ECG Classification With a Convolutional Recurrent Neural Network

Sigurthorsdottir, Halla; Zaen, Jérôme Van; Delgado-Gonzalo, Ricard; Lemay, Mathieu

doi:10.22489/cinc.2020.198

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…Open-source R-peak detectors are not perfect and wrong R-peak detection could be a major source of error. Finally, although the RMS provides good generalization, it is interesting to explore other word embedding methods such as the CNN developed by Sigurthorsdottir et al 2020 [10].…”

Section: Discussionmentioning

confidence: 99%

Generative Pre-Trained Transformer for Cardiac Abnormality Detection

Gaudilliere¹,

Sigurthorsdottir²,

Aguet³

et al. 2021

Preprint

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ECG heartbeat classification plays a vital role in diagnosis of cardiac arrhythmia. The goal of the Physionet/CinC 2021 challenge was to accurately classify clinical diagnosis based on 12, 6, 4, 3 or 2-lead ECG recordings in order to aid doctors in the diagnoses of different heart conditions. Transformers have had great success in the field of natural language processing in the past years. Our team, CinCSEM, proposes to draw the parallel between text and periodic time series signals by viewing the repeated period as "words" and the whole signal as a sequence of such words. In this way, the attention mechanisms of the transformers can be applied to periodic time series signals. In our implementation, we follow the Transformer Encoder architecture, which combines several encoder layers followed by a dense layer with linear or sigmoid activation for generative pre-training or classification, respectively. The use case presented here is multi-label classification of heartbeat abnormalities of ECG recordings shared by the challenge. Our best entry, not exceeding the challenge's hardware limitations, achieved a score of 0.12, 0.07, 0.10, 0.10 and 0.07 on 12lead, 6-lead, 4-lead, 3-lead and 2-lead test set respectively. Unfortunately, our team was unable to be ranked because of a missing pre-print.

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Section: Discussionmentioning

confidence: 99%

Generative Pre-Trained Transformer for Cardiac Abnormality Detection

Gaudilliere¹,

Sigurthorsdottir²,

Aguet³

et al. 2021

Preprint

Self Cite

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show abstract

“…1161/01.CIR.101.23.e215, reference number [18], and China Physiological Signal Challenge dataset at https://doi.org/10. 1166/jmihi.2018.2442, reference number [19]. Personal-specific research data are not shared.…”

Section: Author Contributionsmentioning

confidence: 99%

“…Sigurthorsdottir et al proposed a convolutional recurrent neural network. The blocked convolutional layers extracted features, and a bi-directional gated recurrent unit (GRU) layer and an attention layer is applied to aggregate these features into a single feature vector which is used to classification [19]. Qiao developed a model composed of CNN and Bi-LSTM with multilevel attention to find the abnormal variation in beat-, rhythm-and frequency-level [20].…”

Section: Crnn Model and Its Application In Ecg Diagnosismentioning

confidence: 99%

Automatic cardiac arrhythmias classification using CNN and attention‐based RNN network

Sun

2023

Healthcare Tech Letters

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Cardiac disease has become a severe threat to public health according to the government report. In China, there are 0.29 billion cardiac patients and early diagnosis will greatly reduce mortality and improve life quality. Electrocardiogram (ECG) signal is a priority tool in the diagnosis of heart diseases because it is non-invasive and easily available with a simple diagnostic tool of low cost. The paper proposes an automatic classification model by combing convolutional neural network (CNN) and recurrent neural network (RNN) to distinguish different types of cardiac arrhythmias. Morphology features of the raw ECG signals are extracted by CNN blocks and fed into a bidirectional gated recurrent unit (GRU) network. Attention mechanism is used to highlight specific features of the input sequence and contribute to the performance improvement of classification. The model is evaluated with two datasets considering the class imbalance problem constructed with records from MIT-BIH arrhythmia database and China Physiological Signal Challenge 2018 database. Experimental results show that this model achieves good performance with an average F1 score of 0.9110 on public dataset and 0.9082 on subject-specific dataset, which may have potential practical applications.

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“…Surgen también la combinación entre dos ADL, es decir, la creación de modelos híbridos. Tal es el caso de (Chen et al, 2020;Ma et al, 2020;Rai and Chatterjee, 2021) con RNC+MCP y (Zihlmann et al, 2017;Limam and Precioso, 2017;Van Zaen et al, 2019;Sigurthorsdottir et al, 2020) quienes utilizaron las CNN+RNR. Cada uno de los aportes descritos previamente, en conjunto, tienen algo en común: la arquitectura o los hiperparámetros necesarios para el correcto funcionamiento y la obtención de los resultados publicados; fueron seleccionados de manera artesanal.…”

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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ECG Classification With a Convolutional Recurrent Neural Network

Cited by 7 publications

References 9 publications

Generative Pre-Trained Transformer for Cardiac Abnormality Detection

Generative Pre-Trained Transformer for Cardiac Abnormality Detection

Automatic cardiac arrhythmias classification using CNN and attention‐based RNN network

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

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