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Di Credico A, Perpetuini D, Izzicupo P, Gaggi G, Mammarella N, Di Domenico A, Palumbo R, La Malva P, Cardone D, Merla A, Ghinassi B, Di Baldassarre A. Predicting Sleep Quality through Biofeedback: A Machine Learning Approach Using Heart Rate Variability and Skin Temperature. Clocks Sleep 2024; 6:322-337. [PMID: 39189190 PMCID: PMC11348184 DOI: 10.3390/clockssleep6030023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 07/17/2024] [Accepted: 07/19/2024] [Indexed: 08/28/2024] Open
Abstract
Sleep quality (SQ) is a crucial aspect of overall health. Poor sleep quality may cause cognitive impairment, mood disturbances, and an increased risk of chronic diseases. Therefore, assessing sleep quality helps identify individuals at risk and develop effective interventions. SQ has been demonstrated to affect heart rate variability (HRV) and skin temperature even during wakefulness. In this perspective, using wearables and contactless technologies to continuously monitor HR and skin temperature is highly suited for assessing objective SQ. However, studies modeling the relationship linking HRV and skin temperature metrics evaluated during wakefulness to predict SQ are lacking. This study aims to develop machine learning models based on HRV and skin temperature that estimate SQ as assessed by the Pittsburgh Sleep Quality Index (PSQI). HRV was measured with a wearable sensor, and facial skin temperature was measured by infrared thermal imaging. Classification models based on unimodal and multimodal HRV and skin temperature were developed. A Support Vector Machine applied to multimodal HRV and skin temperature delivered the best classification accuracy, 83.4%. This study can pave the way for the employment of wearable and contactless technologies to monitor SQ for ergonomic applications. The proposed method significantly advances the field by achieving a higher classification accuracy than existing state-of-the-art methods. Our multimodal approach leverages the synergistic effects of HRV and skin temperature metrics, thus providing a more comprehensive assessment of SQ. Quantitative performance indicators, such as the 83.4% classification accuracy, underscore the robustness and potential of our method in accurately predicting sleep quality using non-intrusive measurements taken during wakefulness.
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Affiliation(s)
- Andrea Di Credico
- Department of Medicine and Aging Sciences, “G. D’Annunzio” University of Chieti-Pescara, 66100 Chieti, Italy; (P.I.); (G.G.); (B.G.); (A.D.B.)
- UdA-TechLab, “G. D’Annunzio” University of Chieti-Pescara, 66100 Chieti, Italy;
| | - David Perpetuini
- Department of Engineering and Geology, “G. D’Annunzio” University of Chieti-Pescara, 65127 Pescara, Italy; (D.P.); (D.C.)
| | - Pascal Izzicupo
- Department of Medicine and Aging Sciences, “G. D’Annunzio” University of Chieti-Pescara, 66100 Chieti, Italy; (P.I.); (G.G.); (B.G.); (A.D.B.)
| | - Giulia Gaggi
- Department of Medicine and Aging Sciences, “G. D’Annunzio” University of Chieti-Pescara, 66100 Chieti, Italy; (P.I.); (G.G.); (B.G.); (A.D.B.)
- UdA-TechLab, “G. D’Annunzio” University of Chieti-Pescara, 66100 Chieti, Italy;
| | - Nicola Mammarella
- Department of Psychological, Health and Territorial Sciences, “G. D’Annunzio” University of Chieti-Pescara, 66100 Chieti, Italy; (N.M.); (A.D.D.); (R.P.); (P.L.M.)
| | - Alberto Di Domenico
- Department of Psychological, Health and Territorial Sciences, “G. D’Annunzio” University of Chieti-Pescara, 66100 Chieti, Italy; (N.M.); (A.D.D.); (R.P.); (P.L.M.)
| | - Rocco Palumbo
- Department of Psychological, Health and Territorial Sciences, “G. D’Annunzio” University of Chieti-Pescara, 66100 Chieti, Italy; (N.M.); (A.D.D.); (R.P.); (P.L.M.)
