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The electrocardiogram (ECG) is a valuable signal used to assess variousaspects of heart health, such as heart rate and rhythm. It plays a crucial rolein identifying cardiac conditions and detecting anomalies in ECG data. However,distinguishing between normal and abnormal ECG signals can be a challengingtask. In this paper, we propose an approach that leverages anomaly detection toidentify unhealthy conditions using solely normal ECG data for training.Furthermore, to enhance the information available and build a robust system, wesuggest considering both the time series and time-frequency domain aspects ofthe ECG signal. As a result, we introduce a specialized network called theMultimodal Time and Spectrogram Restoration Network (TSRNet) designedspecifically for detecting anomalies in ECG signals. TSRNet falls into thecategory of restoration-based anomaly detection and draws inspiration from boththe time series and spectrogram domains. By extracting representations fromboth domains, TSRNet effectively captures the comprehensive characteristics ofthe ECG signal. This approach enables the network to learn robustrepresentations with superior discrimination abilities, allowing it todistinguish between normal and abnormal ECG patterns more effectively.Furthermore, we introduce a novel inference method, termed Peak-based Error,that specifically focuses on ECG peaks, a critical component in detectingabnormalities. The experimental result on the large-scale dataset PTB-XL hasdemonstrated the effectiveness of our approach in ECG anomaly detection, whilealso prioritizing efficiency by minimizing the number of trainable parameters.Our code is available at https://github.com/UARK-AICV/TSRNet.

TSRNet: Simple Framework for Real-time ECG Anomaly Detection with Multimodal Time and Spectrogram Restoration Network