Neural Network Based ECG Signal Analysis for Heart Rate Detection

摘要

In this research, Backpropagation Neural Network is used to learn the characteristics of R peak to detect QRS complex. This allows R peak to be differentiated from large peaked T and P waves with higher accuracy and minimizes the problem associated with the noises in the ECG signal includes power line interference, motion artifacts, baseline drift, ECG amplitude modulation and other composite noises. The features that trains the network includes amplitude, differentiation value, duration exceed threshold, RR interval and crossing-zero. The performance was tested using 10 ECG signals data from MIT-BIH Database. The correct positive peak detection gave an accuracy of 91.16% with 8.84% of missing peak count and 6.51% of false positive peak.