CADNet: cardiac arrhythmia detection and classification using unified principal component analysis and 1D-CNN model
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Castillo-Atoche A, et al. Energy efficient framework for a AIoT cardiac arrhythmia detection system wearable during sport. Appl Sci. 2022;12(5):2716
Chieng D, et al. The impact of coffee subtypes on incident cardiovascular disease, arrhythmias, and mortality long-term outcomes from the UK Biobank European. J Prev Cardiol. 2022;29(17):2240–2249
D’Ancona G et al. Deep learning to detect significant coronary artery disease from plain chest radiographs AI4CAD International. J Cardiol. 2023;370:435–441
Daimi H, et al. Genomic and non-genomic regulatory mechanisms of the cardiac sodium channel in cardiac arrhythmias. Int J Mol Sci. 2022;23(3):1381
Ganguly B, et al. Automated detection and classification of arrhythmia from ECG signals using feature-induced long short-term memory network. IEEE Sensors Letters. 2020;4(8):1–4
Gauvrit S, et al. Modeling human cardiac arrhythmias: insights from zebrafish. J Cardiovasc Dev Dis. 2022;9(1):13
Ge B, et al. Detection of pulmonary hypertension associated with congenital heart disease based on time-frequency domain and deep learning features. Biomed Signal Process Control. 2023;81:104316.
Gupta A, et al. Prediction and classification of cardiac arrhythmia Sentimental Analysis and Deep Learning: Proceedings of ICSADL 2021. Springer: Singapore; 2022
Hamilton S, et al. (2022) Ero1α-dependent ERp44 dissociation from RyR2 contributes to cardiac arrhythmia. Circulation Res. 130(5):711–724
Han D, et al. A real-time ppg peak detection method for accurate determination of heart rate during sinus rhythm and cardiac arrhythmia. Biosensors. 2022;12(2): 82
Kim YK, et al. Automatic cardiac arrhythmia classification using residual network combined with long short-term memory. IEEE Trans Instrum Meas. 2022;71:1–17.
Kumar S, et al. Fuzz-ClustNet: coupled fuzzy clustering and deep neural networks for arrhythmia detection from ECG signals. Comput Biol Med. 2023;153:106511
Li Ya, et al. A deep learning approach to cardiovascular disease classification using empirical mode decomposition for ECG feature extraction. Biomed Signal Process Control. 2023;79:104188.
Li J, et al (2022) Two-dimensional ECG-based cardiac arrhythmia classification using DSE-ResNet. Sci Rep 12(1):14485
Mehra R, et al. Sleep-disordered breathing and cardiac arrhythmias in adults: mechanistic insights and clinical implications: a scientific statement from the American Heart Association. Circulation. 2022;146(9):e119-e136
Metan J, et al. Cardiovascular MRI image analysis by using the bio inspired (sand piper optimized) fully deep convolutional network (Bio-FDCN) architecture for an automated detection of cardiac disorders. Biomed Signal Process Control. 2021;70:103002
Pandey SK, Janghel RR. Automated detection of arrhythmia from ECG signal based on new convolutional encoded features with bidirectional long short-term memory network classifier. Phys Eng Sci Med. 2021;44:173–182
Rahman S, Shazzadur R, Bahalul Haque AKM. Automated detection of cardiac arrhythmia based on a hybrid CNN-LSTM network. Emergent Converging Technologies and Biomedical Systems Select Proceedings of ETBS 2021. Singapore: Springer Singapore. 2022;395–414
Rahul J, Lakhan DS. Automatic cardiac arrhythmia classification based on hybrid 1-D CNN and BLSTM model. Biocybern Biomed Eng. 2022;42(1):312–324
Sahoo S, et al. Machine learning approach to detect cardiac arrhythmias in ECG signals: a survey. Irbm. 2020;41(4):185–94.
Sai YP, Rajani Kumari LV. Cognitive assistant DeepNet model for detection of cardiac arrhythmia. Biomed Signal Process Control. 2022;71:103221.
Schenker N, et al. Impact of obesity on acute complications of catheter ablation for cardiac arrhythmia. J Cardiovasc Electrophysiol. 2022;33(4):654–63.
Sutanto H, Heijman J. Integrative computational modeling of cardiomyocyte calcium handling and cardiac arrhythmias: current status and future challenges. Cells. 2022;11(7):1090.
Taylan O et al. Early prediction in classification of cardiovascular diseases with machine learning, neuro-fuzzy and statistical methods. Biology. 2023;12(1):117