FMM-Head：利用先验知识增强基于自动编码器的心电图异常检测
FMM-Head: Enhancing Autoencoder-based ECG anomaly detection with prior knowledge
AI总结：
针对现有自动编码器心电图异常检测模型未考虑导联模式、不可解释的问题，提出基于心电图形态先验的FMM-Head重建头替换解码器，在AUROC最高提升0.31、模型减半的同时实现可解释与实时处理。
摘要：
心电图数据中的异常检测对于识别正常心跳模式的偏差、为高危患者提供及时干预至关重要。已有多种自动编码器模型（AE）被提出用于通过机器学习解决异常检测任务。然而，这些模型未考虑心电图导联的特定模式，且是无法解释的黑盒模型。相比之下，我们将AE的解码部分替换为基于心电图形态先验知识的重建头（即FMM-Head）。我们的模型在异常检测性能上始终优于当前最优模型，ROC曲线下面积（AUROC）最高提升0.31，模型尺寸仅为原模型的一半，且提取的特征具备可解释性。我们的模型处理时间比通过求解优化问题获取相同参数的方式低四个数量级，因此适用于实时心电图参数提取与异常检测。
英文摘要：
Detecting anomalies in electrocardiogram data is crucial to identifying deviations from normal heartbeat patterns and providing timely intervention to at-risk patients. Various AutoEncoder models (AE) have been proposed to tackle the anomaly detection task with ML. However, these models do not consider the specific patterns of ECG leads and are unexplainable black boxes. In contrast, we replace the decoding part of the AE with a reconstruction head (namely, FMM-Head) based on prior knowledge of the ECG shape. Our model consistently achieves higher anomaly detection capabilities than state-of-the-art models, up to 0.31 increase in area under the ROC curve (AUROC), with as little as half the original model size and explainable extracted features. The processing time of our model is four orders of magnitude lower than solving an optimization problem to obtain the same parameters, thus making it suitable for real-time ECG parameters extraction and anomaly detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
