article · Heliyon
Cardiovascular disease diagnosis relies heavily on electrocardiogram (ECG) signal interpretation, but manual assessment remains complex and time-consuming. An automated approach using a deep neural network model with residual blocks has been developed to classify cardiac cycles into six distinct heartbeat categories using single lead-II ECG data. Evaluated against the benchmark MIT-BIH dataset, the system achieved an overall test accuracy of 99.51 per cent, alongside an average sensitivity of 99.7 per cent and specificity of 98.2 per cent. These results surpass other current algorithms evaluated on the same data. The architecture supports both clinical workflows and remote monitoring when paired with single-lead portable ECG equipment. To enable practical use, the classification tool has been embedded within a dedicated web application capable of processing digital ECG inputs and presenting automated diagnostic assessments.
Manual review of electrocardiograms requires significant expertise and time, which can delay urgent medical interventions. Providing high-accuracy automated heartbeat classification simplifies the diagnostic process for clinicians while extending reliable cardiac monitoring to out-of-hospital settings. This allows healthcare systems to identify irregular heart rhythms more swiftly and consistently through accessible, non-invasive digital tools.
The system targets clinical diagnostic services and remote patient monitoring, pairing with single-lead mobile ECG hardware for decentralised healthcare. It appears to be applied and tested, having progressed beyond theoretical modelling through integration into a functional web application for digital ECG analysis. Commercial viability would depend on further clinical trials with diverse patient populations and device-specific hardware integration to establish production readiness for medical software deployment.
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The analysis and processing of electrocardiogram (ECG) signals is a vital step in the diagnosis of cardiovascular disease. ECG offers a non-invasive and risk-free method for monitoring the electrical activity of the heart that can assist in predicting and diagnosing heart diseases. The manual interpretation of the ECG signals, however, can be challenging and time-consuming even for experts. Machine learning techniques are increasingly being utilized to support the research and development of automatic ECG classification, which has emerged as a prominent area of study. In this paper, we propose a deep neural network model with residual blocks (DNN-RB) to classify cardiac cycles into six ECG beat classes. The MIT-BIH dataset was used to validate the model resulting in a test accuracy of 99.51%, average sensitivity of 99.7%, and average specificity of 98.2%. The DNN-RB method has achieved higher accuracy than other state-of-the-art algorithms tested on the same dataset. The proposed method is effective in the automatic classification of ECG signals and can be used for both clinical and out-of-hospital monitoring and classification combined with a single-lead mobile ECG device. The method has also been integrated into a web application designed to accept digital ECG beats as input for analyses and to display diagnostic results.
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DOI: 10.1016/j.heliyon.2023.e17974
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