Wavelet transform for analysis of heart rate variability preceding ventricular arrhythmias in patients with ischemic heart disease

Burri, Haran; Chevalier, Philippe; Arzi, M.; Rubel, P.; Kirkorian, Gilbert; Touboul, P

doi:10.1016/j.ijcard.2005.06.001

Cited by 28 publications

(13 citation statements)

References 28 publications

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“…The band passfiltered signals were digitized at 360 Hz per signal relative to real time using hardware constructed at the MIT Biomedical Engineering Center [8] and at the BIH Biomedical Engineering Laboratory. The sampling frequency was chosen in such a way that it facilitates the implementations of 60 Hz (mains frequency) digital notch filters in arrhythmia detectors [9].…”

Section: Datasetmentioning

confidence: 99%

“…While frequency domain deals with the analysis of mathematical functions or signals with respect to frequency. Frequency domain analysis which is based on power spectral estimation is carried out using fast Fourier transform [13]. The frequency spectrum of a signal is basically the frequency components (spectral components) of that signal.…”

Section: Time and Frequency Analysismentioning

confidence: 99%

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Time and Frequency Exploration of ECG Signal

SharanYadav¹,

Yadav²,

Prachi³

2013

IJCA

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The time and frequency domain analysis for multicomponent non-stationary signals like Electrocardiogram (ECG) is an important issue in signal processing. Because of its non stationary, multicomponent nature, the use of time and frequency domain analysis can be very useful to identify the exact multicomponent structure of these biological signals. In this paper we have analyzed the ECG signal in time domain and calculated various statistical parameters and the study of different plots were done. Then we headed on the frequency analysis where the power spectral density is calculated using Welch method.

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Section: Datasetmentioning

confidence: 99%

Section: Time and Frequency Analysismentioning

confidence: 99%

Time and Frequency Exploration of ECG Signal

SharanYadav¹,

Yadav²,

Prachi³

2013

IJCA

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“…Na análise de Wavelet utiliza-se a técnica de janelamento que possui tamanhos varáveis. Os espectogramas baseados em wavelets foram obtidos tomando o módulo da TWC e elevando-o ao quadrado 18 .…”

Section: Análise Espectral No Domínio Da Frequênciaunclassified

Variabilidade da frequência cardíaca em mulheres com hipermobilidade articular

Nunes

Rubira

et al. 2011

Fisioter. Pesqui.

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, sedentárias e sem uso de medicação. Após diagnóstico da hipermobilidade articular, segundo o escore de Beighton, foram divididas em 2 grupos: 12 hipermóveis (GH) e 15 não hipermóveis (GC). O eletrocardiograma foi realizado durante 10 minutos em supino e em pé para análise da variabilidade da frequência cardíaca. A banda de alta frequência (un) apresentou diminuição da atividade vagal no GH, p<0,03. O incremento de baixa frequência (un) foi maior no GH em relação ao GC, na manobra de ortostatismo, com aumento da atividade simpática, p<0,03. As voluntárias com hipermobilidade articular apresentaram resposta autonômica cardíaca alterada com hiporesponsividade vagal. DEScRitORES: instabilidade articular; células receptoras sensoriais; frequência cardíaca.ABStRAct : Joint hypermobility is the ability to make joint movements greater than normal. The prevalence has large variations determined by race, sex, age, physical activity and variations in characterization criteria. Approximately 30% of adults are considered carriers and present proprioceptive feedback and sensory decreased and joint space positioning altered leading to greater frequency of activation and deformation on the mechanoreceptors in the skeletal muscles and skin. The increase of afferent impulses of the receptors on the bulb cardiovascular area alters the autonomic control on the heart. The objective of the study was to evaluate sympathovagal balance during orthosthatic maneuver in women with hipermobility. Twenty-seven sedentary volunteers participated in this study, with mean age of 19.97±1.79, body mass index below 25 kg/m 2 and without medication. After the joint hipermobility diagnosis according to Beighton score, they were divided into 2 groups: 12 with hipermobility (HG) and 15 without hipermobility (CG). The electrocardiogram was performed during 10 minutes at supine position and stand for analysis of heart rate variability. The band of high frequency (un) presented reduction in vagal activity in HG, p<0.03. The low frequency increment (un) was higher in HG when compared to CG in orthosthatic maneuver with increased sympathetic activity, p<0.03. The joint hypermobility volunteers presented autonomic cardiac response altered with low vagal responsiveness.

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“…21À27 However, Vybrial et al 28 analyzed 24 holter record cases containing VF events, no significant change was found in HRV signal and HR before VT/VF events (5 min). Burri et al 29 analyzed HRV signal with the time-frequency method of short-time Fourier Transform and wavelet transform during 51 min preceding for 41 patients implanted ICD with a history of myocardial infarction. The results had shown that HR significantly increased, but HRV spectrum signature had not significantly changed before the malignant ventricular arrhythmia events.…”

Section: Introductionmentioning

confidence: 99%

The Hilbert Marginal Spectrum Analysis and Feature Extraction of Short-Term Heart Rate Signal of Spontaneous Ventricular Tachyarrhythmia

Dong

Zhang

Hao

2011

Biomed. Eng. Appl. Basis Commun.

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The malignant ventricular tachyarrhythmia including ventricular tachycardia (VT) and ventricular fibrillation (VF) is the major cause of triggering sudden cardiac death (SCD) and it is seriously harmful to human. There is a great significance to predict the VT/VF. In this study, the RR interval series preceding the onset of VT/VF events are used as the study objects, and the 135 RR interval series are recorded by implantable cardioverter defibrillators (ICD) from 78 patients. Instantaneous heart rate (IHR) series are obtained after preprocessing the RR interval series. The Hilbert spectrum and the frequency marginal spectrum of IHR series are analyzed based on the traditional Hilbert-Huang transform (HHT) and the improved HHT. Some signal spectrum features are extracted from the marginal spectrum of IHR series: low frequency amplitude, high frequency amplitude, very high frequency amplitude, total amplitude and the low-to-high frequency amplitude ratio. The statistical analysis shows that the performance of improved HHT is better than that of traditional HHT. High frequency amplitude, very high frequency amplitude and total amplitude of IHR series preceding the onset of VT/VF are significantly higher than that of normal sinus rhythm (p < 0:003), and low-to-high frequency amplitude ratio is significantly lower than that of normal sinus rhythm (p < 0:0002).

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Wavelet transform for analysis of heart rate variability preceding ventricular arrhythmias in patients with ischemic heart disease

Cited by 28 publications

References 28 publications

Time and Frequency Exploration of ECG Signal

Time and Frequency Exploration of ECG Signal

Variabilidade da frequência cardíaca em mulheres com hipermobilidade articular

The Hilbert Marginal Spectrum Analysis and Feature Extraction of Short-Term Heart Rate Signal of Spontaneous Ventricular Tachyarrhythmia

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