Cardiac Disease Detection Using Modified Pan-Tompkins Algorithm

Abstract

The analysis of electrocardiogram (ECG) signals facilitates the detection of various abnormal conditions of the human heart. The QRS complex is the most critical part of the ECG waveform. Further, different diseases can be identified based on the QRS complex. In this paper, a new algorithm based on the well-known Pan-Tompkins algorithm has been proposed. In the proposed scheme, the QRS complex is initially extracted by removing the background noise. Subsequently, the R-R interval and heart rate are calculated to detect whether the ECG is normal or has some abnormalities such as tachycardia and bradycardia. The accuracy of the proposed algorithm is found to be almost the same as the Pan-Tompkins algorithm and increases the R peak detection processing speed. For this work, samples are used from the MIT-BIH Arrhythmia Database, and the simulation is carried out using MATLAB 2016a.

Fig. 1. ECG of a single heartbeat in normal sinus rhythm

Fig. 2. Proposed R-peak detection algorithm

Fig. 3. Raw input ECG signal

Fig. 4. ECG signal after filtering and derivative

Fig. 5. ECG signal after squaring

Fig. 6. ECG signal after R-peak detection

Fig. 7. Pseudo code for disease detection

Fig. 8. Tachycardia detected from the patient ID 213 in 2.55 s.

Fig. 9. Normal ECG signal from patient ID 101.

Fig. 10. Bradycardia detected in test signal 119 in 8.93 s.

Table 1. Functionalities of different waves.

Table 2. Conditions for heart abnormalities.

Table 3. Performance of the existing Pan—Tompkins algorithm for disease detection.

Table 4. Performance of the proposed algorithm for disease detection.