Linear and nonlinear analyses of normal and fatigue heart rate variability signals for miners in high-altitude and cold areas.

Electrocardiography (ECG) Fatigue Heart rate variability (HRV) High-altitude and cold Poincaré plot Sample entropy

Journal

Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513

Informations de publication

Date de publication:
Nov 2020
Historique:
received: 09 01 2020
accepted: 12 07 2020
pubmed: 28 7 2020
medline: 15 5 2021
entrez: 27 7 2020
Statut: ppublish

Résumé

Fatigue is an important cause of operational errors, and human errors are the main cause of accidents. This study is an exploratory study in China. Field tests were conducted on heart rate variability (HRV) parameters and physiological indicators of fatigue among miners in high-altitude, cold and low-oxygen areas. This paper studies heart activity patterns during work fatigue in miners. Fatigue affects both the sympathetic and parasympathetic nervous systems, and it is expressed as an abnormal pattern of HRV parameters. Thirty miners were selected as subjects for a field test, and HRV was extracted from 60 groups of electrocardiography (ECG) datasets as basic signals for fatigue analysis. Then, we analyzed the HRV signals of the miners using linear (time domain and frequency domain) and nonlinear dynamics (Poincaré plot and sample entropy (SampEn)), and a Pearson's correlation coefficient analysis and t-tests were performed on the measured indices. The results showed that the time-domain indices (SDNN, RMSSD, SDSD, pNN50, RRn, heart rate (HR), R-wave humps (RH)) and the coefficient of variation (CV)) and the frequency-domain indices (low frequency/high frequency (LF/HF), LFnorm and HFnorm) clearly changed after fatigue. These features were selected using a Poincaré plot, sample entropy, Pearson's correlation coefficient and a t-test for further analysis. The fatigue characteristics and sensitivity parameters of miners in a high-altitude, cold and hypoxic environment were obtained. This study provides deep insight into the use of linear and nonlinear fatigue characteristics to effectively and reliably identify miner fatigue. Furthermore, the study provides a reference for clinical studies of acute mountain sickness in high-altitude, cold and hypoxic environments.

Sections du résumé

BACKGROUND AND OBJECTIVE OBJECTIVE
Fatigue is an important cause of operational errors, and human errors are the main cause of accidents. This study is an exploratory study in China. Field tests were conducted on heart rate variability (HRV) parameters and physiological indicators of fatigue among miners in high-altitude, cold and low-oxygen areas. This paper studies heart activity patterns during work fatigue in miners.
METHODS METHODS
Fatigue affects both the sympathetic and parasympathetic nervous systems, and it is expressed as an abnormal pattern of HRV parameters. Thirty miners were selected as subjects for a field test, and HRV was extracted from 60 groups of electrocardiography (ECG) datasets as basic signals for fatigue analysis. Then, we analyzed the HRV signals of the miners using linear (time domain and frequency domain) and nonlinear dynamics (Poincaré plot and sample entropy (SampEn)), and a Pearson's correlation coefficient analysis and t-tests were performed on the measured indices.
RESULTS RESULTS
The results showed that the time-domain indices (SDNN, RMSSD, SDSD, pNN50, RRn, heart rate (HR), R-wave humps (RH)) and the coefficient of variation (CV)) and the frequency-domain indices (low frequency/high frequency (LF/HF), LFnorm and HFnorm) clearly changed after fatigue. These features were selected using a Poincaré plot, sample entropy, Pearson's correlation coefficient and a t-test for further analysis. The fatigue characteristics and sensitivity parameters of miners in a high-altitude, cold and hypoxic environment were obtained.
CONCLUSIONS CONCLUSIONS
This study provides deep insight into the use of linear and nonlinear fatigue characteristics to effectively and reliably identify miner fatigue. Furthermore, the study provides a reference for clinical studies of acute mountain sickness in high-altitude, cold and hypoxic environments.

Identifiants

pubmed: 32712570
pii: S0169-2607(20)31500-5
doi: 10.1016/j.cmpb.2020.105667
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105667

Informations de copyright

Copyright © 2020. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare no conflict of interest.

Auteurs

Shoukun Chen (S)

Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China. Electronic address: 314308991@qq.com.

Kaili Xu (K)

Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China. Electronic address: xklsafety2018@163.com.

Xin Zheng (X)

Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China. Electronic address: zhengxin@mail.neu.edu.cn.

Jishuo Li (J)

Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China. Electronic address: jishuoli@163.com.

Bingjie Fan (B)

Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China. Electronic address: 1286175228@qq.com.

Xiwen Yao (X)

Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China. Electronic address: yxw_20061005@126.com.

Zhengrong Li (Z)

Yunnan Diqing Non-ferrous Metals Co., Ltd, Yunnan, 674400, China. Electronic address: lizhengrong-00@163.com.

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Classifications MeSH