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.

翻译：在全球范围内，心血管疾病（CVDs）是导致死亡的主要原因，每年约有1790万人因此丧生。利用心电图（ECG）数据进行心血管疾病的早期检测是关键的临床目标之一，该领域已受到研究界的广泛关注。基于机器学习和深度学习的近期进展在该领域取得了重大突破。然而，现有方法论存在固有局限性，包括不恰当的模型评估和数据泄露问题。在本研究中，我们提出了一种简化的工作流程范式，用于将ECG信号预处理为统一的10秒时长，无需手动特征提取/心跳检测。同时，我们提出了一种由长短期记忆网络（LSTM）与支持向量机（SVM）融合的混合模型用于欺诈检测。该架构包含两个LSTM层和一个SVM分类器，在MIT-BIH心律失常数据集上实现了平均精确率达0.9402的SOTA结果，在MIT-BIH房颤数据集上实现了0.9563的结果。基于上述结果，我们相信该方法能够显著促进心血管疾病的早期检测与管理。