Key result
A 1D-CNN binary classifier for automated ECG anomaly detection achieved high accuracy and recall compared to similar systems on the MIT-BIH database.
Why the study?
Automated detection of suspicious ECG anomalies allows frequent personal heart health monitoring and reduces the burden of manual ECG review by cardiologists.
A 1D-CNN architecture can accurately automate the detection of various cardiac anomalies in ECG signals, potentially enabling efficient personal heart health monitoring on low-complexity smart devices.
May aid device-based ECG monitoring; leaves open prospective clinical validation.
The automated detection of suspicious anomalies in electrocardiogram (ECG) recordings allows frequent personal heart health monitoring and can drastically reduce the number of ECGs that need to be manually examined by the cardiologists, excluding those classified as normal, facilitating healthcare decision-making and reducing a considerable amount of time and money. In this paper, we present a system able to automatically detect the suspect of cardiac pathologies in ECG signals from personal monitoring devices, with the aim to alert the patient to send the ECG to the medical specialist for a correct diagnosis and a proper therapy. The main contributes of this work are: (a) the implementation of a binary classifier based on a 1D-CNN architecture for detecting the suspect of anomalies in ECGs, regardless of the kind of cardiac pathology; (b) the analysis was carried out on 21 classes of different cardiac pathologies classified as anomalous; and (c) the possibility to classify anomalies even in ECG segments containing, at the same time, more than one class of cardiac pathologies. Moreover, 1D-CNN based architectures can allow an implementation of the system on cheap smart devices with low computational complexity. The system was tested on the ECG signals from the MIT-BIH ECG Arrhythmia Database for the MLII derivation. Two different experiments were carried out, showing remarkable performance compared to other similar systems. The best result showed high accuracy and recall, computed in terms of ECG segments and even higher accuracy and recall in terms of patients alerted, therefore considering the detection of anomalies with respect to entire ECG recordings.
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Nannavecchia et al. (2021) studied Cardiac pathologies and ECG anomalies. 1D Convolutional Neural Network (1D-CNN) binary classifier vs. Other similar systems was evaluated on Accuracy and recall for detecting ECG anomalies. A 1D-CNN binary classifier for automated ECG anomaly detection achieved high accuracy and recall compared to similar systems on the MIT-BIH database.
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