Embedded Deep Learning for Sleep Staging

被引：0
|
作者：
Turetken, Engin [1 ]
Van Zaen, Jerome [2 ]
Delgado-Gonzalo, Ricard [3 ]
机构：
[1] CSEM, Embedded Vis Syst Grp, Neuchatel, Switzerland
[2] CSEM, Signal Proc Grp, Neuchatel, Switzerland
[3] CSEM, Embedded Software Grp, Neuchatel, Switzerland
来源：
2019 6TH SWISS CONFERENCE ON DATA SCIENCE (SDS) | 2019年
关键词：
CNN; RNN; deep learning; embedded; SoC; sleep; polysomnography; e-health; m-health;
D O I：
10.1109/SDS.2019.00005
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The rapidly-advancing technology of deep learning (DL) into the world of the Internet of Things (IoT) has not fully entered in the fields of m-Health yet. Among the main reasons are the high computational demands of DL algorithms and the inherent resource-limitation of wearable devices. In this paper, we present initial results for two deep learning architectures used to diagnose and analyze sleep patterns, and we compare them with a previously presented hand-crafted algorithm. The algorithms are designed to be reliable for consumer healthcare applications and to be integrated into low-power wearables with limited computational resources.
引用
收藏
页码：95 / 96
页数：2
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