Literature DB >> 2227970

Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm.

N V Thakor1, Y S Zhu, K Y Pan.   

Abstract

An algorithm for detecting ventricular fibrillation (VF) and ventricular tachycardia (VT) by the method of sequential hypothesis testing is presented. The algorithm first generates a binary sequence by comparing the signal to a threshold. The probability distribution of the time intervals of the binary sequence is obtained, and Wald's sequential hypothesis testing procedure is next employed to discriminate the arrhythmias. Sequential hypothesis testing of 85 cases resulted in identification of 1) 97.64% VF and 97.65% VT episodes after 5 s, and 2) 100% identification of both VF and VT after 7 s. The desired false positive and false negative error probabilities can be preprogrammed into the algorithm. An important feature of the sequential method is that extra time for detection can be traded off for improved accuracy, and vice versa.

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Year:  1990        PMID: 2227970     DOI: 10.1109/10.58594

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  17 in total

1.  Efficient and robust ventricular tachycardia and fibrillation detection method for wearable cardiac health monitoring devices.

Authors:  Eedara Prabhakararao; M Sabarimalai Manikandan
Journal:  Healthc Technol Lett       Date:  2016-07-29

2.  Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators.

Authors:  Anton Amann; Robert Tratnig; Karl Unterkofler
Journal:  Biomed Eng Online       Date:  2005-10-27       Impact factor: 2.819

3.  Recognition of ventricular fibrillation using neural networks.

Authors:  R H Clayton; A Murray; R W Campbell
Journal:  Med Biol Eng Comput       Date:  1994-03       Impact factor: 2.602

4.  Combined wavelet transformation and radial basis neural networks for classifying life-threatening cardiac arrhythmias.

Authors:  A S al-Fahoum; I Howitt
Journal:  Med Biol Eng Comput       Date:  1999-09       Impact factor: 2.602

5.  Detection of life-threatening cardiac arrhythmias using the wavelet transformation.

Authors:  L Khadra; A S al-Fahoum; H al-Nashash
Journal:  Med Biol Eng Comput       Date:  1997-11       Impact factor: 2.602

6.  Adaptive filters for analysis of intra-cardiac signals.

Authors:  N V Thakor
Journal:  Med Biol Eng Comput       Date:  1994-07       Impact factor: 2.602

7.  Comparison of four techniques for recognition of ventricular fibrillation from the surface ECG.

Authors:  R H Clayton; A Murray; R W Campbell
Journal:  Med Biol Eng Comput       Date:  1993-03       Impact factor: 2.602

8.  Sequential algorithm for life threatening cardiac pathologies detection based on mean signal strength and EMD functions.

Authors:  Emran M Abu Anas; Soo Y Lee; Md K Hasan
Journal:  Biomed Eng Online       Date:  2010-09-04       Impact factor: 2.819

9.  Cardiac arrhythmia classification using autoregressive modeling.

Authors:  Dingfei Ge; Narayanan Srinivasan; Shankar M Krishnan
Journal:  Biomed Eng Online       Date:  2002-11-13       Impact factor: 2.819

10.  Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set.

Authors:  Rongru Wan; Yanqi Huang; Xiaomei Wu
Journal:  Sensors (Basel)       Date:  2021-05-19       Impact factor: 3.576

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