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Garcia-Molina G, Winger T, Makaram N, Rao MR, Chernega P, Shcherbakov Y, McGhee L, Chellamuthu V, Veneros E, Mills R, Aloia M, Reid KJ. Unobtrusive Skin Temperature Estimation on a Smart Bed. SENSORS (BASEL, SWITZERLAND) 2024; 24:4882. [PMID: 39123929 PMCID: PMC11315013 DOI: 10.3390/s24154882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 07/09/2024] [Accepted: 07/23/2024] [Indexed: 08/12/2024]
Abstract
The transition from wakefulness to sleep occurs when the core body temperature decreases. The latter is facilitated by an increase in the cutaneous blood flow, which dissipates internal heat into the micro-environment surrounding the sleeper's body. The rise in cutaneous blood flow near sleep onset causes the distal (hands and feet) and proximal (abdomen) temperatures to increase by about 1 °C and 0.5 °C, respectively. Characterizing the dynamics of skin temperature changes throughout sleep phases and understanding its relationship with sleep quality requires a means to unobtrusively and longitudinally estimate the skin temperature. Leveraging the data from a temperature sensor strip (TSS) with five individual temperature sensors embedded near the surface of a smart bed's mattress, we have developed an algorithm to estimate the distal skin temperature with a minute-long temporal resolution. The data from 18 participants who recorded TSS and ground-truth temperature data from sleep during 14 nights at home and 2 nights in a lab were used to develop an algorithm that uses a two-stage regression model (gradient boosted tree followed by a random forest) to estimate the distal skin temperature. A five-fold cross-validation procedure was applied to train and validate the model such that the data from a participant could only be either in the training or validation set but not in both. The algorithm verification was performed with the in-lab data. The algorithm presented in this research can estimate the distal skin temperature at a minute-level resolution, with accuracy characterized by the mean limits of agreement [-0.79 to +0.79 °C] and mean coefficient of determination R2=0.87. This method may enable the unobtrusive, longitudinal and ecologically valid collection of distal skin temperature values during sleep. Therelatively small sample size motivates the need for further validation efforts.
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Affiliation(s)
- Gary Garcia-Molina
- Sleep Number Labs, San Jose, CA 95113, USA; (T.W.); (N.M.); (M.R.R.); (V.C.); (E.V.)
- Department of Psychiatry, University of Wisconsin, Madison, WI 53719, USA
| | - Trevor Winger
- Sleep Number Labs, San Jose, CA 95113, USA; (T.W.); (N.M.); (M.R.R.); (V.C.); (E.V.)
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Nikhil Makaram
- Sleep Number Labs, San Jose, CA 95113, USA; (T.W.); (N.M.); (M.R.R.); (V.C.); (E.V.)
| | - Megha Rajam Rao
- Sleep Number Labs, San Jose, CA 95113, USA; (T.W.); (N.M.); (M.R.R.); (V.C.); (E.V.)
| | | | | | - Leah McGhee
- Sleep Number Corporation, Minneapolis, MN 55404, USA; (L.M.); (R.M.); (M.A.)
| | - Vidhya Chellamuthu
- Sleep Number Labs, San Jose, CA 95113, USA; (T.W.); (N.M.); (M.R.R.); (V.C.); (E.V.)
| | - Erwin Veneros
- Sleep Number Labs, San Jose, CA 95113, USA; (T.W.); (N.M.); (M.R.R.); (V.C.); (E.V.)
| | - Raj Mills
- Sleep Number Corporation, Minneapolis, MN 55404, USA; (L.M.); (R.M.); (M.A.)
| | - Mark Aloia
- Sleep Number Corporation, Minneapolis, MN 55404, USA; (L.M.); (R.M.); (M.A.)
