Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl 105, 1113 Sofia, Bulgaria.
Department of Electronics, Faculty of Electronic Engineering and Technologies, Technical University of Sofia, 8 Kliment Ohridski Blvd., 1000 Sofia, Bulgaria.
Sensors (Basel). 2024 Mar 15;24(6):1883. doi: 10.3390/s24061883.
Research of novel biosignal modalities with application to remote patient monitoring is a subject of state-of-the-art developments. This study is focused on sonified ECG modality, which can be transmitted as an acoustic wave and received by GSM (Global System for Mobile Communications) microphones. Thus, the wireless connection between the patient module and the cloud server can be provided over an audio channel, such as a standard telephone call or audio message. Patients, especially the elderly or visually impaired, can benefit from ECG sonification because the wireless interface is readily available, facilitating the communication and transmission of secure ECG data from the patient monitoring device to the remote server. The aim of this study is to develop an AI-driven algorithm for 12-lead ECG sonification to support diagnostic reliability in the signal processing chain of the audio ECG stream. Our methods present the design of two algorithms: (1) a transformer (ECG-to-Audio) based on the frequency modulation (FM) of eight independent ECG leads in the very low frequency band (300-2700 Hz); and (2) a transformer (Audio-to-ECG) based on a four-layer 1D convolutional neural network (CNN) to decode the audio ECG stream (10 s @ 11 kHz) to the original eight-lead ECG (10 s @ 250 Hz). The CNN model is trained in unsupervised regression mode, searching for the minimum error between the transformed and original ECG signals. The results are reported using the PTB-XL 12-lead ECG database (21,837 recordings), split 50:50 for training and test. The quality of FM-modulated ECG audio is monitored by short-time Fourier transform, and examples are illustrated in this paper and supplementary audio files. The errors of the reconstructed ECG are estimated by a popular ECG diagnostic toolbox. They are substantially low in all ECG leads: amplitude error (quartile range RMSE = 3-7 μV, PRD = 2-5.2%), QRS detector (Se, PPV > 99.7%), P-QRS-T fiducial points' time deviation (<2 ms). Low errors generalized across diverse patients and arrhythmias are a testament to the efficacy of the developments. They support 12-lead ECG sonification as a wireless interface to provide reliable data for diagnostic measurements by automated tools or medical experts.
研究具有远程患者监护应用的新型生物信号模态是最先进发展的主题。本研究专注于可被传输为声波并被全球移动通信系统 (GSM) 麦克风接收的声心电图模态。因此，可以通过音频通道（例如标准电话呼叫或音频消息）在患者模块和云服务器之间提供无线连接。患者，特别是老年人或视力障碍者，可以从心电图声谱图中受益，因为无线接口易于使用，便于从患者监护设备向远程服务器传输安全的心电图数据。本研究的目的是开发一种基于人工智能的 12 导联心电图声谱图算法，以支持音频心电图流信号处理链中的诊断可靠性。我们的方法提出了两种算法的设计：(1) 一种基于在甚低频带（300-2700 Hz）中对八个独立心电图导联进行调频 (FM) 的变压器 (ECG-to-Audio)；(2) 一种基于四层一维卷积神经网络 (CNN) 的变压器 (Audio-to-ECG)，用于将音频心电图流（10 s @ 11 kHz）解码为原始的八导联心电图（10 s @ 250 Hz）。CNN 模型在无监督回归模式下进行训练，搜索变换后和原始心电图信号之间的最小误差。结果使用 PTB-XL 12 导联心电图数据库（21837 条记录）报告，分为 50:50 用于训练和测试。FM 调制心电图音频的质量通过短时傅里叶变换进行监测，本文和补充音频文件中举例说明了这一点。重构心电图的误差由流行的心电图诊断工具包进行估计。在所有心电图导联中，误差都很低：幅度误差（四分位距 RMSE = 3-7 μV，PRD = 2-5.2%），QRS 检测器（Se、PPV > 99.7%），P-QRS-T 基准点时间偏差（<2 ms）。在不同患者和心律失常中广泛存在的低误差证明了开发的有效性。它们支持将 12 导联心电图声谱图作为无线接口，为自动化工具或医学专家的诊断测量提供可靠的数据。
