Classification of Cardiac Arrhythmias Using Interval Type-2 TSK Fuzzy System

Phong, Phan Anh; Thien, Kieu Quang

doi:10.1109/kse.2009.19

Cited by 11 publications

(5 citation statements)

References 7 publications

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“…In [15], using digital Taylor-Fourier transform (DTFT) features and a least square support vector machine (LS-SVM) with linear and radial basis function (RBF), kernels obtained performance values of Acc = 83.75%. [5] 91.84 90.20 91.00 MIT-BIH using EMD & App Entropy [6] 90.47 91.66 91.20 MIT-BIH using KNN [7] 98.10 88.00 93.20 MIT-BIH using RBF [7] 91.53 90.91 91.30 MIT-BIH using Fuzzy [8] 90.90 MIT-BIH using TSK Fuzzy [9] 93.30 MIT-BIH using Mamdani Fuzzy [9] 86.60 MIT-BIH using Random Forest Classifier [12] 95.04 94.78 94.79 CU & MIT-BIH using SVM [13] 95.00 99.00 CU & MIT-BIH using Binary Decision Tree [14] 95 In paper [20], using boosted classification and regression tree (Boosted-CART) on six features for a binary VFVT and non-VFVT classification, a classification of Acc = 98.29% was obtained. To test the method proposed by the authors, three databases were used (Creighton University Ventricular Tachyarrhythmia Database-CUDB, the MIT-BIH Malignant Ventricular Arrhythmia Database-VFDB and the MITBIH arrhythmia database-MITDB) totaling 1888 VT/VF samples and 27,992 non-VT/VF samples.…”

Section: Discussionmentioning

confidence: 99%

“…The use of artificial intelligence in the analysis of medical data or in the processing of biomedical signals is a field that has exploded in terms of the literature and a number of practical implementations in recent years. From monitoring bracelets to deep learning neural networks, from software systems to hardware implementations, from simple classification algorithms like KNN to deep learning neural networks, only the researchers' imagination is the limit that can cause problems regarding the applicability of AI in the processing of medical data and their applicability with the aim of increasing life expectancy and patient compliance [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Prediction and Detection of Ventricular Fibrillation Using Complex Features and AI-Based Classification

Fira,

Costin,

Goras

2024

Applied Sciences

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We analyzed the possibility of detecting and predicting ventricular fibrillation (VF), a medical emergency that may put people’s lives at risk, as the medical prognosis depends on the time in which medical personnel intervene. Therefore, besides immediate detection of VF, the possibility of predicting VF 40 or even 50 min in advance was analyzed. For testing the proposed algorithm, we used ECG signals taken from the MIT-BIH database, namely, Malignant Ventricular Ectopy Database, Sudden Cardiac Death Holter Database and Normal Sinus Rhythm Database. The presented method is based on features extracted from the ECG signals in the time domain, frequency domain and complexity measures. For VF detection, the possibility of identifying the VF episode in the first 3 s after its occurrence was tested. For this, the first 3 s immediately after the appearance of VF were cut out and the features were computed on these sections. For VF prediction, 3 min of the ECG signal clipped 40 or 50 min before VF onset was used. Then, on these pieces of ECG signal, the specific features were calculated for 1 s segments. For the normal signal situation, 3 min was randomly selected from the database with normal ECGs. For the classification or detection stage, both an MLP-type neural network and the classifiers from the Machine Learning toolbox of the MATLAB® environment were used. The results obtained for both detection and classification are over 94% in both cases. The novelty of our results compared to those previously obtained is the time interval with which the possibility of prediction was analyzed, namely, 50 min in advance of the VF installation date. This means that the patient will be informed that it is possible to suffer a VF and has time to take the necessary measures to overcome a possible medical emergency.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction and Detection of Ventricular Fibrillation Using Complex Features and AI-Based Classification

Fira,

Costin,

Goras

2024

Applied Sciences

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“…Sharma and Sunkaria [46] employed random forest algorithm (RFAM) for the categorization of Vfib Vta from non-Vfib Vta and obtained ACCY% of 95.66. Phong and Thien [47] succeeded in obtaining ACCY% of 91.3 by employing RFAM while categorizing Vfib. For computerized external defibrillation and patient monitoring, accurate detection and classification of Vfib, Vfl, and Vta is critical.…”

