Classification of premature ventricular contraction based on Discrete Wavelet Transform for real time applications

Duque, Andrés; Martinez-Tabares, F. J.; Gallego, J.; Rodríguez, Carlos Armando; Mora, I.D.; Castellanos-Domínguez, G.; Bustamante, John

doi:10.1109/pahce.2013.6568330

Cited by 11 publications

(8 citation statements)

References 12 publications

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“…L. Giovangrandi classified the ECG beats obtained from a large set of data using wavelet transforms and the timing information of a neural network classifier [12]. In [13], a PVC detection method based on a Discrete Wavelet Transform (DWT) was proposed. In the study, the wavelet coefficients of the ECG data were used as the feature vector, and a Support Vector Machine (SVM) was used as the classifier.…”

Section: Introductionmentioning

confidence: 99%

“…Peng Li developed a low complexity data-adaptive PVC recognition approach that exhibited good robustness against noise, generalization capabilities, and a PVC recognition accuracy of 98.2%, indicating that it could be effectively used for real-time applications [16]. Using these algorithms, the features of ECGs were manually extracted based on time domain information, such as ECG morphology [6,7,11,12], and transform domain information [4,5,9,[12][13][14], such as the wavelet transforms or statistical parameters [10,15,16]. These processes require artificial experience or specialized knowledge and increase computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression

Yang

Bai

et al. 2015

BME

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Abstract. Premature ventricular contraction (PVC) is one of the most serious arrhythmias. Without early diagnosis and proper treatment, PVC can result in significant complications. In this paper, a novel feature extraction method based on a sparse auto-encoder (SAE) and softmax regression (SR) classifier was used to differentiate PVCs from other common Non-PVC rhythms, including normal sinus (N), left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contraction (APC), and paced beat (PB) rhythms. The proposed method was analyzed using 40 ECG records obtained from the MIT-BIH Arrhythmia Database. The proposed method exhibited an overall accuracy of 99.4%, with a PVC recognition sensitivity and positive predictability of 97.9% and 91.8%, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression

Yang

Bai

et al. 2015

BME

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“…This is one of the main problems with ambulatory monitoring systems, since such interference corrupts the ECG signal, hinders its interpretation, and can lead to misdiagnosis [48], [49]. To remove these interferences from the ECG signal, signal processing techniques based on the use of adaptive filters or the Wavelet transform [46], [48]- [50] are used.…”

Section: Noise Reduction Techniquesmentioning

confidence: 99%

“…To attenuate the noise caused by baseline deviation and power line interference, Orozco-Duque and his collaborators implemented a method based on the Discrete Wavelet Transformed (DWT), which consists of decomposing the signal into frequency scales using the fast DWT and threshold according to the level of the signal-to-noise ratio [50]. Some authors group or classify the techniques mentioned according to the context of their application; for example, Saxena et al [51] suggest the following methodologies to cover problems related to power line interference: Finite Impulse Response Filters (FIR), Infinite Impulse Response Filters (IIR), DWT and Adaptive Normalized LMS Filters, where the linkage of two new methods to the previously named FIR and IIR filters can be observed.…”

Section: Noise Reduction Techniquesmentioning

confidence: 99%

Techniques of Acquisition and Processing of Electrocardiographic Signals in the Detection of Cardiac Arrhythmias

Uribe¹,

Sánchez-Molina²,

Fuentes³

2019

Respuestas

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The development of ambulatory monitoring systems and its electrocardiographic (ECG) signal processing techniques has become an important field of investigation, due to its relevance in the early detection of cardiovascular diseases such as the arrhythmias. The current trend of this technology is oriented to the use of portable equipment and mobile devices such as Smartphones, which have been widely accepted due to the technical characteristics and common integration in daily life. A fundamental characteristic of these systems is their ability to reduce the most common types of noise by means of digital signal processing techniques. Among the most used techniques are the adaptive filters and the Discrete Wavelet Transform (DWT) which have been successfully implemented in several studies. There are systems that integrate classification stages based on artificial intelligence, which increases the performance in the process of arrhythmias detection. These techniques are not only evaluated for their functionality but for their computational cost, since they will be used in real-time applications, and implemented in embedded systems. This paper shows a review of each of the stages in the construction of a standard ambulatory monitoring system, for the contextualization of the reader in this type of technology.

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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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Classification of premature ventricular contraction based on Discrete Wavelet Transform for real time applications

Cited by 11 publications

References 12 publications

A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression

A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression

Techniques of Acquisition and Processing of Electrocardiographic Signals in the Detection of Cardiac Arrhythmias

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

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