Novel logarithmic ECG Feature Extraction Algorithm Based on Pan and Tompkins

ECG signal is an important tool to analyze the heart operation and detect many cardiac diseases. In this paper an improved Pan and Tompkins algorithm in the log(2) domain is proposed. The proposed algorithm successfully detects the QRS complex and significantly reduces noise in the ECG signal. An average sensitivity of 99.68% and predictivity of 100% was obtained using the proposed algorithm. Many normal sinus rhythm ECG signals from the Physionet MIT-BIH database were used to validate the accuracy of the proposed algorithm. The algorithm was also used to measure the average heart rate using the R-R interval, an average error of 1.05% was obtained when compared the measured heart rate with the annotated data. Due to the inherent benefits of simplifying multiplication and division calculations in the log domain, the proposed algorithm provides many benefits over traditional Pan and Tompkins when it comes to hardware realization and a low-power ASIC implementation.