Why the study?
Simultaneous ECG and PCG recording helps analyze electromechanical cardiac microvariations to diagnose cardiovascular diseases, but requires analog feature extraction to reduce power consumption and computational complexity compared to DSP implementation.
The proposed analog front end for simultaneous ECG and PCG acquisition achieves high accuracy in extracting cardiac timing intervals while reducing computational complexity.
Simultaneous recording of an electrocardiogram (ECG) and a phonocardiogram (PCG) is named as electro-phono-cardiogram (EPHONOGRAM). It helps in the analysis of the microvariations between the electrical and mechanical activity of the heart which is useful for diagnosing various cardiovascular diseases (CVDs) including myocardial contractility and coronary syndrome. In this article, analog front end (AFE) for simultaneous acquisition is presented. The ECG and PCG morphologies such as the R peak as well asS1andS2peaks are extracted in the analog domain to reduce the power consumption and computational complexity when compared to implementing the same on the digital signal processor (DSP). The cross-domain microvariations timing intervals such as the R–S1peak interval and the R–S2peak interval are obtained by exploiting the correlation between ECG and PCG timing features. Also, the fundamental heart sounds (S1andS2) from PCG are classified by correlating them with ECG features. The proposed circuit is simulated using UMC 0.18-μm CMOS technology. To analyze the performance of the AFE, performance matrices like input-referred noise (IRN), noise efficiency factor (NEF), power efficiency factor (PEF), and figure of merit (FoM) are used. To clinically validate the proposed system, the ECG and PCG signals are taken from the PhysioBank ATM database. For R peak detection, sensitivity of 100%, positive predictivity (PP) of 94.16%, and accuracy of 94.16% are achieved. Similarly, forS1andS2peaks detection, sensitivity of 97.81%, PP of 97.39%, and accuracy of 95.32% is obtained. The proposed concept is also verified in real time using the prototype built with discrete components and a Basys-3 FPGA board.
No takes yet. Share an insight, caveat, or question.
Barik et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: