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Kurtoglu E. The normalized spectral and nonlinear indexes in heart rate variability analysis. Pediatr Int 2024; 66:e15778. [PMID: 38863301 DOI: 10.1111/ped.15778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 03/24/2024] [Indexed: 06/13/2024]
Affiliation(s)
- Ertugrul Kurtoglu
- Department of Cardiology, Faculty of Medicine, Malatya Turgut Ozal University, Malatya, Turkey
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2
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Casanova-Lizón A, Sarabia JM, Pastor D, Javaloyes A, Peña-González I, Moya-Ramón M. Designing an App to Promote Physical Exercise in Sedentary People Using a Day-to-Day Algorithm to Ensure a Healthy Self-Programmed Exercise Training. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1528. [PMID: 36674281 PMCID: PMC9861270 DOI: 10.3390/ijerph20021528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 06/17/2023]
Abstract
Heart rate variability (HRV) has allowed the implementation of a methodology for daily decision making called day-to-day training, which allows data to be recorded by anyone with a smartphone. The purpose of the present work was to evaluate the validity and reliability of HRV measurements with a new mobile app (Selftraining UMH) in two resting conditions. Twenty healthy people (10 male and 10 female) were measured at rest in supine and seated positions with an electrocardiogram and an application for smartphones at the same time (Selftraining UMH) using recordings obtained through an already validated chest-worn heart rate monitor (Polar H10). The Selftraining UMH app showed no significant differences compared to an electrocardiogram, neither in supine nor in sitting position (p > 0.05) and they presented almost perfect correlation levels (r ≥ 0.99). Furthermore, no significant differences were found between ultra-short (1-min) and short (5-min) length measurements. The intraclass correlation coefficient showed excellent reliability (>0.90) and the standard error of measurement remained below 5%. The Selftraining UMH smartphone app connected via Bluetooth to the Polar H10 chest strap can be used to register daily HRV recordings in healthy sedentary people.
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Affiliation(s)
- Antonio Casanova-Lizón
- Sports Research Centre, Department of Sport Sciences, Miguel Hernández University of Elche, 03202 Elche, Spain
| | - José M. Sarabia
- Sports Research Centre, Department of Sport Sciences, Miguel Hernández University of Elche, 03202 Elche, Spain
- Department of Sport Sciences, Miguel Hernandez University, Alicante Institute for Health and Biomedical Research (ISABIAL), 03010 Alicante, Spain
| | - Diego Pastor
- Department of Sport Sciences, Miguel Hernandez University, Alicante Institute for Health and Biomedical Research (ISABIAL), 03010 Alicante, Spain
| | - Alejandro Javaloyes
- Sports Research Centre, Department of Sport Sciences, Miguel Hernández University of Elche, 03202 Elche, Spain
| | - Iván Peña-González
- Sports Research Centre, Department of Sport Sciences, Miguel Hernández University of Elche, 03202 Elche, Spain
| | - Manuel Moya-Ramón
- Sports Research Centre, Department of Sport Sciences, Miguel Hernández University of Elche, 03202 Elche, Spain
- Department of Sport Sciences, Miguel Hernandez University, Alicante Institute for Health and Biomedical Research (ISABIAL), 03010 Alicante, Spain
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3
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Zimatore G, Gallotta MC, Campanella M, Skarzynski PH, Maulucci G, Serantoni C, De Spirito M, Curzi D, Guidetti L, Baldari C, Hatzopoulos S. Detecting Metabolic Thresholds from Nonlinear Analysis of Heart Rate Time Series: A Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191912719. [PMID: 36232025 PMCID: PMC9564658 DOI: 10.3390/ijerph191912719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 09/30/2022] [Accepted: 10/02/2022] [Indexed: 05/03/2023]
Abstract
Heart rate time series are widely used to characterize physiological states and athletic performance. Among the main indicators of metabolic and physiological states, the detection of metabolic thresholds is an important tool in establishing training protocols in both sport and clinical fields. This paper reviews the most common methods, applied to heart rate (HR) time series, aiming to detect metabolic thresholds. These methodologies have been largely used to assess energy metabolism and to identify the appropriate intensity of physical exercise which can reduce body weight and improve physical fitness. Specifically, we focused on the main nonlinear signal evaluation methods using HR to identify metabolic thresholds with the purpose of identifying a method which can represent a useful tool for the real-time settings of wearable devices in sport activities. While the advantages and disadvantages of each method, and the possible applications, are presented, this review confirms that the nonlinear analysis of HR time series represents a solid, robust and noninvasive approach to assess metabolic thresholds.
