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• W2524141621 abstract "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." @default.
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• W2524141621 date "2016-01-01" @default.
• W2524141621 modified "2023-10-15" @default.
• W2524141621 title "Research on Healthy Anomaly Detection Model Based on Deep Learning from Multiple Time-Series Physiological Signals" @default.
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• W2524141621 doi "https://doi.org/10.1155/2016/5642856" @default.
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