QRS Complexes Detection Using UNet-BiLSTM Model Combined With Adaptive Post-Processing for Multilead Wearable ECG Smart T-Shirt System

In recent years, the rapid development of wearable devices and Internet of Things (IoT) technology has made realtime, continuous dynamic electrocardiogram (ECG) monitoring possible. However, robust universal multi-lead QRS complex detection under noise and high heart rate (HR) conditions remains challenging. In this study, we propose a smart T-shirtbased multi-lead dynamic ECG monitoring system and a highaccuracy QRS complex detection algorithm to solve the above problems. First, we develop a six-lead smart T-shirt system with integrated flexible dry electrodes for real-time acquisition of the ECG under three conditions: standing, walking, and jogging. The electrode structure and hardware circuit are designed to resist noise, while electrodes are combined with medical-grade conductive gels to replace conventional Ag/AgCl wet electrodes, ultimately realizing high signal-to-noise ratio (SNR) signal acquisition under dynamic conditions. The T-shirt-based system design not only enhances user comfort but also facilitates use for non-medical users, making long-term ECG monitoring more feasible. Second, we propose a novel Pro-APP algorithm combining the UNet and bidirectional long short-term memory (UNet-BiLSTM) model with adaptive post-processing (APP), which is robust and outperforms three existing QRS complex detection algorithms. The UNet-BiLSTM model can effectively extract multi-scale features and capture the timing information in the ECG. The APP algorithm further optimizes the R-peak localization by dynamically adjusting parameters. The results show that the system confirms the reliability of its signal quality, HR detection, and electrode performance through the signal quality assessment (SNR ≥ 24 dB), ECG simulator validation (RR interval error.4 ms), and skin-electrode impedance measurement (impedance≤117.27 KΩ at frequencies above 10 Hz). In addition, the Pro-APP algorithm significantly improves the sensitivity (93.67%), precision (97.13%), accuracy (91.08%), and F1-score (95.30%) for detecting QRS complexes, with a mean absolute error of only 3.63 ms, which achieves universal QRS complex detection in multi-lead ECG signals and optimizes wearable dynamic ECG monitoring. The research shows that the smart Tshirt system combined with the QRS complex detection algorithm can provide an important carrier and technical means for the new model of wise information technology of medicine.