Section: Discussionmentioning

confidence: 99%

Pre-trained Bi-LSTM model for automated classification of ventricular arrhythmias using 1-D and 2-D ECG

Chaitanya,

Sharma

2024

Bulletin EEI

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Number of cardiac conditions have been associated with abnormal heartbeat (arrhythmia) such as ventricular fibrillation (Vfib), ventricular flutter (Vfl), and ventricular tachycardia (Vta). This is a difficult and essential job for timely clinical assessment and identification of these potentially life-threatening heart arrhythmias. With the aid of a one-dimensional electrocardiogram (ECG) signal and its associated two-dimensional image, the suggested method provides a strategy for the detection of time-frequency interpretation (Vfib, Vfl, and Vta). A four-stage cascaded Savitzky-Golay (SG) filter is used after a 2-stage median filter to preprocess the ECG signal. This technique employs z-score normalisation after brief (2 sec) ECG readings. The classification of these ECG segments (1-D) and associated time-frequency representation pictures (2-D) was explored separately using a bi-directional long short-term memory-based network. Eight distinct categorization scenarios were examined, and then an average accuracy of 99.67% for 1-D ECG and 99.87% for 2-D ECG signal was attained.

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“…Tan also described the results of using a SOM neural network with poor VT accuracy. Later, Phong et al [43] followed the same line implementing another multiclass classifier using a type-2 TSK fuzzy system, with the same three classes than Tan used; in this case, with better accuracy ratios (Acc(VF) = 93.3%, Acc(VT) = 92.0%, Acc(N) = 100%). They also tried a a type-2 Mandami fuzzy system with lower values.…”

Section: Algorithm An Nc_kn N_an Nc(%) An Nc_kn N_l2rlr(%) An Nc_kn Nmentioning

confidence: 99%

Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction

et al. 2018

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Due the fact that the required therapy to treat Ventricular Fibrillation (V F) is aggressive (electric shock), the lack of a proper detection and recovering therapy could cause serious injuries to the patient or trigger a ventricular fibrillation, or even death. This work describes the development of an automatic diagnostic system for the detection of the occurrence of V F in real time by means of the time-frequency representation (T F R) image of the ECG. The main novelties are the use of the T F R image as input for a classification process, as well as the use of combined classifiers. The feature extraction stage is eliminated and, together with the use of specialized binary classifiers, this method improves the results of the classification. To verify the validity of the method, four different classifiers in different combinations are used: Regression Logistic with L2 Regularization (L 2 R L R), adaptive neural network (A N N C), Bagging (B A G G), and K-nearest neighbor (K N N). The Hierarchical Method (HM) and Voting Majority Method (VMM) combinations are used. ECG signals used for evaluation were obtained from the standard MIT-BIH and AHA databases. When the classifiers were combined, it was observed that the combination of B A G G , K N N , and A N N C using the Hierarchical Method (HM) gave the best results, with a sensitivity of 95.58 ± 0.41%, a 99.31 ± 0.08% specificity, a 98.6 ± 0.04% of overall accuracy, and a precision of 98.25 ± 0.29% for V F . Whereas a sensitivity of 94.02 ± 0.58%, a specificity of 99.31 ± 0.08%, an overall accuracy of 99.14 ± 0.43%, and a precision of 98.59 ± 0.09% was obtained for V T with a run time between 0.07 s and 0.12 s. Results show that the use of T F R image data to feed the combined classifiers yields a reduction in execution time with performance values above to those obtained by individual classifiers. This is of special utility for V F detection in real time.

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Classification of Cardiac Arrhythmias Using Interval Type-2 TSK Fuzzy System

Cited by 11 publications

References 7 publications

Prediction and Detection of Ventricular Fibrillation Using Complex Features and AI-Based Classification

Prediction and Detection of Ventricular Fibrillation Using Complex Features and AI-Based Classification

Pre-trained Bi-LSTM model for automated classification of ventricular arrhythmias using 1-D and 2-D ECG

Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction

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