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Algorithms for Extracting Mental Activity Phases from Heart Beat Rate Streams. / Dubatovka(B), Alina; Mikhailova, Elena; Zotov, Mikhail; Novikov, Boris.

In: Communications in Computer and Information Science, No. 615, 2016, p. 113-125.

Research output: Contribution to journal › Article › peer-review

Harvard

Dubatovka(B), A, Mikhailova, E, Zotov, M & Novikov, B 2016, 'Algorithms for Extracting Mental Activity Phases from Heart Beat Rate Streams', Communications in Computer and Information Science, no. 615, pp. 113-125. <http://link.springer.com/book/10.1007/978-3-319-40180-5>

APA

Dubatovka(B), A., Mikhailova, E., Zotov, M., & Novikov, B. (2016). Algorithms for Extracting Mental Activity Phases from Heart Beat Rate Streams. Communications in Computer and Information Science, (615), 113-125. http://link.springer.com/book/10.1007/978-3-319-40180-5

Vancouver

Dubatovka(B) A, Mikhailova E, Zotov M, Novikov B. Algorithms for Extracting Mental Activity Phases from Heart Beat Rate Streams. Communications in Computer and Information Science. 2016;(615):113-125.

Author

Dubatovka(B), Alina ; Mikhailova, Elena ; Zotov, Mikhail ; Novikov, Boris. / Algorithms for Extracting Mental Activity Phases from Heart Beat Rate Streams. In: Communications in Computer and Information Science. 2016 ; No. 615. pp. 113-125.

BibTeX

@article{0a8d39d3930b49c9940a315a4aedc18d,
title = "Algorithms for Extracting Mental Activity Phases from Heart Beat Rate Streams",
abstract = "The paper presents algorithms for automatic detection of non-stationary periods of cardiac rhythm during professional activity. While working and subsequent rest operator passes through the phases of mobilization, stabilization, work, recovery and the rest. The amplitude and frequency of non-stationary periods of cardiac rhythm indicates the human resistance to stressful conditions. We introduce and analyze a number of algorithms for non-stationary phase extraction: the different approaches to phase preliminary detection, thresholds extraction and final phases extraction are studied experimentally. These algorithms are based on local extremum computation and analysis of linear regression coefficient histograms. The algorithms do not need any labeled datasets for training and could be applied to any person individually. The suggested algorithms were experimentally compared and evaluated by human experts.",
keywords = "Pattern recognition · Signal processing · Mental activity phases · Data stream · Linear regression · Phase extraction",
author = "Alina Dubatovka(B) and Elena Mikhailova and Mikhail Zotov and Boris Novikov",
year = "2016",
language = "English",
pages = "113--125",
journal = "Communications in Computer and Information Science",
issn = "1865-0929",
publisher = "Springer Nature",
number = "615",

}

RIS

TY - JOUR

T1 - Algorithms for Extracting Mental Activity Phases from Heart Beat Rate Streams

AU - Dubatovka(B), Alina

AU - Mikhailova, Elena

AU - Zotov, Mikhail

AU - Novikov, Boris

PY - 2016

Y1 - 2016

N2 - The paper presents algorithms for automatic detection of non-stationary periods of cardiac rhythm during professional activity. While working and subsequent rest operator passes through the phases of mobilization, stabilization, work, recovery and the rest. The amplitude and frequency of non-stationary periods of cardiac rhythm indicates the human resistance to stressful conditions. We introduce and analyze a number of algorithms for non-stationary phase extraction: the different approaches to phase preliminary detection, thresholds extraction and final phases extraction are studied experimentally. These algorithms are based on local extremum computation and analysis of linear regression coefficient histograms. The algorithms do not need any labeled datasets for training and could be applied to any person individually. The suggested algorithms were experimentally compared and evaluated by human experts.

AB - The paper presents algorithms for automatic detection of non-stationary periods of cardiac rhythm during professional activity. While working and subsequent rest operator passes through the phases of mobilization, stabilization, work, recovery and the rest. The amplitude and frequency of non-stationary periods of cardiac rhythm indicates the human resistance to stressful conditions. We introduce and analyze a number of algorithms for non-stationary phase extraction: the different approaches to phase preliminary detection, thresholds extraction and final phases extraction are studied experimentally. These algorithms are based on local extremum computation and analysis of linear regression coefficient histograms. The algorithms do not need any labeled datasets for training and could be applied to any person individually. The suggested algorithms were experimentally compared and evaluated by human experts.

KW - Pattern recognition · Signal processing · Mental activity phases · Data stream · Linear regression · Phase extraction

M3 - Article

SP - 113

EP - 125

JO - Communications in Computer and Information Science

JF - Communications in Computer and Information Science

SN - 1865-0929

IS - 615

ER -

ID: 7575019