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Bacciu D, Morelli D, Pandelea V. Modeling Mood Polarity and Declaration Occurrence by Neural Temporal Point Processes. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:1800-1807. [PMID: 35560083 DOI: 10.1109/tnnls.2022.3172871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Neural point processes provide the flexibility needed to deal with time series of heterogeneous nature within the robust framework of point processes. This aspect is of particular relevance when dealing with real-world data, mixing generative processes characterized by radically different distributions and sampling. This brief discusses a neural point process approach for health and behavioral data, comprising both sparse events coming from user subjective declarations as well as fast-flowing time series from wearable sensors. We propose and empirically validate different neural architectures and we assess the effect of including input sources of different nature. The empirical analysis is built on the top of a challenging original dataset, never published before, and collected as part of a real-world experiment in an uncontrolled setting. Results show the potential of neural point processes both in terms of predicting the next event type as well as in predicting the time to next user interaction.
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Yang Z, Li Z, He X, Yao Z, Xie X, Zhang S, Shen Y, Li S, Qiao S, Hui Z, Gao C, Chen J. The impact of heart rate circadian rhythm on in-hospital mortality in stroke and critically ill patients: insights from the eICU Collaborative Research Database. Heart Rhythm 2022; 19:1325-1333. [PMID: 35367661 DOI: 10.1016/j.hrthm.2022.03.1230] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/17/2022] [Accepted: 03/25/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND Data showing the impact of dysregulated heart rate circadian rhythm in stroke and critically ill patients are scarce. OBJECTIVE The purpose of this study was to investigate whether the circadian rhythm of heart rate was an independent risk factor for in-hospital mortality in stroke and critically ill patients. METHODS Study patients from the recorded eICU Database were included in the current analyses. Three variables, Mesor, Amplitude, and Peak time were used to evaluate the heart rate circadian rhythm. The incremental value of circadian rhythm variables in addition to Acute Physiology and Chronic Health Evaluation (APACHE) IV score to predict in-hospital mortality was also explored. RESULTS A total of 6,201 Patients whose heart rate have cosinor rhythmicity. After adjustments, Mesor per 10 beats per min (bpm) increase was associated with a 1.18-fold (95%CI: 1.12, 1.25, P<0.001) and Amplitude per 5 bpm was associated with a 1.17-fold (95%CI: 1.07, 1.27, P<0.001) increase in the risk of in-hospital mortality, respectively. The risk of in-hospital mortality was highest in patients who had Peak time reached between 12:00-18:00 (OR: 1.35, 95%CI: 1.06, 1.72, P=0.015). Compared with APACHE IV score only (c-index=0.757), combining APACHE IV score and circadian rhythm variables of heart rate (c-index=0.766) was associated with increased discriminative ability (P=0.003). CONCLUSION Circadian rhythm of heart rate is an independent risk factor of the in-hospital mortality in stroke and critically ill patients. Including circadian rhythm variables regarding heart rate might increase the discriminative ability of the risk score to predict the prognosis of patients.
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Affiliation(s)
- Zhengning Yang
- Shaanxi University of Chinese Medicine, 712000, Xian yang, China
| | - Zhe Li
- Department of First Clinical Medicine, Affiliated Hospital of Shaanxi University of Chinese Medicine, 712000, Xian Yang, China
| | - Xu He
- Shaanxi University of Chinese Medicine, 712000, Xian yang, China
| | - Zhen Yao
- Shaanxi University of Chinese Medicine, 712000, Xian yang, China
| | - Xiaoxia Xie
- Shaanxi University of Chinese Medicine, 712000, Xian yang, China
| | - Sha Zhang
- Department of Basic Medicine, Shaanxi University of Chinese Medicine, 712000, Xian Yang, China
| | - Yan Shen
- Department of Encephalopathy, Shaanxi Provincial Hospital of Chinese Medicine, Xi Huamen, Xi'an 710000, Shaanxi, China
| | - Shaowei Li
- Department of Encephalopathy, Shaanxi Provincial Hospital of Chinese Medicine, Xi Huamen, Xi'an 710000, Shaanxi, China
| | - Shuzhen Qiao
- Department of Encephalopathy, Shaanxi Provincial Hospital of Chinese Medicine, Xi Huamen, Xi'an 710000, Shaanxi, China
| | - Zhenliang Hui
- Department of Encephalopathy, Shaanxi Provincial Hospital of Chinese Medicine, Xi Huamen, Xi'an 710000, Shaanxi, China
| | - Chao Gao
- Department of Cardiology, Xijing hospital, Xi'an, China; Department of Cardiology, Radboud University, Nijmegen, The Netherlands.
| | - Jun Chen
- Department of Encephalopathy, Shaanxi Provincial Hospital of Chinese Medicine, Xi Huamen, Xi'an 710000, Shaanxi, China.
