECG history for early detection of heart disease, especially among the young, has been a matter of increasing interest. However, each electrocardiogram, recorded say a few months apart, generates anywhere from 600 to 2400 digitized data, so that statistical methods cannot directly be applied. An information compression step suitable for such data is presented in this paper and a prediction procedure is developed for forecasting the waveform changes. Specifically, each ECG lead is digitized and represented by itsz-domain modes. These modes are found to exhibit continuity in time, from month to month and year to year, except in the event of major physiological changes such as after surgery, thus lending themselves ideally to statistic al prediction. To enhance discrimination of the subtle changes inP, QRS, andT complexes, the derivatives of the waves are employed for extraction of the modes. This signifies a departure from previous efforts in ECG representation. Indeed, otherwise, important changes in the waves can remain undetected through mode extraction while the human eye can perceive them rather easily from the recorded traces.