ECG arrhythmia classification based on optimum-path forest

Luz, Eduardo; Nunes, Thiago; Albuquerque, Victor Hugo C. de; Papa, João Paulo; Menotti, David

doi:10.1016/j.eswa.2012.12.063

Cited by 154 publications

(57 citation statements)

References 40 publications

(62 reference statements)

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“…Suzuki [99] Massive-training neural network (MTANN) Iwashita et al [100], Luz et al [101], Nunes et al [102] and Papa et al [103] Optimum path forest (OPF) …”

Section: Authorsmentioning

confidence: 99%

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

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This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images.Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic * Corresponding author. E-mail: tavares@fe.up.pt. 31, 2015 aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks. Preprint submitted to Computer Methods and Programs in Biomedicine August

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“…Suzuki [99] Massive-training neural network (MTANN) Iwashita et al [100], Luz et al [101], Nunes et al [102] and Papa et al [103] Optimum path forest (OPF) …”

Section: Authorsmentioning

confidence: 99%

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

Self Cite

200

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“…The preprocessing phase is mainly aimed at detecting and attenuating frequencies of the ECG signal related to artifacts, which also usually performs signal normalization and enhancement. After preprocessing, segmentation divides the signal into smaller segments, which can better express the electrical activity of the heart [1]. Nowadays, the researchers can get good results from preprocessing and segmentation by some popular techniques or tools [2].…”

Section: Introductionmentioning

confidence: 99%

ECG Classification Using Wavelet Packet Entropy and Random Forests

Zhou

2016

Entropy

384

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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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“…More detailed descriptions about the supervised OPF algorithm and some of its recent applications can be found, for example, in [36]-classification of ultrasonic signals, [47]-land cover classification, [48,49]-electroencephalogram (EEG) and electrocardiogram (ECG) signal identification and recognition, [50]-characterization of graphite particles in metallographic images, [51,52]-learning-time constrained applications, [53]-segmentation and classification of human intestinal parasites, and in [54]-intrusion detection in computer networks.…”

Section: Machine Learningmentioning

confidence: 99%

Classification of Induced Magnetic Field Signals for the Microstructural Characterization of Sigma Phase in Duplex Stainless Steels

Silva

Marinho

Filho

et al. 2016

Metals

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Duplex stainless steels present excellent mechanical and corrosion resistance properties. However, when heat treated at temperatures above 600 • C, the undesirable tertiary sigma phase is formed. This phase presents high hardness, around 900 HV, and it is rich in chromium, the material toughness being compromised when the amount of this phase is not less than 4%. This work aimed to develop a solution for the detection of this phase in duplex stainless steels through the computational classification of induced magnetic field signals. The proposed solution is based on an Optimum Path Forest classifier, which was revealed to be more robust and effective than Bayes, Artificial Neural Network and Support Vector Machine based classifiers. The induced magnetic field was produced by the interaction between an applied external field and the microstructure. Samples of the 2205 duplex stainless steel were thermal aged in order to obtain different amounts of sigma phases (up to 18% in content). The obtained classification results were compared against the ones obtained by Charpy impact energy test, amount of sigma phase, and analysis of the fracture surface by scanning electron microscopy and X-ray diffraction. The proposed solution achieved a classification accuracy superior to 95% and was revealed to be robust to signal noise, being therefore a valid testing tool to be used in this domain.

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ECG arrhythmia classification based on optimum-path forest

Cited by 154 publications

References 40 publications

Automatic 3D pulmonary nodule detection in CT images: A survey

Automatic 3D pulmonary nodule detection in CT images: A survey

ECG Classification Using Wavelet Packet Entropy and Random Forests

Classification of Induced Magnetic Field Signals for the Microstructural Characterization of Sigma Phase in Duplex Stainless Steels

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