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Maccaro A, Pagliara SM, Zarro M, Piaggio D, Abdulsalami F, Su W, Haleem MS, Pecchia L. Ethics and biomedical engineering for well-being: a cocreation study of remote services for monitoring and support. Sci Rep 2023; 13:14322. [PMID: 37652901 PMCID: PMC10471689 DOI: 10.1038/s41598-023-39834-8] [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: 02/16/2023] [Accepted: 07/31/2023] [Indexed: 09/02/2023] Open
Abstract
The well-being of students and staff directly affects their output and efficiency. This study presents the results of two focus groups conducted in 2022 within a two-phase project led by the Applied Biomedical and Signal Processing Intelligent e-Health Lab, School of Engineering at the University of Warwick, and British Telecom within "The Connected Campus: University of Warwick case study" program. The first phase, by involving staff and students at the University of Warwick, aimed at collecting preliminary information for the subsequent second phase, about the feasibility of the use of Artificial Intelligence and Internet of Things for well-being support on Campus. The main findings of this first phase are interesting technological suggestions from real users. The users helped in the design of the scenarios and in the selection of the key enabling technologies which they considered as the most relevant, useful and acceptable to support and improve well-being on Campus. These results will inform future services to design and implement technologies for monitoring and supporting well-being, such as hybrid, minimal and even intrusive (implantable) solutions. The user-driven co-design of such services, leveraging the use of wearable devices and Artificial Intelligence deployment will increase their acceptability by the users.
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Affiliation(s)
- A Maccaro
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK
| | - S M Pagliara
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK.
- Università di Cagliari, Via Università 40, 09124, Cagliari, Italy.
| | - M Zarro
- Department of Internal Medicine and Medical Therapy, University of Pavia, 27100, Pavia, Italy
| | - D Piaggio
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK
| | - F Abdulsalami
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK
| | - W Su
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK
| | - M S Haleem
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK
| | - L Pecchia
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK
- Università Campus Bio-Medico, Via Álvaro del Portillo, 21, 00128, Rome, Italy
- R&D Blueprint and COVID-19, World Health Organization, Avenue Appia 20, 1202, Geneva, Switzerland
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Kim JW, Seok HS, Shin H. Is Ultra-Short-Term Heart Rate Variability Valid in Non-static Conditions? Front Physiol 2021; 12:596060. [PMID: 33859568 PMCID: PMC8042416 DOI: 10.3389/fphys.2021.596060] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 03/10/2021] [Indexed: 11/20/2022] Open
Abstract
In mobile healthcare, heart rate variability (HRV) is increasingly being used in dynamic patient states. In this situation, shortening of the measurement time is required. This study aimed to validate ultra-short-term HRV in non-static conditions. We conducted electrocardiogram (ECG) measurements at rest, during exercise, and in the post-exercise recovery period in 30 subjects and analyzed ultra-short-term HRV in time and frequency domains by ECG in 10, 30, 60, 120, 180, and 240-s intervals, and compared the values to the 5-min HRV. For statistical analysis, null hypothesis testing, Cohen’s d statistics, Pearson’s correlation coefficient, and Bland-Altman analysis were used, with a statistical significance level of P < 0.05. The feasibility of ultra-short-term HRV and the minimum time required for analysis showed differences in each condition and for each analysis method. If the strict criteria satisfying all the statistical methods were followed, the ultra-short-term HRV could be derived from a from 30 to 240-s length of ECG. However, at least 120 s was required in the post-exercise recovery or exercise conditions, and even ultra-short-term HRV was not measurable in some variables. In contrast, according to the lenient criteria needed to satisfy only one of the statistical criteria, the minimum time required for ultra-short-term HRV analysis was 10–60 s in the resting condition, 10–180 s in the exercise condition, and 10–120 s in the post-exercise recovery condition. In conclusion, the results of this study showed that a longer measurement time was required for ultra-short-term HRV analysis in dynamic conditions. This suggests that the existing ultra-short-term HRV research results derived from the static condition cannot applied to the non-static conditions of daily life and that a criterion specific to the non-static conditions are necessary.
