Comput Methods Programs Biomed - A machine learning approach to multi-level ECG signal quality classification.

Tópicos

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Resumo

Current electrocardiogram (ECG) signal quality assessment studies have aimed to provide a two-level classification: clean or noisy. However, clinical usage demands more specific noise level classification for varying applications. This work outlines a five-level ECG signal quality classification algorithm. A total of 13 signal quality metrics were derived from segments of ECG waveforms, which were labeled by experts. A support vector machine (SVM) was trained to perform the classification and tested on a simulated dataset and was validated using data from the MIT-BIH arrhythmia database (MITDB). The simulated training and test datasets were created by selecting clean segments of the ECG in the 2011 PhysioNet/Computing in Cardiology Challenge database, and adding three types of real ECG noise at different signal-to-noise ratio (SNR) levels from the MIT-BIH Noise Stress Test Database (NSTDB). The MITDB was re-annotated for five levels of signal quality. Different combinations of the 13 metrics were trained and tested on the simulated datasets and the best combination that produced the highest classification accuracy was selected and validated on the MITDB. Performance was assessed using classification accuracy (Ac), and a single class overlap accuracy (OAc), which assumes that an individual type classified into an adjacent class is acceptable. An Ac of 80.26% and an OAc of 98.60% on the test set were obtained by selecting 10 metrics while 57.26% (Ac) and 94.23% (OAc) were the numbers for the unseen MITDB validation data without retraining. By performing the fivefold cross validation, an Ac of 88.07?0.32% and OAc of 99.34?0.07% were gained on the validation fold of MITDB.

Resumo Limpo

current electrocardiogram ecg signal qualiti assess studi aim provid twolevel classif clean noisi howev clinic usag demand specif nois level classif vari applic work outlin fivelevel ecg signal qualiti classif algorithm total signal qualiti metric deriv segment ecg waveform label expert support vector machin svm train perform classif test simul dataset valid use data mitbih arrhythmia databas mitdb simul train test dataset creat select clean segment ecg physionetcomput cardiolog challeng databas ad three type real ecg nois differ signaltonois ratio snr level mitbih nois stress test databas nstdb mitdb reannot five level signal qualiti differ combin metric train test simul dataset best combin produc highest classif accuraci select valid mitdb perform assess use classif accuraci ac singl class overlap accuraci oac assum individu type classifi adjac class accept ac oac test set obtain select metric ac oac number unseen mitdb valid data without retrain perform fivefold cross valid ac oac gain valid fold mitdb

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