  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.
