Algorithm for the classification of phases and stages of sleep in patients with chronic disorders of consciousness based on logical artificial intelligence

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Abstract

BACKGROUND: The analysis of sleep patterns in patients with chronic disorders of consciousness is attracting increasing attention in terms of diagnosis, prognosis, and treatment of severe brain damage. The study describes a software package based on an artificial intelligence (AI) expert system designed to classify the phases and stages of sleep, taking into account the characteristics of impaired cortical rhythm in such patients.

AIM: To develop a specialized AI-based software package focused on patients with chronic impairment of consciousness for automatic classification of sleep phases and stages, with an emphasis on identifying sleep spindles and non-rapid eye movement (REM) and REM sleep phases.

MATERIALS AND METHODS: To ensure the correct operation of the software package, receiver operating characteristic (ROC) curves were analyzed considering the binary classification of slow sleep, REM sleep, and wakefulness.

RESULTS: The average sensitivity and specificity of the algorithm were 87.9 and 70.1, respectively. The average area under the ROC curve was 0.790. The algorithm for determining the REM phase demonstrates low specificity with high sensitivity, and its graph was similar to that of wakefulness, as well as the irregularity of the presence of REMs in the REM sleep phase in patients with CNS and the frequent presence of nystagmus in the waking state. Information about the presence of nystagmus, entered at the start of the program, allowed us to slightly increase the efficiency of the algorithm; however, this aspect probably needs further improvement.

CONCLUSION: A software package that takes into account the features of electroencephalography of patients with chronic disorders of consciousness and analyzes sleep and wakefulness automatically is not only useful as a diagnostic tool for neurologists and somnologists but also contributes to a wider dissemination of this technique in clinical practice.

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About the authors

Yuliya Y. Nekrasova

Federal Scientific and Clinical Center for Resuscitation and Rehabilitation

Email: nekrasova84@yandex.ru
ORCID iD: 0000-0002-4435-8501
SPIN-code: 8947-4230

Cand. Sci. (Engin.)

Russian Federation, Bldg 2, 25, Petrovka Str. Moscow, 127051

Ilya V. Borisov

Federal Scientific and Clinical Center for Resuscitation and Rehabilitation

Email: realzel@gmail.com
ORCID iD: 0000-0002-5707-118X
SPIN-code: 7800-6446

researcher

Russian Federation, Bldg 2, 25, Petrovka Str. Moscow, 127051

Mikhail M. Kanarsky

Federal Scientific and Clinical Center for Resuscitation and Rehabilitation

Email: kanarmm@yandex.ru
ORCID iD: 0000-0002-7635-1048
SPIN-code: 1776-1160

junior researcher

Russian Federation, Bldg 2, 25, Petrovka Str. Moscow, 127051

Pranil Pradhan

Federal Scientific and Clinical Center for Resuscitation and Rehabilitation; Peoples' Friendship University of Russia (PFUR)

Email: pranilpr@yandex.ru
ORCID iD: 0000-0002-3505-7504
SPIN-code: 8647-4329

researcher

Russian Federation, Bldg 2, 25, Petrovka Str. Moscow, 127051; 6, Miklukho Maklaya Str., Moscow, 117198

Larisa A. Mayorova

Federal Scientific and Clinical Center for Resuscitation and Rehabilitation; Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences

Email: major_@bk.ru
ORCID iD: 0000-0001-5112-7878
SPIN-code: 7383-5144

MD, Cand. Sci. (Med.)

Russian Federation, Bldg 2, 25, Petrovka Str. Moscow, 127051; 5A, Butlerova Str., Moscow, 117485

Ivan V. Redkin

Federal Scientific and Clinical Center for Resuscitation and Rehabilitation

Email: iredkin@fnkcrr.ru
ORCID iD: 0000-0001-7008-2038
SPIN-code: 1854-9314

MD, Cand. Sci. (Med.)

Russian Federation, Bldg 2, 25, Petrovka Str. Moscow, 127051

Viktoriya S. Sorokina

Federal Scientific and Clinical Center for Resuscitation and Rehabilitation

Author for correspondence.
Email: vsorokina@fnkcrr.ru
ORCID iD: 0000-0002-1490-1331
SPIN-code: 3407-1625

junior researcher

Russian Federation, Bldg 2, 25, Petrovka Str. Moscow, 127051

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Supplementary files

Supplementary Files
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2. Fig. 1. Twenty-second epoch of the electroencephalography signal in lead C3/A2.

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3. Fig. 2. General view of the slow wave in the electroencephalography record.

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4. Fig. 3. Ten-second epoch of the electroencephalography signal in lead F3/A2.

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5. Fig. 4. Thirty-second epoch of the electroencephalography signal in lead O2 (a) and twenty-second epoch of the electroencephalography signal with muscle artifact in the beta range (b).

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6. Fig. 5. Rapid eye movements detected within a thirty-second epoch and their main parameters.

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7. Fig. 6. An example of a hypnogram obtained using a software package for a patient with a chronic impairment of consciousness

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8. Fig. 7. ROC curves for three groups of data obtained

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СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
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СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
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