Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT

Yochum, Maxime; Renaud, Charlotte; Jacquir, Sabir

doi:10.1016/j.bspc.2015.10.011

Cited by 170 publications

(75 citation statements)

References 29 publications

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“…A typical ECG tracing of the cardiac cycle consists of a P wave, QRS complex, and T wave [3]. Good performance of an ECG analyzing system depends heavily upon the accurate and reliable detection of the QRS complex, as well as the T and P waves [4]. A Bundle Branch Block (BBB) is a delay or obstruction along electrical impulse pathways of the heart manifesting in a prolonged QRS interval usually greater than 120ms.…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm:Neural Network Wrapper Approach for Bundle Branch Block Detection

Allami¹,

Stranieri²,

Balasubramanian³

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm:Neural Network Wrapper Approach for Bundle Branch Block Detection

Allami¹,

Stranieri²,

Balasubramanian³

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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“…Hence, in the last years, several PVC detection system have been proposed for this issue: based on Artificial Neural Network (ANN) (Bortolan et al, 1991;Dalvi et al, 2016;Hu et al, 1997;Inan et al, 2006), Heuristic algorithm (Dotsinsky and Stoyanov, 2004), Bayesian framework (Sayadi et al, 2010), Support Vector Machine (SVM) (Shen et al, 2011), morphology ECG features (Chazal and Reilly, 2006;Chazal et al, 2004;Lek-uthai et al, 2014), Fuzzy Neural Network System (FNNS) (Lim, 2009), Wavelet Transform (Inan et al, 2006;Martis et al, 2013;Nazarahari et al, 2015;Orozco-Duque et al, 2013;Shyu et al, 2004;Yochum et al, 2016) and adaptive filter (Nieminaki et al, 1999;Solosenko et al, 2015). The main feature of most detection methods is a real-time analysis, however some methods have high mathematical complexity, which demands a high computational cost.…”

Section: Real-time Premature Ventricular Contractions Detection Basedmentioning

confidence: 99%

Real-time premature ventricular contractions detection based on Redundant Discrete Wavelet Transform

2018

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Introduction: Premature Ventricular Contraction (PVC) is among the most common types of ventricular cardiac arrhythmia. However, it only poses danger if the person suffers from a heart disease, such as heart failure. Hence, this is an important factor to consider in heart disease people. This paper presents an ECG real-time analysis system for PVC detection. Methods: This system is based on threshold adaptive methods and Redundant Discrete Wavelet Transform (RDWT), with a real-time approach. This analysis is based on wavelet coefficients energy for PVC detection. It is presented also a study to find the most indicated wavelet mother for ECG analysis application among the following wavelet families: Daubechies, Coiflets and Symlets. The system detection performance was validated on the MIT-BIH Arrhythmia Database. Results: The best results were verified with db2 wavelet mother: the Sensitivity Se = 99.18%, Positive Predictive Value P+ = 99.15% and Specificity Sp = 99.94%, on 80.872 annotated beats, and 61.2 s processing speed for a half-hour record. Conclusion: The proposed system exhibits reliable PVC detection, with real-time approach, and a simple algorithmic structure that can be implemented in many platforms.

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“…The detection of the P wave, the T wave and the QRS complex in the ECG is an efficient way to diagnose different arrhythmias. In existing literature, multiple methods and tools are dedicated to this process in [8][9][10][11][12]. A real-time algorithm for detection of the QRS complex in an ECG based upon digital analyses of slope, amplitude and width is presented in [13].…”

Section: Algorithm and Methodsmentioning

confidence: 99%

Automatic Detection of the Wolff:Parkinson:White (WPW) Syndrome from Electrocardiograms (ECGs)

Mahamat¹,

Jacquir²,

Khalil³

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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In this paper, a new method of automatic detection of the Wolff-Parkinson-White (WPW) syndrome is proposed based on electrocardiograms (ECGs) signals. Firstly, with the continuous wavelet transform (CWT), the P wave, the T wave and the QRS complex are identified. Then, their durations are also computed after determination of the boundaries (onsets and offsets of the P, T waves and the QRS complex). Secondly, the PR interval, the QRS complex interval and the area of the QRS complex are determined in order to detect the presence or not of the delta wave. This method has been tested on ECGs signals from patients affected by the WPW syndrome in order to evaluate its robustness. It can provide assistance to cardiologists during the interpretation of the ECG. IntroductionThe WPW syndrome is a cardiac conduction problem associated with reentry tachycardia and it may even be responsible of sudden cardiac death [1]. This congenital abnormality corresponds to a short circuit between atria and ventricles due to the presence of an accessory pathway (AP). In Europe, the prevalence of the WPW syndrome is about 0.15 to 0.31%, and it is the second most common cause of paroxysmal supraventricular tachycardia. The WPW syndrome can be diagnosed on a 12-lead standard ECG, it is described in an ECG by three features [2]: Short PR interval, prolonged QRS duration and presence of a wave so-called delta in the QRS complex. Aside from medical anti-arrhythmic drugs, the main treatment is based on the physical eradication of the AP by applying radiofrequency energy with a dedicated lead on its precise location, using endovascular techniques. Up to now, its precise location around tricuspid or mitral annulus is based on non-practical algorithms that have been published over the years [3]. We have developed a completely new method based on the whole complex QRS morphology analysis, as opposed to previous algorithms based on delta wave patterns only. This method allows us to detect the P wave, the QRS complex and the T wave, and to determine their bounderies (onset and offset of the P, T waves and the QRS complex).In figure 1, the different intervals in a typical ECG are shown, as well as the duration of each wave is illustrated with a heart rate is between 60-90 bpm. This algorithm allows us to effectively detect intervals and durations of the waves (P, QRS complex and T) as described on figure 1. It has been tested on real signals from the University Hospital of Dijon (UHD) database (see section 2) in order to confirm its robustness. Then, one computes the area of the delta wave to complete the detection criteria of the WolffParkinson-White syndrome cited above. In figure 2, we show the difference between a QRS complex with delta wave and a QRS complex without the delta wave: The segment of the QRS complex with the delta

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Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT

Cited by 170 publications

References 29 publications

A Genetic Algorithm:Neural Network Wrapper Approach for Bundle Branch Block Detection

A Genetic Algorithm:Neural Network Wrapper Approach for Bundle Branch Block Detection

Real-time premature ventricular contractions detection based on Redundant Discrete Wavelet Transform

Automatic Detection of the Wolff:Parkinson:White (WPW) Syndrome from Electrocardiograms (ECGs)

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