Arrhythmia Classification using 2D Convolutional Neural Network

Rohmantri, Robby; Surantha, Nico

doi:10.14569/ijacsa.2020.0110427

Cited by 11 publications

(5 citation statements)

References 15 publications

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“…Moreover, the overall F1‐Score results promise a significant improvement over other deep learning techniques. Thus, we have gained 9% compared with our achieved F1‐Score with [32] and 2% compared with [26].…”

Section: Discussionmentioning

confidence: 93%

Automatic heart disease class detection using convolutional neural network architecture‐based various optimizers‐networks

et al. 2021

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Early heart disease class detection is of great interest to reduce the mortality rate. In this context, computational techniques have been proposed to solve this issue. Thus, here, a deep learning architecture is proposed to automatically classify the patient’s Electrocardiogram (ECG) signal into a specific class according to the ANSI–AAMI standards. The proposed methodology is a multi‐stage technique. The first stage combines an R–R peak extraction with a low‐pass filter applied on the ECG data for noise removal. The second stage shows the proposed convolutional neural network‐based fully connected layers architecture, using different network optimizers. Different ECG databases, including challenging tasks, have been used for validation purpose. The whole system is implemented on both CPU and GPU for complexity analysis. For the predicted improved PTB data set, the classification accuracy results achieve 99.37%, 99.15% and 99.31% for training, validation and testing, respectively. Besides, for the MIT‐BIH‐database, the training, validation and testing accuracies are 99.5%, 99.06% and 99.34%, respectively. A top F1‐score of 0.99 is obtained. Experimental results show a high achievement compared to the state‐of‐the‐art models where the implementation of GPU confirms the low computational complexity of the system.

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Section: Discussionmentioning

confidence: 93%

Automatic heart disease class detection using convolutional neural network architecture‐based various optimizers‐networks

et al. 2021

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“…Some of them transformed the 1D time series into spectrograms using wavelet transforms, Fourier transforms, etc. [ 19 , 35 ], whereas others obtained time–domain characteristics by plotting the waveforms directly onto a canvas [ 36 , 37 ]. The former strategy aimed to highlight the time–frequency characteristics, whereas the latter focused on the original time–domain information.…”

Section: Discussionmentioning

confidence: 99%

Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning

Pan

Jia

Liu

et al. 2021

Sensors

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Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient–ventilator asynchrony (PVA) occurs when ventilatory support does not match the needs of the patient and is associated with a series of adverse clinical outcomes. Deep learning methods have shown a strong discriminative ability for PVA detection, but they require a large number of annotated data for model training, which hampers their application to this task. We developed a transfer learning architecture based on pretrained convolutional neural networks (CNN) and used it for PVA recognition based on small datasets. The one-dimensional signal was converted to a two-dimensional image, and features were extracted by the CNN using pretrained weights for classification. A partial dropping cross-validation technique was developed to evaluate model performance on small datasets. When using large datasets, the performance of the proposed method was similar to that of non-transfer learning methods. However, when the amount of data was reduced to 1%, the accuracy of transfer learning was approximately 90%, whereas the accuracy of the non-transfer learning was less than 80%. The findings suggest that the proposed transfer learning method can obtain satisfactory accuracies for PVA detection when using small datasets. Such a method can promote the application of deep learning to detect more types of PVA under various ventilation modes.

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“…Por otra parte, (Schwab et al, 2017;Limam and Precioso, 2017;Singh et al, 2018;Mostayed et al, 2018;Banerjee et al, 2019;Park and Yun, 2019;Simanjuntak et al, 2020) hicieron uso de la RNR, la MCP fue utilizada por (Gao et al, 2019;Hou et al, 2019;Yildirim et al, 2019;Saadatnejad et al, 2019;Kim and Pyun, 2020;Wang, 2021). Finalmente, como ya se había mencionado con anterioridad las CNN tienen una mayor aportación con los trabajos realizados por (Kachuee et al, 2018b;Yıldırım et al, 2018;Jun et al, 2018;Salem et al, 2018;S ¸en and Özkurt, 2019;Izci et al, 2019;Liu et al, 2019;Rohmantri and Surantha, 2020;Wang et al, 2020;Atal and Singh, 2020;Ferretti et al, 2021;Mathunjwa et al, 2021;Zhang et al, 2021). Surgen también la combinación entre dos ADL, es decir, la creación de modelos híbridos.…”

Section: Antecedentes Y Trabajo Relacionadounclassified

Clasificación de Arritmias Cardíacas mediante Redes Neuronales Convolucionales y Optimización por Enjambre de Partículas

Santander-Baños

Hernández-Romero

Barragan-Vite

et al. 2022

ICBI

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Una arritmia cardíaca es un latido irregular del corazón que se traduce en un impulso eléctrico anormal, y su tipo se define por el ritmo y duración. Su clasificación ha sido abordada en diferentes campos de la ciencia, destacando el uso de algoritmos de aprendizaje profundo. La presente investigación, utilizó un modelo híbrido entre Redes Neuronales Convolucionales y el algoritmo metaheurístico de Optimización por Enjambre de Partículas; para la clasificación de arritmias cardíacas. El metaheurístico se encargó de optimizar la arquitectura de capas de la red neuronal, a través de la minización de la pérdida durante el entrenamiento y prueba. Los datos se obtuvieron del MIT-BIH Arrhythmia dataset, donde se describen cinco categorías de arritmias. Los resultados logrados demostraron que el metaheurístico es un algoritmo confiable en la búsqueda de la mejor arquitectura de capas, logrando obtener una exactitud del 97%, lo que significa que el uso de técnicas metaheurísticas es una opción que se debe tomar en consideración a la hora de optimizar el rendimiento de las redes neuronales convolucionales.

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Arrhythmia Classification using 2D Convolutional Neural Network

Cited by 11 publications

References 15 publications

Automatic heart disease class detection using convolutional neural network architecture‐based various optimizers‐networks

Automatic heart disease class detection using convolutional neural network architecture‐based various optimizers‐networks

Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning

Clasificación de Arritmias Cardíacas mediante Redes Neuronales Convolucionales y Optimización por Enjambre de Partículas

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