Cardiac arrhythmia classification using tunable Q-wavelet transform based features and support vector machine classifier

Jha, Chandan Kumar; Kolekar, Maheshkumar H.

doi:10.1016/j.bspc.2020.101875

Cited by 78 publications

(66 citation statements)

References 42 publications

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“…The online subsampling results in signal denoising, decomposition and dimension reduction with a reduced computational cost when compared with the fix-rate counterparts [ 12 , 14 , 15 , 16 , 17 , 22 , 23 ]. In fix-rate counterparts the

is acquired and processed at

.…”

Section: Resultsmentioning

confidence: 99%

“…The multi-resolution time-frequency examination of the non-stationary signals could result in an efficient extraction of features [ 12 , 15 , 16 , 22 , 23 ]. In this context, the Wavelet Transform (WT) is commonly adopted.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, automated ECG signal processing and analysis approaches have been proposed [ 6 , 7 , 12 , 14 , 15 , 16 , 17 , 22 , 23 , 24 , 25 ]. In the event of a poorly tolerated rhythm disorder, the type of arrhythmia should be diagnosed quickly in order to start treatment as quickly as possible [ 6 , 7 , 12 , 14 ]. Depending on the type of arrhythmia, several medications may be prescribed.…”

Section: Introductionmentioning

confidence: 99%

“…In [ 7 ] Qaisar et al used wavelet decomposition-based subbands statistical features extraction approach with random forest (RF) for arrhythmia classification. In [ 12 ] Jha et al Used TQWT for features extraction and support vector machine (SVM) for the arrhythmia classification. In [ 14 ] Sharma et al used DWT with fuzzy and Renyi entropy plus the fractal dimension approach for features extraction.…”

Section: Introductionmentioning

confidence: 99%

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Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification

Qaisar

Mihoub

Krichen

et al. 2021

Sensors

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The usage of wearable gadgets is growing in the cloud-based health monitoring systems. The signal compression, computational and power efficiencies play an imperative part in this scenario. In this context, we propose an efficient method for the diagnosis of cardiovascular diseases based on electrocardiogram (ECG) signals. The method combines multirate processing, wavelet decomposition and frequency content-based subband coefficient selection and machine learning techniques. Multirate processing and features selection is used to reduce the amount of information processed thus reducing the computational complexity of the proposed system relative to the equivalent fixed-rate solutions. Frequency content-dependent subband coefficient selection enhances the compression gain and reduces the transmission activity and computational cost of the post cloud-based classification. We have used MIT-BIH dataset for our experiments. To avoid overfitting and biasness, the performance of considered classifiers is studied by using five-fold cross validation (5CV) and a novel proposed partial blind protocol. The designed method achieves more than 12-fold computational gain while assuring an appropriate signal reconstruction. The compression gain is 13 times compared to fixed-rate counterparts and the highest classification accuracies are 97.06% and 92.08% for the 5CV and partial blind cases, respectively. Results suggest the feasibility of detecting cardiac arrhythmias using the proposed approach.

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is acquired and processed at

.…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification

Qaisar

Mihoub

Krichen

et al. 2021

Sensors

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“…Therefore, SVM is generally known as a linear classifier. Researchers have detected arrhythmias using SVM [96], [98], [101] with Sequential Minimal Optimization-SVM (SMO-SVM)) [102], Multi-class Support Vector Machine (MSVM)/Complex Support Vector Machine (CSVM) [104] and in conjunction with other ML methods such as Ensemble-SVM [97]. Even though SVM is a linear classifier, it can still capture nonlinear relationships in the cardiovascular functionalities, often making highly accurate predictions such as classifying ECG as Normal versus Abnormal [99], [100] and detecting different heartbeats [103].…”

Section: ) Traditional Ecg Classification Approachesmentioning

confidence: 99%

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

et al. 2020

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Electrocardiogram (ECG) gives essential information about different cardiac conditions of the human heart. Its analysis has been the main objective among the research community to detect and prevent life threatening cardiac circumstances. Traditional signal processing methods, machine learning and its subbranches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications for early detection and treatment of cardiac conditions and arrhythmias. A detailed literature survey regarding ECG signal analysis is presented in this paper. We first introduce a stages-based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. The model describes both traditional time/frequency-domain and advanced machine learning techniques reported in the published literature at every stage of analysis, starting from ECG data acquisition to its classification for both simulations and real-time monitoring systems. We present a comprehensive literature review of real-time ECG signal acquisition, prerecorded clinical ECG data, ECG signal processing and denoising, detection of ECG fiducial points based on feature engineering and ECG signal classification along with comparative discussions among the reviewed studies. This study also presents a detailed literature review of ECG signal analysis and feature engineering for ECG-based body sensor networks in portable and wearable ECG devices for real-time cardiac status monitoring. Additionally, challenges and limitations are discussed and tools for research in this field as well as suggestions for future work are outlined. INDEX TERMS ECG analysis, cardiac arrhythmias, QRS and ST detection, ECG classification, deep learning.

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Impact of ECG Signal Preprocessing and Filtering on Arrhythmia Classification Using Machine Learning Techniques

Ayala-Cucas

Mora-Piscal²,

Mayorca-Torres³

et al. 2022

Advances in Artificial Intelligence – IBERAMIA 2022

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Cardiac arrhythmia classification using tunable Q-wavelet transform based features and support vector machine classifier

Cited by 78 publications

References 42 publications

Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification

Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

Impact of ECG Signal Preprocessing and Filtering on Arrhythmia Classification Using Machine Learning Techniques

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