An ECG feature extraction with wavelet algorithm for personal healthcare

This paper presents an electrocardiogram (ECG) feature extraction with wavelet algorithm for personal healthcare monitoring. Developing a wireless ECG examination system employed to monitor the cardiovascular disease (CVD) is signification, especially uses a low-power device anywhere and anytime detecting the real-time ECG signal for self-examination applications. The continuous time Mexican-Hat wavelet transform (CTMHWT) algorithm is quickly and easily applied to analyze the approximation of P-QRS-T complex fiducial points. The database, MIT-BIH discrete points, is adopted to efficiently extract the ECG signal according to CTMHWT algorithm with Matlab simulator. Moreover, the simulation result reveals that the feature extraction of ECG signal is satisfied to the required error range.