Key result
A novel deep learning model combining stacked denoising autoencoders and bidirectional LSTM with cost-sensitive learning achieved 98.85% accuracy and 99.86% specificity in classifying ECG arrhythmias.
Why the study?
Coronary artery disease is a leading cause of heart disease globally, making fast and accurate identification of abnormal heartbeats on ECG significant.
Does a deep learning model combining stacked denoising autoencoders and bidirectional LSTM improve the accuracy and efficiency of ECG arrhythmia classification?
Does a deep learning model combining stacked denoising autoencoders and bidirectional LSTM improve the accuracy and efficiency of ECG arrhythmia classification?
Answers are grounded in this paper and cite their sources.
A novel deep learning architecture combining SDAE and Bi-LSTM with cost-sensitive learning provides highly accurate and computationally efficient automated classification of ECG arrhythmias.
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.
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Wang et al. (2019) studied Arrhythmia (n=100,900). SDAE and Bi-LSTM with cost-sensitive learning vs. State-of-the-art machine learning methods was evaluated on Classification accuracy. A novel deep learning model combining stacked denoising autoencoders and bidirectional LSTM with cost-sensitive learning achieved 98.85% accuracy and 99.86% specificity in classifying ECG arrhythmias.
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