An Efficient Signal Processing Algorithm for accurate detection of Characteristic points in Abnormal ECG signals

A lot of information on the normal and pathological physiology of heart can be obtained in the form of ECG. The irregularity of heart resembles the shape of ECG. One cardiac cycle of ECG signal consists of characteristic points P-QRS-T. The amplitudes and intervals values of P-QRS-T segment determine the functioning of heart of every human. If the electrical activity of the heart is irregular, faster, or slower than normal this is considered as abnormality in ECG signals. In this paper a Signal processing algorithm, which is the combination of both Frequency and Time analysis is used to identify P-QRS-T points. R-peak detection is the first step in characteristic points detection, for identifying R-peak, wavelet transform (sym4) decomposition method (Frequency domain) is used. After R-peak detection other characteristic points are detected by tracing to and fro from R-peak (Time domain). In this five cases of ECG signals are tested specifically for a Normal Person; Normal Sinus Rhythm (NSR), Atrial fibrillation (AF), Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Cardiac Ischemia (CI) and Sudden Cardiac Arrest (SCA). A total of 73 Abnormal ECG signals are taken from MIT-BIH arrhythmias database and Results are evaluated using MATLAB R2014a software.