Analysis of the High-Frequency Content in Human QRS Complexes by the Continuous Wavelet Transform: An Automatized Analysis for the Prediction of Sudden Cardiac Death
Background: Fragmentation and delayed potentials in the QRS signal of patients have been postulated as risk markers for Sudden Cardiac Death (SCD). The analysis of the high-frequency spectral content may be useful for quantification. Methods: Forty-two consecutive patients with prior history of SCD...
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doaj-128918386dc34f13882480f33b04cef32020-11-24T21:17:41ZengMDPI AGSensors1424-82202018-02-0118256010.3390/s18020560s18020560Analysis of the High-Frequency Content in Human QRS Complexes by the Continuous Wavelet Transform: An Automatized Analysis for the Prediction of Sudden Cardiac DeathDaniel García Iglesias0Nieves Roqueñi Gutiérrez1Francisco Javier De Cos2David Calvo3Arrhythmia Unit, Hospital Universitario Central de Asturias, 33011 Oviedo, SpainGrupo para la Modelización Matemática Avanzada (MOMA), Universidad de Oviedo, 33004 Oviedo, SpainGrupo para la Modelización Matemática Avanzada (MOMA), Universidad de Oviedo, 33004 Oviedo, SpainArrhythmia Unit, Hospital Universitario Central de Asturias, 33011 Oviedo, SpainBackground: Fragmentation and delayed potentials in the QRS signal of patients have been postulated as risk markers for Sudden Cardiac Death (SCD). The analysis of the high-frequency spectral content may be useful for quantification. Methods: Forty-two consecutive patients with prior history of SCD or malignant arrhythmias (patients) where compared with 120 healthy individuals (controls). The QRS complexes were extracted with a modified Pan-Tompkins algorithm and processed with the Continuous Wavelet Transform to analyze the high-frequency content (85–130 Hz). Results: Overall, the power of the high-frequency content was higher in patients compared with controls (170.9 vs. 47.3 103nV2Hz−1; p = 0.007), with a prolonged time to reach the maximal power (68.9 vs. 64.8 ms; p = 0.002). An analysis of the signal intensity (instantaneous average of cumulative power), revealed a distinct function between patients and controls. The total intensity was higher in patients compared with controls (137.1 vs. 39 103nV2Hz−1s−1; p = 0.001) and the time to reach the maximal intensity was also prolonged (88.7 vs. 82.1 ms; p < 0.001). Discussion: The high-frequency content of the QRS complexes was distinct between patients at risk of SCD and healthy controls. The wavelet transform is an efficient tool for spectral analysis of the QRS complexes that may contribute to stratification of risk.http://www.mdpi.com/1424-8220/18/2/560electrocardiographic analysiswavelet transformhigh-frequency contentsudden cardiac death |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Daniel García Iglesias Nieves Roqueñi Gutiérrez Francisco Javier De Cos David Calvo |
spellingShingle |
Daniel García Iglesias Nieves Roqueñi Gutiérrez Francisco Javier De Cos David Calvo Analysis of the High-Frequency Content in Human QRS Complexes by the Continuous Wavelet Transform: An Automatized Analysis for the Prediction of Sudden Cardiac Death Sensors electrocardiographic analysis wavelet transform high-frequency content sudden cardiac death |
author_facet |
Daniel García Iglesias Nieves Roqueñi Gutiérrez Francisco Javier De Cos David Calvo |
author_sort |
Daniel García Iglesias |
title |
Analysis of the High-Frequency Content in Human QRS Complexes by the Continuous Wavelet Transform: An Automatized Analysis for the Prediction of Sudden Cardiac Death |
title_short |
Analysis of the High-Frequency Content in Human QRS Complexes by the Continuous Wavelet Transform: An Automatized Analysis for the Prediction of Sudden Cardiac Death |
title_full |
Analysis of the High-Frequency Content in Human QRS Complexes by the Continuous Wavelet Transform: An Automatized Analysis for the Prediction of Sudden Cardiac Death |
title_fullStr |
Analysis of the High-Frequency Content in Human QRS Complexes by the Continuous Wavelet Transform: An Automatized Analysis for the Prediction of Sudden Cardiac Death |
title_full_unstemmed |
Analysis of the High-Frequency Content in Human QRS Complexes by the Continuous Wavelet Transform: An Automatized Analysis for the Prediction of Sudden Cardiac Death |
title_sort |
analysis of the high-frequency content in human qrs complexes by the continuous wavelet transform: an automatized analysis for the prediction of sudden cardiac death |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2018-02-01 |
description |
Background: Fragmentation and delayed potentials in the QRS signal of patients have been postulated as risk markers for Sudden Cardiac Death (SCD). The analysis of the high-frequency spectral content may be useful for quantification. Methods: Forty-two consecutive patients with prior history of SCD or malignant arrhythmias (patients) where compared with 120 healthy individuals (controls). The QRS complexes were extracted with a modified Pan-Tompkins algorithm and processed with the Continuous Wavelet Transform to analyze the high-frequency content (85–130 Hz). Results: Overall, the power of the high-frequency content was higher in patients compared with controls (170.9 vs. 47.3 103nV2Hz−1; p = 0.007), with a prolonged time to reach the maximal power (68.9 vs. 64.8 ms; p = 0.002). An analysis of the signal intensity (instantaneous average of cumulative power), revealed a distinct function between patients and controls. The total intensity was higher in patients compared with controls (137.1 vs. 39 103nV2Hz−1s−1; p = 0.001) and the time to reach the maximal intensity was also prolonged (88.7 vs. 82.1 ms; p < 0.001). Discussion: The high-frequency content of the QRS complexes was distinct between patients at risk of SCD and healthy controls. The wavelet transform is an efficient tool for spectral analysis of the QRS complexes that may contribute to stratification of risk. |
topic |
electrocardiographic analysis wavelet transform high-frequency content sudden cardiac death |
url |
http://www.mdpi.com/1424-8220/18/2/560 |
work_keys_str_mv |
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