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Anbuselvam B, Gunasekaran BM, Srinivasan S, Ezhilan M, Rajagopal V, Nesakumar N. Wearable biosensors in cardiovascular disease. Clin Chim Acta 2024; 561:119766. [PMID: 38857672 DOI: 10.1016/j.cca.2024.119766] [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: 05/23/2024] [Revised: 06/05/2024] [Accepted: 06/05/2024] [Indexed: 06/12/2024]
Abstract
This review provides a comprehensive overview of the latest advancements in wearable biosensors, emphasizing their applications in cardiovascular disease monitoring. Initially, the key sensing signals and biomarkers crucial for cardiovascular health, such as electrocardiogram, phonocardiography, pulse wave velocity, blood pressure, and specific biomarkers, are highlighted. Following this, advanced sensing techniques for cardiovascular disease monitoring are examined, including wearable electrophysiology devices, optical fibers, electrochemical sensors, and implantable cardiac devices. The review also delves into hydrogel-based wearable electrochemical biosensors, which detect biomarkers in sweat, interstitial fluids, saliva, and tears. Further attention is given to flexible electronics-based biosensors, including resistive, capacitive, and piezoelectric force sensors, as well as resistive and pyroelectric temperature sensors, flexible biochemical sensors, and sensor arrays. Moreover, the discussion extends to polymer-based wearable sensors, focusing on innovations in contact lens, textile-type, patch-type, and tattoo-type sensors. Finally, the review addresses the challenges associated with recent wearable biosensing technologies and explores future perspectives, highlighting potential groundbreaking avenues for transforming wearable sensing devices into advanced diagnostic tools with multifunctional capabilities for cardiovascular disease monitoring and other healthcare applications.
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Affiliation(s)
- Bhavadharani Anbuselvam
- School of Chemical & Biotechnology (SCBT), SASTRA Deemed University, Thanjavur 613 401, Tamil Nadu, India
| | - Balu Mahendran Gunasekaran
- School of Chemical & Biotechnology (SCBT), SASTRA Deemed University, Thanjavur 613 401, Tamil Nadu, India; Center for Nanotechnology & Advanced Biomaterials (CENTAB), SASTRA Deemed University, Thanjavur 613 401, Tamil Nadu, India
| | - Soorya Srinivasan
- Department of Mechanical Engineering, IIT Madras, Chennai 600036, Tamil Nadu, India
| | - Madeshwari Ezhilan
- Department of Biomedical Engineering, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Vel Nagar, Avadi, Chennai 600062, Tamil Nadu, India.
| | - Venkatachalam Rajagopal
- Centre for Advanced Materials and Industrial Chemistry (CAMIC), School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - Noel Nesakumar
- School of Chemical & Biotechnology (SCBT), SASTRA Deemed University, Thanjavur 613 401, Tamil Nadu, India; Center for Nanotechnology & Advanced Biomaterials (CENTAB), SASTRA Deemed University, Thanjavur 613 401, Tamil Nadu, India.
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Gudigar A, Kadri NA, Raghavendra U, Samanth J, Maithri M, Inamdar MA, Prabhu MA, Hegde A, Salvi M, Yeong CH, Barua PD, Molinari F, Acharya UR. Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013-2023). Comput Biol Med 2024; 172:108207. [PMID: 38489986 DOI: 10.1016/j.compbiomed.2024.108207] [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: 12/27/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 03/17/2024]
Abstract
Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources.
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Affiliation(s)
- Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Nahrizul Adib Kadri
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia
| | - U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
| | - Jyothi Samanth
- Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, 576104, India
| | - M Maithri
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Mahesh Anil Inamdar
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Mukund A Prabhu
- Department of Cardiology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Ajay Hegde
- Manipal Hospitals, Bengaluru, Karnataka, 560102, India
| | - Massimo Salvi
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnicodi Torino, Turin, Italy
| | - Chai Hong Yeong
- School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500, Subang Jaya, Malaysia
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW, 2010, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD, 4350, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnicodi Torino, Turin, Italy
| | - U Rajendra Acharya
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD, 4300, Australia; Centre for Health Research, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
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Liu XX, Huang PH, Wang YJ, Gao Y. Effects of Aerobic Exercise Combined With Attentional Bias Modification in the Care of Male Patients With a Methamphetamine Use Disorder. J Addict Nurs 2024; 35:E2-E14. [PMID: 38574107 DOI: 10.1097/jan.0000000000000565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
OBJECTIVE It remains unclear which individual or combined strategies are most beneficial for methamphetamine use disorders (MUDs). We compared the effects of aerobic exercise, attentional bias modification, and combined intervention on male patients with MUD. METHOD One hundred male patients with MUD were randomly assigned to combined intervention, aerobic exercise, attentional bias modification, or control groups (25 patients per group). The 8-week intervention protocol included three 60-minute sessions of aerobic exercises per week. Primary outcomes included high- and low-frequency heart rate variability, executive function, and cardiorespiratory fitness measured by customized software, computerized tests, and the Harvard step test, respectively. Secondary outcomes included psychiatric symptoms, drug craving, training acceptability, and persistence. RESULTS Participant characteristics were matched between groups at baseline. Executive function, heart rate variability, cardiorespiratory fitness, drug craving, and most psychiatric symptoms had significant time-group interactions at posttest (p < .05, η2 = .08-.28). Compared with the attentional bias modification and control groups, the combined intervention and aerobic exercise groups improved significantly in executive function, heart rate variability, cardiorespiratory fitness, and most secondary outcomes. In addition, high-frequency heart rate variability and cardiorespiratory fitness in the aerobic exercise group were significantly higher than those in the combined intervention group. CONCLUSIONS Combination strategies showed comparable efficacy to aerobic exercise alone in improving executive function, psychiatric symptoms, and drug craving and significantly exceeded other conditions. For heart rate variability and cardiorespiratory fitness, aerobic exercise alone was the most effective. For acceptability and persistence, combination strategies were preferred over single-domain training and health education intervention.