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Affiliation(s)
- Giovanna Zimatore
- Department of Theoretical and Applied Sciences, eCampus University, 22060 Novedrate, Italy
- IMM-CNR, 40129 Bologna, Italy
- Correspondence: (G.Z.); (G.M.)
| | - Maria Chiara Gallotta
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, 00185 Roma, Italy
| | - Matteo Campanella
- Department of Theoretical and Applied Sciences, eCampus University, 22060 Novedrate, Italy
| | - Piotr H. Skarzynski
- Department of Teleaudiology and Screening, World Hearing Center, Institute of Physiology and Pathology of Hearing, 02-042 Warsaw, Poland
- Heart Failure and Cardiac Rehabilitation Department, Faculty of Medicine, Medical University of Warsaw, 03-042 Warsaw, Poland
- Institute of Sensory Organs, 05-830 Warsaw, Poland
| | - Giuseppe Maulucci
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Neuroscience Department, Biophysics Section, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Correspondence: (G.Z.); (G.M.)
| | - Cassandra Serantoni
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Neuroscience Department, Biophysics Section, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Marco De Spirito
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Neuroscience Department, Biophysics Section, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Davide Curzi
- Department Unicusano, Niccolò Cusano University, 00166 Rome, Italy
| | - Laura Guidetti
- Department Unicusano, Niccolò Cusano University, 00166 Rome, Italy
| | - Carlo Baldari
- Department of Theoretical and Applied Sciences, eCampus University, 22060 Novedrate, Italy
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4
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Faust O, Hong W, Loh HW, Xu S, Tan RS, Chakraborty S, Barua PD, Molinari F, Acharya UR. Heart rate variability for medical decision support systems: A review. Comput Biol Med 2022; 145:105407. [DOI: 10.1016/j.compbiomed.2022.105407] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/09/2022] [Accepted: 03/12/2022] [Indexed: 12/22/2022]
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Hammad M, Kandala RN, Abdelatey A, Abdar M, Zomorodi‐Moghadam M, Tan RS, Acharya UR, Pławiak J, Tadeusiewicz R, Makarenkov V, Sarrafzadegan N, Khosravi A, Nahavandi S, EL-Latif AAA, Pławiak P. Automated detection of shockable ECG signals: A review. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.05.035] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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Singh V, Gupta A, Sohal JS, Singh A, Bakshi S. Age induced interactions between heart rate variability and systolic blood pressure variability using approximate entropy and recurrence quantification analysis: a multiscale cross correlation analysis. Phys Eng Sci Med 2021; 44:497-510. [PMID: 33939105 DOI: 10.1007/s13246-021-01000-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 04/03/2021] [Indexed: 10/21/2022]
Abstract
The purpose of this study is to study the effect of age on the correlation between heart rate variability (HRV) and blood pressure variability (BPV). To meet this end, multi-scale cross correlation (CC) analysis of HRV and systolic blood pressure variability (SBPV) was performed. The Approximate Entropy (ApEn) and Recurrence Quantification Analysis (RQA) derived indices, calculated from RR interval series (RRi) and systolic blood pressure (SBP) series at multiple temporal scales, are the basis of this CC analysis. For the computation of ApEn and RQA indices, the tolerance threshold (r) is chosen by either: (i) selecting any arbitrary value (0.2) within the recommended range (0.1-0.25) times standard deviation (SD) of time series, and (ii) taking the 'r' (ropt) corresponding to maximum ApEn (ApEnmax) as tolerance threshold. It is found that (i) at each time scale (τ), a lower SD is observed when indices are computed using ropt than [Formula: see text] (r0.2), for RRi as well as SBP series, (ii) descriptive indices of RRi are found significant (p < 0.05) at all scales (τ), however for SBP, these are found insignificant (p > 0.05) at most of the scales, (iii) CC values of descriptive statistics viz., mean and SD are not significant (p > 0.05) irrespective of τ, barring τ = 1, (iv) CC values of ApEn and RQA indices, found using ropt, are found significant (p < 0.05) and provide enhanced stratification at τ = 1, 2 and 3, whereas this significant correlation and strong classification is missing for indices calculated using r0.2, and (v) Lastly as τ increases, ApEn and RQA indices, computed with ropt, reverse their trend but manage to provide significant difference in elder and younger subjects. It is concluded that HRV and SBPV interactions gets altered with age. Descriptive indicators however are not enough to capture these changes. These complex interactions can only be deciphered using complexity-based methods such as approximate entropy and that too at the multiple scale level.
