A-phase classification using convolutional neural networks.
A series of short events, called A-phases, can be observed in the human electroencephalogram (EEG) during Non-Rapid Eye Movement (NREM) sleep. These events can be classified in three groups (A1, A2, and A3) according to their spectral contents, and are thought to play a role in the transitions betwe...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 5; pp. 1003 - 1015 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142943690&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142943690 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2020 vid: 58 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142943690 142943690 NLM32124224 10.1007/s11517-020-02144-6 NLM32124224 142943690 ppf: 1003 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A-phase classification using convolutional neural networks. aug: au: Arce-Santana, Edgar R. Alba, Alfonso Mendez, Martin O. Arce-Guevara, Valdemar affil: Laboratorio Nacional Centro de Investigación en Imagenología e Instrumentación Médica, Facultad de Ciencias & CICSaB, Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico sug: ab: A series of short events, called A-phases, can be observed in the human electroencephalogram (EEG) during Non-Rapid Eye Movement (NREM) sleep. These events can be classified in three groups (A1, A2, and A3) according to their spectral contents, and are thought to play a role in the transitions between the different sleep stages. A-phase detection and classification is usually performed manually by a trained expert, but it is a tedious and time-consuming task. In the past two decades, various researchers have designed algorithms to automatically detect and classify the A-phases with varying degrees of success, but the problem remains open. In this paper, a different approach is proposed: instead of attempting to design a general classifier for all subjects, we propose to train ad-hoc classifiers for each subject using as little data as possible, in order to drastically reduce the amount of time required from the expert. The proposed classifiers are based on deep convolutional neural networks using the log-spectrogram of the EEG signal as input data. Results are encouraging, achieving average accuracies of 80.31% when discriminating between A-phases and non A-phases, and 71.87% when classifying among A-phase sub-types, with only 25% of the total A-phases used for training. When additional expert-validated data is considered, the sub-type classification accuracy increases to 78.92%. These results show that a semi-automatic annotation system with assistance from an expert could provide a better alternative to fully automatic classifiers. Graphical abstract A/N Deep Learning Classifier. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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