Analysis of unpredictable intra-QRS potentials based on multi-step linear prediction modeling for evaluating the risk of ventricular arrhythmias

Lin, Chun‐Cheng; Hu, Wei-Chih

doi:10.1109/cic.2007.4745605

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Standards have proposed the automatic estimation of the initial and final points of the filtered QRS complex [27]. However, the incomplete characterization of reentrant activity, poor accuracy of positive prediction [28], and impossibility in detecting VLPs in patients with bundle branch block pathology [29] are the major limitations of time domain analysis.…”

Section: Prior Researchmentioning

confidence: 99%

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

2019

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The analysis of cardiac signals is still regarded as attractive by both the academic community and industry because it helps physicians in detecting abnormalities and improving the diagnosis and therapy of diseases. Electrocardiographic signal processing for detecting irregularities related to the occurrence of low-amplitude waveforms inside the cardiac signal has a considerable workload as cardiac signals are heavily contaminated by noise and other artifacts. This paper presents an effective approach for the detection of ventricular late potential occurrences which are considered as markers of sudden cardiac death risk. Three stages characterize the implemented method which performs a beat-to-beat processing of high-resolution electrocardiograms (HR-ECG). Fifteen lead HR-ECG signals are filtered and denoised for the improvement of signal-to-noise ratio. Five features were then extracted and used as inputs of a classifier based on a machine learning approach. For the performance evaluation of the proposed method, a HR-ECG database consisting of real ventricular late potential (VLP)-negative and semi-simulated VLP-positive patterns was used. Experimental results show that the implemented system reaches satisfactory performance in terms of sensitivity, specificity accuracy, and positive predictivity; in fact, the respective values equal to 98.33%, 98.36%, 98.35%, and 98.52% were achieved.

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Section: Prior Researchmentioning

confidence: 99%

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

2019

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“…Standards propose the automatic estimation of the initial and final points of the filtered QRS complex [9]. Moreover, the incomplete characterization of re-entrant activity, the poor accuracy of positive prediction [10], and the impossibility to detect VLPs in patients with bundle branch block pathology [11] are the major limitations of time-domain analysis.…”

Section: Introductionmentioning

confidence: 99%

Marginal Component Analysis of ECG Signals for Beat-to-Beat Detection of Ventricular Late Potentials

2019

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Heart condition diagnosis based on electrocardiogram signal analysis is the basic method used in prevention of cardiovascular diseases, which are recognized as the leading cause of death globally. To anticipate the occurrence of ventricular arrhythmia, the detection of Ventricular Late Potentials (VLPs) is clinically worthwhile. VLPs are low-amplitude and high-frequency signals appearing at the end part of QRS complexes in the electrocardiogram, which can be considered as a robust feature for arrhythmia risk stratification in patients with cardiac diseases. This paper proposes a beat-to-beat VLP detection method based on the the marginal component analysis and investigates its performance taking into account different ratios between QRS and VLP power. After a denoising phase, performed adopting the singular vector decomposition technique, heartbeats characterized by VLP onsets are identified and extracted taking into account the vector magnitude of each high resolution ECG (HR-ECG) record. To evaluate the proposed method performance, a 15-lead HR-ECG database consisting of real VLP-negative and simulated VLP-positive patterns was used. The achieved results highlight the method validity for VLP detection.

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Analysis of unpredictable intra-QRS potentials based on multi-step linear prediction modeling for evaluating the risk of ventricular arrhythmias

Abstract: The abnormal intra-QRS potentials (AIQP)

Cited by 2 publications

References 13 publications

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

Marginal Component Analysis of ECG Signals for Beat-to-Beat Detection of Ventricular Late Potentials

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