Signal Processing Techniques for ECG analysis

The electrocardiogram (ECG) signals provide important information about human heart status. The information of the human heart, such as the normal or irregular heartbeat rhythm, the heartbeat rate, and the working behaviors of heart, can be used to interpret healthy or unhealthy states of heart. An automatic ECG waveform analysis algorithm with high accuracy and efficiency is helpful for cardiac disease diagnosis and health monitoring. A typical heartbeat consists of the dominant points of P, Q, R, S, and T peaks. The most important one is the R-wave peak. When the position of the R-wave peak is found, P, Q, S, and T peaks can be determined according to the relative positions to the R-wave peak. After detecting P, Q, R, S, and T peaks, their locations, heights, widths, and distances are extracted as the basic features for heartbeat classification. The accuracy of cardiac disease problem analysis, such as premature ventricular contraction (VPC), atrial premature contraction (APC), and atrial fibrillation (AF) analysis, significantly depends on whether the features of an ECG signal can be extracted accurately. In the dissertation, we propose a time-domain-based algorithm, which is very effective and efficient, to analyze an ECG signal for heart disease diagnosis and health monitoring. Based on the signal processing techniques of the gradient varying weighting function for baseline subtraction of an ECG signal, the Haar-like matched filter, noise-like peaks removal by the variation ratio test, adaptive thresholds for R-wave peak sifting, and the Mexican-hat matched filter for detection P, Q, S, and T peaks, the intra-heartbeat and inter-heartbeat features can be extracted precisely. Moreover, a rule based weighted classifier with product-form score functions, a ratio variation hypothesis test method, and a two-class cluster splitting method by the Gini index are also applied for VPC heartbeat, APC heartbeat, and AF episode classification. The proposed real-time detection algorithm is tested in the MIT-BIH arrhythmia database, the atrial fibrillation database, the QT database, and the AHA database, which consist of two-lead ECG signals. Simulations show that the proposed algorithm achieves higher sensitivity value (SE), positive prediction rate (+P), detection error rate (DER), and specificity value (SP) than those of other existing algorithms. With the proposed signal processing techniques for ECG signal analysis, the PVC heartbeats, APC heartbeats, and AF episodes can be determined in an accurate way.