Arrhythmia classification based on wavelet transformation and random forests

Pan, Guolin; Xin, Zhuo; Shi, Si; Jin, Dawei

doi:10.1007/s11042-017-5225-5

Cited by 21 publications

(10 citation statements)

References 13 publications

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“…in [4], proposed system-based approach for computer-assisted detection of five classes of ECG arrhythmia beats by adopting DWT as a feature to train Support Vector Machine (SVM). In [5] developed a comprehensive model based on random forest techniques and discrete wavelet for arrhythmia classification. The authors in [6]used the DWT to capture features from the ECG signal based on ST-segment elevation and inverted T wave logic.…”

Section: Introductionmentioning

confidence: 99%

CNN-LSTM Based Model for ECG Arrhythmias and Myocardial Infarction Classification

Abdullah¹,

Al-Ani²

2020

Adv. sci. technol. eng. syst. j.

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ECG analysis is commonly used by medical practitioners and cardiologists for monitoring cardiac health. A high-performance automatic ECG classification system is a challenging area because there is difficulty in detecting and clustering various waveforms in the signal, especially in the manual analysis of electrocardiogram (ECG) signals. In this paper, an accurate (ECG) classification and monitoring system are proposed using the implementation of 1D Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM). The learned features are captured from the CNN model, and then fed to the LSTM model. No handcraft features are required for the model for the ECG classification. The result of the CNN-LSTM model has demonstrated superior performance than several state-of-the-arts that cited in the result section. The proposed models are evaluated on MIT-BIH arrhythmia and PTB Diagnostics datasets. Based on the obtained results, the CNN-LSTM method can improve the accuracy rate, such that 98.1 % and 98.66 % on Myocardial Infarction (MI) and arrhythmia classification, respectively.

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Section: Introductionmentioning

confidence: 99%

CNN-LSTM Based Model for ECG Arrhythmias and Myocardial Infarction Classification

Abdullah¹,

Al-Ani²

2020

Adv. sci. technol. eng. syst. j.

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“…Specifically, DWT was used to remove high-frequency noise and baseline drift, while DWT, autocorrelation, PCA, variances, and other mathematical methods are used to extract frequency-domain features, time-domain features, and morphology features. Moreover, an arrhythmia classification system was developed, and its availability was verified that the proposed scheme could significantly be used for guidance and reference in clinical arrhythmia automatic classification [63]. Sahoo (2017) proposed an improved algorithm to find QRS complex features based on the wavelet transform to classify four kids of ECG beats: Normal (N), left bundle branch block (LBBB), right bundle branch block (RBBB), and Paced beats (P); using NN and SVM classifier.…”

Section: Dwtmentioning

confidence: 99%

A Review Study for Electrocardiogram Signal Classification

Abdulla

Al-Ani

2020

UHD J SCI TECH

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An electrocardiogram (ECG) signal is a recording of the electrical activity generated by the heart. The analysis of the ECG signal has been interested in more than a decade to build a model to make automatic ECG classification. The main goal of this work is to study and review an overview of utilizing the classification methods that have been recently used such as Artificial Neural Network, Convolution Neural Network (CNN), discrete wavelet transform, Support Vector Machine (SVM), and K-Nearest Neighbor. Efficient comparisons are shown in the result in terms of classification methods, features extraction technique, dataset, contribution, and some other aspects. The result also shows that the CNN has been most widely used for ECG classification as it can obtain a higher success rate than the rest of the classification approaches.

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“…More recently, Alickovic and Subasi [12] also proposed the use of RF with the DWT of the ECG signal but taking a set of different statistical features extracted from the distribution of wavelet coefficients instead of the coefficient themselves. A similar approach that proposes an extensive use of the DWT was also recently proposed by Pan et al [13]. RF was similarly explored by Ganesh Kumar and Kumaraswamy [14] but with the use of features obtained from the Discrete Cosine Transform (DCT) over the interbeat interval signal.…”

Section: Introductionmentioning

confidence: 99%

Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification

Saenz-Cogollo

Agelli

2020

Algorithms

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Finding an optimal combination of features and classifier is still an open problem in the development of automatic heartbeat classification systems, especially when applications that involve resource-constrained devices are considered. In this paper, a novel study of the selection of informative features and the use of a random forest classifier while following the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) and an inter-patient division of datasets is presented. Features were selected using a filter method based on the mutual information ranking criterion on the training set. Results showed that normalized beat-to-beat (R–R) intervals and features relative to the width of the ventricular depolarization waves (QRS complex) are the most discriminative among those considered. The best results achieved on the MIT-BIH Arrhythmia Database were an overall accuracy of 96.14% and F1-scores of 97.97%, 73.06%, and 90.85% in the classification of normal beats, supraventricular ectopic beats, and ventricular ectopic beats, respectively. In comparison with other state-of-the-art approaches tested under similar constraints, this work represents one of the highest performances reported to date while relying on a very small feature vector.

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Arrhythmia classification based on wavelet transformation and random forests

Cited by 21 publications

References 13 publications

CNN-LSTM Based Model for ECG Arrhythmias and Myocardial Infarction Classification

CNN-LSTM Based Model for ECG Arrhythmias and Myocardial Infarction Classification

A Review Study for Electrocardiogram Signal Classification

Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification

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