Author:
Momynaliev K. T.,Ivanov I. V.
Publisher
Springer Science and Business Media LLC
Subject
Medical Laboratory Technology,Biomedical Engineering,Medicine (miscellaneous)
Reference22 articles.
1. Delmastro, F., Martino, F. D., and Dolciotti, C., “Cognitive training and stress detection in MCI frail older people through wearable sensors and machine learning,” IEEE Access, 8, 65,573–65,590 (2020).
2. Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., and Ng, A. Y., “Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network,” Nat. Med., 25, No. 1, 65–69 (2019).
3. Kwon, S., Hong, J., Choi, E. K., Lee, B., Baik, C., Lee, E., Jeong, E. R., Koo, B. K., Oh S, and Yi, Y., “Detection of atrial fibrillation using a ring-type wearable device (CardioTracker) and deep learning analysis of photoplethysmography signals: Prospective observational proof-of-concept study,” J. Med. Internet. Res., 22, No. 5, e16443 (2020).
4. Buettner, R., Frick, J., and Rieg, T., “High-performance detection of epilepsy in seizure-free EEG recordings,” in: Proceedings of the International Conference on Information Systems, Munich, Germany, (2019), p. 12.
5. Can, Y. S., Arnrich, B., and Ersoy, C., “Stress detection in daily life scenarios using smart phones and wearable sensors: A survey,” J. Biomed. Inform., 92, Art No. 103139 (2019).