Multi-Scale Heart Rate Anomaly Detection Based on Temporal Convolutional Networks

This study aims to utilize the Temporal Convolutional Network (TCN) to develop a multi-scale heart rate anomaly detection method, enhancing the accuracy and efficiency of anomaly recognition. Utilizing the MIT-BIH Arrhythmia Dataset, this research preprocesses ECG data with a Butterworth bandpass filter and combines time-domain and frequency-domain analyses to extract signal features at key time points through PCA. We designed a novel TCN model that captures high-frequency and low-frequency information of heart rate signals using varying convolutional kernel scales, integrated with ResNet technology to improve the learning of abstract features. The experimental results show that this model excels in multi-class heart rate anomaly detection with an accuracy of up to 99.41%, significantly outperforming comparable models. Additionally, by aggregating features through a multi-head attention mechanism, the model's capability to capture complex nonlinear characteristics of ECG signals is further enhanced, validating its feasibility and effectiveness.