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Bester M, Nichting TJ, Joshi R, Aissati L, Oei GS, Mischi M, van Laar JOEH, Vullings R. Changes in Maternal Heart Rate Variability and Photoplethysmography Morphology after Corticosteroid Administration: A Prospective, Observational Study. J Clin Med 2024; 13:2442. [PMID: 38673715 PMCID: PMC11051424 DOI: 10.3390/jcm13082442] [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: 03/23/2024] [Revised: 04/17/2024] [Accepted: 04/18/2024] [Indexed: 04/28/2024] Open
Abstract
Background: Owing to the association between dysfunctional maternal autonomic regulation and pregnancy complications, assessing non-invasive features reflecting autonomic activity-e.g., heart rate variability (HRV) and the morphology of the photoplethysmography (PPG) pulse wave-may aid in tracking maternal health. However, women with early pregnancy complications typically receive medication, such as corticosteroids, and the effect of corticosteroids on maternal HRV and PPG pulse wave morphology is not well-researched. Methods: We performed a prospective, observational study assessing the effect of betamethasone (a commonly used corticosteroid) on non-invasively assessed features of autonomic regulation. Sixty-one women with an indication for betamethasone were enrolled and wore a wrist-worn PPG device for at least four days, from which five-minute measurements were selected for analysis. A baseline measurement was selected either before betamethasone administration or sufficiently thereafter (i.e., three days after the last injection). Furthermore, measurements were selected 24, 48, and 72 h after betamethasone administration. HRV features in the time domain and frequency domain and describing heart rate (HR) complexity were calculated, along with PPG morphology features. These features were compared between the different days. Results: Maternal HR was significantly higher and HRV features linked to parasympathetic activity were significantly lower 24 h after betamethasone administration. Features linked to sympathetic activity remained stable. Furthermore, based on the PPG morphology features, betamethasone appears to have a vasoconstrictive effect. Conclusions: Our results suggest that administering betamethasone affects maternal autonomic regulation and cardiovasculature. Researchers assessing maternal HRV in complicated pregnancies should schedule measurements before or sufficiently after corticosteroid administration.
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Affiliation(s)
- Maretha Bester
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Patient Care and Monitoring, Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Thomas J. Nichting
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Department of Obstetrics and Gynecology, Máxima Medical Centrum, 5504 DB Veldhoven, The Netherlands
| | - Rohan Joshi
- Patient Care and Monitoring, Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Lamyae Aissati
- Faculty of Health, Medicine and Life Science, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Guid S. Oei
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Department of Obstetrics and Gynecology, Máxima Medical Centrum, 5504 DB Veldhoven, The Netherlands
| | - Massimo Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Judith O. E. H. van Laar
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Department of Obstetrics and Gynecology, Máxima Medical Centrum, 5504 DB Veldhoven, The Netherlands
| | - Rik Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
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Bester M, Almario Escorcia MJ, Fonseca P, Mollura M, van Gilst MM, Barbieri R, Mischi M, van Laar JOEH, Vullings R, Joshi R. The impact of healthy pregnancy on features of heart rate variability and pulse wave morphology derived from wrist-worn photoplethysmography. Sci Rep 2023; 13:21100. [PMID: 38036597 PMCID: PMC10689737 DOI: 10.1038/s41598-023-47980-2] [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: 04/21/2023] [Accepted: 11/20/2023] [Indexed: 12/02/2023] Open
Abstract
Due to the association between dysfunctional maternal autonomic regulation and pregnancy complications, tracking non-invasive features of autonomic regulation derived from wrist-worn photoplethysmography (PPG) measurements may allow for the early detection of deteriorations in maternal health. However, even though a plethora of these features-specifically, features describing heart rate variability (HRV) and the morphology of the PPG waveform (morphological features)-exist in the literature, it is unclear which of these may be valuable for tracking maternal health. As an initial step towards clarity, we compute comprehensive sets of HRV and morphological features from nighttime PPG measurements. From these, using logistic regression and stepwise forward feature elimination, we identify the features that best differentiate healthy pregnant women from non-pregnant women, since these likely capture physiological adaptations necessary for sustaining healthy pregnancy. Overall, morphological features were more valuable for discriminating between pregnant and non-pregnant women than HRV features (area under the receiver operating characteristics curve of 0.825 and 0.74, respectively), with the systolic pulse wave deterioration being the most valuable single feature, followed by mean heart rate (HR). Additionally, we stratified the analysis by sleep stages and found that using features calculated only from periods of deep sleep enhanced the differences between the two groups. In conclusion, we postulate that in addition to HRV features, morphological features may also be useful in tracking maternal health and suggest specific features to be included in future research concerning maternal health.
