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Research on Healthy Anomaly Detection Model Based on Deep Learning from Multiple Time-Series Physiological Signals
Joint Authors
Wang, Kai
Xiong, Qingyu
Zhao, Youjin
Fan, Min
Sun, Guotan
Ma, Longkun
Liu, Tong
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-09-26
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Health is vital to every human being.
To further improve its already respectable medical technology, the medical community is transitioning towards a proactive approach which anticipates and mitigates risks before getting ill.
This approach requires measuring the physiological signals of human and analyzes these data at regular intervals.
In this paper, we present a novel approach to apply deep learning in physiological signals analysis that allows doctor to identify latent risks.
However, extracting high level information from physiological time-series data is a hard problem faced by the machine learning communities.
Therefore, in this approach, we apply model based on convolutional neural network that can automatically learn features from raw physiological signals in an unsupervised manner and then based on the learned features use multivariate Gauss distribution anomaly detection method to detect anomaly data.
Our experiment is shown to have a significant performance in physiological signals anomaly detection.
So it is a promising tool for doctor to identify early signs of illness even if the criteria are unknown a priori.
American Psychological Association (APA)
Wang, Kai& Zhao, Youjin& Xiong, Qingyu& Fan, Min& Sun, Guotan& Ma, Longkun…[et al.]. 2016. Research on Healthy Anomaly Detection Model Based on Deep Learning from Multiple Time-Series Physiological Signals. Scientific Programming،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1118289
Modern Language Association (MLA)
Wang, Kai…[et al.]. Research on Healthy Anomaly Detection Model Based on Deep Learning from Multiple Time-Series Physiological Signals. Scientific Programming No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1118289
American Medical Association (AMA)
Wang, Kai& Zhao, Youjin& Xiong, Qingyu& Fan, Min& Sun, Guotan& Ma, Longkun…[et al.]. Research on Healthy Anomaly Detection Model Based on Deep Learning from Multiple Time-Series Physiological Signals. Scientific Programming. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1118289
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1118289