Automated Screening of Arrhythmia Using Wavelet Based Machine Learning Techniques

Martis, Roshan Joy; Krishnan, M. Muthu Rama; Chakraborty, Chandan; Pal, Sarbajit; Sarkar, Debranjan; Mandana, Kayapanda; Ray, Ajoy Kumar

doi:10.1007/s10916-010-9535-7

Cited by 52 publications

(13 citation statements)

References 17 publications

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“…An accuracy of 100 % was achieved on a small, private, database. (Martis et al, 2012) compared the performance of support vector machines, neural network and Gaussian mixture model in the distinction of normal and 12 different beat types. Features were obtained after a feature selection method was applied to the wavelet coefficients.…”

Section: State-of-the-artmentioning

confidence: 99%

Spectral and Time Domain Parameters for The Classification of Atrial Fibrillation

Batista¹,

Fred

2015

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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Atrial fibrillation (AF) is the most common type of arrhythmia. This work presents a pattern analysis approach to automatically classify electrocardiographic (ECG) records as normal sinus rhythm or AF. Both spectral and time domain features were extracted and their discrimination capability was assessed individually and in combination. Spectral features were based on the wavelet decomposition of the signal and time parameters translated heart rate characteristics. The performance of three classifiers was evaluated: k-nearest neighbour (kNN), artificial neural network (ANN) and support vector machine (SVM). The MIT-BIH arrhythmia database was used for validation. The best results were obtained when a combination of spectral and time domain features was used. An overall accuracy of 99.08 % was achieved with the SVM classifier.

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Section: State-of-the-artmentioning

confidence: 99%

Spectral and Time Domain Parameters for The Classification of Atrial Fibrillation

Batista¹,

Fred

2015

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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“…For electrocardiographic (ECG) analysis, Martis et al [12] developed an approach for discriminating arrhythmia from normal sinus rhythm based on feature extraction using discrete Daubechies-4 wavelet transform as well as feature reduction using PCA. Korurek and Nizam [13] employed the PCA compressed discrete Daubechies-5 wavelet coefficients to classify normal heart beats and five types of arrhythmia for the diagnosis of cardiovascular disease.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Two-Directional Two-Dimensional Principal Component Analysis and Its Application to High-Dimensional Biomedical Signal Classification

Xie

Zhou

Guo

et al. 2016

IEEE Trans. Biomed. Eng.

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Goal: Time-frequency analysis incorporating the wavelet transform followed by principal component analysis (WT-PCA) has been a powerful approach for the analysis of biomedical signals such as electromyography (EMG), electroencephalography (EEG), electrocardiography (ECG), and Doppler ultrasound. Time-frequency coefficients at various scales were usually transformed into a one-dimensional array using only a single or a few signal channels. The steady improvement of biomedical recording techniques has increasingly permitted the registration of a high number of channels. However, WT-PCA is not applicable to high-dimensional recordings due to the curse of dimensionality and small sample size problem. In this study, we present a multi-scale two-directional two-dimensional principal component analysis (MS2D 2 PCA) method for the efficient and effective extraction of essential feature information from high-dimensional signals. Multi-scale matrices constructed in the first step incorporate the spatial correlation and physiological characteristics of sub-band signals among channels. In the second step, the two-directional two-dimensional principal component analysis operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify 20 hand movements using 89-channel EMG signals recorded in stroke survivors, which illustrates the efficiency and effectiveness of the proposed method for high-dimensional biomedical signal analysis.

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“…Diger yandan, temel bileşen analizi (TBA) [4], yüksek dereceli istatistikler (YDİ) [5], bagımsız bileşen analizi (BBA) [6] ve form faktörü (FF) [2] gibi gelişmiş zaman bölgesi sinyal işleme yöntemleri kullanılarak öznitelikler başarı ile elde edilmektedir. Zaman bölgesi yöntemlerinin gürültüye olan hassasiyeti sebebiyle, zaman-frekans analiz yöntemi olan ayrık dalgacık dönüşümü (ADD) diger birçok biyolojik sinyallerin analizinde oldugu gibi EKG sinyallerinde de başarı ile önerilmektedir [7]. Daha gelişmiş özellikleri ile karmaşık dalgacık dönüşümü (KDD) [8] ve çift agaç karmaşık dalgacık dönüşümü (ÇAKDD) [1] ise bu alanda özgün yöntemler arasındadır.…”

Section: Introductionunclassified

ECG signal analysis based on variational mode decomposition and bandwidth property

Mert

2016

2016 24th Signal Processing and Communication Application Conference (SIU)

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Özetçe -Bu çalışmada, elektrokardiyogram (EKG) sinyallerinden aritmi analizi için varyasyonel kip ayrışım (VKA) yöntemi ve bant genişligi özelligi incelenmiştir. VKA yöntemi, görgül kip ayrışımı (GKA) algoritmasını esas alarak geliştirilmiş dogrusal ve duragan olmayan sinyal işleme yöntemidir. Bu yöntem ile sinyal, kendini oluşturan sınırlı bant genişligine sahip kiplerin toplamı olarak ayrıştırılır. MIT-BIH aritmi veri tabanından alınan EKG sinyalleri VKA ile ayrıştırılır ve elde edilin kiplerin genlik modülasyon bant genişligi (BAM ), frekans modülasyonu bant genişligi (BF M ) ve toplam bant genişligi (B) öznitelik vektörü olarak kullanılır. Normal (N), prematüre ventriküler kasılma (PVC), sol dal blok vuru (LBBB), sag dal blok vuru (RBBB), yapay vuru (PB) ve atriyal prematüre vuru (APB) için EKG sinyalleri analiz edilerek bant genişlikleri (BAM , BF M , B) ve bunların hesabı için kullanılan anlık genlik (IA) ve anlık frekans (IF) özelliklerine deginilmiştir. Son olarak k-en yakın komşu, yapay sinir agı ve karar agacı gibi tek sınıflandırıcı ile bunların topluluk sınıflandırıcıları ile performansı irdelenmiştir.Anahtar Kelimeler-Varyasyonel kip ayrışımı, elektrokardiyogram, aritmi, sınıflandırma.Abstract-In this paper, the bandwidth properties of the modes obtained using the variational mode decomposition (VMD) are analyzed to detect arrhythmia electrocardiogram (ECG) beats. The VMD is an enhanced version of the empirical mode decomposition (EMD) algorithm to analyze non-linear and nonstationary signals. It decomposes the signal into a set of bandlimited amplitude and frequency modulated oscillations called modes. ECG signals from MIT-BIH arrhythmia database are decomposed using the VMD, and the amplitude modulation bandwidth BAM , frequency modulation bandwidth BF M and total bandwidth B of the modes are deployed as feature vector. Heart beats such as normal (N), premature ventricular contraction (PVC), left bundle branch block (LBBB), right bundle branch block (RBBB), paced beat( PB) and atrial premature beat (APB) are classified using these features. Class discrimination capability of the VMD based features are indicated giving different instantaneous frequency (IF) and amplitude (IA) spectra. Finally, single classifiers such as k-nearest neighbor, artificial neural network and decision tree with their ensemble methods are used to evaluate the performance of the proposed method.

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Automated Screening of Arrhythmia Using Wavelet Based Machine Learning Techniques

Cited by 52 publications

References 17 publications

Spectral and Time Domain Parameters for The Classification of Atrial Fibrillation

Spectral and Time Domain Parameters for The Classification of Atrial Fibrillation

Multiscale Two-Directional Two-Dimensional Principal Component Analysis and Its Application to High-Dimensional Biomedical Signal Classification

ECG signal analysis based on variational mode decomposition and bandwidth property

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