6533b858fe1ef96bd12b6489

RESEARCH PRODUCT

Adaptive Threshold, Wavelet and Hilbert Transform for QRS Detection in Electrocardiogram Signals

Jiri BilaJolanta Mizera-pietraszkoEdgar A. Martinez GarciaRicardo Rodríguez JorgeRafael Torres Córdoba

subject

Computer sciencebusiness.industryNoise (signal processing)010401 analytical chemistryWavelet transformPattern recognition02 engineering and technology01 natural sciencesSignal0104 chemical sciencessymbols.namesakeQRS complexWavelet0202 electrical engineering electronic engineering information engineeringsymbols020201 artificial intelligence & image processingHilbert transformArtificial intelligenceEcg signalbusiness

description

This paper combines Hilbert and Wavelet transforms and an adaptive threshold technique to detect the QRS complex of electrocardiogram signals. The method is performed in a window framework. First, the Wavelet transform is applied to the ECG signal to remove noise. Next, the Hilbert transform is applied to detect dominant peak points in the signal. Finally, the adaptive threshold technique is applied to detect R-peaks, Q, and S points. The performance of the algorithm is evaluated against the MIT-BIH arrhythmia database, and the numerical results indicated significant detection accuracy.

yearjournalcountryeditionlanguage
2017-11-03
10.1007/978-3-319-69835-9_73https://link.springer.com/chapter/10.1007/978-3-319-69835-9_73