The electrocardiogram (ECG) is a valuable signal used to assess various
aspects of heart health, such as heart rate and rhythm. It plays a crucial role
in identifying cardiac conditions and detecting anomalies in ECG data. However,
distinguishing between normal and abnormal ECG signals can be a challenging
task. In this paper, we propose an approach that leverages anomaly detection to
identify unhealthy conditions using solely normal ECG data for training.
Furthermore, to enhance the information available and build a robust system, we
suggest considering both the time series and time-frequency domain aspects of
the ECG signal. As a result, we introduce a specialized network called the
Multimodal Time and Spectrogram Restoration Network (TSRNet) designed
specifically for detecting anomalies in ECG signals. TSRNet falls into the
category of restoration-based anomaly detection and draws inspiration from both
the time series and spectrogram domains. By extracting representations from
both domains, TSRNet effectively captures the comprehensive characteristics of
the ECG signal. This approach enables the network to learn robust
representations with superior discrimination abilities, allowing it to
distinguish 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 detecting
abnormalities. The experimental result on the large-scale dataset PTB-XL has
demonstrated the effectiveness of our approach in ECG anomaly detection, while
also prioritizing efficiency by minimizing the number of trainable parameters.
Our code is available at this https URL

通过利用正常心电图数据进行训练，从而利用异常检测来识别不健康的条件；提出了一种名为 TSRNet 的专用网络，该网络能够通过从时间序列和时频领域提取的特征有效地捕捉心电图信号的综合特征，并通过引入基于峰值错误的推理方法来增强心电图异常检测的效果。