Ventricular Fibrillation Detection by an Improved Time Domain Algorithm Combined with SVM

2014 ◽  
Author(s):  
Zhongjie Hou ◽  
Yue Zhang
Keyword(s):  
Ventricular Fibrillation ◽  
Time Domain

A simple time domain algorithm for the detection of ventricular fibrillation in electrocardiogram

2009 ◽  
Vol 5 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Muhammad Abdullah Arafat ◽  
Abdul Wadud Chowdhury ◽  
Md. Kamrul Hasan
Keyword(s):  
Ventricular Fibrillation ◽  
Time Domain

A COMPREHENSIVE APPROACH TOWARDS CLASSIFICATION AND PREDICTION OF VENTRICULAR TACHYCARDIA AND VENTRICULAR FIBRILLATION

2017 ◽  
Vol 29 (05) ◽  
pp. 1750039 ◽  
Author(s):  
Mohammadreza Noruzi ◽  
Malihe Sabeti ◽  
Reza Boostani
Keyword(s):  
Ventricular Tachycardia ◽  
Ventricular Fibrillation ◽  
Power Spectrum ◽  
Time Domain ◽  
Correlation Dimension ◽  
Heart Rhythm ◽  
Surrogate Data ◽  
Stressful Situation ◽  
Wrong Decision ◽  
The Time Domain

Ventricular tachycardia (VT) is a fast heart rate that arises from improper electrical activity in the ventricular of the heart. VT may eventually lead to lethal ventricular fibrillation (VF) which is characterized by fast and irregular heart rhythm. Since difference between VT and VF is diagnosed by specialist in a critical and stressful situation, the possibility of wrong decision is not low. Here, various set of ECG features belonged to different domains are implemented to investigate the predictability and discriminability of VT and VF episodes. Informative features from different domains such as correlation dimension (phase space) and power spectrum (frequency domain) were elicited from electrocardiogram (ECG) signals to describe the amount of irregularity/variation through the attack. In addition raw signal samples were used to assess the classification task based on the time domain features. Applying correlation dimension, power spectrum and the raw samples of ECGs to artificial neural network (ANN) classifier provides 91%, 92% and 71% classification accuracy between VT and VF signals, respectively. However, to enrich the time domain features, surrogate data was generated and the results of time domain is increased up to 87% which represents that ANNs are able to learn the dynamic nature of chaotic signals.

scholarly journals Time-Domain T-Wave Alternans is Strongly Associated with a History of Ventricular Fibrillation in Patients with Brugada Syndrome

2014 ◽  
Vol 25 (9) ◽  
pp. 1021-1027 ◽  
Author(s):  
YUKO UCHIMURA-MAKITA ◽  
YUKIKO NAKANO ◽  
TAKEHITO TOKUYAMA ◽  
MAI FUJIWARA ◽  
YOSHIKAZU WATANABE ◽  
...  
Keyword(s):  
Ventricular Fibrillation ◽  
Time Domain ◽  
Brugada Syndrome ◽  
T Wave ◽  
History Of ◽  
T Wave Alternans
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