2013
DOI: 10.1016/j.bspc.2013.01.005
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ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform

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Cited by 620 publications
(310 citation statements)
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“…Therefore, selection of significant features plays a vital role in the classification, particularly when the ECG data is noisy. Current reduction/optimization methods to solve the feature reduction problem in ECG classification include Genetic Algorithm (GA) with Support Vector Machine (SVM) [5], Principal Component Analysis (PCA) with SVM, Linear Discriminant Analysis (LDA) with SVM [6], Cartesian Genetic Programming (CGP) with Neural Network (NN) [7], Firefly and Particle Swarm Optimization (FFPSO) technique with Levenberg Marquardt Neural Network (LMNN) [8]. Existing methods were tested on noise-free ECG data, which produced accurate classification results [9].…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, selection of significant features plays a vital role in the classification, particularly when the ECG data is noisy. Current reduction/optimization methods to solve the feature reduction problem in ECG classification include Genetic Algorithm (GA) with Support Vector Machine (SVM) [5], Principal Component Analysis (PCA) with SVM, Linear Discriminant Analysis (LDA) with SVM [6], Cartesian Genetic Programming (CGP) with Neural Network (NN) [7], Firefly and Particle Swarm Optimization (FFPSO) technique with Levenberg Marquardt Neural Network (LMNN) [8]. Existing methods were tested on noise-free ECG data, which produced accurate classification results [9].…”
Section: Introductionmentioning
confidence: 99%
“…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%
“…a j yaklaşım katsayıları ve d j detay katsayıları denklem (2)'deki gibi hesaplanır [21]. (2) ADD analizinde, orijinal sinyal birbirini tamamlayan alçak ve yüksek geçiren iki filtre yoluyla eşit olarak alçak ve yüksek frekanslı bileşenlere ayrılır [22,23]. Alçak geçiren filtre yöntemiyle işlenen sinyalden elde edilen yüksek ölçekli düşük frekanslı yeniden oluşturma bileşenine yaklaşım adı verilmekte ve "A" harfi ile gösterilmektedir.…”
Section: Marmara Fenunclassified