Документы

  • Ampilova CEMA22-final

    Конечная издательская версия, 869 KB, Документ PDF

Time series are widely used for representation data of different types. Along the traditional methods the approach of nonlinear dynamics – reconstruction of the attractor of the system generating the series – is successfully applied. It allows us to calculate correlation dimension of the attractor of the system under study (if it exists) or to establish that the system does not have any attractor.
In this work we apply this method to solve a practical problem to analyze EEG records for revealing the patients with epileptic activity. Additionally we calculate entropy of a signal on amplitude coverage. This approach resulted in separation of 15 records into 2 classes – epileptic activity and other pathologies, and it is in accordance with the expert conclusions.
The implemented program system may be used both for investigations and educational purpose, and the method may be applied to time series of other types.
Переведенное названиеИсследование временных рядов методами нелинейной динамики
Язык оригиналаанглийский
Название основной публикации16 TH INTERNATIONAL CONFERENCE ON COMMUNICATIONS, ELECTROMAGNETICS AND MEDICAL APPLICATIONS
Страницы8-12
Число страниц5
СостояниеПринято в печать - 20 окт 2022
Событие16 INTERNATIONAL CONFERENCE ON COMMUNICATIONS, ELECTROMAGNETICS AND MEDICAL APPLICATIONS - Софийский Технический Университет, София, Болгария
Продолжительность: 20 окт 202220 окт 2022
Номер конференции: 16
http://rcvt.tu-sofia.bg/CEMA/proceedings.html

Серия публикаций

НазваниеCommunication, Electromagnetics and Medical Application
ISSN (печатное издание)1314-2100

конференция

конференция16 INTERNATIONAL CONFERENCE ON COMMUNICATIONS, ELECTROMAGNETICS AND MEDICAL APPLICATIONS
Сокращенное названиеCEMA’22
Страна/TерриторияБолгария
ГородСофия
Период20/10/2220/10/22
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    Предметные области Scopus

  • Компьютерные науки (все)
  • Математика (все)

    Области исследований

  • restoration of attractor, Takens method, time series, correlation dimension

ID: 104298289