Key result
The novel ECG signal quality assessment method achieved 97.10% accuracy, 94.59% sensitivity, and 98.38% specificity for two-class classification, outperforming existing methods.
Why the study?
ECG signals recorded in unsupervised environments are prone to noise and errors, but existing signal quality assessment methods are complex and perform poorly in the presence of certain arrhythmias.
Does the proposed SQA method improve ECG signal quality assessment compared to existing methods?
Does the proposed SQA method improve ECG signal quality assessment compared to existing methods?
The proposed SQA method achieves high accuracy and specificity with low computational time, making it suitable for telemedicine ECG monitoring.
May aid automated ECG quality checks in telemedicine; leaves open prospective validation versus existing methods.
Electrocardiogram (ECG) signals recorded by paramedics in an unsupervised environment are prone to errors and noise due to factors such as electrode misplacements and daily activities during prolonged monitoring. Signal Quality Assessment (SQA) systems can prevent false diagnoses by automated arrhythmia detection systems and reduce the workload of cardiologists. However, current SQA methods are quite complex and/or exhibit poor performances in presence of certain arrhythmias. This study proposed a novel SQA method consisting of three main steps: (1) feasibility conditions check, (2) average beat correlation algorithm, and (3) beat clustering algorithm. The system uses empirical probability functions to automatically adapt the quality score according to the presence of pathological beats and/or noises. We evaluated the proposed method on three datasets and considered both three-quality classes ('Bad', 'HR' and 'Diagnostic') and two-quality classes ('Acceptable' and 'Unacceptable'). For the three-class classification, we obtained an average accuracy of 81.42 % while for the two-class, our algorithm achieved an average sensitivity of 94.59 %, a specificity of 98.38 %, and an accuracy of 97.10 %. The results show that our method achieves high accuracy, specificity and generalizability, outperforming the simple heuristic rule, machine learning, and deep learning methods. Additionally, the algorithm requires less computational time, making it suitable for telemedicine applications.
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Kuetche et al. (2023) studied ECG signal quality assessment. Novel ECG Signal Quality Assessment (SQA) method vs. Simple heuristic rule, machine learning, and deep learning methods was evaluated on Two-class quality classification ('Acceptable' and 'Unacceptable'). The novel ECG signal quality assessment method achieved 97.10% accuracy, 94.59% sensitivity, and 98.38% specificity for two-class classification, outperforming existing methods.
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