Investigating Cardiac Arrhythmia in ECG using Random Forest Classification

Kumar, Rajesh; Kumaraswamy, Y. S.

doi:10.5120/4599-6557

Cited by 34 publications

(14 citation statements)

References 14 publications

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“…They may be able to capture changes in the heart rhythm, such as sinus rhythm versus fibrillation, in which the complexes exhibit different morphologies. Some works focus on time interval features to characterize the dynamics of ECG phenomena such as QRS duration, QT interval or heart rate, defined as the number of beats per unit of time [22,23,31,46,55]. Morphological features include the coefficients of the Hermite transform, the wavelet transform or the discrete cosine transform [29,32] that aim to model the ECG beat instead of extracting features from the raw data.…”

Section: Feature Extraction and Dimensionality Reductionmentioning

confidence: 99%

“…A useful property of random forests is their ability to rank the variables according to their importance in the classification and therefore allow feature selection to avoid overfitting. Ganeshkumar & Kumaraswamy [23] investigated arrhythmia detection by identifying six heartbeat classes (normal, PVC, paced, atrial premature beat, and left and right bundle branch block) using random forest. They reached an accuracy of 92.16% on 150 beats extracted from the MIT-BIH database with 30 trees but their method was not validated over an independent testing set.…”

Section: Random Forestsmentioning

confidence: 99%

See 1 more Smart Citation

Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

Lyon

Mincholé

Martínez

et al. 2018

J. R. Soc. Interface.

179

102

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Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data. This review describes the computational methods in use for ECG analysis, with a focus on machine learning and 3D computer simulations, as well as their accuracy, clinical implications and contributions to medical advances. The first section focuses on heartbeat classification and the techniques developed to extract and classify abnormal from regular beats. The second section focuses on patient diagnosis from whole recordings, applied to different diseases. The third section presents real-time diagnosis and applications to wearable devices. The fourth section highlights the recent field of personalized ECG computer simulations and their interpretation. Finally, the discussion section outlines the challenges of ECG analysis and provides a critical assessment of the methods presented. The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.

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Section: Feature Extraction and Dimensionality Reductionmentioning

confidence: 99%

Section: Random Forestsmentioning

confidence: 99%

Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

Lyon

Mincholé

Martínez

et al. 2018

J. R. Soc. Interface.

179

102

View full text Add to dashboard Cite

show abstract

“…Melgani et al [10] applied the idea of particle swarm optimization to an SVM classifier and achieved a total accuracy of 89.72% for the classification of six arrhythmias. Kumar et al [11] proposed the use of random forests (RF) to distinguish RR intervals and Park et al [12] proposed a K-nearest neighbor (K-NN) classifier for detecting 17 types of ECG beats. Yuzhen et al [13] attempted to use BP neural networks to classify ECG beats and achieved an accuracy rate of 93.9%.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification System of Arrhythmias Using 12-Lead ECGs with a Deep Neural Network Based on an Attention Mechanism

Zhao

et al. 2020

Symmetry

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Nowadays, a series of social problems caused by cardiovascular diseases are becoming increasingly serious. Accurate and efficient classification of arrhythmias according to an electrocardiogram is of positive significance for improving the health status of people all over the world. In this paper, a new neural network structure based on the most common 12-lead electrocardiograms was proposed to realize the classification of nine arrhythmias, which consists of Inception and GRU (Gated Recurrent Units) primarily. Moreover, a new attention mechanism is added to the model, which makes sense for data symmetry. The average F1 score obtained from three different test sets was over 0.886 and the highest was 0.919. The accuracy, sensitivity, and specificity obtained from the PhysioNet public database were 0.928, 0.901, and 0.984, respectively. As a whole, this deep neural network performed well in the multi-label classification of 12-lead ECG signals and showed better stability than other methods in the case of more test samples.

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“…ECG features mainly include time domain, frequency domain, morphological and nonlinear features. Classification methods mainly focus on random forest [19], linear discriminant classification, neural networks (NNs), support vector machine (SVM), etc. As ECG waveforms in time domains are complicated and easily interfered by noise, classic time domain analysis had low accuracy in extracting features [20,21].…”

Section: Introductionmentioning

confidence: 99%

“…As ECG waveforms in time domains are complicated and easily interfered by noise, classic time domain analysis had low accuracy in extracting features [20,21]. Thereafter, the transform and morphological methods are studied to obtain ECG features containing lots of time and frequency information [19,22]. However, these features can not reflect more accurate characteristics of ECG for achieving high classification accuracy.…”

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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Investigating Cardiac Arrhythmia in ECG using Random Forest Classification

Cited by 34 publications

References 14 publications

Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

Automatic Classification System of Arrhythmias Using 12-Lead ECGs with a Deep Neural Network Based on an Attention Mechanism

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

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