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Time Series Analysis using Embedding Dimension on Heart Rate Variability

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Time Series Analysis using Embedding Dimension on Heart Rate Variability. / Bhavsar, Ronakben; Davey, Neil; Helian, Na; Sun, Yi; Steffert, Tony; Mayor, David.

In: Procedia Computer Science, Vol. 145, 11.12.2018, p. 89-96.

Research output: Contribution to journalConference articlepeer-review

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Bhavsar, Ronakben ; Davey, Neil ; Helian, Na ; Sun, Yi ; Steffert, Tony ; Mayor, David. / Time Series Analysis using Embedding Dimension on Heart Rate Variability. In: Procedia Computer Science. 2018 ; Vol. 145. pp. 89-96.

Bibtex

@article{cad71a09b98f43d1a09477fb785c4485,
title = "Time Series Analysis using Embedding Dimension on Heart Rate Variability",
abstract = "Heart Rate Variability (HRV) is the measurement sequence with one or more visible variables of an underlying dynamic system, whose state changes with time. In practice, it is difficult to know what variables determine the actual dynamic system. In this research, Embedding Dimension (ED) is used to find out the nature of the underlying dynamical system. False Nearest Neighbour(FNN) method of estimating ED has been adapted for analysing and predicting variables responsible for HRV time series. It shows that the ED can provide the evidence of dynamic variables which contribute to the HRV time series. Also, the embedding of the HRV time series into a four-dimensional space produced the smallest number of FNN. This result strongly suggests that the Autonomic Nervous System that drives the heart is a two features dynamic system: sympathetic and parasympathetic nervous system.",
keywords = "Embedding Dimension, False Nearest Neighbours, HRV, Linear Regression, Parasympathetic, Sympathetic, Time series analysis",
author = "Ronakben Bhavsar and Neil Davey and Na Helian and Yi Sun and Tony Steffert and David Mayor",
year = "2018",
month = dec,
day = "11",
doi = "10.1016/j.procs.2018.11.015",
language = "English",
volume = "145",
pages = "89--96",
journal = "Procedia Computer Science",
issn = "1877-0509",
publisher = "Elsevier BV",
note = "The 9th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2018 ; Conference date: 24-08-2018 Through 28-08-2018",
url = "http://bica2018.bicasociety.org/",

}

RIS

TY - JOUR

T1 - Time Series Analysis using Embedding Dimension on Heart Rate Variability

AU - Bhavsar, Ronakben

AU - Davey, Neil

AU - Helian, Na

AU - Sun, Yi

AU - Steffert, Tony

AU - Mayor, David

N1 - Conference code: The 9th

PY - 2018/12/11

Y1 - 2018/12/11

N2 - Heart Rate Variability (HRV) is the measurement sequence with one or more visible variables of an underlying dynamic system, whose state changes with time. In practice, it is difficult to know what variables determine the actual dynamic system. In this research, Embedding Dimension (ED) is used to find out the nature of the underlying dynamical system. False Nearest Neighbour(FNN) method of estimating ED has been adapted for analysing and predicting variables responsible for HRV time series. It shows that the ED can provide the evidence of dynamic variables which contribute to the HRV time series. Also, the embedding of the HRV time series into a four-dimensional space produced the smallest number of FNN. This result strongly suggests that the Autonomic Nervous System that drives the heart is a two features dynamic system: sympathetic and parasympathetic nervous system.

AB - Heart Rate Variability (HRV) is the measurement sequence with one or more visible variables of an underlying dynamic system, whose state changes with time. In practice, it is difficult to know what variables determine the actual dynamic system. In this research, Embedding Dimension (ED) is used to find out the nature of the underlying dynamical system. False Nearest Neighbour(FNN) method of estimating ED has been adapted for analysing and predicting variables responsible for HRV time series. It shows that the ED can provide the evidence of dynamic variables which contribute to the HRV time series. Also, the embedding of the HRV time series into a four-dimensional space produced the smallest number of FNN. This result strongly suggests that the Autonomic Nervous System that drives the heart is a two features dynamic system: sympathetic and parasympathetic nervous system.

KW - Embedding Dimension

KW - False Nearest Neighbours

KW - HRV

KW - Linear Regression

KW - Parasympathetic

KW - Sympathetic

KW - Time series analysis

UR - http://www.scopus.com/inward/record.url?scp=85059471250&partnerID=8YFLogxK

U2 - 10.1016/j.procs.2018.11.015

DO - 10.1016/j.procs.2018.11.015

M3 - Conference article

VL - 145

SP - 89

EP - 96

JO - Procedia Computer Science

JF - Procedia Computer Science

SN - 1877-0509

T2 - The 9th Annual International Conference on Biologically Inspired Cognitive Architectures

Y2 - 24 August 2018 through 28 August 2018

ER -