Rajendran Jegan, Wilson Sukumari Nimi, Jose P Subha Hency, Rajendran Manikandan, Saikia Manob Jyoti
Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA.
Biomedical Engineering Department, Karunya Institute of Technology and Sciences, Coimbatore 641114, Tamil Nadu, India.
Bioengineering (Basel). 2024 Dec 11;11(12):1252. doi: 10.3390/bioengineering11121252.
A battery-operated biomedical wearable device gradually assists in clinical tasks to monitor patients' health states regarding early diagnosis and detection. This paper presents the development of a self-powered portable electronic module by integrating an onboard energy-harvesting facility for electrocardiogram (ECG) signal processing and personalized health monitoring. The developed electronic module provides a customizable approach to power the device using a lithium-ion battery, a series of silicon photodiode arrays, and a solar panel. The new architecture and techniques offered by the developed method include an analog front-end unit, a signal processing unit, and a battery management unit for the acquiring and processing of real-time ECG signals. The dynamic multi-level wavelet packet decomposition framework has been used and applied to an ECG signal to extract the desired features by removing overlapped and repeated samples from an ECG signal. Further, a random forest with deep decision tree (RFDDT) architecture has been designed for offline ECG signal classification, and experimental results provide the highest accuracy of 99.72%. One assesses the custom-developed sensor by comparing its data with those of conventional biosensors. The onboard energy-harvesting and battery management circuits are designed with a BQ25505 microprocessor with the support of silicon photodiodes and solar cells which detect the ambient light variations and provide a maximum of 4.2 V supply to enable the continuous operation of an entire module. The measurements conducted on each unit of the proposed method demonstrate that the proposed signal-processing method significantly reduces the overlapping samples from the raw ECG data and the timing requirement criteria for personalized and wearable health monitoring. Also, it improves temporal requirements for ECG data processing while achieving excellent classification performance at a low computing cost.
一种电池供电的生物医学可穿戴设备逐步协助临床任务，以监测患者有关早期诊断和检测的健康状况。本文介绍了一种自供电便携式电子模块的开发，该模块集成了用于心电图（ECG）信号处理和个性化健康监测的板载能量收集设施。所开发的电子模块提供了一种可定制的方法，使用锂离子电池、一系列硅光电二极管阵列和太阳能电池板为设备供电。所开发方法提供的新架构和技术包括一个模拟前端单元、一个信号处理单元和一个用于采集和处理实时ECG信号的电池管理单元。动态多级小波包分解框架已被用于ECG信号，通过去除ECG信号中的重叠和重复样本提取所需特征。此外，还设计了一种具有深度决策树（RFDDT）架构的随机森林用于离线ECG信号分类，实验结果提供了高达99.72%的准确率。通过将定制开发的传感器的数据与传统生物传感器的数据进行比较来评估该传感器。板载能量收集和电池管理电路采用BQ25505微处理器设计，在硅光电二极管和太阳能电池的支持下，检测环境光变化并提供最大4.2V的电源，以使整个模块能够连续运行。对所提出方法的每个单元进行的测量表明，所提出的信号处理方法显著减少了原始ECG数据中的重叠样本以及个性化和可穿戴健康监测的定时要求标准。此外，它提高了ECG数据处理的时间要求，同时以低计算成本实现了优异的分类性能。