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Affiliation(s)
- M Bester
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands.
- Patient Care and Monitoring, Philips Research, 5656 AE, Eindhoven, The Netherlands.
| | - M J Almario Escorcia
- Patient Care and Monitoring, Philips Research, 5656 AE, Eindhoven, The Netherlands
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milan, MI, Italy
| | - P Fonseca
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Patient Care and Monitoring, Philips Research, 5656 AE, Eindhoven, The Netherlands
| | - M Mollura
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milan, MI, Italy
| | - M M van Gilst
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, 5591 VE, Heeze, The Netherlands
| | - R Barbieri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milan, MI, Italy
| | - M Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
| | - J O E H van Laar
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Department of Obstetrics and Gynecology, Máxima Medical Centrum, De Run 4600, 5504 DB, Veldhoven, The Netherlands
| | - R Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
| | - R Joshi
- Patient Care and Monitoring, Philips Research, 5656 AE, Eindhoven, The Netherlands
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Moscato S, Orlandi S, Di Gregorio F, Lullini G, Pozzi S, Sabattini L, Chiari L, La Porta F. Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol. BMJ Open 2023; 13:e073534. [PMID: 37993169 PMCID: PMC10668325 DOI: 10.1136/bmjopen-2023-073534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 07/28/2023] [Indexed: 11/24/2023] Open
Abstract
INTRODUCTION Millions of people survive injuries to the central or peripheral nervous system for which neurorehabilitation is required. In addition to the physical and cognitive impairments, many neurorehabilitation patients experience pain, often not widely recognised and inadequately treated. This is particularly true for multiple sclerosis (MS) patients, for whom pain is one of the most common symptoms. In clinical practice, pain assessment is usually conducted based on a subjective estimate. This approach can lead to inaccurate evaluations due to the influence of numerous factors, including emotional or cognitive aspects. To date, no objective and simple to use clinical methods allow objective quantification of pain and the diagnostic differentiation between the two main types of pain (nociceptive vs neuropathic). Wearable technologies and artificial intelligence (AI) have the potential to bridge this gap by continuously monitoring patients' health parameters and extracting meaningful information from them. Therefore, we propose to develop a new automatic AI-powered tool to assess pain and its characteristics during neurorehabilitation treatments using physiological signals collected by wearable sensors. METHODS AND ANALYSIS We aim to recruit 15 participants suffering from MS undergoing physiotherapy treatment. During the study, participants will wear a wristband for three consecutive days and be monitored before and after their physiotherapy sessions. Measurement of traditionally used pain assessment questionnaires and scales (ie, painDETECT, Doleur Neuropathique 4 Questions, EuroQoL-5-dimension-3-level) and physiological signals (photoplethysmography, electrodermal activity, skin temperature, accelerometer data) will be collected. Relevant parameters from physiological signals will be identified, and AI algorithms will be used to develop automatic classification methods. ETHICS AND DISSEMINATION The study has been approved by the local Ethical Committee (285-2022-SPER-AUSLBO). Participants are required to provide written informed consent. The results will be disseminated through contributions to international conferences and scientific journals, and they will also be included in a doctoral dissertation. TRIAL REGISTRATION NUMBER NCT05747040.
