Optimizing the Detection of Characteristic Waves in ECG Based on Processing Methods Combinations
| Autor: | Goran Krstačić, Mario Cifrek, Alan Jovic, Krešimir Friganović, Davor Kukolja |
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| Rok vydání: | 2018 |
| Předmět: |
General Computer Science
Computer science 0206 medical engineering Word error rate 02 engineering and technology 01 natural sciences Reduction (complexity) QRS complex characteristic waves medicine automatic detection algorithms Waveform General Materials Science expert system medicine.diagnostic_test ECG Noise (signal processing) business.industry 010401 analytical chemistry P wave General Engineering Wavelet transform Pattern recognition Filter (signal processing) 020601 biomedical engineering 0104 chemical sciences ECG characteristic waves automatic detection algorithms clustering expert system biomedical signal analysis lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business lcsh:TK1-9971 Electrocardiography clustering biomedical signal analysis |
| Zdroj: | IEEE Access, Vol 6, Pp 50609-50626 (2018) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2018.2869943 |
| Popis: | Accurate detection of characteristic electrocardiogram (ECG) waves is necessary for ECG analysis and interpretation. In this paper, we distinguish four processing steps of detection algorithms: noise and artefacts reduction, transformations, fiducial marks selection of wave candidates, and decision rule. Processing steps combinations from several detection algorithms are used to find QRS, P, and T wave peaks. Additionally, we consider the search window parameter modification based on waveform templates extracted by heart cycles clustering. The methods are extensively evaluated on two public ECG databases containing QRS, P, and T wave peaks annotations. We found that the combination of morphological mathematical filtering with Elgendi's algorithm works best for QRS detection on MIT-BIH Arrhythmia Database (detection error rate (DER = 0.48%, Lead I). The combination of modified Martinez’s PT and wavelet transform (WT) methods gave the best results for P wave peaks detection on both databases, when both leads are considered (MIT- BIH Arrhythmia Database: DER = 32.13%, Lead I, DER = 42.52%, Lead II ; QT Database: DER = 21.23%, Lead I, DER = 26.80%, Lead II). Waveform templates in combination with Martinez's WT obtained the best results for T wave peaks detection on QT Database (DER = 25.15%, Lead II). Our work demonstrates that combining some of the best proposed methods in literature leads to improvements over the original methods for ECG waves detection, while maintaining satisfactory computation times. |
| Databáze: | OpenAIRE |
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