- Department of Medicine, National Jewish Health, Denver, CO 80206, USA
| | - Kathryn J. Reid
- Center for Circadian and Sleep Medicine, Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
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Winger T, Chellamuthu V, Guzenko D, Aloia M, Barr S, DeFranco S, Gorski B, Mushtaq F, Garcia-Molina G. Fine tuned personalized machine learning models to detect insomnia risk based on data from a smart bed platform. Front Neurol 2024; 15:1303978. [PMID: 38419714 PMCID: PMC10899690 DOI: 10.3389/fneur.2024.1303978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 01/24/2024] [Indexed: 03/02/2024] Open
Abstract
Introduction Insomnia causes serious adverse health effects and is estimated to affect 10-30% of the worldwide population. This study leverages personalized fine-tuned machine learning algorithms to detect insomnia risk based on questionnaire and longitudinal objective sleep data collected by a smart bed platform. Methods Users of the Sleep Number smart bed were invited to participate in an IRB approved study which required them to respond to four questionnaires (which included the Insomnia Severity Index; ISI) administered 6 weeks apart from each other in the period from November 2021 to March 2022. For 1,489 participants who completed at least 3 questionnaires, objective data (which includes sleep/wake and cardio-respiratory metrics) collected by the platform were queried for analysis. An incremental, passive-aggressive machine learning model was used to detect insomnia risk which was defined by the ISI exceeding a given threshold. Three ISI thresholds (8, 10, and 15) were considered. The incremental model is advantageous because it allows personalized fine-tuning by adding individual training data to a generic model. Results The generic model, without personalizing, resulted in an area under the receiving-operating curve (AUC) of about 0.5 for each ISI threshold. The personalized fine-tuning with the data of just five sleep sessions from the individual for whom the model is being personalized resulted in AUCs exceeding 0.8 for all ISI thresholds. Interestingly, no further AUC enhancements resulted by adding personalized data exceeding ten sessions. Discussion These are encouraging results motivating further investigation into the application of personalized fine tuning machine learning to detect insomnia risk based on longitudinal sleep data and the extension of this paradigm to sleep medicine.
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Affiliation(s)
- Trevor Winger
- Sleep Number Labs, Sleep Number, San Jose, CA, United States
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, United States
| | | | | | - Mark Aloia
- Sleep Number Corporation, Minneapolis, MN, United States
- National Jewish Health, Denver, CO, United States
| | - Shawn Barr
- Sleep Number Labs, Sleep Number, San Jose, CA, United States
| | - Susan DeFranco
- Sleep Number Corporation, Minneapolis, MN, United States
| | - Brandon Gorski
- Sleep Number Corporation, Minneapolis, MN, United States
| | - Faisal Mushtaq
- Sleep Number Labs, Sleep Number, San Jose, CA, United States
| | - Gary Garcia-Molina
- Sleep Number Labs, Sleep Number, San Jose, CA, United States
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
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Garcia-Molina G, Chellamuthu V, Le B, Aloia M, Wu M, Mills R. Observational study to understand the effect of timing and regularity on sleep metrics and cardiorespiratory parameters using data from a smart bed. Chronobiol Int 2024; 41:213-225. [PMID: 38153128 DOI: 10.1080/07420528.2023.2298267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 12/17/2023] [Indexed: 12/29/2023]
Abstract
Sleep regularity and chronotype can affect health, performance, and overall well-being. This observational study examines how sleep regularity and chronotype affect sleep quality and cardiorespiratory metrics. Data was collected from 1 January 2019 through 30 December 2019 from over 330 000 Sleep Number smart bed users across the United States who opted into this at-home study. A pressure signal from the smart bed reflected bed presence, movements, heart rate (HR), and breathing rate (BR). Participants (mean age: 55.69 years [SD: 14.0]; 51.2% female) were categorized by chronotype (16.8% early; 62.2% intermediate, 20.9% late) and regularity of sleep timing. Participants who were regular sleepers (66.1%) experienced higher percent restful sleep and lower mean HR and BR compared to the 4.8% categorized as irregular sleepers. Regular early-chronotype participants displayed better sleep and cardiorespiratory parameters compared to those with regular late-chronotypes. Significant variations were noted in sleep duration (Cohen's d = 1.54 and 0.88, respectively) and restful sleep (Cohen's d = 1.46 and 0.82, respectively) between early and late chronotypes, particularly within regular and irregular sleep patterns. This study highlights how sleep regularity and chronotype influence sleep quality and cardiorespiratory metrics. Irrespective of chronotype, sleep regularity demonstrated a substantial effect. Further research is necessary to confirm these findings.
