A deep convolutional neural network model to classify heartbeats

By U. Acharya, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, M. Adam, Arkadiusz Gertych, Ruyan Tan. Comput. Biol. Medicine, 2017

1,287 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/6974d29613bc027fa4c3e77086c14ebdd246dc95

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In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

Citations in the corpus

Automated detection of COVID-19 cases using deep neural networks with X-ray images (2020, methodology): “Deep learning techniques have been successfully applied in many problems such as arrhythmia detection [28 – 30], skin cancer classification [31,32], breast cancer detection [33,34], brain disease classification [35], pneumonia detection from chest X-ray images [36], fundus image segmentation [37],…”
1-D Convolutional Neural Networks for Signal Processing Applications (2019, neutral): “Other deep 1D CNN approaches have been recently followed by [27]-[29] for anomaly detection in ECG signals.”
Automated Atrial Fibrillation Detection using a Hybrid CNN-LSTM Network on Imbalanced ECG Datasets (2021, methodology): “[45] made use of the same database in order to achieve beat classification by a CNN containing three convolutional, three max-pooling and three fully-connected layers.”
CoEdge: Cooperative DNN Inference With Adaptive Workload Partitioning Over Heterogeneous Edge Devices (2020, background): “progress in smart healthcare, security inspection and disease detection [4]–[6].”
Deep Learning on 1-D Biosignals: a Taxonomy-based Survey (2018, methodology): “D CNN models [19, 45–49, 58–60, 62, 65, 81]; two were based on auto-encoders [23, 73]; one used an RNN model [67]; and one used an RBM model [78].”
Deep learning for healthcare applications based on physiological signals: A review (2018, background): “, 2017 [142] Heart beat classification CNN MIT-BIH arrhythmia DB [129] accuracy: 94.”
Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications (2017, neutral): “3 [96] HB NN FF 17 / / 95 [97] HB CNN NFF 47 96.”
1D Convolutional Neural Networks and Applications: A Survey (2019, methodology): “Other deep 1D CNN approaches have been recently proposed by [61–64] for anomaly detection in ECG signals.”
Machine Learning-Based Heart Disease Diagnosis: A Systematic Literature Review (2021, background): “For instance, some studies emphasize their study result based on accuracy [8, 26], while some research claimed their best model based on AUC, ROC, sensitivity, and speciﬁcity [11, 54, 34, 48].”