| | - Pasquale La Malva
- Department of Psychological, Health and Territorial Sciences, “G. D’Annunzio” University of Chieti-Pescara, 66100 Chieti, Italy; (N.M.); (A.D.D.); (R.P.); (P.L.M.)
| | - Daniela Cardone
- Department of Engineering and Geology, “G. D’Annunzio” University of Chieti-Pescara, 65127 Pescara, Italy; (D.P.); (D.C.)
| | - Arcangelo Merla
- UdA-TechLab, “G. D’Annunzio” University of Chieti-Pescara, 66100 Chieti, Italy;
- Department of Engineering and Geology, “G. D’Annunzio” University of Chieti-Pescara, 65127 Pescara, Italy; (D.P.); (D.C.)
| | - Barbara Ghinassi
- Department of Medicine and Aging Sciences, “G. D’Annunzio” University of Chieti-Pescara, 66100 Chieti, Italy; (P.I.); (G.G.); (B.G.); (A.D.B.)
- UdA-TechLab, “G. D’Annunzio” University of Chieti-Pescara, 66100 Chieti, Italy;
| | - Angela Di Baldassarre
- Department of Medicine and Aging Sciences, “G. D’Annunzio” University of Chieti-Pescara, 66100 Chieti, Italy; (P.I.); (G.G.); (B.G.); (A.D.B.)
- UdA-TechLab, “G. D’Annunzio” University of Chieti-Pescara, 66100 Chieti, Italy;
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Yang J, Pan Y, Wang T, Zhang X, Wen J, Luo Y. Sleep-Dependent Directional Interactions of the Central Nervous System-Cardiorespiratory Network. IEEE Trans Biomed Eng 2020; 68:639-649. [PMID: 32746063 DOI: 10.1109/tbme.2020.3009950] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE We investigated the nature of interactions between the central nervous system (CNS) and the cardiorespiratory system during sleep. METHODS Overnight polysomnography recordings were obtained from 33 healthy individuals. The relative spectral powers of five frequency bands, three ECG morphological features and respiratory rate were obtained from six EEG channels, ECG, and oronasal airflow, respectively. The synchronous feature series were interpolated to 1 Hz to retain the high time-resolution required to detect rapid physiological variations. CNS-cardiorespiratory interaction networks were built for each EEG channel and a directionality analysis was conducted using multivariate transfer entropy. Finally, the difference in interaction between Deep, Light, and REM sleep (DS, LS, and REM) was studied. RESULTS Bidirectional interactions existed in central-cardiorespiratory networks, and the dominant direction was from the cardiorespiratory system to the brain during all sleep stages. Sleep stages had evident influence on these interactions, with the strength of information transfer from heart rate and respiration rate to the brain gradually increasing with the sequence of REM, LS, and DS. Furthermore, the occipital lobe appeared to receive the most input from the cardiorespiratory system during LS. Finally, different ECG morphological features were found to be involved with various central-cardiac and cardiac-respiratory interactions. CONCLUSION These findings reveal detailed information regarding CNS-cardiorespiratory interactions during sleep and provide new insights into understanding of sleep control mechanisms. SIGNIFICANCE Our approach may facilitate the investigation of the pathological cardiorespiratory complications of sleep disorders.
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Yang J, Pan Y, Luo Y. Investigation of brain-heart network during sleep. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3343-3346. [PMID: 33018720 DOI: 10.1109/embc44109.2020.9175305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Interactions between brain and heart play an important role for sleep quality and control. However, the influence mechanism was still unclear. This study aimed to further investigate this mechanism according to build an information transfer network of brain-heart coupling. This study included 24 healthy individuals and both of them underwent overnight polysomnography. The relative spectral powers of five frequency bands and the high frequency power of heart rate variability were extracted from six electroencephalogram (EEG) channels and electrocardiography (ECG) respectively. For each EEG channel, brain-heart interaction networks were built and a directionality analysis was conducted by using multivariate transfer entropy. Results revealed the bidirectionality of information transfer between brain and heart during sleep, and the information was dominantly transfer from heart to brain. The information transfer strength between brain and heart were significantly stronger than which between frequency bands in each EEG channels. Besides, the frequency bands and EEG channels had evident influence on these interactions. This study exposed more detailed characteristics of brain-heart interaction, which will facilitate the future study about the sleep control and the diagnose of sleep related disease.
