Optimization of ECG Classification by Means of Feature Selection

Mar, T.; Zaunseder, Sebastian; Martínez, Juan Pablo; Llamedo, Mariano; Poll, R.

doi:10.1109/tbme.2011.2113395

Cited by 273 publications

(163 citation statements)

References 46 publications

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“…For this purpose, some studies include an optimization step testing different feature combinations (via an optimization algorithm such as genetic algorithm or particle swarm optimization, or statistical distribution analysis such as Gini's index) and retrieving only the relevant features for further analysis [20,22,57]. For example, Mar et al [58] performed classification between normal, ventricular, premature ventricular and fusion beats based on the idea of Llamedo & Martínez [4] to use the sequential forward floating search (SFFS) feature selection procedure. This improved the classification accuracy of the multi-layer perceptron (MLP) classifier from 79% using 71 features to 90% with only nine features.…”

Section: Feature Extraction and Dimensionality Reductionmentioning

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.

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174

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%

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.

Self Cite

174

102

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“…We assume that i, j ∈ {N, S, V, F}, C i,j is the number of heartbeats of class i classified as j. If ∀ i ≠ j, then C i,j is an incorrectly classified heartbeat, whereas C i,i is a correctly classified heartbeat [7]. We define B j = ∑ ∀ j C i,j as the total number of examples originally belonging to class i; A j = ∑ ∀ i C i,j as the total number of examples labeled as class j; and C total = ∑ ∀ i ∀ j C i,j .…”

Section: Evaluation Criterionmentioning

confidence: 99%

“…Various kinds of comprehensive features have been extracted to describe ECG; these features can be divided into three categories, including temporal, morphological, and statistical features [7]. Temporal features are exclusively acquired from timedomain signals and consist of RR-and heartbeat interval features.…”

Section: Introductionmentioning

confidence: 99%

“…More discriminating features can be extracted in a wavelet transform (WT) domain than in a time domain [8]. Morphological and statistical features can be obtained with WT of the ECG signal, which provides good resolution in time and frequency domains [7]. However, WT only displays sufficient frequency resolution at low frequencies but poor frequency resolution at high frequencies.…”

Section: Introductionmentioning

confidence: 99%

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Fast multi-scale feature fusion for ECG heartbeat classification

Yang

Wang

et al. 2015

EURASIP J. Adv. Signal Process.

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Electrocardiogram (ECG) is conducted to monitor the electrical activity of the heart by presenting small amplitude and duration signals; as a result, hidden information present in ECG data is difficult to determine. However, this concealed information can be used to detect abnormalities. In our study, a fast feature-fusion method of ECG heartbeat classification based on multi-linear subspace learning is proposed. The method consists of four stages. First, baseline and high frequencies are removed to segment heartbeat. Second, as an extension of wavelets, wavelet-packet decomposition is conducted to extract features. With wavelet-packet decomposition, good time and frequency resolutions can be provided simultaneously. Third, decomposed confidences are arranged as a two-way tensor, in which feature fusion is directly implemented with generalized N dimensional ICA (GND-ICA). In this method, co-relationship among different data information is considered, and disadvantages of dimensionality are prevented; this method can also be used to reduce computing compared with linear subspace-learning methods (PCA). Finally, support vector machine (SVM) is considered as a classifier in heartbeat classification. In this study, ECG records are obtained from the MIT-BIT arrhythmia database. Four main heartbeat classes are used to examine the proposed algorithm. Based on the results of five measurements, sensitivity, positive predictivity, accuracy, average accuracy, and t-test, our conclusion is that a GND-ICA-based strategy can be used to provide enhanced ECG heartbeat classification. Furthermore, large redundant features are eliminated, and classification time is reduced.

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“…This software component is implemented under the form of a desktop application that runs on one or several computers inside the monitoring centre [3][4][5][6][7][8]. By means of this application, the specialist doctor has access to the medical record data, the current ECG investigation as well as to the history of all ECG investigations made for the patient in the past, no matter the place where these were realized (another family doctor).…”

Section: Ecg Monitoring Modulementioning

confidence: 99%

Integrated Information System of Monitoring and Management for Heart Centers

Brezulianu¹,

Fira²

2012

IJARAI

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Abstract-The integrated information system presented in this paper is focused on increasing the quality of the medical services offered to the patients by rendering the activities more efficient and by corroborating the family doctors and the cardiologists' efforts. In order to accomplish the mentioned objectives we propose implementing an integrated information system by means of which the integrated management of the medical data will be done. In addition, it has been offered a collaborative instrument, patient oriented, that it will be used both by the family doctor and by the specialist doctor.

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Optimization of ECG Classification by Means of Feature Selection

Cited by 273 publications

References 46 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

Fast multi-scale feature fusion for ECG heartbeat classification

Integrated Information System of Monitoring and Management for Heart Centers

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