Short-Term HRV Analysis Using Nonparametric Sample Entropy for Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is associated with reduced heart rate variability (HRV) and autonomic nervous system dysfunction. Sample entropy (SampEn) is commonly used for regularity analysis. However, it has limitations in processing short-term segments of HRV signals due to the extreme dependence...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Duan Liang [verfasserIn] Shan Wu [verfasserIn] Lan Tang [verfasserIn] Kaicheng Feng [verfasserIn] Guanzheng Liu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	heart rate variability (HRV) nonparametric sample entropy (NPSampEn) obstructive sleep apnea (OSA) short-term HRV analysis

Übergeordnetes Werk:	In: Entropy - MDPI AG, 2003, 23(2021), 3, p 267
Übergeordnetes Werk:	volume:23 ; year:2021 ; number:3, p 267

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/e23030267

Katalog-ID:	DOAJ079347266

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520			\|a Obstructive sleep apnea (OSA) is associated with reduced heart rate variability (HRV) and autonomic nervous system dysfunction. Sample entropy (SampEn) is commonly used for regularity analysis. However, it has limitations in processing short-term segments of HRV signals due to the extreme dependence of its functional parameters. We used the nonparametric sample entropy (NPSampEn) as a novel index for short-term HRV analysis in the case of OSA. The manuscript included 60 6-h electrocardiogram recordings (20 healthy, 14 mild-moderate OSA, and 26 severe OSA) from the PhysioNet database. The NPSampEn value was compared with the SampEn value and frequency domain indices. The empirical results showed that NPSampEn could better differentiate the three groups (<i<p</i< < 0.01) than the ratio of low frequency power to high frequency power (LF/HF) and SampEn. Moreover, NPSampEn (83.3%) approached a higher OSA screening accuracy than the LF/HF (73.3%) and SampEn (68.3%). Compared with SampEn (\|r\| = 0.602, <i<p</i< < 0.05), NPSampEn (\|r\| = 0.756, <i<p</i< < 0.05) had a significantly stronger association with the apnea-hypopnea index (AHI). Hence, NPSampEn can fully overcome the influence of individual differences that are prevalent in biomedical signal processing, and might be useful in processing short-term segments of HRV signal.
650		4	\|a heart rate variability (HRV)
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allfieldsSound	10.3390/e23030267 doi (DE-627)DOAJ079347266 (DE-599)DOAJ3882a93bf79e4d639a60e97578378965 DE-627 ger DE-627 rakwb eng QB460-466 QC1-999 Duan Liang verfasserin aut Short-Term HRV Analysis Using Nonparametric Sample Entropy for Obstructive Sleep Apnea 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Obstructive sleep apnea (OSA) is associated with reduced heart rate variability (HRV) and autonomic nervous system dysfunction. Sample entropy (SampEn) is commonly used for regularity analysis. However, it has limitations in processing short-term segments of HRV signals due to the extreme dependence of its functional parameters. We used the nonparametric sample entropy (NPSampEn) as a novel index for short-term HRV analysis in the case of OSA. The manuscript included 60 6-h electrocardiogram recordings (20 healthy, 14 mild-moderate OSA, and 26 severe OSA) from the PhysioNet database. The NPSampEn value was compared with the SampEn value and frequency domain indices. The empirical results showed that NPSampEn could better differentiate the three groups (<i<p</i< < 0.01) than the ratio of low frequency power to high frequency power (LF/HF) and SampEn. Moreover, NPSampEn (83.3%) approached a higher OSA screening accuracy than the LF/HF (73.3%) and SampEn (68.3%). Compared with SampEn (\|r\| = 0.602, <i<p</i< < 0.05), NPSampEn (\|r\| = 0.756, <i<p</i< < 0.05) had a significantly stronger association with the apnea-hypopnea index (AHI). Hence, NPSampEn can fully overcome the influence of individual differences that are prevalent in biomedical signal processing, and might be useful in processing short-term segments of HRV signal. heart rate variability (HRV) nonparametric sample entropy (NPSampEn) obstructive sleep apnea (OSA) short-term HRV analysis Science Q Astrophysics Physics Shan Wu verfasserin aut Lan Tang verfasserin aut Kaicheng Feng verfasserin aut Guanzheng Liu verfasserin aut In Entropy MDPI AG, 2003 23(2021), 3, p 267 (DE-627)316340359 (DE-600)2014734-X 10994300 nnns volume:23 year:2021 number:3, p 267 https://doi.org/10.3390/e23030267 kostenfrei https://doaj.org/article/3882a93bf79e4d639a60e97578378965 kostenfrei https://www.mdpi.com/1099-4300/23/3/267 kostenfrei https://doaj.org/toc/1099-4300 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2021 3, p 267
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callnumber-first	Q - Science
author	Duan Liang
spellingShingle	Duan Liang misc QB460-466 misc QC1-999 misc heart rate variability (HRV) misc nonparametric sample entropy (NPSampEn) misc obstructive sleep apnea (OSA) misc short-term HRV analysis misc Science misc Q misc Astrophysics misc Physics Short-Term HRV Analysis Using Nonparametric Sample Entropy for Obstructive Sleep Apnea
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topic_title	QB460-466 QC1-999 Short-Term HRV Analysis Using Nonparametric Sample Entropy for Obstructive Sleep Apnea heart rate variability (HRV) nonparametric sample entropy (NPSampEn) obstructive sleep apnea (OSA) short-term HRV analysis
