Assessment and comparison of different methods for heartbeat classification

Jekova, Irena; Bortolan, G.; Christov, Ivaylo

doi:10.1016/j.medengphy.2007.02.003

Cited by 81 publications

(52 citation statements)

References 23 publications

(38 reference statements)

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“…The processing of the information by the heart is reflected in dynamical changes of electrical activity in time, frequency and space. Mostly, features in time [7] and frequency [8] were extracted and combined with efficient classifiers.…”

Section: Morphological-based Feature Extractionmentioning

confidence: 99%

Classification of ECG Signals Using Hybrid Feature Extraction and Classifier with Hybrid ABC-GA Optimization

Muthuvel

Suresh

Alexander

2015

Proceedings of the International Conference on Soft Computing Systems

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In this research, an efficient technique has been developed to classify the five abnormal beat (Afonso et al., IEEE Trans Biomed Eng 46:192-202 (1999) [1]; Kohler et al., IEEE Eng Med Biol Mag 21:42-57 (2002) [2] signals which includes Left Bundle Branch Block beat (LBBB), Right Bundle Branch Block beat (RBBB), Premature Ventricular Contraction (PVC), Atrial Premature Beat (APB), and Nodal (junction) Premature Beat (NPB) along with the normal beat. The proposed technique is composed into three stages, (1) preprocessing (2) Hybrid feature extraction (3) classifier. For efficient feature extraction, hybrid feature extractor is used. Hybrid feature extraction is done in two steps, (i) Morphological-based feature extraction (ii) Haar wavelet-based feature extraction. Once the features are extracted, a Feed Forward Neural Network (FFNN) classifier classifies the beat signal. Artificial Bee Colony (ABC) combined with genetic algorithm has been used for training the neural network. A best crossover rate has been chosen in order to achieve higher accuracy. The proposed technique gives an accuracy of 81 %, sensitivity of 75 %, and specificity of 79 % for the crossover rate of 0.8.

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Section: Morphological-based Feature Extractionmentioning

confidence: 99%

Classification of ECG Signals Using Hybrid Feature Extraction and Classifier with Hybrid ABC-GA Optimization

Muthuvel

Suresh

Alexander

2015

Proceedings of the International Conference on Soft Computing Systems

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“…There are various mechanism to extract ECG features, such as morphological characteristic like amplitude [12] and Hermite based function [13]. There is also study by Sani et al in sleep apnea detection from ECG signal on its optimal features, principal components, and nonlinearity [14].…”

Section: Introductionmentioning

confidence: 99%

Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation

Jatmiko¹,

Setiawan²,

Akbar³

et al. 2016

MST

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Cardiac disease is one of the major causes of death in the world. Early diagnose of the symptoms depends on abnormality on heart beat pattern, known as Arrhythmia. A novel fuzzy neuro generalized learning vector quantization for automatic Arrhythmia heart beat classification is proposed. The algorithm is an extension from the GLVQ algorithm that employs a fuzzy logic concept as the discriminant function in order to develop a robust algorithm and improve the classification performance. The algorithm is tested against MIT-BIH arrhythmia database to measure the performance. Based on the experiment result, FN-GLVQ is able to increase the accuracy of GLVQ by a soft margin. As we intend to build a device with automated Arrhythmia detection, FN-GLVQ is then implemented into Field Gate Programmable Array to prototype the system into a real device. AbstrakDeteksi Otomatis Detak Arrhythmia: Algoritma, Sistem dan Implementasi. Penyakit jantung adalah salah satu penyebab utama kematian di dunia. Diagnosa awal dari penyakit tersebut didasarkan pada ketidaknormalan pola detak jantung, atau disebut juga sebagai Arrhythmia. Sebuah metode baru bernama fuzzy neuro generalized learning vector quantization (FN-GLCQ) kami kembangkan untuk melakukan klasifikasi terhadap pola tersebut. Algoritma tersebut dimodifikasi dari algoritma GLVQ dengan mengimplementasikan konsep fuzzy logic sebagai fungsi diskriminan untuk meningkatkan performa klasifikasi. Algoritma tersebut dites dengan menggunakan MIT-BIH arrythmia database untuk dianalisis performanya. Berdasarkan uji coba yang telah dilakukan, FN-GLVQ dapat meningkatkan akurasi dari performa GLVQ. Sesuai dengan tujuan kami untuk membangun alat pendeteksian otomatis, FN-GLVQ juga diimplementasikan pada Field Gate Programmable Array sebagai prototipe dari perangkat yang akan dibangun.

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“…Thus far, simple classifiers, such as linear discriminants [3] and K-nearest neighbor classifier [4], and complex classifiers, including chaotic modeling, spectral coherence analysis, artificial neural networks, and support vector machine, have been extensively applied. Classifier combination is also used in ECG heartbeat classification to improve accuracy [5].…”

Section: Introductionmentioning

confidence: 99%

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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Assessment and comparison of different methods for heartbeat classification

Cited by 81 publications

References 23 publications

Classification of ECG Signals Using Hybrid Feature Extraction and Classifier with Hybrid ABC-GA Optimization

Classification of ECG Signals Using Hybrid Feature Extraction and Classifier with Hybrid ABC-GA Optimization

Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation

Fast multi-scale feature fusion for ECG heartbeat classification

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