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Affiliation(s)
- Gary Garcia-Molina
- Sleep Number Labs, San Jose, California, USA
- Center for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | | | - Brandon Le
- Sleep Number Labs, San Jose, California, USA
| | - Mark Aloia
- Department of Medicine, National Jewish Health, Denver, Colorado, USA
- Sleep Number Corporation, Minneapolis, Minnesota, USA
| | - Michael Wu
- Sleep Number Labs, San Jose, California, USA
| | - Rajasi Mills
- Sleep Number Corporation, Minneapolis, Minnesota, USA
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Garcia-Molina G. Feasibility of Unobtrusively Estimating Blood Pressure Using Load Cells under the Legs of a Bed. SENSORS (BASEL, SWITZERLAND) 2023; 24:96. [PMID: 38202958 PMCID: PMC10780971 DOI: 10.3390/s24010096] [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: 11/03/2023] [Revised: 12/08/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024]
Abstract
The ability to monitor blood pressure unobtrusively and continuously, even during sleep, may promote the prevention of cardiovascular diseases, enable the early detection of cardiovascular risk, and facilitate the timely administration of treatment. Publicly available data from forty participants containing synchronously recorded signals from four force sensors (load cells located under each leg of a bed) and continuous blood pressure waveforms were leveraged in this research. The focus of this study was on using a deep neural network with load-cell data as input composed of three recurrent layers to reconstruct blood pressure (BP) waveforms. Systolic (SBP) and diastolic (DBP) blood pressure values were estimated from the reconstructed BP waveform. The dataset was partitioned into training, validation, and testing sets, such that the data from a given participant were only used in a single set. The BP waveform reconstruction performance resulted in an R2 of 0.61 and a mean absolute error < 0.1 mmHg. The estimation of the mean SBP and DBP values was characterized by Bland-Altman-derived limits of agreement in intervals of [-11.99 to 15.52 mmHg] and [-7.95 to +3.46 mmHg], respectively. These results may enable the detection of abnormally large or small variations in blood pressure, which indicate cardiovascular health degradation. The apparent contrast between the small reconstruction error and the limit-of-agreement width owes to the fact that reconstruction errors manifest more prominently at the maxima and minima, which are relevant for SBP and DBP estimation. While the focus here was on SBD and DBP estimation, reconstructing the entire BP waveform enables the calculation of additional hemodynamic parameters.
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Affiliation(s)
- Gary Garcia-Molina
- Sleep Number Labs, San Jose, CA 95113, USA; or
- Center for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
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Nguyen A, Pogoncheff G, Dong BX, Bui N, Truong H, Pham N, Nguyen L, Nguyen-Huu H, Bui-Diem K, Vu-Tran-Thien Q, Duong-Quy S, Ha S, Vu T. A comprehensive study on the efficacy of a wearable sleep aid device featuring closed-loop real-time acoustic stimulation. Sci Rep 2023; 13:17515. [PMID: 37845236 PMCID: PMC10579321 DOI: 10.1038/s41598-023-43975-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 09/30/2023] [Indexed: 10/18/2023] Open
Abstract
Difficulty falling asleep is one of the typical insomnia symptoms. However, intervention therapies available nowadays, ranging from pharmaceutical to hi-tech tailored solutions, remain ineffective due to their lack of precise real-time sleep tracking, in-time feedback on the therapies, and an ability to keep people asleep during the night. This paper aims to enhance the efficacy of such an intervention by proposing a novel sleep aid system that can sense multiple physiological signals continuously and simultaneously control auditory stimulation to evoke appropriate brain responses for fast sleep promotion. The system, a lightweight, comfortable, and user-friendly headband, employs a comprehensive set of algorithms and dedicated own-designed audio stimuli. Compared to the gold-standard device in 883 sleep studies on 377 subjects, the proposed system achieves (1) a strong correlation (0.89 ± 0.03) between the physiological signals acquired by ours and those from the gold-standard PSG, (2) an 87.8% agreement on automatic sleep scoring with the consensus scored by sleep technicians, and (3) a successful non-pharmacological real-time stimulation to shorten the duration of sleep falling by 24.1 min. Conclusively, our solution exceeds existing ones in promoting fast falling asleep, tracking sleep state accurately, and achieving high social acceptance through a reliable large-scale evaluation.
