ECG beat classification by using discrete wavelet transform and Random Forest algorithm

Emanet, Nahit

doi:10.1109/icsccw.2009.5379457

Cited by 39 publications

(20 citation statements)

References 12 publications

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“…Note that the proposed method is different from the previous work [41,42]. Firstly, we used WPD and entropy instead of DWT coefficients to extract features.…”

Section: Introductionmentioning

confidence: 99%

ECG Classification Using Wavelet Packet Entropy and Random Forests

Zhou

2016

Entropy

412

200

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Abstract:The electrocardiogram (ECG) is one of the most important techniques for heart disease diagnosis. Many traditional methodologies of feature extraction and classification have been widely applied to ECG analysis. However, the effectiveness and efficiency of such methodologies remain to be improved, and much existing research did not consider the separation of training and testing samples from the same set of patients (so called inter-patient scheme). To cope with these issues, in this paper, we propose a method to classify ECG signals using wavelet packet entropy (WPE) and random forests (RF) following the Association for the Advancement of Medical Instrumentation (AAMI) recommendations and the inter-patient scheme. Specifically, we firstly decompose the ECG signals by wavelet packet decomposition (WPD), and then calculate entropy from the decomposed coefficients as representative features, and finally use RF to build an ECG classification model. To the best of our knowledge, it is the first time that WPE and RF are used to classify ECG following the AAMI recommendations and the inter-patient scheme. Extensive experiments are conducted on the publicly available MIT-BIH Arrhythmia database and influence of mother wavelets and level of decomposition for WPD, type of entropy and the number of base learners in RF on the performance are also discussed. The experimental results are superior to those by several state-of-the-art competing methods, showing that WPE and RF is promising for ECG classification.

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“…Note that the proposed method is different from the previous work [41,42]. Firstly, we used WPD and entropy instead of DWT coefficients to extract features.…”

Section: Introductionmentioning

confidence: 99%

ECG Classification Using Wavelet Packet Entropy and Random Forests

Zhou

2016

Entropy

412

200

View full text Add to dashboard Cite

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“…Result of the proposed method indicates its potentiality in biomedical engineering applications such as Alzheimer disease, breast cancer. Nahit Emanet [11] has proposed method for classification of heartbeat from ECG signal into their respective classes. In this proposed system, preprocessing is performed in order to remove artifact signal from ECG signal.…”

Section: Related Workmentioning

confidence: 99%

“…The computational complexity of Random Forest is based on the number of features, number of training cases. It is given by [11] √MSlog(S) [1] Where 'M' is the number of features of dataset and 'S' is the number of the training case. As random forest injects randomness in selection of features, reducing 'M' which implies selected features for classification which results into reducing strength of individual tree and the correlation between them.…”

Section: Mathematical Modelingmentioning

confidence: 99%

An ensemble classifier approach for disease diagnosis using Random Forest

Pachange

Joglekar

Kulkarni³

2015

2015 Annual IEEE India Conference (INDICON)

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Massive amount of diagnostic data is generated everyday as a part of daily diagnosis, related to various types of diseases and disorders. For knowledge discovery from this diagnostic data, efficient data mining techniques play a very important role. Ensemble classifier is one of the data classification techniques related to data mining, in which decision of multiple base classifiers is combined for accurate prediction of the presence or absence of abnormality. Here, we have considered retinal images of diabetic patients, PET scan of brain of Alzheimer and MRI of brain cancer and classification is performed irrespective of whether normality or abnormality is present. The ensemble method proves to be very efficient in classification of records from available patient database, as it involves the process of considering opinion from multiple base classifiers, as opposed to the single classifier method. This leads to very accurate and precise inference, as uncorrelated errors are removed because of multiple base classifiers.

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“…They reached an accuracy of 89% validated with fivefold cross validation. Similar random forest classifiers for ECG classification were developed by Sathish & Vimal [64], or Emanet [65].…”

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

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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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ECG beat classification by using discrete wavelet transform and Random Forest algorithm

Cited by 39 publications

References 12 publications

ECG Classification Using Wavelet Packet Entropy and Random Forests

ECG Classification Using Wavelet Packet Entropy and Random Forests

An ensemble classifier approach for disease diagnosis using Random Forest

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

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