Discrete Cosine Transform based ECG Signal Analysis and Processing

Abstract

Cardiovascular disease (CADs) is considered the primary leading cause of death. Irregular activity of heart, these disease can be detected and classified by Electrocardiogram (ECG), which is constructed from using electrodes placed on human skin to record the electrical activity of the heart. Because QRS complex represents the basic part of the ECG signal, these components should be recognized in order to analysis the other characteristics of the signal. Different methods and algorithms are proposed to analysis and processing the ECG signal. In this paper, a new QRS complex recognition method are proposed based on discrete cosine transform (DCT) with variable adaptive threshold method, which is used to determine threshold based on characteristic of each ECG signal to detect upper and lower levels of threshold to detect the peak of the signal. At first, the DCT is applied to the ECG signal to isolate it into different coefficients and eliminate or reduce the noises of the signal based on processing of high frequency components of DCT coefficients, which have less information, then the ECG is reconstructed by cropping the most important coefficients to be used in threshold determination. The basic idea is that the reconstructed signal have high differences between the components of the signal, and this facilitates the process of calculating the threshold value, which is used later to find peaks of ECG signal. The proposed method is tested and its performance are determined based on three different datasets, which are MITBIH Arrhythmia dataset, (LTSTDB) and (EDB) and the performance are evaluated using different metrics, which are Detection rate, accuracy, specificity and sensitivity. The experimental results show that the proposed method is performed or outperformed other works, therefore it can be used in peak detection applications.