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<p>The Photoplethysmogram (PPG) is commonly used to determine Heart Rate (HR) and analyze Heart Rate Variability (HRV). It has been widely used in recent research to detect many psychological and physical problems. More new and advanced programming-based signal processing techniques are needed to be developed to analyze PPG signals more precisely and with added advanced features. This paper presented Python and pulse sensor-based, a low-cost technique for PPG signal analysis. In the PPG signals, the peaks were detected using the Peakutils library. The time differences of consecutive peaks were estimated to estimate the HR and analyze the HRV. The HR was estimated using three different methods. Peakutils library has successfully detected the peaks in the signals. The results obtained by the three methods were almost the same. The HRV was analyzed very comfortably in two different terms. Signal processing has been given a lot of focus in this technique, and it has produced good results. It will help in developing new devices (including IoT-based) and other advanced research on signal analysis, such as advanced investigation of P, Q, R, S, and T waves in the ECG signals and analysis of HRV of psychological and cardiac problematic patients.</p>

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Python and Photoplethysmogram-Based Signal Processing Technique for Heart Rate Estimation, HRV Analysis and Other Relevant Processing of ECG and PPG Signals

  • 期刊论文

  • 发表日期：

    2025年6月9日

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  • Sultan Ahamad

摘要

The Photoplethysmogram (PPG) is commonly used to determine Heart Rate (HR) and analyze Heart Rate Variability (HRV). It has been widely used in recent research to detect many psychological and physical problems. More new and advanced programming-based signal processing techniques are needed to be developed to analyze PPG signals more precisely and with added advanced features. This paper presented Python and pulse sensor-based, a low-cost technique for PPG signal analysis. In the PPG signals, the peaks were detected using the Peakutils library. The time differences of consecutive peaks were estimated to estimate the HR and analyze the HRV. The HR was estimated using three different methods. Peakutils library has successfully detected the peaks in the signals. The results obtained by the three methods were almost the same. The HRV was analyzed very comfortably in two different terms. Signal processing has been given a lot of focus in this technique, and it has produced good results. It will help in developing new devices (including IoT-based) and other advanced research on signal analysis, such as advanced investigation of P, Q, R, S, and T waves in the ECG signals and analysis of HRV of psychological and cardiac problematic patients.