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Charlton PH, Kyriacou PA, Mant J, Marozas V, Chowienczyk P, Alastruey J. Wearable Photoplethysmography for Cardiovascular Monitoring. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2022; 110:355-381. [PMID: 35356509 PMCID: PMC7612541 DOI: 10.1109/jproc.2022.3149785] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 01/06/2022] [Accepted: 01/27/2022] [Indexed: 05/29/2023]
Abstract
Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.
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Affiliation(s)
- Peter H. Charlton
- Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging SciencesKing’s College London, King’s Health PartnersLondonSE1 7EUU.K.
- Research Centre for Biomedical Engineering, CityUniversity of LondonLondonEC1V 0HBU.K.
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeCB1 8RNU.K.
| | - Panicos A. Kyriacou
- Research Centre for Biomedical Engineering, CityUniversity of LondonLondonEC1V 0HBU.K.
| | - Jonathan Mant
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeCB1 8RNU.K.
| | - Vaidotas Marozas
- Department of Electronics Engineering and the Biomedical Engineering Institute, Kaunas University of Technology44249KaunasLithuania
| | - Phil Chowienczyk
- Department of Clinical PharmacologyKing’s College LondonLondonSE1 7EHU.K.
| | - Jordi Alastruey
- Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging SciencesKing’s College London, King’s Health PartnersLondonSE1 7EUU.K.
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Lutin E, Schiweck C, Cornelis J, De Raedt W, Reif A, Vrieze E, Claes S, Van Hoof C. The cumulative effect of chronic stress and depressive symptoms affects heart rate in a working population. Front Psychiatry 2022; 13:1022298. [PMID: 36311512 PMCID: PMC9606467 DOI: 10.3389/fpsyt.2022.1022298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 09/26/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Chronic stress and depressive symptoms have both been linked to increased heart rate (HR) and reduced HR variability. However, up to date, it is not clear whether chronic stress, the mechanisms intrinsic to depression or a combination of both cause these alterations. Subclinical cases may help to answer these questions. In a healthy working population, we aimed to investigate whether the effect of chronic stress on HR circadian rhythm depends on the presence of depressive symptoms and whether chronic stress and depressive symptoms have differential effects on HR reactivity to an acute stressor. METHODS 1,002 individuals of the SWEET study completed baseline questionnaires, including psychological information, and 5 days of electrocardiogram (ECG) measurements. Complete datasets were available for 516 individuals. In addition, a subset (n = 194) of these participants completed a stress task on a mobile device. Participants were grouped according to their scores for the Depression Anxiety Stress Scale (DASS) and Perceived Stress Scale (PSS). We explored the resulting groups for differences in HR circadian rhythm and stress reactivity using linear mixed effect models. Additionally, we explored the effect of stress and depressive symptoms on night-time HR variability [root mean square of successive differences (RMSSD)]. RESULTS High and extreme stress alone did not alter HR circadian rhythm, apart from a limited increase in basal HR. Yet, if depressive symptoms were present, extreme chronic stress levels did lead to a blunted circadian rhythm and a lower basal HR. Furthermore, blunted stress reactivity was associated with depressive symptoms, but not chronic stress. Night-time RMSSD data was not influenced by chronic stress, depressive symptoms or their interaction. CONCLUSION The combination of stress and depressive symptoms, but not chronic stress by itself leads to a blunted HR circadian rhythm. Furthermore, blunted HR reactivity is associated with depressive symptoms and not chronic stress.
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Affiliation(s)
- Erika Lutin
- Electrical Engineering-ESAT, KU Leuven, Leuven, Belgium.,Imec, Leuven, Belgium
| | - Carmen Schiweck
- Department of Psychiatry, Psychosomatics and Psychotherapy, Goethe University, Frankfurt am Main, Germany
| | | | | | - Andreas Reif
- Department of Psychiatry, Psychosomatics and Psychotherapy, Goethe University, Frankfurt am Main, Germany
| | - Elske Vrieze
- Department of Neurosciences, Psychiatry Research Group, KU Leuven, Leuven, Belgium.,University Psychiatric Centre KU Leuven, Leuven, Belgium
| | - Stephan Claes
- Department of Neurosciences, Psychiatry Research Group, KU Leuven, Leuven, Belgium.,University Psychiatric Centre KU Leuven, Leuven, Belgium
| | - Chris Van Hoof
- Electrical Engineering-ESAT, KU Leuven, Leuven, Belgium.,Imec, Leuven, Belgium.,OnePlanet Research Center, Wageningen, Netherlands
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Rossi A, Pappalardo L, Cintia P. A Narrative Review for a Machine Learning Application in Sports: An Example Based on Injury Forecasting in Soccer. Sports (Basel) 2021; 10:sports10010005. [PMID: 35050970 PMCID: PMC8822889 DOI: 10.3390/sports10010005] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/09/2021] [Accepted: 12/22/2021] [Indexed: 11/28/2022] Open
Abstract
In the last decade, the number of studies about machine learning algorithms applied to sports, e.g., injury forecasting and athlete performance prediction, have rapidly increased. Due to the number of works and experiments already present in the state-of-the-art regarding machine-learning techniques in sport science, the aim of this narrative review is to provide a guideline describing a correct approach for training, validating, and testing machine learning models to predict events in sports science. The main contribution of this narrative review is to highlight any possible strengths and limitations during all the stages of model development, i.e., training, validation, testing, and interpretation, in order to limit possible errors that could induce misleading results. In particular, this paper shows an example about injury forecaster that provides a description of all the features that could be used to predict injuries, all the possible pre-processing approaches for time series analysis, how to correctly split the dataset to train and test the predictive models, and the importance to explain the decision-making approach of the white and black box models.
