# A Chest Strap-Based System for Electrocardiogram Monitoring
## 1 Introduction  
Nowadays, sudden cardiac events during physical activity have become a major hidden threat to human health, drawing increasing attention to the real-time monitoring of cardiac function. Electrocardiogram (ECG) monitoring, as the most direct and effective method for assessing cardiac activity, plays a critical role in evaluating heart function [1, 2]. Although traditional ECG monitoring systems offer accurate signal acquisition, their bulky and restrictive designs hinder user mobility, limiting their suitability for continuous and portable daily monitoring [3]. Furthermore, the Ag/AgCl gel electrodes commonly used in conventional systems exhibit poor breathability during prolonged use, often leading to skin irritation or allergic reactions, which further constrains the widespread adoption of portable monitoring systems [4, 5, 6]. In recent years, advances in flexible electronics, sensor materials, and integrated circuit technologies have paved the way for the development of flexible wearable monitoring systems as a promising solution [7, 8, 9, 10, 11, 12, 13, 14, 15]. Numerous studies have demonstrated that by optimizing electrode materials and structural design, combined with low-power, high-performance signal conditioning circuits and algorithms, high-quality, real-time ECG monitoring can be achieved [16, 17, 18, 19, 20, 21, 22, 23, 24]. [PERSON] et al. proposed a vest-type wearable wireless Tele-ECG monitoring system based on fabric electrodes, enabling remote physiological signal monitoring while ensuring user comfort. The filtered digital signal achieved a signal-to-noise ratio (SNR) of up to45.62 [25]. [PERSON] et al. designed a printed fabric-based hybrid electric circuit that remains conductive and reliable under repeated stretching and bending. This flexible circuit provides 1600-times amplification and multi-stage filtering, effectively capturing and processing weak electrophysiological signals [26]. [PERSON] et al. developed a portable ECG monitoring device using flexible non-hydrogel electrodes, demonstrating its potential for long-term ECG monitoring in real-world scenarios [27]. However, current research still faces challenges, including low system integration, bulky device designs, and limited wearability. Moreover, most studies emphasize ECG signal acquisition, with insufficient focus on advanced signal analysis and intelligent processing. These shortcomings hinder the development of practical ECG monitoring systems that meet modern demands for portability, intelligence, and rich information extraction. During physical activity, signal quality and system stability are more susceptible to motion artifacts, unstable electrode contact, and sweat interference [28; 29]. Therefore, developing a portable system capable of acquiring high-quality ECG signals under dynamic conditions remains a key challenge in current research. At the same time, with the continuous advancement of artificial intelligence, particularly deep learning, analyzing physiological signals using deep learning techniques has become a major research focus [30; 31; 32; 33]. [PERSON] et al. proposed a self-powered, wearable AIoT system for athletes, which uses a convolutional neural network (CNN) to accurately analyze and classify ECG signals during exercise. However, the system still faces issues such as large size and limited portability [34]. [PERSON] et al. introduced an algorithm that employs an autoencoder with long short-term memory network (LSTM) layers and incorporates an adaptive filter to extract ECG features, enabling high-precision automatic identification and classification of ECG signals [35]. [PERSON] et al. developed a classification and prediction model for cardiac arrhythmia based on a CNN-LSTM-SE fusion algorithm, achieving high classification accuracy and recall [36].