Globally, cardiovascular diseases (CVDs) are the leading cause of mortality,
accounting for an estimated 17.9 million deaths annually. One critical clinical
objective is the early detection of CVDs using electrocardiogram (ECG) data, an
area that has received significant attention from the research community.
Recent advancements based on machine learning and deep learning have achieved
great progress in this domain. However, existing methodologies exhibit inherent
limitations, including inappropriate model evaluations and instances of data
leakage. In this study, we present a streamlined workflow paradigm for
preprocessing ECG signals into consistent 10-second durations, eliminating the
need for manual feature extraction/beat detection. We also propose a hybrid
model of Long Short-Term Memory (LSTM) with Support Vector Machine (SVM) for
fraud detection. This architecture consists of two LSTM layers and an SVM
classifier, which achieves a SOTA results with an Average precision score of
0.9402 on the MIT-BIH arrhythmia dataset and 0.9563 on the MIT-BIH atrial
fibrillation dataset. Based on the results, we believe our method can
significantly benefit the early detection and management of CVDs.

本研究提出了一种用于心血管疾病早期检测的流程，结合了机器学习和深度学习的技术，并利用长短时记忆神经网络（LSTM）和支持向量机（SVM）的混合模型进行欺诈检测，取得了卓越的成果。这种方法对于心血管疾病的早期检测和管理具有显著的益处。

一种紧凑的 LSTM-SVM 融合模型用于长期心血管疾病检测

A Compact LSTM-SVM Fusion Model for Long-Duration Cardiovascular  Diseases Detection

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.

通过使用基于心电图形状先验知识的重建头（即 FMM-Head）替代自编码器（AE）的解码部分来提高异常检测能力和解释性特征的心电图异常检测模型。