A novel LMS algorithm for ECG signal preprocessing and KNN classifier based abnormality detection

Abstract

ECG signal abnormality detection is useful for identifying heart related problems. Two popular abnormality detection techniques are ischaemic beat classification and arrhythmic beat classification. In this work, ECG signal preprocessing and KNN based arrhythmic beat classification are performed to categorize into normal and abnormal subjects. LMS based adaptive filters are used in ECG signal preprocessing, but they consume more time for processing due to long critical path. To overcome this problem, a novel adaptive filter with delayed error normalized LMS algorithm is utilized to attain high speed and low latency design. Low power design is achieved in this design by applying pipelining concept in the error feedback path. R-peak detection is carried out in the preprocessed signal using wavelets for HRV feature extraction. Arrhythmic beat classification is carried out by KNN classifier on HRV feature extracted signal. Classification performance reveals that the proposed DWT with KNN classifier provides the accuracy of 97.5% which is better than other machine leaning techniques.