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Affiliation(s)
- Vikramjit Singh
- Department of Electronics and Communication Engineering, I K G Punjab Technical University, Jalandhar, Punjab, India.
| | - Amit Gupta
- Department of Electronics and Communication Engineering, I K G Punjab Technical University, Jalandhar, Punjab, India
| | - J S Sohal
- Ludhiana College of Engineering and Technology, Ludhiana, Punjab, India
| | | | - Surbhi Bakshi
- Department of Electrical Engineering, Chandigarh University, Mohali, India
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Lee J, Lee H, Shin M. Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals. SENSORS 2021; 21:s21072381. [PMID: 33808147 PMCID: PMC8038071 DOI: 10.3390/s21072381] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 03/12/2021] [Accepted: 03/26/2021] [Indexed: 11/17/2022]
Abstract
Mental stress can lead to traffic accidents by reducing a driver’s concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers’ stress in advance to prevent dangerous situations increased. Thus, we propose a novel method for detecting driving stress using nonlinear representations of short-term (30 s or less) physiological signals for multimodal convolutional neural networks (CNNs). Specifically, from hand/foot galvanic skin response (HGSR, FGSR) and heart rate (HR) short-term input signals, first, we generate corresponding two-dimensional nonlinear representations called continuous recurrence plots (Cont-RPs). Second, from the Cont-RPs, we use multimodal CNNs to automatically extract FGSR, HGSR, and HR signal representative features that can effectively differentiate between stressed and relaxed states. Lastly, we concatenate the three extracted features into one integrated representation vector, which we feed to a fully connected layer to perform classification. For the evaluation, we use a public stress dataset collected from actual driving environments. Experimental results show that the proposed method demonstrates superior performance for 30-s signals, with an overall accuracy of 95.67%, an approximately 2.5–3% improvement compared with that of previous works. Additionally, for 10-s signals, the proposed method achieves 92.33% classification accuracy, which is similar to or better than the performance of other methods using long-term signals (over 100 s).
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8
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Gaudez C, Mouzé-Amady M. Which subject-related variables contribute to movement variability during a simulated repetitive and standardised occupational task? Recurrence quantification analysis of surface electromyographic signals. ERGONOMICS 2021; 64:366-382. [PMID: 33026299 DOI: 10.1080/00140139.2020.1834148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 10/04/2020] [Indexed: 06/11/2023]
Abstract
Movement variability is a component of human movement. This study applied recurrence quantification analysis (RQA) on electromyographic signals to determine the effects of two types of variables on movement variability during a short, simulated repetitive and standardised occupational clip-fitting task. The electrical activity of six muscles in the dominant upper limb was recorded in 21 participants. Variables related to the task performance (insertion force and movements performed when fitting clips) affected RQA measures: recurrence rate (RR), percentage of determinism (DET) and diagonal line length entropy (ENT). Variables related to participant's characteristics (sex, age, and BMI) affected only DET and ENT. A constrasting variability was observed such as a high-DET value combined with a high-ENT value and inversely. Variables affected mainly the recurrences organisation of the more distal muscles. Even if movement variability is complex, it should be considered by ergonomists and work place designers to better understanding of operators' movements. Practitioner summary: It is essential to consider the complexity of operators' movement variability to understand their activities. Based on intrinsic movement variability knowledge, ergonomists and work place designers will be able to modulate the movement variability by acting on workstation designs and occupational organisation with the aim of preserving operators' health. Abbreviations: RR: recurrence rate; DET: percentage of determinism; ENT: diagonal line length entropy; BMI: body mass index; FDS: flexor digitorum superficialis; EXT: extensor digitorum communis; BIC: biceps brachii; TRI: triceps brachii; DEL: deltoideus anterior; TRA: trapezius pars descendens; F: female; M: male; S: supinated; P: pronated; CM: continuous movement; DM: discontinuous movement.
