Detección de Fibrilación Ventricular Mediante Tiempo-Frecuencia y Clasificador KNN sin Extracción de Parámetros

Mjahad, Azeddine; Muñoz, Andrés Pedreño; Mompean, Manuel Bataller; Villora, José V. Francés; Martínez, Juan Francisco Guerrero

doi:10.4995/riai.2017.8833

Cited by 3 publications

(4 citation statements)

References 29 publications

(28 reference statements)

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“…In this first test, individual classifiers are used. The results are obtained using four different classification algorithms: L2RLR, ANNC, BAGG, and KNN [8,31]. After several trials, the parameters for the classifiers were the following:…”

Section: Results For Individual Classifiersmentioning

confidence: 99%

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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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Section: Results For Individual Classifiersmentioning

confidence: 99%

“…Some algorithms propose the use of four classes [8,31]. However, we can combine different binary algorithms for two-class separation so that they can provide important complementary information about the representation of the data.…”

Section: Classification Algorithmsmentioning

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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“…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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“…The detection of the QRS complex of the ECG signal was assessed in [ 5 , 6 ]. From a medical point of view, essential information present in the ECG signal are included in the P wave, the QRS complex, and the T wave.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of the ECG Signal by Means of a Linear Regression Algorithm

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Ochoa-Brust

Felix

et al. 2019

Sensors

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The monitoring and processing of electrocardiogram (ECG) beats have been actively studied in recent years: new lines of research have even been developed to analyze ECG signals using mobile devices. Considering these trends, we proposed a simple and low computing cost algorithm to process and analyze an ECG signal. Our approach is based on the use of linear regression to segment the signal, with the goal of detecting the R point of the ECG wave and later, to separate the signal in periods for detecting P, Q, S, and T peaks. After pre-processing of ECG signal to reduce the noise, the algorithm was able to efficiently detect fiducial points, information that is transcendental for diagnosis of heart conditions using machine learning classifiers. When tested on 260 ECG records, the detection approach performed with a Sensitivity of 97.5% for Q-point and 100% for the rest of ECG peaks. Finally, we validated the robustness of our algorithm by developing an ECG sensor to register and transmit the acquired signals to a mobile device in real time.

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Detección de Fibrilación Ventricular Mediante Tiempo-Frecuencia y Clasificador KNN sin Extracción de Parámetros

Cited by 3 publications

References 29 publications

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

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

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

Segmentation of the ECG Signal by Means of a Linear Regression Algorithm

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