Sleep scoring using artificial neural networks

被引:176
|
作者
Ronzhina, Marina [1 ]
Janousek, Oto [1 ]
Kolarova, Jana [1 ]
Novakova, Marie [2 ]
Honzik, Petr [3 ]
Provaznik, Ivo [1 ]
机构
[1] Brno Univ Technol, Dept Biomed Engn, Fac Elect Engn & Commun, Brno 61200, Czech Republic
[2] Masaryk Univ, Fac Med, Dept Physiol, Brno 62500, Czech Republic
[3] Brno Univ Technol, Dept Control & Instrumentat, Fac Elect Engn & Commun, Brno 61200, Czech Republic
关键词
Polysomnographic data; Sleep scoring; Features extraction; Artificial neural networks; ARTIFACT REMOVAL; AUTOMATIC-ANALYSIS; REM-SLEEP; EEG; CLASSIFICATION; RECHTSCHAFFEN; MODEL; KALES; AASM; VALIDATION;
D O I
10.1016/j.smrv.2011.06.003
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Rapid development of computer technologies leads to the intensive automation of many different processes traditionally performed by human experts. One of the spheres characterized by the introduction of new high intelligence technologies substituting analysis performed by humans is sleep scoring. This refers to the classification task and can be solved next to other classification methods by use of artificial neural networks (ANN). ANNs are parallel adaptive systems suitable for solving of nonlinear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparation of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:251 / 263
页数:13
相关论文
共 50 条
  • [21] SLEEP CLASSIFICATION IN INFANTS BASED ON ARTIFICIAL NEURAL NETWORKS
    PFURTSCHELLER, G
    FLOTZINGER, D
    MATUSCHIK, K
    [J]. BIOMEDIZINISCHE TECHNIK, 1992, 37 (06): : 122 - 130
  • [22] Characterization of Physiological Networks in Sleep Apnea Patients Using Artificial Neural Networks for Granger Causality Computation
    Cardenas, Jhon
    Orjuela-Canon, Alvaro D.
    Cerquera, Alexander
    Ravelo, Antonio
    [J]. 13TH INTERNATIONAL CONFERENCE ON MEDICAL INFORMATION PROCESSING AND ANALYSIS, 2017, 10572
  • [23] Prediction of sleep-inducing activity of barbiturates using artificial neural networks.
    Tauer, TP
    Darsey, JA
    [J]. ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2002, 223 : U223 - U223
  • [24] Diagnosis of Obstructive Sleep Apnea Using Logistic Regression and Artificial Neural Networks Models
    Sheta, Alaa
    Turabieh, Hamza
    Braik, Malik
    Surani, Salim R.
    [J]. PROCEEDINGS OF THE FUTURE TECHNOLOGIES CONFERENCE (FTC) 2019, VOL 1, 2020, 1069 : 766 - 784
  • [25] Demosaicing using artificial neural networks
    Kapah, O
    Hel-Or, HZ
    [J]. APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS IN IMAGE PROCESSING V, 2000, 3962 : 112 - 120
  • [26] Validation of an automatic sleep scoring program using neural network
    Hsieh, J
    Robinson, EL
    Fuller, CA
    [J]. SLEEP, 2004, 27 : 366 - 367
  • [27] Credit scoring using neural networks and discriminant analysis
    Lee, TS
    Chiu, CC
    Lu, CJ
    [J]. PROCEEDINGS OF THE 6TH JOINT CONFERENCE ON INFORMATION SCIENCES, 2002, : 1098 - 1101
  • [28] Routing in computer networks using artificial neural networks
    Pierre, S
    Said, H
    Probst, WG
    [J]. ARTIFICIAL INTELLIGENCE IN ENGINEERING, 2000, 14 (04): : 295 - 305
  • [29] DEEP CONVOLUTIONAL NEURAL NETWORKS FOR INTERPRETABLE ANALYSIS OF EEG SLEEP STAGE SCORING
    Vilamala, Albert
    Madsen, Kristoffer H.
    Hansen, Lars K.
    [J]. 2017 IEEE 27TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, 2017,
  • [30] Clinical Practice for Diagnostic Causes for Obstructive Sleep Apnea Using Artificial Intelligent Neural Networks
    Alsalamah, Mashail
    Amin, Saad
    Palade, Vasile
    [J]. EMERGING TECHNOLOGIES IN COMPUTING, ICETIC 2018, 2018, 200 : 259 - 272