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

页码：95 / 96

页数：2

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