Synchronous Acquisition and Processing of Electro- and Phono-Cardiogram Signals for Accurate Systolic Times'
Roberto De Fazio1,2, Ilaria Cascella1, Şule Esma Yalçınkaya1
1Department of Innovation Engineering, University of Salento, Road to Monteroni, Building 'O', 73100 Lecce, Italy.
Insights
This study introduces a new method combining electrocardiography (ECG) and phonocardiography (PCG) for better cardiovascular disease monitoring. The developed algorithm accurately extracts key heart parameters, improving diagnostic potential.
Area of Science:
- Biomedical Engineering
- Cardiology
- Signal Processing
Background:
- Cardiovascular diseases are a leading cause of mortality globally.
- Electrocardiography (ECG) is standard for heart electrical activity but insufficient for conditions like valvular disorders.
- Phonocardiography (PCG) analyzes heart sounds, enhancing diagnostic accuracy when combined with ECG.
Purpose of the Study:
- To develop and validate a method for simultaneous ECG and PCG signal acquisition and analysis.
- To extract key systolic time intervals (EMAT, PEP, LVET, LVST) for cardiovascular disease monitoring.
- To assess the reliability and robustness of the developed adaptive segmentation algorithm.
Main Methods:
- Simultaneous acquisition of ECG and PCG signals using a compact system with an AD8232 analog front-end and a digital stethoscope.
- Positioning ECG electrodes and microphone on the chest for spatial signal alignment.
- Development of an adaptive segmentation algorithm to identify key waves (Q, R, S) and heart sounds (S1, S2) for parameter extraction.
Main Results:
- Measured average systolic time intervals (EMAT, PEP, LVET, LVST) were consistent with reference standards.
- The algorithm demonstrated robustness across different recording conditions, with extracted intervals closely matching literature values.
- Validation on the BSSLAB Localized ECG Data dataset confirmed the method's reliability.
Conclusions:
- Joint ECG and PCG analysis shows significant potential for improving long-term cardiovascular disease monitoring.
- The developed adaptive segmentation algorithm provides accurate extraction of vital cardiovascular parameters.
- This integrated approach offers a promising tool for enhanced cardiac diagnostics and patient management.
Abstract:
Cardiovascular diseases remain one of the leading causes of mortality worldwide, highlighting the importance of effective monitoring and early diagnosis. While electrocardiography (ECG) is the standard technique for evaluating the heart's electrical activity and detecting rhythm and conduction abnormalities, it alone is insufficient for identifying certain conditions, such as valvular disorders. Phonocardiography (PCG) allows the recording and analysis of heart sounds and improves the diagnostic accuracy when combined with ECG. In this study, ECG and PCG signals were simultaneously acquired from a resting adult subject using a compact system comprising an analog front-end (model AD8232, manufactured by Analog Devices, Wilmington, MA, USA) for ECG acquisition and a digital stethoscope built around a condenser electret microphone (model HM-9250, manufactured by HMYL, Anqing, China). Both the ECG electrodes and the microphone were positioned on the chest to ensure the spatial alignment of the signals. An adaptive segmentation algorithm was developed to segment PCG and ECG signals based on their morphological and temporal features. This algorithm identifies the onset and peaks of S1 and S2 heart sounds in the PCG and the Q, R, and S waves in the ECG, enabling the extraction of the systolic time intervals such as EMAT, PEP, LVET, and LVST parameters proven useful in the diagnosis and monitoring of cardiovascular diseases. Based on the segmented signals, the measured averages (EMAT = 74.35 ms, PEP = 89.00 ms, LVET = 244.39 ms, LVST = 258.60 ms) were consistent with the reference standards, demonstrating the reliability of the developed method. The proposed algorithm was validated on synchronized ECG and PCG signals from multiple subjects in an open-source dataset (BSSLAB Localized ECG Data). The systolic intervals extracted using the proposed method closely matched the literature values, confirming the robustness across different recording conditions; in detail, the mean Q-S1 interval was 40.45 ms (≈45 ms reference value, mean difference: -4.85 ms, LoA: -3.42 ms and -6.09 ms) and the R-S1 interval was 14.09 ms (≈15 ms reference value, mean difference: -1.2 ms, LoA: -0.55 ms and -1.85 ms). In conclusion, the results demonstrate the potential of the joint ECG and PCG analysis to improve the long-term monitoring of cardiovascular diseases.
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