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Affiliation(s)
- Alessio Rossi
- Department of Computer Science, University of Pisa, 56127 Pisa, Italy;
- Correspondence:
| | - Luca Pappalardo
- Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy;
| | - Paolo Cintia
- Department of Computer Science, University of Pisa, 56127 Pisa, Italy;
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Baka T, Simko F. Monitoring Non-dipping Heart Rate by Consumer-Grade Wrist-Worn Devices: An Avenue for Cardiovascular Risk Assessment in Hypertension. Front Cardiovasc Med 2021; 8:711417. [PMID: 34368261 PMCID: PMC8342801 DOI: 10.3389/fcvm.2021.711417] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 06/30/2021] [Indexed: 12/23/2022] Open
Affiliation(s)
- Tomas Baka
- Institute of Pathophysiology, Faculty of Medicine, Comenius University, Bratislava, Slovakia
| | - Fedor Simko
- Institute of Pathophysiology, Faculty of Medicine, Comenius University, Bratislava, Slovakia.,3rd Department of Internal Medicine, Faculty of Medicine, Comenius University, Bratislava, Slovakia.,Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia
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Morelli D, Rossi A, Bartoloni L, Cairo M, Clifton DA. SDNN24 Estimation from Semi-Continuous HR Measures. SENSORS (BASEL, SWITZERLAND) 2021; 21:1463. [PMID: 33672456 PMCID: PMC7923410 DOI: 10.3390/s21041463] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 02/12/2021] [Accepted: 02/15/2021] [Indexed: 12/31/2022]
Abstract
The standard deviation of the interval between QRS complexes recorded over 24 h (SDNN24) is an important metric of cardiovascular health. Wrist-worn fitness wearable devices record heart beats 24/7 having a complete overview of users' heart status. Due to motion artefacts affecting QRS complexes recording, and the different nature of the heart rate sensor used on wearable devices compared to ECG, traditionally used to compute SDNN24, the estimation of this important Heart Rate Variability (HRV) metric has never been performed from wearable data. We propose an innovative approach to estimate SDNN24 only exploiting the Heart Rate (HR) that is normally available on wearable fitness trackers and less affected by data noise. The standard deviation of inter-beats intervals (SDNN24) and the standard deviation of the Average inter-beats intervals (ANN) derived from the HR (obtained in a time window with defined duration, i.e., 1, 5, 10, 30 and 60 min), i.e., ANN=60HR (SDANNHR24), were calculated over 24 h. Power spectrum analysis using the Lomb-Scargle Peridogram was performed to assess frequency domain HRV parameters (Ultra Low Frequency, Very Low Frequency, Low Frequency, and High Frequency). Due to the fact that SDNN24 reflects the total power of the power of the HRV spectrum, the values estimated from HR measures (SDANNHR24) underestimate the real values because of the high frequencies that are missing. Subjects with low and high cardiovascular risk show different power spectra. In particular, differences are detected in Ultra Low and Very Low frequencies, while similar results are shown in Low and High frequencies. For this reason, we found that HR measures contain enough information to discriminate cardiovascular risk. Semi-continuous measures of HR throughout 24 h, as measured by most wrist-worn fitness wearable devices, should be sufficient to estimate SDNN24 and cardiovascular risk.
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Affiliation(s)
- Davide Morelli
- Huma Therapeutics Limited, London SW1P 4QP, UK; (L.B.); (M.C.)