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Affiliation(s)
- Serena Moscato
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" - DEI, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Silvia Orlandi
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" - DEI, Alma Mater Studiorum University of Bologna, Bologna, Italy
- Health Science and Technologies - Interdepartmental Center for Industrial Research (CIRI-SDV), Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Francesco Di Gregorio
- UOC Medicina Riabilitativa e Neuroriabilitazione, Azienda Unità Sanitaria Locale di Bologna, Bologna, Italy
- Centro studi e ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Cesena, Italy
| | - Giada Lullini
- IRCCS Istituto delle Scienze Neurologuche di Bologna, Bologna, Italy
| | - Stefania Pozzi
- DATER Riabilitazione Ospedaliera, UA Riabilitazione, Azienda Unità Sanitaria Locale di Bologna, Bologna, Italy
| | | | - Lorenzo Chiari
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" - DEI, Alma Mater Studiorum University of Bologna, Bologna, Italy
- Health Science and Technologies - Interdepartmental Center for Industrial Research (CIRI-SDV), Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Fabio La Porta
- IRCCS Istituto delle Scienze Neurologuche di Bologna, Bologna, Italy
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Udhayakumar R, Rahman S, Buxi D, Macefield VG, Dawood T, Mellor N, Karmakar C. Measurement of stress-induced sympathetic nervous activity using multi-wavelength PPG. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221382. [PMID: 37650068 PMCID: PMC10465208 DOI: 10.1098/rsos.221382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 08/02/2023] [Indexed: 09/01/2023]
Abstract
The onset of stress triggers sympathetic arousal (SA), which causes detectable changes to physiological parameters such as heart rate, blood pressure, dilation of the pupils and sweat release. The objective quantification of SA has tremendous potential to prevent and manage psychological disorders. Photoplethysmography (PPG), a non-invasive method to measure skin blood flow changes, has been used to estimate SA indirectly. However, the impact of various wavelengths of the PPG signal has not been investigated for estimating SA. In this study, we explore the feasibility of using various statistical and nonlinear features derived from peak-to-peak (AC) values of PPG signals of different wavelengths (green, blue, infrared and red) to estimate stress-induced changes in SA and compare their performances. The impact of two physical stressors: and Hand Grip are studied on 32 healthy individuals. Linear (Mean, s.d.) and nonlinear (Katz, Petrosian, Higuchi, SampEn, TotalSampEn) features are extracted from the PPG signal's AC amplitudes to identify the onset, continuation and recovery phases of those stressors. The results show that the nonlinear features are the most promising in detecting stress-induced sympathetic activity. TotalSampEn feature was capable of detecting stress-induced changes in SA for all wavelengths, whereas other features (Petrosian, AvgSampEn) are significant (AUC ≥ 0.8) only for IR and Red wavelengths. The outcomes of this study can be used to make device design decisions as well as develop stress detection algorithms.
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Affiliation(s)
| | - Saifur Rahman
- School of Information Technology Deakin University, Geelong 3225, Australia
| | | | | | - Tye Dawood
- Baker Heart and Diabetes Institute, Melbourne, Australia
| | | | - Chandan Karmakar
- School of Information Technology Deakin University, Geelong 3225, Australia
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Yu SN, Lin IM, Wang SY, Hou YC, Yao SP, Lee CY, Chang CJ, Chu CS, Lin TH. Affective Computing Based on Morphological Features of Photoplethysmography for Patients with Hypertension. SENSORS (BASEL, SWITZERLAND) 2022; 22:8771. [PMID: 36433366 PMCID: PMC9698908 DOI: 10.3390/s22228771] [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: 09/26/2022] [Revised: 10/31/2022] [Accepted: 11/10/2022] [Indexed: 06/16/2023]
Abstract
Negative and positive emotions are the risk and protective factors for the cause and prognosis of hypertension. This study aimed to use five photoplethysmography (PPG) waveform indices and affective computing (AC) to discriminate the emotional states in patients with hypertension. Forty-three patients with essential hypertension were measured for blood pressure and PPG signals under baseline and four emotional conditions (neutral, anger, happiness, and sadness), and the PPG signals were transformed into the mean standard deviation of five PPG waveform indices. A support vector machine was used as a classifier. The performance of the classifier was verified by using resubstitution and six-fold cross-validation (CV) methods. Feature selectors, including full search and genetic algorithm (GA), were used to select effective feature combinations. Traditional statistical analyses only differentiated between the emotional states and baseline, whereas AC achieved 100% accuracy in distinguishing between the emotional states and baseline by using the resubstitution method. AC showed high accuracy rates when used with 10 waveform features in distinguishing the records into two, three, and four classes by applying a six-fold CV. The GA feature selector further boosted the accuracy to 78.97%, 74.22%, and 67.35% in two-, three-, and four-class differentiation, respectively. The proposed AC achieved high accuracy in categorizing PPG records into distinct emotional states with features extracted from only five waveform indices. The results demonstrated the effectiveness of the five indices and the proposed AC in patients with hypertension.
