同步采集心电图和心音图信号对心脏机电谱的研究

D. Damani, Divaakar Siva Baala Sundaram, S. Damani, Anoushka Kapoor, Adelaide Olson, S. P. Arunachalam
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引用次数: 0

摘要

心脏病是世界上导致死亡的主要原因。心电图(ECG)和心音图(PCG)信号在各种心脏疾病的诊断中具有重要作用。心电和心电信号的同时采集为心脏的机电分析开辟了新的信号处理途径。然而,目前还没有标准的方法来确保高保真度的同步数据采集,从而使这些新技术的发展成为可能。在这项工作中,作者报告了各种数据捕获位置的结果,这些位置可能导致同时收集ECG和PCG数据的标准化。肺音的存在、姿势的变化、呼吸的深度和频率都会导致ECG-PCG记录的信号不同。这就需要一种标准的方法来记录和解释收集到的数据。作者使用数字听诊器同时记录6名健康受试者的ECG-PCG,以了解正常呼吸和深呼吸条件下不同记录体位(俯卧、仰卧、俯卧、半卧、站立、左侧卧和坐姿)的信号质量差异。采集到的数字化信号使用定制的MATLAB信噪比软件离线处理信号质量。结果表明,不同记录位置的信号质量差异极小。需要用更大的数据集验证该技术。未来的工作将研究体位和呼吸方式对心电图和PCG特征的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
INVESTIGATION OF SYNCHRONIZED ACQUISITION OF ELECTROCARDIOGRAM AND PHONOCARDIOGRAM SIGNALS TOWARDS ELECTROMECHANICAL PROFILING OF THE HEART
Cardiac diseases are the leading cause of death in the world. Electrocardiogram (ECG and Phonocardiogram (PCG signals play a significant role in the diagnosis of various cardiac diseases. Simultaneous acquisition of ECG and PCG signals can open new avenues of signal processing approaches for electromechanical profiling of the heart. However, there are no standard approaches to ensure high fidelity synchronous data acquisition to enable the development of such novel technologies. In this work, the authors report results on various data capture positions that could lead to standardization of simultaneous ECG and PCG data collection. Presence of lung sounds, variations in posture, depth and frequency of breathing can lead to differences in the ECG-PCG signals recorded. This necessitates a standard approach to record and interpret the data collected. The authors recorded ECG-PCG simultaneously in six healthy subjects using a digital stethoscope to understand the differences in signal quality in various recording positions (prone, supine, bending, semi recumbent, standing, left lateral and sitting with normal and deep breathing conditions. The collected digitized signals are processed offline for signal quality using custom MATLAB software for SNR. The results indicate minimal differences in signal quality across different recording positions. Validation of this technique with larger dataset is required. Future work will investigate changes in characteristic ECG and PCG features due to position and breathing patterns.
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