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Affiliation(s)
- Jin Woong Kim
- Department of Biomedical Engineering, Chonnam National University, Yeosu-si, South Korea
| | - Hyeon Seok Seok
- Department of Biomedical Engineering, Chonnam National University, Yeosu-si, South Korea
| | - Hangsik Shin
- Department of Biomedical Engineering, Chonnam National University, Yeosu-si, South Korea
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Recognition of the Impulse of Love at First Sight Based on Electrocardiograph Signal. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:6631616. [PMID: 33833790 PMCID: PMC8012126 DOI: 10.1155/2021/6631616] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 02/19/2021] [Accepted: 03/10/2021] [Indexed: 11/17/2022]
Abstract
The impulse of love at first sight (ILFS) is a well known but rarely studied phenomenon. Despite the privacy of these emotions, knowing how attractive one finds a partner may be beneficial for building a future relationship in an open society, where partners are accepting each other. Therefore, this study adopted the electrocardiograph (ECG) signal collection method, which has been widely used in wearable devices, to collect signals and conduct corresponding recognition analysis. First, we used photos to induce ILFS and obtained ECG signals from 46 healthy students (24 women and 22 men) in a laboratory. Second, we extracted the time- and frequency-domain features of the ECG signals and performed a nonlinear analysis. We subsequently used a feature selection algorithm and a set of classifiers to classify the features. Combined with the sequence floating forward selection and random forest algorithms, the identification accuracy of the ILFS was 69.07%. The sensitivity, specificity, F1, and area under the curve of the other parameters were all greater than 0.6. The classification of ECG signals according to their characteristics demonstrated that the signals could be recognized. Through the information provided by the ECG signals, it can be determined whether the participant possesses the desire to fall in love, helping to determine the right partner in the fastest time; this is conducive to establishing a romantic relationship.
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Kim SW, Park HY, Jung WS, Lim K. Predicting Heart Rate Variability Parameters in Healthy Korean Adults: A Preliminary Study. INQUIRY: THE JOURNAL OF HEALTH CARE ORGANIZATION, PROVISION, AND FINANCING 2021; 58:469580211056201. [PMID: 34841954 PMCID: PMC8673878 DOI: 10.1177/00469580211056201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The purpose of the study was to examine the development of a multiple linear regression model to estimate heart rate variability (HRV) parameters using easy-to-measure independent variables in preliminary experiments. HRV parameters (time domain: SDNN, RMSSD, NN50, pNN50; frequency domain: TP, VLF, LF, HF) and the independent variables (e.g., sex, age, body height, body weight, BMI, HR, HRmax, HRR) were measured in 75 healthy adults (male n = 27, female n = 48) for estimating HRV. The HRV estimation multiple linear regression model was developed using the backward elimination technique. The regression model’s coefficient of determination for the time domain variables was significantly high (SDNN = R2: 72.2%, adjusted R2: 69.8%, P < .001; RMSSD = R2: 93.1%, adjusted R2: 92.1%, P < .001; NN50 = R2: 78.0%, adjusted R2: 74.9%, P < .001; pNN50 = R2: 89.1%, adjusted R2: 87.4%, P < .001). The coefficient of determination of the regression model for the frequency domain variable was moderate (TP = R2: 75.6%, adjusted R2: 72.6%, P < .001; VLF = R2: 41.6%, adjusted R2: 40.3%, P < .001; LF = R2: 54.6%, adjusted R2: 49.2%, P < .001; HF = R2: 67.5%, adjusted R2: 63.4%, P < .001). The coefficient of determination of time domain variables in the developed multiple regression models was shown to be very high (adjusted R2: 69.8%–92.1%, P < .001), but the coefficient of determination of frequency domain variables was moderate (adjusted R2: 40.3%–72.6%, P < .001). In addition to the equipment used for measuring HRV in clinical trials, this study confirmed that simple physiological variables could predict HRV.