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Affiliation(s)
- Anh Nguyen
- Department of Computer Science, University of Montana, Missoula, MT, 59812, USA.
| | | | | | - Nam Bui
- Department of Electrical Engineering, University of Colorado Denver, Denver, CO, 80204, USA
| | - Hoang Truong
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, 80309, USA
| | - Nhat Pham
- School of Computer Science and Informatics, Cardiff University, Cardiff, CF24 4AG, UK
| | | | - Hoang Nguyen-Huu
- University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Khue Bui-Diem
- University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Quan Vu-Tran-Thien
- University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Sy Duong-Quy
- Lam Dong Medical College, Da Lat City, Lam Dong Province, Vietnam
- Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Vietnam
- Hershey Medical Center, Penn State College of Medicine, Hershey, PA, 17033, USA
| | - Sangtae Ha
- Earable Inc., Boulder, CO, 80309, USA
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, 80309, USA
| | - Tam Vu
- Earable Inc., Boulder, CO, 80309, USA
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, 80309, USA
- Department of Computer Science, University of Oxford, Oxford, OX1 3QD, UK
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Ravindran KKG, della Monica C, Atzori G, Lambert D, Hassanin H, Revell V, Dijk DJ. Contactless and longitudinal monitoring of nocturnal sleep and daytime naps in older men and women: a digital health technology evaluation study. Sleep 2023; 46:zsad194. [PMID: 37471049 PMCID: PMC10566241 DOI: 10.1093/sleep/zsad194] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/16/2023] [Indexed: 07/21/2023] Open
Abstract
STUDY OBJECTIVES To compare the 24-hour sleep assessment capabilities of two contactless sleep technologies (CSTs) to actigraphy in community-dwelling older adults. METHODS We collected 7-14 days of data at home from 35 older adults (age: 65-83), some with medical conditions, using Withings Sleep Analyser (WSA, n = 29), Emfit QS (Emfit, n = 17), a standard actigraphy device (Actiwatch Spectrum [AWS, n = 34]), and a sleep diary (n = 35). We compared nocturnal and daytime sleep measures estimated by the CSTs and actigraphy without sleep diary information (AWS-A) against sleep-diary-assisted actigraphy (AWS|SD). RESULTS Compared to sleep diary, both CSTs accurately determined the timing of nocturnal sleep (intraclass correlation [ICC]: going to bed, getting out of bed, time in bed >0.75), whereas the accuracy of AWS-A was much lower. Compared to AWS|SD, the CSTs overestimated nocturnal total sleep time (WSA: +92.71 ± 81.16 minutes; Emfit: +101.47 ± 75.95 minutes) as did AWS-A (+46.95 ± 67.26 minutes). The CSTs overestimated sleep efficiency (WSA: +9.19% ± 14.26%; Emfit: +9.41% ± 11.05%), whereas AWS-A estimate (-2.38% ± 10.06%) was accurate. About 65% (n = 23) of participants reported daytime naps either in bed or elsewhere. About 90% in-bed nap periods were accurately determined by WSA while Emfit was less accurate. All three devices estimated 24-hour sleep duration with an error of ≈10% compared to the sleep diary. CONCLUSIONS CSTs accurately capture the timing of in-bed nocturnal sleep periods without the need for sleep diary information. However, improvements are needed in assessing parameters such as total sleep time, sleep efficiency, and naps before these CSTs can be fully utilized in field settings.
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Affiliation(s)
- Kiran K G Ravindran
- Surrey Sleep Research Centre, University of Surrey, Guildford, UK
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, UK, and University of Surrey, Guildford, UK
| | - Ciro della Monica
- Surrey Sleep Research Centre, University of Surrey, Guildford, UK
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, UK, and University of Surrey, Guildford, UK
| | - Giuseppe Atzori
- Surrey Sleep Research Centre, University of Surrey, Guildford, UK
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, UK, and University of Surrey, Guildford, UK
| | - Damion Lambert
- Surrey Sleep Research Centre, University of Surrey, Guildford, UK
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, UK, and University of Surrey, Guildford, UK
| | - Hana Hassanin
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, UK, and University of Surrey, Guildford, UK
- Surrey Clinical Research Facility, Faculty of Health and Medical Sciences, School of Biosciences and Medicine, University of Surrey, Guildford, UK
| | - Victoria Revell
- Surrey Sleep Research Centre, University of Surrey, Guildford, UK
| | - Derk-Jan Dijk
- Surrey Sleep Research Centre, University of Surrey, Guildford, UK
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, UK, and University of Surrey, Guildford, UK
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7
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Yoon H, Choi SH. Technologies for sleep monitoring at home: wearables and nearables. Biomed Eng Lett 2023; 13:313-327. [PMID: 37519880 PMCID: PMC10382403 DOI: 10.1007/s13534-023-00305-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/17/2023] [Accepted: 07/03/2023] [Indexed: 08/01/2023] Open
Abstract
Sleep is an essential part of our lives and daily sleep monitoring is crucial for maintaining good health and well-being. Traditionally, the gold standard method for sleep monitoring is polysomnography using various sensors attached to the body; however, it is limited with regards to long-term sleep monitoring in a home environment. Recent advancements in wearable and nearable technology have made it possible to monitor sleep at home. In this review paper, the technologies that are currently available for sleep stages and sleep disorder monitoring at home are reviewed using wearable and nearable devices. Wearables are devices that are worn on the body, while nearables are placed near the body. These devices can accurately monitor sleep stages and sleep disorder in a home environment. In this study, the benefits and limitations of each technology are discussed, along with their potential to improve sleep quality.
