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A statistical index for early diagnosis of ventricular arrhythmia from the trend analysis of ECG phase-portraits

A statistical index for early diagnosis of ventricular arrhythmia from the trend analysis of ECG phase-portraits
A statistical index for early diagnosis of ventricular arrhythmia from the trend analysis of ECG phase-portraits
In this paper, we propose a novel statistical index for the early diagnosis of ventricular arrhythmia (VA) using the time delay phase-space reconstruction (PSR) technique, from the electrocardiogram (ECG) signal. Patients with two classes of fatal VA - with preceding ventricular premature beats (VPBs) and with no VPBs have been analysed using extensive simulations. Three subclasses of VA with VPBs viz. ventricular tachycardia (VT), ventricular fibrillation (VF) and VT followed by VF are analyzed using the proposed technique. Measures of descriptive statistics like mean (µ), standard deviation (?), coefficient of variation (CV = ?/µ), skewness (?) and kurtosis (?) in phase-space diagrams are studied for a sliding window of 10 beats of ECG signal using the box-counting technique. Subsequently, a hybrid prediction index which is composed of a weighted sum of CV and kurtosis has been proposed for predicting the impending arrhythmia before its actual occurrence. The early diagnosis involves crossing the upper bound of a hybrid index which is capable of predicting an impending arrhythmia 356 ECG beats, on average (with 192 beats standard deviation) before its onset when tested with 32 VA patients (both with and without VPBs). The early diagnosis result is also verified using a leave out cross-validation (LOOCV) scheme with 96.88% sensitivity, 100% specificity and 98.44% accuracy
0967-3334
107-131
Cappiello, Grazia
0105af1b-7ef6-4b45-87c0-42b3d3bca789
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Mazomenos, Evangelos
23983827-c7e7-4ee1-bfc8-986aa3594279
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Koulaouzidis, George
3f78927b-a7f3-457f-ab26-da6f256b2804
Morgan, John M.
ac98099e-241d-4551-bc98-709f6dfc8680
Puddu, Paolo Emilio
4daadd4b-cc61-49e6-954c-0107300b7025
Cappiello, Grazia
0105af1b-7ef6-4b45-87c0-42b3d3bca789
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Mazomenos, Evangelos
23983827-c7e7-4ee1-bfc8-986aa3594279
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Koulaouzidis, George
3f78927b-a7f3-457f-ab26-da6f256b2804
Morgan, John M.
ac98099e-241d-4551-bc98-709f6dfc8680
Puddu, Paolo Emilio
4daadd4b-cc61-49e6-954c-0107300b7025

Cappiello, Grazia, Das, Saptarshi, Mazomenos, Evangelos, Maharatna, Koushik, Koulaouzidis, George, Morgan, John M. and Puddu, Paolo Emilio (2015) A statistical index for early diagnosis of ventricular arrhythmia from the trend analysis of ECG phase-portraits. Physiological Measurement, 36 (1), 107-131. (PMID:25500749)

Record type: Article

Abstract

In this paper, we propose a novel statistical index for the early diagnosis of ventricular arrhythmia (VA) using the time delay phase-space reconstruction (PSR) technique, from the electrocardiogram (ECG) signal. Patients with two classes of fatal VA - with preceding ventricular premature beats (VPBs) and with no VPBs have been analysed using extensive simulations. Three subclasses of VA with VPBs viz. ventricular tachycardia (VT), ventricular fibrillation (VF) and VT followed by VF are analyzed using the proposed technique. Measures of descriptive statistics like mean (µ), standard deviation (?), coefficient of variation (CV = ?/µ), skewness (?) and kurtosis (?) in phase-space diagrams are studied for a sliding window of 10 beats of ECG signal using the box-counting technique. Subsequently, a hybrid prediction index which is composed of a weighted sum of CV and kurtosis has been proposed for predicting the impending arrhythmia before its actual occurrence. The early diagnosis involves crossing the upper bound of a hybrid index which is capable of predicting an impending arrhythmia 356 ECG beats, on average (with 192 beats standard deviation) before its onset when tested with 32 VA patients (both with and without VPBs). The early diagnosis result is also verified using a leave out cross-validation (LOOCV) scheme with 96.88% sensitivity, 100% specificity and 98.44% accuracy

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Published date: January 2015
Organisations: Electronic & Software Systems

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Local EPrints ID: 372824
URI: https://eprints.soton.ac.uk/id/eprint/372824
ISSN: 0967-3334
PURE UUID: a9caaa6b-3264-45f2-b973-0ee97064a1f3

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Date deposited: 19 Dec 2014 15:35
Last modified: 10 Nov 2017 19:54

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