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Affiliation(s)
- Sung-Nien Yu
- Department of Electrical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan
- Pervasive Artificial Intelligence Research (PAIR) Labs, Hsinchu 300093, Taiwan
| | - I-Mei Lin
- Pervasive Artificial Intelligence Research (PAIR) Labs, Hsinchu 300093, Taiwan
- Department of Psychology, College of Humanities and Social Sciences, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
| | - San-Yu Wang
- Department of Psychology, College of Humanities and Social Sciences, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Yi-Cheng Hou
- Department of Electrical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan
| | - Sheng-Po Yao
- Department of Electrical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan
| | - Chun-Ying Lee
- Division of Family Medicine, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
- Departments of Family Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Chai-Jan Chang
- Division of Family Medicine, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
- Departments of Family Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Chih-Sheng Chu
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
- Department of Internal Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Tsung-Hsien Lin
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
- Department of Internal Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
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Moscato S, Lo Giudice S, Massaro G, Chiari L. Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22155831. [PMID: 35957395 PMCID: PMC9370973 DOI: 10.3390/s22155831] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/21/2022] [Accepted: 08/02/2022] [Indexed: 06/12/2023]
Abstract
Photoplethysmographic (PPG) signals are mainly employed for heart rate estimation but are also fascinating candidates in the search for cardiovascular biomarkers. However, their high susceptibility to motion artifacts can lower their morphological quality and, hence, affect the reliability of the extracted information. Low reliability is particularly relevant when signals are recorded in a real-world context, during daily life activities. We aim to develop two classifiers to identify PPG pulses suitable for heart rate estimation (Basic-quality classifier) and morphological analysis (High-quality classifier). We collected wrist PPG data from 31 participants over a 24 h period. We defined four activity ranges based on accelerometer data and randomly selected an equal number of PPG pulses from each range to train and test the classifiers. Independent raters labeled the pulses into three quality levels. Nineteen features, including nine novel features, were extracted from PPG pulses and accelerometer signals. We conducted ten-fold cross-validation on the training set (70%) to optimize hyperparameters of five machine learning algorithms and a neural network, and the remaining 30% was used to test the algorithms. Performances were evaluated using the full features and a reduced set, obtained downstream of feature selection methods. Best performances for both Basic- and High-quality classifiers were achieved using a Support Vector Machine (Acc: 0.96 and 0.97, respectively). Both classifiers outperformed comparable state-of-the-art classifiers. Implementing automatic signal quality assessment methods is essential to improve the reliability of PPG parameters and broaden their applicability in a real-world context.
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Affiliation(s)
- Serena Moscato
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”—DEI, University of Bologna, 40136 Bologna, Italy;
| | - Stella Lo Giudice
- School of Engineering (Digital Technology Engineering), Pulsed Academy, Fontys University of Applied Science, 5612 MA Eindhoven, The Netherlands;
| | - Giulia Massaro
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy;
| | - Lorenzo Chiari
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”—DEI, University of Bologna, 40136 Bologna, Italy;
- Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, 40136 Bologna, Italy
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Moscato S, Orlandi S, Giannelli A, Ostan R, Chiari L. Automatic pain assessment on cancer patients using physiological signals recorded in real-world contexts. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1931-1934. [PMID: 36086417 DOI: 10.1109/embc48229.2022.9871990] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Pain assessment represents the first fundamental stage for proper pain management, but currently, methods applied in clinical practice often lack in providing a satisfying characterization of the pain experience. Automatic methods based on the analysis of physiological signals (e.g., photoplethysmography, electrodermal activity) promise to overcome these limitations, also providing the possibility to record these signals through wearable devices, thus capturing the physiological response in everyday life. After applying preprocessing, feature extraction and feature selection methods, we tested several machine learning algorithms to develop an automatic classifier fed with physiological signals recorded in real-world contexts and pain ratings from 21 cancer patients. The best algorithm achieved up to 72% accuracy. Although performance can be improved by enlarging the dataset, preliminary results proved the feasibility of assessing pain by using physiological signals recorded in real-world contexts.
