Literature DB >> 25576717

Prompt and accurate diagnosis of ventricular arrhythmias with a novel index based on phase space reconstruction of ECG.

George Koulaouzidis1, Saptarshi Das2, Grazia Cappiello2, Evangelos B Mazomenos2, Koushik Maharatna2, Paolo E Puddu3, John M Morgan4.   

Abstract

AIM: To develop a statistical index based on the phase space reconstruction (PSR) of the electrocardiogram (ECG) for the accurate and timely diagnosis of ventricular tachycardia (VT) and ventricular fibrillation (VF).
METHODS: Thirty-two ECGs with sinus rhythm (SR) and 32 ECGs with VT/VF were analyzed using the PSR technique. Firstly, the method of time delay embedding were employed with the insertion of delay "τ" in the original time-series X(t), which produces the Y(t)=X(t-τ). Afterwards, a PSR diagram was reconstructed by plotting Y(t) against X(t). The method of box counting was applied to analyze the behavior of the PSR trajectories. Measures as mean (μ), standard deviation (σ) and coefficient of variation (CV=σ/μ), kurtosis (β) for the box counting of PSR diagrams were reported.
RESULTS: During SR, CV was always <0.05, while with the onset of arrhythmia CV increased >0.05. A similar pattern was observed with β, where <6 was considered as the cut-off point between SR and VT/VF. Therefore, the upper threshold for SR was considered CVth=0.05 and βth<6. For optimisation of the accuracy, a new index (J) was proposed: J=wCVCVth+1-wββth. During SR the upper limit of J was the value of 1. Furthermore CV, β and J crossed the cut-off point timely before the onset of arrhythmia (average time: 4min 31s; SD: 2min 30s); allowing sufficient time for preventive therapy.
CONCLUSION: The J index improved ECG utility for arrhythmia monitoring and detection utility, allowing the prompt and accurate diagnosis of ventricular arrhythmias.
Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Index; Phase space reconstruction of ECG; Timely diagnosis; Ventricular arrhythmias

Mesh:

Year:  2014        PMID: 25576717     DOI: 10.1016/j.ijcard.2014.12.067

Source DB:  PubMed          Journal:  Int J Cardiol        ISSN: 0167-5273            Impact factor:   4.164


  3 in total

Review 1.  Artificial Intelligence in Cardiology-A Narrative Review of Current Status.

Authors:  George Koulaouzidis; Tomasz Jadczyk; Dimitris K Iakovidis; Anastasios Koulaouzidis; Marc Bisnaire; Dafni Charisopoulou
Journal:  J Clin Med       Date:  2022-07-05       Impact factor: 4.964

2.  Phase Space Reconstruction Based CVD Classifier Using Localized Features.

Authors:  Naresh Vemishetty; Ramya Lakshmi Gunukula; Amit Acharyya; Paolo Emilio Puddu; Saptarshi Das; Koushik Maharatna
Journal:  Sci Rep       Date:  2019-10-10       Impact factor: 4.379

3.  Prediction of Ventricular Tachycardia One Hour before Occurrence Using Artificial Neural Networks.

Authors:  Hyojeong Lee; Soo-Yong Shin; Myeongsook Seo; Gi-Byoung Nam; Segyeong Joo
Journal:  Sci Rep       Date:  2016-08-26       Impact factor: 4.379

  3 in total

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