Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network

By U. Acharya, Hamido Fujita, O. Lih, Yuki Hagiwara, Jen Hong Tan, M. Adam. Information Sciences, 2017

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Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals (2017, methodology): “9% using 2-second and 5-second ECG segments, respectively [28].”
Deep learning for healthcare applications based on physiological signals: A review (2018, methodology): “, 2017 [128] Arrhythmia detection using different intervals of tachycardia ECG segments CNN MIT-BIH arrhythmia database [129] Accuracy of 92.”
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].”
Dynamically Weighted Balanced Loss: Class Imbalanced Learning and Confidence Calibration of Deep Neural Networks (2021, background): “neural net (1-D CNN) models have shown to be effective in various applications in sequence processing, such as anomaly detection [41], speech processing [42], and biomedical data classification [43].”
1D Convolutional Neural Networks and Applications: A Survey (2019, background): “Several studies on arrhythmia detection and identification have been proposed ever since, e.g., [46,62–64].”