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Zhu Z, Feng J, Wang X, Xu Y, Zhou H, Sun J, Jiang W, Chen H. Emotion Recognition Based on Energy-related Features of Peripheral Physiological Signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1895-1901. [PMID: 36086319 DOI: 10.1109/embc48229.2022.9871935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The interest in development of methods and tools for recognizing human emotions has increased continuously. Using physiological information, especially the peripheral physiological signals, to identify emotions is an important direction for this area. This paper proposes an approach for emotion recognition based on energy-related features extracted from peripheral physiological signals. Three emotions: calm, happiness and fear, were elicited in 54 volunteers using video clips while three peripheral physiological signals were recorded: Electrocardiography (ECG), Photoplethysmography (PPG) and Respiration. Given that energy-related features of physiological signals are closely related to autonomic nervous systems activities, nine energy-related features were extracted from the recorded physiological signals. To find the optimal feature subset to represent the target emotions, the correlation between features and emotion state, as well as the discrimination ability of feature for emotion recognition were both analyzed. Four optimal features were then selected for further classification. Moreover, models based on Decision Tree (DT) were built to evaluate the performance of these features for purpose of recognition of emotion states of calm, happiness, and fear. The results show that the DT models based on these four optimal features could distinguish fear from calm (AUC=0.879, Accuracy=87.8%), happiness from calm (AUC=0.915, Accuracy=91.8%), and fear from happiness (AUC=0.822, Accuracy=81.8%), with a global recognition accuracy of 70.8%. These results indicate that energy-related features of peripheral physiological signals can reliably identify emotions, especially intense emotions.
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Evaluation of Novel Entropy-Based Complex Wavelet Sub-bands Measures of PPG in an Emotion Recognition System. J Med Biol Eng 2020. [DOI: 10.1007/s40846-020-00526-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Zhu J, Ji L, Liu C. Heart rate variability monitoring for emotion and disorders of emotion. Physiol Meas 2019; 40:064004. [PMID: 30974428 DOI: 10.1088/1361-6579/ab1887] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
BACKGROUND Emotion is composed of cognitive processing, physiological response and behavioral reaction. Heart rate variability (HRV) refers to the fluctuations between consecutive heartbeat cycles, and is considered as a non-invasive method for evaluating cardiac autonomic function. HRV analysis plays an important role in emotional study and detection. OBJECTIVE In this paper, the physiological foundation of HRV is briefly described, and then the relevant literature relating to HRV-based emotion studies for the performance of HRV in different emotions, emotion recognition, the evaluation of emotional disorders, HRV biofeedback, as well as HRV-based emotion analysis and management enhanced by wearable devices, are reviewed. SIGNIFICANCE It is suggested that HRV is an effective tool for the measurement and regulation of emotional response, with a broad application prospect.
