Comparison of neuro-fuzzy approaches with artificial neural networks for the detection of Ischemia in ECG signals

Tonekabonipour, Hoda; Emam, Ali; Teshnelab, Mohammad; Shoorehdeli, Mahdi Aliyari

doi:10.1109/icsmc.2010.5642196

Cited by 5 publications

(6 citation statements)

References 16 publications

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“…Tonekabonipour et al [ 29 ] compared and verified the fuzzy neural algorithm and the network neural algorithm in the accuracy of ECG processing, and the results showed that the fuzzy neural algorithm is superior to ECG. Lai et al [ 30 ] believed that the SVM combined with particle swarm optimization (PSO) algorithm has greatly improved the accuracy of classification and recognition and general application compared with other classification methods.…”

Section: Resultsmentioning

confidence: 99%

Construction of Community Medical Communication Service and Rehabilitation Model for Elderly Patients under the Internet of Things

Zhang

Wang

et al. 2022

Journal of Healthcare Engineering

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The objective of this study was to discuss the health management of elderly patients in the community and the management of community rehabilitation under the support of the new Internet of Things (IoT). The IoT technology was adopted to monitor the wearable devices through mobile medical physiological data. The heart rate, blood pressure, respiratory rate, and other physiological indicators of the elderly were collected in real time. The support vector machine (SVM) algorithm was selected as the core algorithm for the elderly physiological index disease risk assessment, the fuzzy comprehensive evaluation method was adopted as the core method of the elderly disease risk quantitative assessment model to process the physiological indicators, and finally, a reasonable physiological index processing model and quantitative indicators of disease risk were obtained. The data on vascular disease were selected from the MIMIC database. In addition, the advantages and disadvantages of the SVM algorithm and the Backpropagation Neural Network (BPNN) algorithm were compared and analysed. The final verification results showed that the fusion accuracy of the SVM processing MIMIC database and the University of California Irvine (UCI) dataset was 0.8327 and 0.8045, respectively, while the fusion accuracy of the BPNN algorithm in processing the same data was 0.7792 and 0.7288, respectively. It was obvious that the fusion accuracy of the SVM algorithm was higher than that of the BPNN algorithm, and the accuracy difference of the SVM algorithm was lower than that of the BPNN algorithm in different groups of data. In the verification of the elderly disease risk quantitative assessment model, the results were consistent with the selected data, which verified the effectiveness of the design model in this study. Therefore, it can be used as a quantitative assessment model of general elderly physiological indicators of disease risk and can be applied to the community medical communication management system. It proved that the model of medical communication and rehabilitation services for elderly patients in the community constructed in this study can definitely help the development of community service for the elderly.

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Section: Resultsmentioning

confidence: 99%

Construction of Community Medical Communication Service and Rehabilitation Model for Elderly Patients under the Internet of Things

Zhang

Wang

et al. 2022

Journal of Healthcare Engineering

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“…Thus, for a particular feature vector of a subject the NFC will produce both training recognition rate as well as testing Figure 9. Generalized ANFIS architecture [28] (a); ANFIS structure used for BMA classification using NFC on Matlab platform (b). Figure 10.…”

Section: Nfc Pa Classification Resultsmentioning

confidence: 99%

“…The complexity of this simple structure will increase depending upon the type of application and nature of the classification problem. The neuro-fuzzy classifier and ANFIS system, in addition to general data classification, have also been used for many applications in ECG signals like feature analysis & ECG signal classification; Ischaemia prediction and detection as found in other studies [25][26][27][28][29].…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Physical activities recognition from ambulatory ECG signals using neuro-fuzzy classifiers and support vector machines

Kher¹,

Pawar²,

Thakar³

et al. 2015

Journal of Medical Engineering & Technology

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The use of wearable recorders for long-term monitoring of physiological parameters has increased in the last few years. The ambulatory electrocardiogram (A-ECG) signals of five healthy subjects with four body movements or physical activities (PA)-left arm up down, right arm up down, waist twisting and walking-have been recorded using a wearable ECG recorder. The classification of these four PAs has been performed using neuro-fuzzy classifier (NFC) and support vector machines (SVM). The PA classification is based on the distinct, time-frequency features of the extracted motion artifacts contained in recorded A-ECG signals. The motion artifacts in A-ECG signals have been separated first by the discrete wavelet transform (DWT) and the time-frequency features of these motion artifacts have then been extracted using the Gabor transform. The Gabor energy feature vectors have been fed to the NFC and SVM classifiers. Both the classifiers have achieved a PA classification accuracy of over 95% for all subjects.

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“…In this paper, information from the whole ECG cycle was used. This is considerably different from the previous ischemia detection algorithms [6,8,9,12] in that (1) it avoids the use of the J-point and the ST-segment whose detection is often difficult and time consuming, (2) the training sets are slices from the frequency domain and (3) the effective power of the HOS in preserving nonlinearity was exploited by using features from the higher-order domain. These features are one-dimensional slices and can be calculated within few seconds.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, methods including NN and neurofuzzy models have been used for ECG signal classification [3]. In [7,8] NN models were applied, while [3,9,10] utilized neurofuzzy approaches and NNs, principal component analysis [11], and multilayer perceptron [9,12]. Features extracted from higher-order spectra of heart rate variability were used for detecting cardiac abnormalities [13].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Based Automated ECG Signal Classifier

El-Khafif

El-Brawany

2013

ISRN Biomedical Engineering

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The ECG signal is well known for its nonlinear dynamic behavior and a key characteristic that is utilized in this research; the nonlinear component of its dynamics changes more significantly between normal and abnormal conditions than does the linear one. As the higher-order statistics (HOS) preserve phase information, this study makes use of one-dimensional slices from the higher-order spectral domain of normal and ischemic subjects. A feedforward multilayer neural network (NN) with error back-propagation (BP) learning algorithm was used as an automated ECG classifier to investigate the possibility of recognizing ischemic heart disease from normal ECG signals. Different NN structures are tested using two data sets extracted from polyspectrum slices and polycoherence indices of the ECG signals. ECG signals from the MIT/BIH CD-ROM, the Normal Sinus Rhythm Database (NSR-DB), and European ST-T database have been utilized in this paper. The best classification rates obtained are 93% and 91.9% using EDBD learning rule with two hidden layers for the first structure and one hidden layer for the second structure, respectively. The results successfully showed that the presented NN-based classifier can be used for diagnosis of ischemic heart disease.

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Comparison of neuro-fuzzy approaches with artificial neural networks for the detection of Ischemia in ECG signals

Cited by 5 publications

References 16 publications

Construction of Community Medical Communication Service and Rehabilitation Model for Elderly Patients under the Internet of Things

Construction of Community Medical Communication Service and Rehabilitation Model for Elderly Patients under the Internet of Things

Physical activities recognition from ambulatory ECG signals using neuro-fuzzy classifiers and support vector machines

Artificial Neural Network-Based Automated ECG Signal Classifier

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