Classification of ECG signals using LDA with factor analysis method as feature reduction technique

Kaur, Manpreet; Arora, Ajat Shatru

doi:10.3109/03091902.2012.702851

Cited by 18 publications

(9 citation statements)

References 14 publications

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“…Rai et al [23] produced the second-best classification accuracy score where the calculated achievement was 99.60%. Kaur et al [22] also produced the 99.06% accuracy score. Khalaf et al [24], Dong et al [2] and Thomas et al [22] produced 98.60%, 97.78% and 97.68% accuracy scores, respectively.…”

Section: Experimental Work and Resultsmentioning

confidence: 95%

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Local feature descriptors based ECG beat classification

Abdullah

Akpınar

Şengür

2020

Health Inf Sci Syst

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ECG beat type analysis is important in the detection of various heart diseases. The ECG beats give useful information about the status of the monitored heart condition. Up to now, various artificial intelligence-based methods have been proposed for ECG based heart failure detection. These methods were generally based on either time or frequency domain signal processing routines. In this study, we propose a different approach for ECG beat classification. The proposed approach is based on image processing. Thus, the initial step of the proposed work is converting the ECG beat signals to the ECG beat images. To do that, the ECG beat snapshots are initially saved as ECG beat images and then local feature descriptors are considered for feature extraction from ECG beat images. Eight local feature descriptors namely Local Binary Patterns, Frequency Decoded LBP, Quaternionic Local Ranking Binary Pattern, Binary Gabor Pattern, Local Phase Quantization, Binarized Statistical Image Features, CENsus TRansform hISTogram and Pyramid Histogram of Oriented Gradients are considered for feature extraction. The Support Vector Machines (SVM) classifier is used in the classification stage of the study. Linear, Quadratic, Cubic and Gaussian kernel functions are used in the SVM classifier. Five types of ECG beats from the MIT-BIH arrhythmia dataset are considered in experiments and the classification accuracy is used for performance measure. To construct a balanced training and test sets, 5000 and 10,000 ECG beat samples are randomly selected and are used in experiments in tenfold cross-validation fashion. The obtained resultsshow that the proposed method is quite efficient where the calculated accuracy score is 99.9% and the comparisons with the state-of-the-art method show that the proposed method outperforms other methods.

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Section: Experimental Work and Resultsmentioning

confidence: 95%

“…Thomas et al [21] used dual-tree-complex WT and multi-layered NN for the classification of cardiac arrhythmias. Kaur et al [22] used wavelet coefficients and feature reduction methods for efficient cardiac dysfunctions classification. Rai et al [23] used Daubechies WT and RBFNN for five types of ECG beat classification.…”

Section: Experimental Work and Resultsmentioning

confidence: 99%

Local feature descriptors based ECG beat classification

Abdullah

Akpınar

Şengür

2020

Health Inf Sci Syst

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“…A better method of feature extraction can be achieved by combining principal component analysis (PCA) with independent component analysis (ICA) in what is called PCA-ICA algorithm [26]. Linear discriminant analysis (LDA) is also used to reduce the ECG features [27]. ML algorithms are used to construct classification models for the ECG features [9,13,15,28].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification

Obeidat¹,

Alqudah²

2021

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In this paper we have utilized a hybrid lightweight 1D deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods for accurate, fast, and automated beat-wise ECG classification. The CNN and LSTM models were designed separately to compare with the hybrid CNN-LSTM model in terms of accuracy, number of parameters, and the time required for classification. The hybrid CNN-LSTM system provides an automated deep feature extraction and classification for six ECG beats classes including Normal Sinus Rhythm (NSR), atrial fibrillation (AFIB), atrial flutter (AFL), atrial premature beat (APB), left bundle branch block (LBBB), and right bundle branch block (RBBB). The hybrid model uses the CNN blocks for deep feature extraction and selection from the ECG beat. While the LSTM layer will learn how to extract contextual time information. The results show that the proposed hybrid CNN-LSTM model achieves high accuracy and sensitivity of 98.22% and 98.23% respectively. This model is light and fast in classifying ECG beats and superior to other previously used models which makes it very suitable for embedded systems designs that can be used in clinical applications for monitoring heart diseases in faster and more efficient manner.

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“…However, a choice of an optimal wavelet is still challenging [18] and the approach has low efficiency in smoothing ECG signals. Other algorithms tested for such needs include the principal component analysis (PCA) [19], linear discriminant analysis (LDA) [20], independent component analysis (ICA) [21], support vector machine [22], and neural networks [23].…”

Section: Introductionmentioning

confidence: 99%

ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing

Lastre-Dominguez

Shmaliy

Ibarra-Manzano

et al. 2019

BioMed Research International

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Methods of the electrocardiography (ECG) signal features extraction are required to detect heart abnormalities and different kinds of diseases. However, different artefacts and measurement noise often hinder providing accurate features extraction. One of the standard techniques developed for ECG signals employs linear prediction. Referring to the fact that prediction is not required for ECG signal processing, smoothing can be more efficient. In this paper, we employ the p-shift unbiased finite impulse response (UFIR) filter, which becomes smooth by p<0. We develop this filter to have an adaptive averaging horizon: optimal for slow ECG behaviours and minimal for fast excursions. It is shown that the adaptive UFIR algorithm developed in such a way provides better denoising and suboptimal features extraction in terms of the output signal-noise ratio (SNR). The algorithm is developed to detect durations and amplitudes of the P-wave, QRS-complex, and T-wave in the standard ECG signal map. Better performance of the algorithm designed is demonstrated in a comparison with the standard linear predictor, UFIR filter, and UFIR predictive filter based on real ECG data associated with normal heartbeats.

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Classification of ECG signals using LDA with factor analysis method as feature reduction technique

Cited by 18 publications

References 14 publications

Local feature descriptors based ECG beat classification

Local feature descriptors based ECG beat classification

A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification

ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing

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