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Abstract
Coronary artery disease (CAD) has been one of main causes of heart diseases globally. The electrocardiogram (ECG) is a widely used diagnostic tool to monitor patients’ heart activities, and medical personnel need to judge whether there are abnormal heartbeats according to captured results. Therefore, it is significant to identify ECG signals accurately and fast. In this paper, a fast and accurate electrocardiogram (ECG) classification system based on deep learning is proposed. In our model, stacked denoising autoencoders (SDAE), as encoder, automatically learns semantic encoding of heartbeats without any complex feature extraction in unsupervised way. Then bidirectional LSTM (Bi-LSTM) classifier achieves classification of heartbeats with semantic encoding. SDAE implements noise-reduction while Bi-LSTM takes full advantage of temporal information in data. At the same time, this method relieves impacts from unbalanced data by employing cost-sensitive loss function. We validate our model on MIT-BIH Arrhythmias Database, SVDB and NSTDB respectively. Compared with state-of-art methods, the final result verify that this newly proposed method not only has high accuracy but also boosts classifying efficiency.
Highlights
N OWADAYS, coronary artery disease (CAD) has been one of main causes of global increase in the fatality rate, and arrhythmia is a disease belonging to cardiovascular
We propose a new architecture employing cost sensitive loss function, and construct a bidirectional long short-term memory (LSTM) (Bi-LSTM) classifier based on stacked denoising autoencoders (SDAE)
To classify heartbeats more efficiently and accurately, we propose a Bi-LSTM classifier based on SDAE combining with cost-sensitive learning
Summary
N OWADAYS, coronary artery disease (CAD) has been one of main causes of global increase in the fatality rate, and arrhythmia is a disease belonging to cardiovascular. The final outcome shows the newly proposed method can improve accuracy without manual intervention and save a lot of time compared with state-of-art methods It reduces impacts from unbalanced data and makes classifying process more robust by SDAE. 2) MIT-BIH SUPRAVENTRICULAR ARRHYTHMIA DATABASE (SVDB) This database consists of 78 two-lead recordings of approximately 30 min and samples at 128 Hz. The beat type annotations of the recordings were first automatically performed, by the Marquette Electronics 8000 Holter scanner and later reviewed and corrected by medical students. The noise recordings which samples at 360Hz, were made by adding calibrated amounts of noise to unpolluted ECG recording from MIT-BIH Arrhythmia Database. Taking recordings in MIT-BIH arrhythmia database as example, we observe that one recording has 650000 sampled points and contains 3100 or so heartbeats at most.
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