Joint HW/SW Signal Co-Conditioning Strategy for Unobtrusive Single-Arm Cardiac Surveillance

The electrocardiogram (ECG) is a crucial tool for cardiac health monitoring, but existing ECG monitoring systems often lack effective signal conditioning coordination between hardware (HW) and software (SW), resulting in poor signal quality and monitoring effectiveness. To address this issue, this study presents a novel single-arm ECG monitoring system based on the Internet of Things (IoT) that comprehensively considers both hardware and software aspects. The system mainly consists of an ECG measurement device and a signal reconstruction algorithm. For the device, impedance matching circuits and preamplifier circuits were specially designed to measure the weak single-arm ECG signal. Additionally, to improve wearing comfort, a metal dry electrode and an armband were developed. On the software side, an ECG signal reconstruction algorithm based on a diffusion model is proposed, and the reverse process of the diffusion model and the noise prediction model are improved according to the characteristics of the ECG signal. The feasibility of the system was verified through monitoring experiments conducted in real environments on healthy subjects and heart disease patients. The experimental results show that the proposed measurement device is capable of long-term ECG monitoring and that the signal reconstruction algorithm effectively removes various real-world noises. The R-wave detection precision, sensitivity, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula>-score of the reconstructed signal improved from 75.90% to 85.87%, 69.16% to 83.40%, and 72.37% to 84.62%, respectively. This demonstrates that the system provides a new solution for efficient cardiac monitoring in daily life. The open-source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/CHENJIAR3/ECG_Denoising/tree/Linfei</uri>.