topic	misc QB460-466 misc QC1-999 misc heart rate variability (HRV) misc nonparametric sample entropy (NPSampEn) misc obstructive sleep apnea (OSA) misc short-term HRV analysis misc Science misc Q misc Astrophysics misc Physics
topic_unstemmed	misc QB460-466 misc QC1-999 misc heart rate variability (HRV) misc nonparametric sample entropy (NPSampEn) misc obstructive sleep apnea (OSA) misc short-term HRV analysis misc Science misc Q misc Astrophysics misc Physics
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title	Short-Term HRV Analysis Using Nonparametric Sample Entropy for Obstructive Sleep Apnea
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title_full	Short-Term HRV Analysis Using Nonparametric Sample Entropy for Obstructive Sleep Apnea
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title_sort	short-term hrv analysis using nonparametric sample entropy for obstructive sleep apnea
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title_auth	Short-Term HRV Analysis Using Nonparametric Sample Entropy for Obstructive Sleep Apnea
abstract	Obstructive sleep apnea (OSA) is associated with reduced heart rate variability (HRV) and autonomic nervous system dysfunction. Sample entropy (SampEn) is commonly used for regularity analysis. However, it has limitations in processing short-term segments of HRV signals due to the extreme dependence of its functional parameters. We used the nonparametric sample entropy (NPSampEn) as a novel index for short-term HRV analysis in the case of OSA. The manuscript included 60 6-h electrocardiogram recordings (20 healthy, 14 mild-moderate OSA, and 26 severe OSA) from the PhysioNet database. The NPSampEn value was compared with the SampEn value and frequency domain indices. The empirical results showed that NPSampEn could better differentiate the three groups (<i<p</i< < 0.01) than the ratio of low frequency power to high frequency power (LF/HF) and SampEn. Moreover, NPSampEn (83.3%) approached a higher OSA screening accuracy than the LF/HF (73.3%) and SampEn (68.3%). Compared with SampEn (\|r\| = 0.602, <i<p</i< < 0.05), NPSampEn (\|r\| = 0.756, <i<p</i< < 0.05) had a significantly stronger association with the apnea-hypopnea index (AHI). Hence, NPSampEn can fully overcome the influence of individual differences that are prevalent in biomedical signal processing, and might be useful in processing short-term segments of HRV signal.
abstractGer	Obstructive sleep apnea (OSA) is associated with reduced heart rate variability (HRV) and autonomic nervous system dysfunction. Sample entropy (SampEn) is commonly used for regularity analysis. However, it has limitations in processing short-term segments of HRV signals due to the extreme dependence of its functional parameters. We used the nonparametric sample entropy (NPSampEn) as a novel index for short-term HRV analysis in the case of OSA. The manuscript included 60 6-h electrocardiogram recordings (20 healthy, 14 mild-moderate OSA, and 26 severe OSA) from the PhysioNet database. The NPSampEn value was compared with the SampEn value and frequency domain indices. The empirical results showed that NPSampEn could better differentiate the three groups (<i<p</i< < 0.01) than the ratio of low frequency power to high frequency power (LF/HF) and SampEn. Moreover, NPSampEn (83.3%) approached a higher OSA screening accuracy than the LF/HF (73.3%) and SampEn (68.3%). Compared with SampEn (\|r\| = 0.602, <i<p</i< < 0.05), NPSampEn (\|r\| = 0.756, <i<p</i< < 0.05) had a significantly stronger association with the apnea-hypopnea index (AHI). Hence, NPSampEn can fully overcome the influence of individual differences that are prevalent in biomedical signal processing, and might be useful in processing short-term segments of HRV signal.
abstract_unstemmed	Obstructive sleep apnea (OSA) is associated with reduced heart rate variability (HRV) and autonomic nervous system dysfunction. Sample entropy (SampEn) is commonly used for regularity analysis. However, it has limitations in processing short-term segments of HRV signals due to the extreme dependence of its functional parameters. We used the nonparametric sample entropy (NPSampEn) as a novel index for short-term HRV analysis in the case of OSA. The manuscript included 60 6-h electrocardiogram recordings (20 healthy, 14 mild-moderate OSA, and 26 severe OSA) from the PhysioNet database. The NPSampEn value was compared with the SampEn value and frequency domain indices. The empirical results showed that NPSampEn could better differentiate the three groups (<i<p</i< < 0.01) than the ratio of low frequency power to high frequency power (LF/HF) and SampEn. Moreover, NPSampEn (83.3%) approached a higher OSA screening accuracy than the LF/HF (73.3%) and SampEn (68.3%). Compared with SampEn (\|r\| = 0.602, <i<p</i< < 0.05), NPSampEn (\|r\| = 0.756, <i<p</i< < 0.05) had a significantly stronger association with the apnea-hypopnea index (AHI). Hence, NPSampEn can fully overcome the influence of individual differences that are prevalent in biomedical signal processing, and might be useful in processing short-term segments of HRV signal.
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container_issue	3, p 267
title_short	Short-Term HRV Analysis Using Nonparametric Sample Entropy for Obstructive Sleep Apnea
url	https://doi.org/10.3390/e23030267 https://doaj.org/article/3882a93bf79e4d639a60e97578378965 https://www.mdpi.com/1099-4300/23/3/267 https://doaj.org/toc/1099-4300
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Short-Term HRV Analysis Using Nonparametric Sample Entropy for Obstructive Sleep Apnea

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