Key result
A Deep Belief Network pipelined onto a Logistic Regression classifier achieved a 5% and 10% increase in accuracy for arrhythmia detection compared to 1D convolution and no feature extraction.
Why the study?
Accurate categorization of heartbeats from ECG signals is an important step for the diagnosis of arrhythmia.
Effect estimate: 5% and 10% increase in accuracy
A Deep Belief Network combined with Logistic Regression improves ECG arrhythmia classification accuracy by 5-10% compared to 1D convolution or plain logistic regression.
May support DBN-LR for ECG arrhythmia detection; leaves open clinical utility pending prospective validation.
Arrhythmia is a disorder of the heart caused by the erratic nature of heartbeats occurring due to conduction failures of the electrical signals in the cardiac muscle. In recent years, research galore has been done towards accurate categorization of heartbeats and electrocardiogram (ECG)-based heartbeat processing. Accurate categorization of different heartbeats is an important step for diagnosis of arrhythmia. This paper primarily focuses on effective feature extraction of the ECG signals for model performance enhancement using an unsupervised Deep Belief Network (DBN) pipelined onto a simple Logistic Regression (LR) classifier. We compare and evaluate the results of data feature enrichment against plain, non-enriched data based on the metrics of precision, recall, specificity, and F1-score and report the extent of increase in performance. Also, we compare the performance of the DBN-LR pipeline with a 1D convolution technique and find that the DBN-LR algorithm achieves a 5% and 10% increase in accuracy when compared to 1D convolution and no feature extraction using DBN respectively.
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Gourisaria et al. (2021) studied Arrhythmia. Deep Belief Network (DBN) pipelined onto a Logistic Regression (LR) classifier vs. 1D convolution technique and no feature extraction was evaluated on Accuracy of heartbeat categorization (5% and 10% increase in accuracy). A Deep Belief Network pipelined onto a Logistic Regression classifier achieved a 5% and 10% increase in accuracy for arrhythmia detection compared to 1D convolution and no feature extraction.
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