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Affiliation(s)
- Clarisse Gaudez
- INRS - Institut National de Recherche et de Sécurité, Vandoeuvre cedex, France
| | - Marc Mouzé-Amady
- INRS - Institut National de Recherche et de Sécurité, Vandoeuvre cedex, France
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de Faria Cardoso C, Ohe NT, Bader Y, Afify N, Al-Homedi Z, Alwedami SM, O'Sullivan S, Campos LA, Baltatu OC. Heart Rate Variability Indices as Possible Biomarkers for the Severity of Post-traumatic Stress Disorder Following Pregnancy Loss. Front Psychiatry 2021; 12:700920. [PMID: 35058809 PMCID: PMC8763675 DOI: 10.3389/fpsyt.2021.700920] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 11/02/2021] [Indexed: 12/30/2022] Open
Abstract
Background: Psychological distress, such as posttraumatic stress disorder (PTSD), is commonly evaluated using subjective questionnaires, a method prone to self-report bias. The study's working hypothesis was that levels of autonomic dysfunction determined by heart rate variability (HRV) measures are associated with the severity of PTSD in women following pregnancy loss. Methods: This was an observational prospective cohort study with 53 patients enrolled. The DSM-5 (Diagnostic and Statistical Manual of Mental Disorders) PTSD scale (PCL-5) was used to assess the severity of PTSD in women after pregnancy loss. The cardiac autonomic function was assessed using HRV measurements during a deep breathing test using an HRV scanner system with wireless ECG enabling real-time data analysis and visualization. HRV measures were: standard deviation (SD) of normal R-R wave intervals [SDNN, ms], square root of the mean of the sum of the squares of differences between adjacent normal R wave intervals [RMSSD, ms], and the number of all R-R intervals in which the change in consecutive normal sinus intervals exceeds 50 milliseconds divided by the total number of R-R intervals measured [pNN50 = (NN50/n-1)*100%] [pNN50%]. Results: The PCL-5 scores had a statistically significant association with HRV indices (SDNN; RMSSD, and pNN50%). Patients with PTSD had similar mean heart rate values as compared to patients without PTSD (PCL-5), but significantly higher SDNN [median[IQR, interquartile range]: 90.1 (69.1-112.1) vs. 52.5 (36.8-65.6)], RMSSD [59.4 (37.5-74.9) vs. 31.9 (19.3 - 44.0)], and PNN50% values [25.7 (16.4-37.7) vs. 10.6 (1.5-21.9)]. The SDNN of the deep breathing test HRV was effective at distinguishing between patients with PTSD and those without, with an AUC = 0.83 +/- 0.06 (95 % CI 0.94, p = 0.0001) of the ROC model. Conclusions: In this study, HRV indices as biomarkers of cardiac dysautonomia were found to be significantly related to the severity of PTSD symptoms in women after pregnancy loss.
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Affiliation(s)
- Cláudia de Faria Cardoso
- Center of Innovation, Technology and Education (CITE), Anhembi Morumbi University, Sao Jose dos Campos, Brazil
| | - Natalia Tiemi Ohe
- Center of Innovation, Technology and Education (CITE), Anhembi Morumbi University, Sao Jose dos Campos, Brazil
| | - Yazan Bader
- Emory University, Atlanta, GA, United States
| | - Nariman Afify
- College of Medicine and Health Sciences, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Zahrah Al-Homedi
- College of Medicine and Health Sciences, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Salma Malalla Alwedami
- College of Medicine and Health Sciences, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Siobhán O'Sullivan
- College of Medicine and Health Sciences, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Luciana Aparecida Campos
- Center of Innovation, Technology and Education (CITE), Anhembi Morumbi University, Sao Jose dos Campos, Brazil.,College of Medicine and Health Sciences, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Ovidiu Constantin Baltatu
- Center of Innovation, Technology and Education (CITE), Anhembi Morumbi University, Sao Jose dos Campos, Brazil.,College of Medicine and Health Sciences, Khalifa University, Abu Dhabi, United Arab Emirates
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Shang H, Li Y, Xu J, Qi B, Yin J. A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E1039. [PMID: 33286808 PMCID: PMC7597099 DOI: 10.3390/e22091039] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 09/15/2020] [Accepted: 09/15/2020] [Indexed: 01/14/2023]
Abstract
To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD.
