Detection of life-threatening cardiac arrhythmias using the wavelet transformation

Khadra, L.; Al–Fahoum, Amjed S.; Al‐Nashash, Hasan

doi:10.1007/bf02510970

Cited by 130 publications

(50 citation statements)

References 16 publications

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“…Initially 1024 samples are selected randomly and used as first window. By Àtrous algorithm the signal is decomposed into six levels 2 1 , 2 2 ,… 2 6 .The QRS signal is having maximum energy in level 2 4 ( 0.1 -30 Hz) [42]. This algorithm searches for maximum modulus lines exceeding some threshold at scales from 2 1 to 2 4 .…”

Section: From Mit -Bih Arrhythmia Data Base Ecg Signals Are Takenmentioning

confidence: 99%

Ventricular Arrhythmias Detection using Wavelet Decomposition

Ilankumaran¹,

Thamaraiselvi²

2011

IJCA

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In this paper an algorithm has been proposed to detect and classify the cardiac arrhythmia from a normal Electro Cardio Graphic (ECG) signal based on wavelet decomposition with adaptive threshold. The MIT -BIH arrhythmia and malignant ventricular arrhythmia database has been utilized for evaluating the algorithm. The performance of the algorithm is compared with some existing algorithms in terms of signal duration time (episode length), sensitivity, specificity and positive selectivity. The analysis shows that the proposed algorithm gives satisfactory results.

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Section: From Mit -Bih Arrhythmia Data Base Ecg Signals Are Takenmentioning

confidence: 99%

Ventricular Arrhythmias Detection using Wavelet Decomposition

Ilankumaran¹,

Thamaraiselvi²

2011

IJCA

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“…The detection of arrhythmia is an important task in clinical reasons which can initiate life saving operations. Quick availability of ECG signal from remote location and providing proper filtering circuit on time can help in analyzing the signal for arrhythmia [1].From early times several detection algorithms have been proposed, such as the sequential hypothesis testing [3], the threshold-crossing intervals [4], algorithms based on neural-networks [6], and wavelets [7]. The classification of detected arrhythmias is also a research field of great interest.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Classification of Cardiac Arrhythmia Using ECG Signals

Bhardwaj¹,

Choudhary²,

Dayama³

2012

IJCA

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ECG is a graphical record of the electrical tension of heart and has established as one the most important bio-signal used by cardiologists for diagnostic purposes and further to adopt an appropriate course of treatment. The difficulties faced in interpretation of ECG signals forced researchers to study about automatic detection of cardiac arrhythmia disorders. The data analysis techniques using specific computer software could easily interpret complex ECG signals, predict presence or absence of cardiac arrhythmia. This provides real time analysis and further facilitates for timely diagnosis. In this paper, Support Vector Machine (SVM) technique, using LibSVM3.1 has been applied to ECG dataset for arrhythmia classification in five categories. Out of these five categories, one is normal and four are arrhythmic beat categories. The dataset used in this study is 3003 arrhythmic beats out of which 2101 beats (70%) are used for training and remaining 902 beats (30%) have been used for testing purpose. Total performance accuracy is found to be around 95.21 % in this case.

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“…In addition, wavelet transforms (Khadra et al, 1997;Abbas et al, 2004;Nawarvar et al, 2004) and nonlinear analysis (Jekova et al, 2002;Sun et al, 2005;Daoming et al, 2007) were proposed as detection techniques for the classification of VF signals.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Ventricular Tachycardia and Fibrillation Using Semantic Mining

Othman¹,

Safri²,

Ghani³

et al. 2012

CIS

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Ventricular tachycardia (VT) and ventricular fibrillation (VF) are potentially life-threatening forms of cardiac arrhythmia. Fast and accurate detection of these conditions can save lives. We used semantic mining to characterize VT and VF episodes by extracting three significant parameters (frequency, damping coefficient and input signal) from electrocardiogram (ECG) signal. This method was used to analyze four-second ECG signals from a widely recognized database at the Massachusetts Institute of Technology (MIT). The method achieved a high sensitivity and specificity of 96.7% and 98.3%, respectively, and was capable of detecting normal sinus rhythm (N) from VT and VF signals without false detection, with a sensitivity of 100%. VT and VF signals were recognized from each other, with a recognition sensitivity of 96% and 94%, respectively. This newly proposed method using semantic mining shows strong potential for clinical applications because it is able to recognize VT and VF signals with higher accuracy and faster recognition times compare to existing methods.

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Detection of life-threatening cardiac arrhythmias using the wavelet transformation

Cited by 130 publications

References 16 publications

Ventricular Arrhythmias Detection using Wavelet Decomposition

Ventricular Arrhythmias Detection using Wavelet Decomposition

Analysis and Classification of Cardiac Arrhythmia Using ECG Signals

Characterization of Ventricular Tachycardia and Fibrillation Using Semantic Mining

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