Multichannel QRS Morphology Clustering - Data Preprocessing for Ultra-High-Frequency ECG Analysis

Plešinger, Filip; Jurco, Juraj; Halámek, Josef; Leinveber, Pavel; Reichlova, Tereza; Jurák, Pavel

doi:10.5220/0005604200110019

Cited by 9 publications

(7 citation statements)

References 16 publications

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“…At the first step, QRS complexes were detected [8] and sorted [9]. Amplitude envelopes in 12 frequency windows (bandwidth 100 Hz, middle frequency f0 from 100 to 650 Hz, in steps of 50 Hz) were computed and regular (sinus beats) HFQRS complexes were averaged.…”

Section: Discussionmentioning

confidence: 99%

High Frequency QRS Analysis From Orthogonal Leads

Halámek

Leinveber

Malík

et al. 2018

2018 Computing in Cardiology Conference (CinC)

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We analysed high frequency averaged QRS (HFQRS) in orthogonal leads and different passbands. Three groups of subjects were compared: healthy subjects, ischemic heart disease (IHD) and dilated cardiomyopathy (DCM) patients. Among the IHD group, those with heart failure (HF) symptoms were identified. Investigated parameters included HFQRS maximal amplitude, HFQRS power, and HFQRS fragmentation based on normalized length of the HFQRS line. The study aimed at assessing (1) group differences in relation to the passband, lead, and parameter, and (2) the reproducibility of parameters. Results: Significant differences were found between healthy subjects and IHD or DCM in all parameters and passbands. Some singularities of significance existed between IHD and DCM. Significant differences were also found between IHD subgroups with and without HF symptoms, and these existed over more frequency bands. Conclusion: HFQRS parameters are frequency dependent and this dependency should be tested to eliminate singularities in statistical significances. Differences between groups with or without HF symptoms were found mainly at higher passbands, regardless of deterioration of reproducibility. Lead X appeared to be the lead with maximal differences between groups.

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Section: Discussionmentioning

confidence: 99%

High Frequency QRS Analysis From Orthogonal Leads

Halámek

Leinveber

Malík

et al. 2018

2018 Computing in Cardiology Conference (CinC)

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“…QRS complexes were detected [7] and clusterized as the first step [8]. Amplitude envelopes in passbands 100 Hz wide, with 100 Hz step from 50 Hz to 1000 Hz, were computed and regular HFQRS complexes were averaged.…”

Section: Discussionmentioning

confidence: 99%

Attenuation of QRS Power in the Frequency Range from 0.05 to 1 kHz

Halámek¹,

Leinveber²,

Plešinger³

et al. 2017

Computing in Cardiology Conference (CinC)

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The high-frequency QRS (HFQRS) signal was analyzed in four groups of subjects: healthy, patients suffering from coronary artery disease, heart transplanted patients and patients with left bundle branch block (LBBB). Subjects were measured in the supine position with a high-quality ECG system (fs=5 kHz, ADC 24 bits). The dependency of HFQRS power, maximal amplitude and HFQRS width on the passband used was analyzed. Results: The dependency between HFQRS power and frequency in logarithmic scales is nearly linear across all groups. Significant differences exist in HFQRS power attenuation in regard to frequency between groups of subjects, P<0.001. This attenuation is maximal in healthy subjects-48.1±3.7 [dB per decade], minimal in LBBB-40.2±2.7. Based on significant differences between healthy and patient groups, we assume that QRS power attenuation may represent new information in ECG analysis. This parameter is independent of the choice of the analyzed frequency band. The dependency of HFQRS shape on frequency is presented by analysis of a healthy subject. Explanation of this significant dependency is a task for the future.

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“…The precise R-wave detection and exact categorization of the different QRS morphologies was performed prior QRS averaging. The QRS complexes were detected and sorted using a robust multichannel correlation algorithm [9,10]. Only regular beats were included into further analysis.…”

Section: Methodsmentioning

confidence: 99%

High Frequency QRS for Detection of Myocardial Ischemia

Leinveber

Halámek

Jurák

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Several studies investigating the high frequency QRS (HF-QRS) behavior have reported the superiority of the HF-QRS method compared to the standard ST-segment changes in acute coronary syndrome and ischemic heart disease (IHD). The potential diagnostic contribution of five selected parameters was tested in three frequency ranges-HFQRS (150-250Hz), VHFQRS (250-500Hz) and UHFQRS (500-1000Hz) in healthy subjects and IHD patients. Significant differences were found between healthy and IHD subjects in all tested parameters and analyzed passbands. All parameters have comparable diagnostic contribution with minimal or no effect of the different frequency ranges. Despite significant differences between the groups, the parameters evince very large interindividual variation that limits the clinical usefulness of the parameters to discriminate between healthy subjects and IHD patients. The implementation of a heart rhythm excitation would allow analyzing the changes of the parameters during increased oxygen demand of myocardium.

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Multichannel QRS Morphology Clustering - Data Preprocessing for Ultra-High-Frequency ECG Analysis

Cited by 9 publications

References 16 publications

High Frequency QRS Analysis From Orthogonal Leads

High Frequency QRS Analysis From Orthogonal Leads

Attenuation of QRS Power in the Frequency Range from 0.05 to 1 kHz

High Frequency QRS for Detection of Myocardial Ischemia

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