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Affiliation(s)
- Jianping Zhu
- School of Life Science, Shandong Normal University, Jinan 250014, People's Republic of China
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11
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Zhao L, Yang L, Su Z, Liu C. Cardiorespiratory Coupling Analysis Based on Entropy and Cross-Entropy in Distinguishing Different Depression Stages. Front Physiol 2019; 10:359. [PMID: 30984033 PMCID: PMC6449862 DOI: 10.3389/fphys.2019.00359] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 03/14/2019] [Indexed: 12/15/2022] Open
Abstract
Aims This study used entropy- and cross entropy-based methods to explore the cardiorespiratory coupling of depressive patients, and thus to assess the values of those entropy methods for identifying depression patients with different disease severities. Methods Electrocardiogram (ECG) and respiration signals from 69 depression patients were recorded simultaneously for 5 min. Patients were classified into three groups according to the Hamilton Depression Rating Scale (HDRS) scores: group Non-De (HDRS 0–7), Mid-De (HDRS 8–17), and Con-De (HDRS >17). Sample entropy (SEn), fuzzy measure entropy (FMEn) and high-frequency power (HF) were computed on the original RR interval time series and breath-to-breath interval time series. Cross sample entropy (CSEn) and cross fuzzy measure entropy (CFMEn) were computed on interval time series resampled at 2 Hz and 4 Hz, respectively. The difference among three patient groups and correlation between entropy values and HDRS scores were analyzed by statistical analysis. Surrogate data were also employed to confirm the validation of entropy measures in this study. Results A consistent increasing trend has been found among most entropy measures from Non-De, to Mid-De, and to Con-De groups, and a significant (p < 0.05) difference in FMEn of RR intervals exists between Non-De and Mid-De or Con-De groups. Significant differences have been also found in all cross entropies, between Non-De and Con-De groups and between Mid-De and Con-De groups. Furthermore, significant correlations also exist between HDRS scores and FMEn of RR intervals (R = 0.24, p < 0.05), CSEn at 4 Hz (R = 0.26, p < 0.05) or 2 Hz (R = 0.28, p < 0.05) resampling, and CFMEn at 4 Hz (R = 0.31, p < 0.01) or 2 Hz (R = 0.30, p < 0.05) resampling. A significant difference of cardiorespiratory coupling parameters between different depression stages and significant correlations between entropy measures and depression severity both indicate central autonomic dysregulation in depression patients and reflect varying degrees of vagal modulation reduction among different depression levels. Analysis based on surrogate data confirms that the non-linear properties of the physiological signals played a major role in depression recognition. Conclusion The current study demonstrates the potential of cardiorespiratory coupling in the auxiliary diagnosis of depression based on the entropy method.
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Affiliation(s)
- Lulu Zhao
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Licai Yang
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Zhonghua Su
- Second Affiliated Hospital of Jining Medical College, Jining, China
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing, China
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Jiang X, Wei S, Zheng D, Huang P, Liu C. Changes in the bilateral pulse transit time difference with a moving arm. Technol Health Care 2018; 26:113-119. [PMID: 29710744 PMCID: PMC6005010 DOI: 10.3233/thc-174256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
BACKGROUND: Changes of pulse transit time (PTT) induced by arm position were studied for unilateral arm. However, consistency of the PTT changes was not validated for both arm sides. OBJECTIVE: We aimed to quantify the PTT changes between horizontal and non-horizontal positions from right arm and left arm in order to explore the consistency of both arms. METHODS: Twenty-four normal subjects aged between 21 and 50 (14 male and 10 female) years were enrolled. Left and right radial artery pulses were synchronously recorded from 24 healthy subjects with one arm (left or right) at five angles (90∘, 45∘, 0∘, -45∘ and -90∘) and the other arm at the horizontal level (0∘) for reference. RESULTS: The overall mean PTT changes at the five angles (from 90∘ to -90∘) in the left arm (right as reference) were 16.1, 12.3, -0.5, -2.5 and -2.6 ms, respectively, and in the right arm (left as reference) were 18.0, 12.6, 1.6, -1.6 and -2.0 ms, respectively. CONCLUSIONS: Obvious differences were not found in the PTT changes between the two arms (left arm moving or right arm moving) under each of the five different positions (all P> 0.05).
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Affiliation(s)
- Xinge Jiang
- School of Control Science and Engineering, Shandong University, Jinan 250061, Shandong, China.,Shandong College of Electronic Technology, Jinan 250200, Shandong, China
| | - Shoushui Wei
- School of Control Science and Engineering, Shandong University, Jinan 250061, Shandong, China
| | - Dingchang Zheng
- Health and Well Being Academy, Faculty of Medical Science, Anglia Ruskin University, Chelmsford, CM1 1SQ, UK
| | - Peng Huang
- School of Control Science and Engineering, Shandong University, Jinan 250061, Shandong, China
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, Jiangsu, China
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Jiang X, Wei S, Zheng D, Liu F, Zhang S, Zhang Z, Liu C. Change of bilateral difference in radial artery pulse morphology with one-side arm movement. Artery Res 2017. [DOI: 10.1016/j.artres.2017.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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