Publication Type : Conference Paper
Thematic Areas : Wireless Network and Application
Publisher : IEEE International Conference on Biomedical and Health Informatics, Orlando, Florida.
Source : IEEE International Conference on Biomedical and Health Informatics, Orlando, Florida, p.293-296 (2017)
Url : https://www.researchgate.net/publication/317001271_Instantaneous_heart_rate_as_a_robust_feature_for_sleep_apnea_severity_detection_using_deep_learning
Campus : Coimbatore, Amritapuri
School : School of Engineering
Center : Amrita Center for Wireless Networks and Applications (AmritaWNA), Computational Engineering and Networking
Department : Mechanical Engineering, Electronics and Communication
Year : 2017
Abstract : Automated sleep apnea detection and severity identification has largely focused on multivariate sensor data in the past two decades. Clinically too, sleep apnea is identified using a combination of markers including blood oxygen saturation, respiration rate etc. More recently, scientists have begun to investigate the use of instantaneous heart rates for detection and severity measurement of sleep apnea. However, the best-known techniques that use heart rate and its derivatives have been able to achieve less than 85% accuracy in classifying minute-to-minute apnea data. In our research reported in this paper, we apply a deep learning technique called LSTM-RNN (long short-term memory recurrent neural network) for identification of sleep apnea and its severity based only on instantaneous heart rates. We have tested this model on multiple sleep apnea datasets and obtained perfect accuracy. Furthermore, we have also tested its robustness on an arrhythmia dataset (that is highly probable in mimicking sleep apnea heart rate variability) and found that the model is highly accurate in distinguishing between the two.
Cite this Research Publication : Rahul K Pathinarupothi, Vinaykumar R, Ekanath Srihari Rangan, Dr. E. A. Gopalakrishnan, and Dr. Soman K. P., “Instantaneous heart rate as a robust feature for sleep apnea severity detection using deep learning”, in IEEE International Conference on Biomedical and Health Informatics, Orlando, Florida, 2017, pp. 293-296.