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford OX1 2JD, UK;
| | - Alessio Rossi
- Department of Computer Science, University of Pisa, 56126 Pisa, Italy;
| | | | - Massimo Cairo
- Huma Therapeutics Limited, London SW1P 4QP, UK; (L.B.); (M.C.)
| | - David A. Clifton
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford OX1 2JD, UK;
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A Public Dataset of 24-h Multi-Levels Psycho-Physiological Responses in Young Healthy Adults. DATA 2020. [DOI: 10.3390/data5040091] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Wearable devices now make it possible to record large quantities of physiological data, which can be used to obtain a clearer view of a person’s health status and behavior. However, to the best of our knowledge, there are no open datasets in the literature that provide psycho-physiological data. The Multilevel Monitoring of Activity and Sleep in Healthy people (MMASH) dataset presented in this paper provides 24 h of continuous psycho-physiological data, that is, inter-beat intervals data, heart rate data, wrist accelerometry data, sleep quality index, physical activity (i.e., number of steps per second), psychological characteristics (e.g., anxiety status, stressful events, and emotion declaration), and sleep hormone levels for 22 participants. The MMASH dataset will enable the investigation of possible relationships between the physical and psychological characteristics of people in daily life. Data were validated through different analyses that showed their compatibility with the literature.
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van Kraaij AWJ, Schiavone G, Lutin E, Claes S, Van Hoof C. Relationship Between Chronic Stress and Heart Rate Over Time Modulated by Gender in a Cohort of Office Workers: Cross-Sectional Study Using Wearable Technologies. J Med Internet Res 2020; 22:e18253. [PMID: 32902392 PMCID: PMC7511872 DOI: 10.2196/18253] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 06/12/2020] [Accepted: 07/07/2020] [Indexed: 01/24/2023] Open
Abstract
Background Chronic stress is increasing in prevalence and is associated with several physical and mental disorders. Although it is proven that acute stress changes physiology, much less is known about the relationship between physiology and long-term stress. Continuous measurement of vital signs in daily life and chronic stress detection algorithms could serve this purpose. For this, it is paramount to model the effects of chronic stress on human physiology and include other cofounders, such as demographics, enabling the enrichment of a population-wide approach with individual variations. Objective The main objectives of this study were to investigate the effect of chronic stress on heart rate (HR) over time while correcting for weekdays versus weekends and to test a possible modulation effect by gender and age in a healthy cohort. Methods Throughout 2016 and 2017, healthy employees of technology companies were asked to participate in a 5-day observation stress study. They were required to wear two wearables, of which one included an electrocardiogram sensor. The derived HR was averaged per hour and served as an output for a mixed design model including a trigonometric fit over time with four harmonics (periods of 24, 12, 8, and 6 hours), gender, age, whether it was a workday or weekend day, and a chronic stress score derived from the Perceived Stress Scale (PSS) as predictors. Results The study included 328 subjects, of which 142 were female and 186 were male participants, with a mean age of 38.9 (SD 10.2) years and a mean PSS score of 13.7 (SD 6.0). As main effects, gender (χ21=24.02, P<.001); the hour of the day (χ21=73.22, P<.001); the circadian harmonic (χ22=284.4, P<.001); and the harmonic over 12 hours (χ22=242.1, P<.001), over 8 hours (χ22=23.78, P<.001), and over 6 hours (χ22=82.96, P<.001) had a significant effect on HR. Two three-way interaction effects were found. The interaction of age, whether it was a workday or weekend day, and the circadian harmonic over time were significantly correlated with HR (χ22=7.13, P=.03), as well as the interaction of gender, PSS score, and the circadian harmonic over time (χ22=7.59, P=.02). Conclusions The results show a relationship between HR and the three-way interaction of chronic stress, gender, and the circadian harmonic. The modulation by gender might be related to evolution-based energy utilization strategies, as suggested in related literature studies. More research, including daily cortisol assessment, longer recordings, and a wider population, should be performed to confirm this interpretation. This would enable the development of more complete and personalized models of chronic stress.
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Affiliation(s)
- Alex Wilhelmus Jacobus van Kraaij
- Holst Centre, imec-the Netherlands, Eindhoven, Netherlands.,OnePlanet Research Center, imec-the Netherlands, Wageningen, Netherlands.,Faculty of Natural Sciences, Math and Informatics (FNWI), Radboud University, Nijmegen, Netherlands
| | - Giuseppina Schiavone
- Holst Centre, imec-the Netherlands, Eindhoven, Netherlands.,OnePlanet Research Center, imec-the Netherlands, Wageningen, Netherlands
| | - Erika Lutin
- Electrical Engineering-ESAT, KU Leuven, Leuven, Belgium.,imec-Belgium, Heverlee, Belgium
| | - Stephan Claes
- University Psychiatric Center & Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Chris Van Hoof
- Holst Centre, imec-the Netherlands, Eindhoven, Netherlands.,OnePlanet Research Center, imec-the Netherlands, Wageningen, Netherlands.,Electrical Engineering-ESAT, KU Leuven, Leuven, Belgium.,imec-Belgium, Heverlee, Belgium
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