Ventricular Fibrillation and Tachycardia detection from surface ECG using time-frequency representation images as input dataset for machine learning

Mjahad, Azeddine; Rosado-Muoz, A.; Bataller-Mompen, M.; Francs-Vllora, J.V.; Guerrero-Martnez, J.F.

doi:10.1016/j.cmpb.2017.02.010

Cited by 56 publications

(34 citation statements)

References 33 publications

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“…To select candidate areas from EEG signals could be helpful a computerized analysis of the EEG [8,9]. As with other pathologies [10][11][12], machine learning has been applied in epilepsy at many works [13][14][15] to classify EEG signals as normal versus epileptic or seizure versus inter-ictal. However, the most challenging classification problem is focal (F) versus non-focal (NF).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation

et al. 2020

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The epileptogenic focus is a brain area that may be surgically removed to control of epileptic seizures. Locating it is an essential and crucial step prior to the surgical treatment. However, given the difficulty of determining the localization of this brain region responsible of the initial seizure discharge, many works have proposed machine learning methods for the automatic classification of focal and non-focal electroencephalographic (EEG) signals. These works use automatic classification as an analysis tool for helping neurosurgeons to identify focal areas off-line, out of surgery, during the processing of the huge amount of information collected during several days of patient monitoring. In turn, this paper proposes an automatic classification procedure capable of assisting neurosurgeons online, during the resective epilepsy surgery, to refine the localization of the epileptogenic area to be resected, if they have doubts. This goal requires a real-time implementation with as low a computational cost as possible. For that reason, this work proposes both a feature set and a classifier model that minimizes the computational load while preserving the classification accuracy at 95.5%, a level similar to previous works. In addition, the classification procedure has been implemented on a FPGA device to determine its resource needs and throughput. Thus, it can be concluded that such a device can embed the whole classification process, from accepting raw signals to the delivery of the classification results in a cost-effective Xilinx Spartan-6 FPGA device. This real-time implementation begins providing results after a 5 s latency, and later, can deliver floating-point classification results at 3.5 Hz rate, using overlapped time-windows.

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Section: Introductionmentioning

confidence: 99%

Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation

et al. 2020

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“…Support Vector Machine (SVM) [13], Random Forest (RF) [14], Artificial Neural Networks (ANN) [15] have been used. In [16] arrhythmias. Loss of ECG beat characteristics in noise filtering, selecting not optimal features for classification step, low classification performance are examples of these limitations that directly affect success of the studies [18].…”

Section: Introductionmentioning

confidence: 99%

ECG Heartbeat Classification Based on Signal-to-Image Transformation Using Convolutional Neural Networks

Degirmenci¹,

Özdemir²,

Izci³

et al. 2020

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Background: Electrocardiogram (ECG) is a method of recording the electrical activity of the heart and provides a diagnostic mean for heart-related diseases. An arrhythmia is any irregularity of heartbeat that causes an abnormality in one’s heart rhythm. Early detection of arrhythmia has great importance to prevent many diseases. Manual analysis of ECG signal is not sufficient for quickly identifying arrhythmias that may cause sudden deaths. Hence, many studies have been presented to developed computer-aided diagnosis (CAD) systems to automatically identify arrhythmias.Methods: This paper proposes a novel deep learning approach to identify arrhythmias in ECG signals. The signals are obtained from MIT-BIH arrhythmia database and are categorized according to five arrhythmia types. The proposed approach identifies arrhythmia classes by using Convolutional Neural Network (CNN) architecture trained by two-dimensional (2D) ECG beat images. CNN architecture is selected due to high image recognition performance. ECG signals are segmented into heartbeats, then each heartbeat is transformed into a 2D grayscale image. The heartbeat images are used as input for the CNN. Results: The proposed CNN model is compared to other common CNN architectures such as LeNet and ResNet-50 to evaluate the performance of our study. Overall, the proposed study achieved 99.7% test accuracy in the classification of five different ECG arrhythmias.Conclusions: Testing results demonstrate that CNN trained by ECG image representations provide outstanding classification performance of arrhythmic ECG signals and outperforms similar network architectures. Hence, the proposed approach provides a robust method for the classification of ECG arrhythmias.

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“…Several studies have used mathematical models that combine temporal and spectral information in the same representation. This technique of Time-Frequency Representation (TFR) is very important in the treatment of non-stationary signals such as the ECG signal, as it distributes the energy of the signal in a two-dimensional time-frequency space [8,9]. In addition, multiple factors might alter the acquisition and recording of the ECG signal: The influence of the environment, 50-60 Hz mains interference, variations of the base line of low-frequency interference in the range of 0 Hz to 0.5 Hz [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…There are many examples in the literature that have used the combination of classifiers focused towards the field of bioinformatics and biomedical research, geophysical analysis and remote sensing, among others. Out of the most frequently used multi classifiers, Random Forests [12], Bagging [8], Boosting [13], or Random Subspaces are the most commonly employed multiclassifiers. In the case of Random Subspaces, different subsets of attributes are used to train each individual classifier.…”

Section: Introductionmentioning

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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Ventricular Fibrillation and Tachycardia detection from surface ECG using time-frequency representation images as input dataset for machine learning

Cited by 56 publications

References 33 publications

Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation

Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation

ECG Heartbeat Classification Based on Signal-to-Image Transformation Using Convolutional Neural Networks

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

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