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Affiliation(s)
- Haikun Shang
- Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China; (Y.L.); (J.X.); (B.Q.)
| | - Yucai Li
- Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China; (Y.L.); (J.X.); (B.Q.)
| | - Junyan Xu
- Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China; (Y.L.); (J.X.); (B.Q.)
| | - Bing Qi
- Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China; (Y.L.); (J.X.); (B.Q.)
| | - Jinliang Yin
- School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin 300384, China;
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Dimitriev D, Saperova EV, Dimitriev A, Karpenko Y. Recurrence Quantification Analysis of Heart Rate During Mental Arithmetic Stress in Young Females. Front Physiol 2020; 11:40. [PMID: 32116754 PMCID: PMC7026015 DOI: 10.3389/fphys.2020.00040] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Accepted: 01/20/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
- Dimitriy Dimitriev
- Department of Biology, Chuvash State Pedagogical University named I Ya Yakovlev, Cheboksary, Russia
| | - Elena V Saperova
- Department of Biology, Chuvash State Pedagogical University named I Ya Yakovlev, Cheboksary, Russia
| | - Aleksey Dimitriev
- Department of Biology, Chuvash State Pedagogical University named I Ya Yakovlev, Cheboksary, Russia
| | - Yuriy Karpenko
- Centre for Strategic Planning, Russian Ministry of Health, Moscow, Russia
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Liu J, Huang R, Xiao Y, Lin S. ApEn for assessing hypoxemia severity in obstructive sleep apnea hypopnea syndrome patients. Sleep Breath 2020; 24:1481-1486. [PMID: 31919715 DOI: 10.1007/s11325-019-02004-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 12/09/2019] [Accepted: 12/19/2019] [Indexed: 11/26/2022]
Abstract
OBJECTIVE A new index, approximate entropy (ApEn) of oxygen saturation, was used to assess the severity of hypoxemia in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS), determine the correlation with other parameters, and explore its clinical value. METHODS A retrospective analysis was performed on 1200 patients with OSAHS and snorers (normal control). All subjects underwent sleep apnea monitoring for 6 h. Subjects were divided into four subgroups by apnea-hypopnea index (AHI): normal control (AHI < 5), mild OSAHS (5 ≤ AHI < 15), moderate OSAHS (15 ≤ AHI < 30), and severe OSAHS 104 (AHI ≥ 30). ApEn was initially compared among the subgroups. Then a correlation analysis of AHI with ApEn and a correlation analysis of ApEn with oxygen desaturation index (ODI), lowest oxygen saturation (LO2), and T < 90% were performed. (2) The AHI was used as the gold standard, and an attempt was performed to determine the value of ApEn to assess the severity of hypoxemia in OSAHS. RESULTS Among the 1200 subjects, 822 subjects were men (72%) and mean age was 53.2 ± 15.2 years (range 24-95 years). The ApEn in each group was significantly different (P <0.001), and the ApEn synchronously increased with AHI. Furthermore, a significant difference in ApEn was found among the groups (P <0.001). In addition, ApEn had a good correlation with ODI, LO2, and T <90%. According to the ROC analysis results, the boundary value of ApEn to judge OSAHS patients with mild, moderate, and severe hypoxia was 16.72, 17.84, and 20.06, respectively. CONCLUSION ApEn synchronously increased with the AHI and had a good correlation with AHI, ODI, LO2, and T <90%. These findings suggest that ApEn may have clinical value for assessing hypoxia severity in OSAHS patients.
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Affiliation(s)
- Jie Liu
- Department of health management department, Peking Union Medical College Hospital, Beijing, 100730, China
| | - Rong Huang
- Department of Respiratory, Peking Union Medical College Hospital, Beijing, 100730, China
| | - Yi Xiao
- Department of Respiratory, Peking Union Medical College Hospital, Beijing, 100730, China
| | - Songbai Lin
- Department of health management department, Peking Union Medical College Hospital, Beijing, 100730, China.
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