Spike detection in biomedical signals using midprediction filter.

Spikes such as QRS complex in ECG, epileptic seizures in EEG, fine crackles in vesicular sound and glottal closure instants in voiced sound are of diagnostic importance. Various methods of spike detection use the amplitude and frequency characteristics of the spikes. Because of the high frequency content, the spikes appear in the error signal when a linear prediction filtering scheme is used. The authors use the method of midprediction filtering for the detection of the spikes. In this method, the present sample is predicted as a weighted average of p recent past and p immediate future samples. The symmetrical nature of midprediction causes the spikes to appear in the error signal with their original basewidths. This can help in improving the reliability of spike detection, as both the amplitude and the duration of the spike can be considered as decision making parameters. It is observed that the high frequency gain of the midprediction filter is higher compared to the high frequency gain of the LPC or endprediction filter. As a result, this method works better than linear prediction for the detection of spikes.