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Affiliation(s)
- Sung-Woo Kim
- Physical Activity and Performance Institute, Konkuk University, Seoul, Republic of Korea
| | - Hun-Young Park
- Physical Activity and Performance Institute, Konkuk University, Seoul, Republic of Korea
- Department of Sports Medicine and Science, Graduate School, Konkuk University, Seoul, Republic of Korea
| | - Won-Sang Jung
- Physical Activity and Performance Institute, Konkuk University, Seoul, Republic of Korea
| | - Kiwon Lim
- Physical Activity and Performance Institute, Konkuk University, Seoul, Republic of Korea
- Department of Sports Medicine and Science, Graduate School, Konkuk University, Seoul, Republic of Korea
- Department of Physical Education, Konkuk University, Seoul, Republic of Korea
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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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Pourmohammadi S, Maleki A. Stress detection using ECG and EMG signals: A comprehensive study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 193:105482. [PMID: 32408236 DOI: 10.1016/j.cmpb.2020.105482] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 03/27/2020] [Accepted: 03/27/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE In recent years, stress and mental health have been considered as important worldwide concerns. Stress detection using physiological signals such as electrocardiogram (ECG), skin conductance (SC), electromyogram (EMG) and electroencephalogram (EEG) is a traditional approach. However, the effect of stress on the EMG signal of different muscles and the efficacy of combination of the EMG and other biological signals for stress detection have not been taken into account yet. This paper presents a comprehensive review of the EMG signal of the right and left trapezius and right and left erector spinae muscles for multi-level stress recognition. Also, the ECG signal was employed to evaluate the efficacy of EMG signals for stress detection. METHODS Both EMG and ECG signals were acquired simultaneously from 34 healthy students (23 females and 11 males, aged 20-37 years). Mental arithmetic, Stroop color-word test, time pressure, and stressful environment were employed to induce stress in the laboratory. RESULTS The accuracies of stress recognition in two, three and four levels were 100%, 97.6%, and 96.2%, respectively, obtained from the distinct combination of feature selection and machine learning algorithms. CONCLUSIONS The comparison of stress detection accuracies resulted from EMG and ECG indicators demonstrated the strong ability and the effectiveness of EMG signal for multi-level stress detection.
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Affiliation(s)
| | - Ali Maleki
- Biomedical Engineering Department, Semnan University, Semnan, Iran.
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Castaldo R, Montesinos L, Melillo P, James C, Pecchia L. Ultra-short term HRV features as surrogates of short term HRV: a case study on mental stress detection in real life. BMC Med Inform Decis Mak 2019; 19:12. [PMID: 30654799 PMCID: PMC6335694 DOI: 10.1186/s12911-019-0742-y] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Accepted: 01/10/2019] [Indexed: 11/24/2022] Open
Abstract
Background This paper suggests a method to assess the extent to which ultra-short Heart Rate Variability (HRV) features (less than 5 min) can be considered as valid surrogates of short HRV features (nominally 5 min). Short term HRV analysis has been widely investigated for mental stress assessment, whereas the validity of ultra-short HRV features remains unclear. Therefore, this study proposes a method to explore the extent to which HRV excerpts can be shortened without losing their ability to automatically detect mental stress. Methods ECGs were acquired from 42 healthy subjects during a university examination and resting condition. 23 features were extracted from HRV excerpts of different lengths (i.e., 30 s, 1 min, 2 min, 3 min, and 5 min). Significant differences between rest and stress phases were investigated using non-parametric statistical tests at different time-scales. Features extracted from each ultra-short length were compared with the standard short HRV features, assumed as the benchmark, via Spearman’s rank correlation analysis and Bland-Altman plots during rest and stress phases. Using data-driven machine learning approaches, a model aiming to detect mental stress was trained, validated and tested using short HRV features, and assessed on the ultra-short HRV features. Results Six out of 23 ultra-short HRV features (MeanNN, StdNN, MeanHR, StdHR, HF, and SD2) displayed consistency across all of the excerpt lengths (i.e., from 5 to 1 min) and 3 out of those 6 ultra-short HRV features (MeanNN, StdHR, and HF) achieved good performance (accuracy above 88%) when employed in a well-dimensioned automatic classifier. Conclusion This study concluded that 6 ultra-short HRV features are valid surrogates of short HRV features for mental stress investigation. Electronic supplementary material The online version of this article (10.1186/s12911-019-0742-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- R Castaldo
- School of Engineering, University of Warwick, CV47AL, Coventry, UK.,Institute of Advanced Studies, University of Warwick, CV47AL, Coventry, UK
| | - L Montesinos
- School of Engineering, University of Warwick, CV47AL, Coventry, UK
| | - P Melillo
- Multidisciplinary Department of Medical, Surgical and Dental Sciences, University of Campania Luigi Vanvitelli, Naples, Italy
| | - C James
- School of Engineering, University of Warwick, CV47AL, Coventry, UK
| | - L Pecchia
- School of Engineering, University of Warwick, CV47AL, Coventry, UK.
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