Principal component analysis in high resolution electrocardiogram for risk stratification of sustained monomorphic ventricular tachycardia

Nasario-Junior, Olivassé; Benchimol-Barbosa, Paulo Roberto; Nadal, Jurandir

doi:10.1016/j.bspc.2013.10.003

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Principal Component Analysis (Pca)1

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Cited by 3 publications

(2 citation statements)

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“…Promising approaches seem to include transformations performed using independent components obtained using PCA [34,83,89,90]. The error between the measured ECG and reversely transformed ECG can be optimized; and the use of non-linear regression methods.…”

Section: Further Developmentmentioning

confidence: 99%

See 1 more Smart Citation

Methods for derivation of orthogonal leads from 12-lead electrocardiogram: A review

Vozda

Cerny

2015

Biomedical Signal Processing and Control

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Section: Further Developmentmentioning

confidence: 99%

“…The most common PCA applications include: ECG compression, heart rate detection, artifact noise suppression, classification, obtaining indications, signal separation and others [89,90,[93][94][95]. PCA has also been used to derive orthogonal leads [35,39].…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Methods for derivation of orthogonal leads from 12-lead electrocardiogram: A review

Vozda

Cerny

2015

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

An Empirical Evaluation of Savitzky-Golay (SG) Filter for Denoising ST Segment

Roopa¹,

Harish²

2018

Communications in Computer and Information Science

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Analysis of Abnormal Intra-QRS Potentials in Signal-Averaged Electrocardiograms Using a Radial Basis Function Neural Network

Lin

2016

Sensors

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Abnormal intra-QRS potentials (AIQPs) are commonly observed in patients at high risk for ventricular tachycardia. We present a method for approximating a measured QRS complex using a non-linear neural network with all radial basis functions having the same smoothness. We extracted the high frequency, but low amplitude intra-QRS potentials using the approximation error to identify possible ventricular tachycardia. With a specified number of neurons, we performed an orthogonal least squares algorithm to determine the center of each Gaussian radial basis function. We found that the AIQP estimation error arising from part of the normal QRS complex could cause clinicians to misjudge patients with ventricular tachycardia. Our results also show that it is possible to correct this misjudgment by combining multiple AIQP parameters estimated using various spread parameters and numbers of neurons. Clinical trials demonstrate that higher AIQP-to-QRS ratios in the X, Y and Z leads are visible in patients with ventricular tachycardia than in normal subjects. A linear combination of 60 AIQP-to-QRS ratios can achieve 100% specificity, 90% sensitivity, and 95.8% total prediction accuracy for diagnosing ventricular tachycardia.

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Principal component analysis in high resolution electrocardiogram for risk stratification of sustained monomorphic ventricular tachycardia

Cited by 3 publications

References 34 publications

Methods for derivation of orthogonal leads from 12-lead electrocardiogram: A review

Methods for derivation of orthogonal leads from 12-lead electrocardiogram: A review

An Empirical Evaluation of Savitzky-Golay (SG) Filter for Denoising ST Segment

Analysis of Abnormal Intra-QRS Potentials in Signal-Averaged Electrocardiograms Using a Radial Basis Function Neural Network

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