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
相关论文
共 50 条
  • [1] Review of Deep Learning Methods for Automated Sleep Staging
    Malekzadeh, Masoud
    Hajibabaee, Parisa
    Heidari, Maryam
    Berlin, Brett
    2022 IEEE 12TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC), 2022, : 80 - 86
  • [2] Sleep staging from electrocardiography and respiration with deep learning
    Sun, Haoqi
    Ganglberger, Wolfgang
    Panneerselvam, Ezhil
    Leone, Michael J.
    Quadri, Syed A.
    Goparaju, Balaji
    Tesh, Ryan A.
    Akeju, Oluwaseun
    Thomas, Robert J.
    Westover, M. Brandon
    SLEEP, 2020, 43 (07)
  • [3] Deep Learning Automatic Sleep Staging Method Based on Multidimensional Sleep Data
    Yang, Jian
    Meng, Yao
    Cheng, Qian
    Li, Huafei
    Cai, Wenpeng
    Wang, Tengjiao
    IEEE ACCESS, 2024, 12 : 168360 - 168369
  • [4] CareSleepNet: A Hybrid Deep Learning Network for Automatic Sleep Staging
    Wang, Jiquan
    Zhao, Sha
    Jiang, Haiteng
    Zhou, Yangxuan
    Yu, Zhenghe
    Li, Tao
    Li, Shijian
    Pan, Gang
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2024, 28 (12) : 7392 - 7405
  • [5] Deep Learning in Automatic Sleep Staging With a Single Channel Electroencephalography
    Fu, Mingyu
    Wang, Yitian
    Chen, Zixin
    Li, Jin
    Xu, Fengguo
    Liu, Xinyu
    Hou, Fengzhen
    FRONTIERS IN PHYSIOLOGY, 2021, 12
  • [6] Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea
    Korkalainen, Henri
    Aakko, Juhani
    Duce, Brett
    Kainulainen, Samu
    Leino, Akseli
    Nikkonen, Sami
    Afara, Isaac O.
    Myllymaa, Sami
    Toyras, Juha
    Leppanen, Timo
    SLEEP, 2020, 43 (11)
  • [7] SLEEP STAGING CLASSIFICATION FROM WEARABLE SIGNALS USING DEEP LEARNING
    Heneghan, Conor
    Gillard, Ryan
    Niehaus, Logan
    Schneider, Logan
    Guerard, Marius
    SLEEP, 2024, 47
  • [8] DEEP TRANSFER LEARNING FOR SLEEP STAGING USING EAR-EEG
    Specht, A.
    Berent, J.
    Dong, C.
    Saini, P.
    Rye, D.
    Ehrens, D.
    SLEEP MEDICINE, 2024, 115 : 404 - 405
  • [9] Comparison of deep transfer learning algorithms and transferability measures for wearable sleep staging
    Waters, Samuel H.
    Clifford, Gari D.
    BIOMEDICAL ENGINEERING ONLINE, 2022, 21 (01)
  • [10] Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning
    Phan, Huy
    Chen, Oliver Y.
    Koch, Philipp
    Lu, Zongqing
    McLoughlin, Ian
    Mertins, Alfred
    De Vos, Maarten
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2021, 68 (06) : 1787 - 1798
12345