Number №2, 2020 - page 10-16

Prediction and remote monitoring of epileptic seizures based on changes in autonomic regulation and motor activity DOI: 10.29188/2542-2413-2020-6-2-10-16

For citation: Lebedev G.S., Nagornyak A.V., Shaderkin I.A., Shaderkina A.I. Prediction and remote monitoring of epileptic seizures based on changes in autonomic regulation and motor activity. Journal of Telemedicine and E-Health 2020;(2):10-16
  • Lebedev G.S. – Dr. Sc., professor, Head of the Department of Information and Internet Technologies at Sechenov University; Head of the Department of Innovative Development and Scientific Design of the Central Research Institute of Organization and Informatization of Health Care of the Ministry of Health of the Russian Federation, geramail@rambler.ru
  • Nagornyak A.V. – 6st year student Institute of Clinical Medicine of the First Moscow State Medical University them I.M. Sechenov (Sechenov University), nagartem@gmail.com, ORCID 0000-0002-0903-2178.
  • Shaderkin I.A. – PhD, Head of the Laboratory of Electronic Health, Institute of Digital Medicine, Sechenov University, info@uroweb.ru, ORCID 0000-0001-8669-2674
  • Shaderkina A.I. – 1st year student Institute of Clinical Medicine of the First Moscow State Medical University them I.M. Sechenov (Sechenov University), nastyashade01@yandex.ru, ORCID 0000-0003-0639-3274
136

Aim. Automatic detection of epileptic seizures provides an opportunity for remotely observing epileptic patients, minimizing of seizure complications, improvement of healthcare delivery. Vegetative changes often precede ictal electroencephalographic signs and therefore they could be perspective tool for prediction and early detection of epileptic seizures. Otherwise, specific patterns may represent value for seizure predicting and their automated analysis could be effective for seizure detection.

Matireals and methods. A search and analysis of original researches on people which verify algorithm of automated seizure detection due to autonomic function changes was conducted on PubMed and Google Scholar. 103 studies were found.

Results.12 of 103 studies were included. Unimodal algorithm based on heart rate variability (HRV) (n=5), heart rate (HR) (n=5), SpO2 (n=2) and unimodal algorithm based on several combinations of parameters (n=3) are presented in the n studies. The quality of presented studies isn’t high enough basically due to short observation periods. The majority of studies is retrospective with small size of sample and short observation period and only 2 studies have a prospective verification. Retrospective studies using unimodal algorithms present a better sensibility and frequency of false positive lower than in the studies using unit modality.

Conclusions. Presented parameters of autonomic system and specific activity are valuable and promising tools for epileptic seizures detection. It is necessary to make an equipment that allows to trace and analyze simultaneously these parameters for long term verification of presented algorithms and further implementation in clinical practice.

Conflict of interest. The authors declare no conflict of interest.

AttachmentSize
Download90.94 KB
remote monitoring, epilepsy, heart rate variability, electrodermal activity, oxygen saturation