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Affiliation(s)
- Heenam Yoon
- Department of Human-Centered Artificial Intelligence, Sangmyung University, Seoul, 03016 Korea
| | - Sang Ho Choi
- School of Computer and Information Engineering, Kwangwoon University, Seoul, 01897 Korea
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8
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Yin J, Xu J, Ren TL. Recent Progress in Long-Term Sleep Monitoring Technology. BIOSENSORS 2023; 13:395. [PMID: 36979607 PMCID: PMC10046225 DOI: 10.3390/bios13030395] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/11/2023] [Accepted: 03/14/2023] [Indexed: 06/18/2023]
Abstract
Sleep is an essential physiological activity, accounting for about one-third of our lives, which significantly impacts our memory, mood, health, and children's growth. Especially after the COVID-19 epidemic, sleep health issues have attracted more attention. In recent years, with the development of wearable electronic devices, there have been more and more studies, products, or solutions related to sleep monitoring. Many mature technologies, such as polysomnography, have been applied to clinical practice. However, it is urgent to develop wearable or non-contacting electronic devices suitable for household continuous sleep monitoring. This paper first introduces the basic knowledge of sleep and the significance of sleep monitoring. Then, according to the types of physiological signals monitored, this paper describes the research progress of bioelectrical signals, biomechanical signals, and biochemical signals used for sleep monitoring. However, it is not ideal to monitor the sleep quality for the whole night based on only one signal. Therefore, this paper reviews the research on multi-signal monitoring and introduces systematic sleep monitoring schemes. Finally, a conclusion and discussion of sleep monitoring are presented to propose potential future directions and prospects for sleep monitoring.
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Affiliation(s)
- Jiaju Yin
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Jiandong Xu
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Tian-Ling Ren
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
- Center for Flexible Electronics Technology, Tsinghua University, Beijing 100084, China
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Benedetti D, Menghini L, Vallat R, Mallett R, Kiss O, Faraguna U, Baker FC, de Zambotti M. Call to action: an open-source pipeline for standardized performance evaluation of sleep-tracking technology. Sleep 2023; 46:6972313. [PMID: 36611112 DOI: 10.1093/sleep/zsac304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Affiliation(s)
- Davide Benedetti
- Center for Health Sciences, SRI International, Menlo Park, CA, USA.,Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy
| | - Luca Menghini
- Department of General Psychology, University of Padova, Padova, Italy
| | - Raphael Vallat
- Center for Human Sleep Science, Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Remington Mallett
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Orsolya Kiss
- Center for Health Sciences, SRI International, Menlo Park, CA, USA
| | - Ugo Faraguna
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy.,Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Pisa, Italy
| | - Fiona C Baker
- Center for Health Sciences, SRI International, Menlo Park, CA, USA
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Balali P, Rabineau J, Hossein A, Tordeur C, Debeir O, van de Borne P. Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22239565. [PMID: 36502267 PMCID: PMC9737480 DOI: 10.3390/s22239565] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 11/11/2022] [Accepted: 11/28/2022] [Indexed: 05/29/2023]
Abstract
Ballistocardiography (BCG) and seismocardiography (SCG) are non-invasive techniques used to record the micromovements induced by cardiovascular activity at the body's center of mass and on the chest, respectively. Since their inception, their potential for evaluating cardiovascular health has been studied. However, both BCG and SCG are impacted by respiration, leading to a periodic modulation of these signals. As a result, data processing algorithms have been developed to exclude the respiratory signals, or recording protocols have been designed to limit the respiratory bias. Reviewing the present status of the literature reveals an increasing interest in applying these techniques to extract respiratory information, as well as cardiac information. The possibility of simultaneous monitoring of respiratory and cardiovascular signals via BCG or SCG enables the monitoring of vital signs during activities that require considerable mental concentration, in extreme environments, or during sleep, where data acquisition must occur without introducing recording bias due to irritating