Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification

Dutta, Saibal; Chatterjee, Amitava; Munshi, Sugata

doi:10.1016/j.medengphy.2010.08.007

Cited by 86 publications

(36 citation statements)

References 19 publications

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“…al., [54] have made an attempt to develop a robust heart beat recognition algorithm that can automatically classify normal/ PVC/other heart beats. The work proposes cross-correlation as a formidable feature extraction tool, which when coupled with the LS-SVM classifiers, can be efficiently employed as an automated ECG beat classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Survey on Support Vector Machine in Data Mining Tasks: Applications & Challenges

Nayak¹,

Naik²,

Behera³

2015

IJDTA

136

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Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Survey on Support Vector Machine in Data Mining Tasks: Applications & Challenges

Nayak¹,

Naik²,

Behera³

2015

IJDTA

136

View full text Add to dashboard Cite

show abstract

“…The C3 channel is cross correlated with the data of the remaining channels and the cross-correlation sequences are obtained using the reference channel and any one of other channels. The detailed description of the CC technique is available in reference [7,22]. 7.…”

Section: Cc-ls-svm Algorithmmentioning

confidence: 99%

“…This method has been successfully used in many applications like ECG beat detection [7,8], gait signal processing [9,10], emotional speech recognition [11], heart rate variability classification [12], signal to noise enhancement [13] and seizure prediction [14]. This study intends to apply the CC technique for feature extraction from the MI EEG data as all the channels on the head do not provide independent information and there are high correlations between the channels in EEG [15].…”

Section: Introductionmentioning

confidence: 99%

Comparisons Between Motor Area Eeg and All-Channels Eeg for Two Algorithms in Motor Imagery Task Classification

Siuly

Wen

2014

Biomed. Eng. Appl. Basis Commun.

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Abstract. This article reports on a comparative study to identify electroencephalography (EEG) signals during motor imagery (MI) for motor area EEG and all-channels EEG in the Brain Computer Interface (BCI) application. In this paper, we present two algorithms: CC-LS-SVM and CC-LR for MI tasks classification. The CC-LS-SVM algorithm combines the cross-correlation (CC) technique and the least square support vector machine (LS-SVM). The CC-LR algorithm assembles the cross-correlation (CC) technique and binary logistic regression (LR) model. These two algorithms are implemented on the motor area EEG and the all-channels EEG to investigate how well they perform and also to test which area EEG is better for the MI classification. These two algorithms are also compared with some existing methods which reveal their competitive performance during classification. Results on both datasets, IVa and IVb from BCI Competition III, show that the CC-LS-SVM algorithm performs better than the CC-LR algorithm on both the motor area EEG and the all-channels EEG. The results also demonstrate that the CC-LS-SVM algorithm performs much better for the all-channels EEG than for the motor area EEG. Furthermore, the LS-SVM based approach can correctly identify the discriminative MI tasks, demonstrating the algorithm's superiority in classification performance over some existing methods.

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“…Lin extracted normalized R-R interval and morphological features as the input features to train and test the linear discriminant classifier in the classification of heartbeats [24]. A cross-correlation-based approach was used to extract suitable features, and a least squares SVM (LSSVM) classifier was developed to classify ECG beats [25]. Several independent component analysis (ICA) algorithms were tested and analysed to identify various components with high accuracy in a particular algorithm based on biomedical data for classification [26].…”

Section: Introductionmentioning

confidence: 99%

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

Yuan

Wang

et al. 2016

Sensors

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Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

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Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification

Cited by 86 publications

References 19 publications

A Comprehensive Survey on Support Vector Machine in Data Mining Tasks: Applications & Challenges

A Comprehensive Survey on Support Vector Machine in Data Mining Tasks: Applications & Challenges

Comparisons Between Motor Area Eeg and All-Channels Eeg for Two Algorithms in Motor Imagery Task Classification

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

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