@inproceedings{917e87b5919246d0995fc7ad87a79ed9,
title = "Wavelet-based signal quality assessment: Noise detection by temporal feature and heuristics-based",
abstract = "Signal Quality Assessment (SQA) is an approach to determine whether an electrocardiogram (ECG) signal can be used for clinical assessment or not. Unacceptable ECG signal contains one or more noises. This study proposes a method to determine the ECG signal quality using heuristic rules and temporal features. The experiment is conducted using 1000 data consist of 703 acceptable ECG signal and 297 unacceptable ECG signal. The Sensitivity, specificity, and accuracy are used to assess the performance of the method. The result shows that Daubechies filter is the best wavelet basis function to detect and classify the ECG signal for clinical assessment. Among the variability of the order used, the Daubechies filter with order eight has the best performance with sensitivity 0.85, specificity 0.86, and accuracy 85.6%.",
keywords = "Electrocardiogram, Heuristic rule, Signal quality, Temporal features, Wavelet",
author = "Indra Hermawan and Nina Sevani and Ma'sum, {Muhammad Anwar} and Noverina Alfiany and Wisnu Jatmiko",
year = "2019",
month = aug,
doi = "10.1109/CYBERNETICSCOM.2019.8875655",
language = "English",
series = "Proceedings: CYBERNETICSCOM 2019 - 2019 IEEE International Conference on Cybernetics and Computational Intelligence: Towards a Smart and Human-Centered Cyber World",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "103--108",
booktitle = "Proceedings",
address = "United States",
note = "2019 IEEE International Conference on Cybernetics and Computational Intelligence, CYBERNETICSCOM 2019 ; Conference date: 22-08-2019 Through 24-08-2019",
}