monitoring equipment. This work aims to provide a theoretical and practical overview of cardiopulmonary interaction based on BCG and SCG signals. It covers the recent improvements in extracting respiratory signals, computing markers of the cardiorespiratory interaction with practical applications, and investigating sleep breathing disorders, as well as a comparison of different sensors used for these applications. According to the results of this review, recent studies have mainly concentrated on a few domains, especially sleep studies and heart rate variability computation. Even in those instances, the study population is not always large or diversified. Furthermore, BCG and SCG are prone to movement artifacts and are relatively subject dependent. However, the growing tendency toward artificial intelligence may help achieve a more accurate and efficient diagnosis. These encouraging results bring hope that, in the near future, such compact, lightweight BCG and SCG devices will offer a good proxy for the gold standard methods for assessing cardiorespiratory function, with the added benefit of being able to perform measurements in real-world situations, outside of the clinic, and thus decrease costs and time.
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Affiliation(s)
- Paniz Balali
- Laboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Laboratory of Image Synthesis and Analysis, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Jeremy Rabineau
- Laboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Amin Hossein
- Laboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Cyril Tordeur
- Laboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Olivier Debeir
- Laboratory of Image Synthesis and Analysis, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Philippe van de Borne
- Laboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Department of Cardiology, Erasme Hospital, Université Libre de Bruxelles, 1050 Brussels, Belgium
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Toften S, Kjellstadli JT, Thu OKF, Ellingsen OJ. Noncontact Longitudinal Respiratory Rate Measurements in Healthy Adults Using Radar-Based Sleep Monitor (Somnofy): Validation Study. JMIR BIOMEDICAL ENGINEERING 2022; 7:e36618. [PMID: 38875674 PMCID: PMC11041471 DOI: 10.2196/36618] [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: 01/20/2022] [Revised: 06/21/2022] [Accepted: 07/23/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Respiratory rate (RR) is arguably the most important vital sign to detect clinical deterioration. Change in RR can also, for example, be associated with the onset of different diseases, opioid overdoses, intense workouts, or mood. However, unlike for most other vital parameters, an easy and accurate measuring method is lacking. OBJECTIVE This study aims to validate the radar-based sleep monitor, Somnofy, for measuring RRs and investigate whether events affecting RR can be detected from personalized baselines calculated from nightly averages. METHODS First, RRs from Somnofy for 37 healthy adults during full nights of sleep were extensively validated against respiratory inductance plethysmography. Then, the night-to-night consistency of a proposed filtered average RR was analyzed for 6 healthy participants in a pilot study in which they used Somnofy at home for 3 months. RESULTS Somnofy measured RR 84% of the time, with mean absolute error of 0.18 (SD 0.05) respirations per minute, and Bland-Altman 95% limits of agreement adjusted for repeated measurements ranged from -0.99 to 0.85. The accuracy and coverage were substantially higher in deep and light sleep than in rapid eye movement sleep and wake. The results were independent of age, sex, and BMI, but dependent on supine sleeping position for some radar orientations. For nightly filtered averages, the 95% limits of agreement ranged from -0.07 to -0.04 respirations per minute. In the longitudinal part of the study, the nightly average was consistent from night to night, and all substantial deviations coincided with self-reported illnesses. CONCLUSIONS RRs from Somnofy were more accurate than those from any other alternative method suitable for longitudinal measurements. Moreover, the nightly averages were consistent from night to night. Thus, several factors affecting RR should be detectable as anomalies from personalized baselines, enabling a range of applications. More studies are necessary to investigate its potential in children and older adults or in a clinical setting.
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Affiliation(s)
- Ståle Toften
- Department of Data Science and Research, VitalThings AS, Tønsberg, Norway
| | | | - Ole Kristian Forstrønen Thu
- VitalThings AS, Tønsberg, Norway
- Department of Anesthesiology and Intensive Care Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
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