Heartbeat detection using mHDFT filter along with modified Pan Tompkins algorithm

This article aims to calculate the heartbeat from a raw electrocardiogram (ECG) signal by using a modified Pan-Tompkins algorithm. The raw ECG signal, when acquired, has frequency noises along with power line interference as well as baseline wandering issues. For this, a modulated hopping Discrete Fourier transform (mHDFT) is designed to work as a low-pass filter to remove the high-frequency noises and the power line interference from the raw ECG signal. Normalization is applied to the ECG signal so that the signal is scaled to a consistent range. Segmentation is applied to the filtered ECG signal to divide it into 30 segments to calculate the heartbeat in each 1-minute interval. The heartbeat detection is achieved using the peak detection technique of the QRS complex. The modified Pan-Tompkins algorithm is applied to demonstrate enhanced accuracy in identifying R-peaks, enabling precise heart rate calculation. Using the MIT-BIH Arrhythmia Database, the suggested approach is validated and shows good handling of various ECG morphologies and arrhythmic patterns. This method shows potential for real-time cardiac monitoring and arrhythmia identification in clinical and wearable contexts.