Why the study?
Cardiac function abnormalities occur irregularly and involve multimodal electrical, mechanical, and acoustic alterations, requiring an integrated model for complete macroscopic cardiac function mapping.
The development of a portable Electro-Mechano-Acoustic mapping system demonstrates high accuracy in extracting cardiac mechanical and electrical features for dynamic monitoring.
Abnormalities in cardiac function arise irregularly and typically involve multimodal electrical, mechanical vibrations, and acoustics alterations. This article proposes an Electro-Mechano-Acoustic (EMA) activity model for mapping the complete macroscopic cardiac function to refine the systematic interpretation of cardiac multimodal assessment. We abstract this activity pattern and build the mapping system by analyzing the functional comparison of the heart pump and Electronic Fuel Injection (EFI) system from the multimodal characteristics of the heart. Electrocardiogram (ECG), seismocardiogram (SCG) & Ultra-Low Frequency seismocardiogram (ULF-SCG), and Phonocardiogram (PCG) are selected to implement the EMA mapping respectively. First, a novel low-frequency cardiograph compound sensor capable of extracting both SCG and ULF-SCG is proposed, which is integrated with ECG and PCG modules on a single hardware device for portable dynamic acquisition. Afterward, a multimodal signal processing chain further analyses the acquired synchronized signals, and the extracted ULF-SCG is shown to indicate changes in heart volume. In particular, the proposed method based on waveform curvature is used to extract 9 feature points of the SCG signal, and the overall recognition accuracy reaches over 90% in the data collected by EMA portable device. Ultimately, we integrate the portable device and signal processing chains to form the EMA cardiovascular mapping system (EMACMS). As a next-generation system solution for cardiac daily dynamic monitoring, which can map the mechanical coupling and electromechanical coupling process, extract multi-characteristic heart rate variability (HRV), and enable extraction of important time intervals of cardiac activity to assess cardiac function.
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Gao et al. (2023) studied this question.
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