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Sleep disorders, nocturnal blood pressure, and cardiovascular risk: A translational perspective. Auton Neurosci 2019; 218:31-42. [DOI: 10.1016/j.autneu.2019.02.006] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2019] [Revised: 02/16/2019] [Accepted: 02/21/2019] [Indexed: 12/12/2022]
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Zhang H, Liang B, Li T, Zhou Y, Shang D, Du Z. Orexin A Suppresses Oxidized LDL Induced Endothelial Cell Inflammation via
MAPK p38 and NF-κB Signaling Pathway. IUBMB Life 2018; 70:961-968. [PMID: 30207631 DOI: 10.1002/iub.1890] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 04/13/2018] [Accepted: 05/02/2018] [Indexed: 12/22/2022]
Affiliation(s)
- Haiyang Zhang
- Department of Emergency; Shandong Provincial Hospital Affiliated to Shandong University; Jinan China
| | - Bin Liang
- Division of Respiration, Department of Internal Medicine; Shandong Provincial Hospital Affiliated to Shandong University; Jinan China
| | - Tao Li
- Department of Emergency; Shandong Provincial Hospital Affiliated to Shandong University; Jinan China
| | - Yi Zhou
- Department of Emergency; Shandong Provincial Hospital Affiliated to Shandong University; Jinan China
| | - Deya Shang
- Department of Emergency; Shandong Provincial Hospital Affiliated to Shandong University; Jinan China
| | - Zhongjun Du
- Department of Toxicology; Shandong Academy of Occupational Health and Occupational Medicine, Shandong Academy of Medical Sciences; Jinan China
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de Zambotti M, Trinder J, Silvani A, Colrain IM, Baker FC. Dynamic coupling between the central and autonomic nervous systems during sleep: A review. Neurosci Biobehav Rev 2018; 90:84-103. [PMID: 29608990 PMCID: PMC5993613 DOI: 10.1016/j.neubiorev.2018.03.027] [Citation(s) in RCA: 99] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 02/16/2018] [Accepted: 03/24/2018] [Indexed: 12/19/2022]
Abstract
Sleep is characterized by coordinated cortical and cardiac oscillations reflecting communication between the central (CNS) and autonomic (ANS) nervous systems. Here, we review fluctuations in ANS activity in association with CNS-defined sleep stages and cycles, and with phasic cortical events during sleep (e.g., arousals, K-complexes). Recent novel analytic methods reveal a dynamic organization of integrated physiological networks during sleep and indicate how multiple factors (e.g., sleep structure, age, sleep disorders) affect "CNS-ANS coupling". However, these data are mostly correlational and there is a lack of clarity of the underlying physiology, making it challenging to interpret causality and direction of coupling. Experimental manipulations (e.g., evoking K-complexes or arousals) provide information on the precise temporal sequence of cortical-cardiac activity, and are useful for investigating physiological pathways underlying CNS-ANS coupling. With the emergence of new analytical approaches and a renewed interest in ANS and CNS communication during sleep, future work may reveal novel insights into sleep and cardiovascular interactions during health and disease, in which coupling could be adversely impacted.
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Affiliation(s)
| | - John Trinder
- Melbourne School of Psychological Sciences, University of Melbourne, Parkville, Victoria, Australia.
| | - Alessandro Silvani
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Italy.
| | - Ian M Colrain
- Center for Health Sciences, SRI International, Menlo Park, CA, USA; Melbourne School of Psychological Sciences, University of Melbourne, Parkville, Victoria, Australia.
| | - Fiona C Baker
- Center for Health Sciences, SRI International, Menlo Park, CA, USA; Brain Function Research Group, School of Physiology, University of the Witwatersrand, Johannesburg, South Africa.
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