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Electrocardiogram (ECG) signal quality assessment (SQA) is a crucial step for improving the performance and reliability of medical diagnostics in computer-aided monitoring systems. Most of the existing ECG SQA methods utilize QRS complex and R-peak information-based features, where the quality assessment performance degrades due to improper detection of R-peaks. Unlike the existing machine learning-based ECG SQA approaches, this work proposes an efficient wavelet sub-bands features-based SQA scheme by avoiding the utilization of R-peak-based features. Sub-band levels features are extracted by incorporating the frequency localization property of wavelet transform (WT). Furthermore, the features are applied to machine learning-based classifier to assess the signal quality. Simulation study and results show that the proposed scheme exhibits an assessment accuracy of 98.11% on the simulated dataset, 99.12% on single channel ECG dataset of Physionet: Computing in Cardiology Challenge (PCinCC) and 97.32% on real arrhythmia dataset: Massachusetts Institute of Technology-Beth Israel Hospital database (MITDB). An in-depth study of the results illustrates that the proposed ECG SQA technique can be utilized for improving the reliability of modern automated computer-aided cardiac monitoring systems.

Wavelet Sub-bands Features-based ECG Signal Quality Assessment Scheme for Computer-aided Monitoring System