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Stojmenski A, Gusev M, Chorbev I, Tudjarski S, Poposka L, Vavlukis M. Age and Gender Impact on Heart Rate Variability towards Noninvasive Glucose Measurement. SENSORS (BASEL, SWITZERLAND) 2023; 23:8697. [PMID: 37960397 PMCID: PMC10647381 DOI: 10.3390/s23218697] [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: 08/18/2023] [Revised: 10/01/2023] [Accepted: 10/17/2023] [Indexed: 11/15/2023]
Abstract
Heart rate variability (HRV) parameters can reveal the performance of the autonomic nervous system and possibly estimate the type of its malfunction, such as that of detecting the blood glucose level. Therefore, we aim to find the impact of other factors on the proper calculation of HRV. In this paper, we research the relation between HRV and the age and gender of the patient to adjust the threshold correspondingly to the noninvasive glucose estimator that we are developing and improve its performance. While most of the literature research so far addresses healthy patients and only short- or long-term HRV, we apply a more holistic approach by including both healthy patients and patients with arrhythmia and different lengths of HRV measurements (short, middle, and long). The methods necessary to determine the correlation are (i) point biserial correlation, (ii) Pearson correlation, and (iii) Spearman rank correlation. We developed a mathematical model of a linear or monotonic dependence function and a machine learning and deep learning model, building a classification detector and level estimator. We used electrocardiogram (ECG) data from 4 different datasets consisting of 284 subjects. Age and gender influence HRV with a moderate correlation value of 0.58. This work elucidates the intricate interplay between individual input and output parameters compared with previous efforts, where correlations were found between HRV and blood glucose levels using deep learning techniques. It can successfully detect the influence of each input.
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Affiliation(s)
- Aleksandar Stojmenski
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, 1000 Skopje, North Macedonia; (M.G.); (I.C.); (S.T.)
| | - Marjan Gusev
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, 1000 Skopje, North Macedonia; (M.G.); (I.C.); (S.T.)
| | - Ivan Chorbev
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, 1000 Skopje, North Macedonia; (M.G.); (I.C.); (S.T.)
| | - Stojancho Tudjarski
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, 1000 Skopje, North Macedonia; (M.G.); (I.C.); (S.T.)
| | - Lidija Poposka
- Faculty of Medicine, Ss. Cyril and Methodius University in Skopje, 1000 Skopje, North Macedonia
| | - Marija Vavlukis
- Faculty of Medicine, Ss. Cyril and Methodius University in Skopje, 1000 Skopje, North Macedonia
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Tan J, Wang J, Guo Y, Lu C, Tang W, Zheng L. Effects of 8 months of high-intensity interval training on physical fitness and health-related quality of life in substance use disorder. Front Psychiatry 2023; 14:1093106. [PMID: 37621972 PMCID: PMC10445760 DOI: 10.3389/fpsyt.2023.1093106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 07/12/2023] [Indexed: 08/26/2023] Open
Abstract
Objective This study aimed to investigate the effect of 8 months of high-intensity interval training (HIIT) on physical fitness and health-related quality of life in substance use disorder. Methods Sixty substance use disorder were randomly assigned to either the HIIT group or the control group according to a random sampling method. The HIIT group received 8 months of four 60-min sessions per week under supervision. Weight, waist circumference, body fat percentage, heart rate, blood pressure, VO2max, reaction time, grip strength, standing on one foot with eyes closed, sitting forward flexion, and quadrant jumping, standing on one foot with eyes closed, the number of push-ups, quality of life (SF-36) score, and craving (VAS) scored were monitored in the HIIT and control groups at baseline, 4 months, and 8 months. SPSS 22.0 was used to conduct repeated measurement analysis of variance and Pearson correlation analysis on the collected subject data. Results Compared with baseline, weight (p < 0.001), waist circumference (p < 0.001), body fat percentage (p < 0.001), heart rate (p < 0.05), Systolic blood pressure (p < 0.01), systolic blood pressure (p < 0.05), reaction time (p < 0.001),PSQI (p < 0.001), Total cholesterol (p < 0.001), Triglyceride (p < 0.001), Blood sugar (p < 0.001) and VAS score (p < 0.001) were significantly decreased after 8 months of exercise intervention. Contrastingly, VO2max (p < 0.05), grip strength (p < 0.05), eyes closed and one foot Standing (p < 0.001), sitting forward flexion (p < 0.001), quadrant jumping (p < 0.001), push-ups (p < 0.001), PCS (p < 0.001), and MCS (p < 0.001) were significantly increased. VO2max was significantly negatively correlated with VAS (r = -0.434, p < 0.001), and significantly positively correlated with PCS (r = 0.425, p < 0.001). There was a positive correlation between standing on one foot with closed eyes and MCS (r = 0.283, p < 0.05). Conclusion Eight months of HIIT can comprehensively improve the physical health level and health-related quality of life of men with substance use disorders, reduce the desire for drugs, and lay the foundation for better starting a happy life.
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Affiliation(s)
- Jun Tan
- Hunan Normal University, Changsha, Hunan, China
- Hunan International Economics University, Changsha, Hunan, China
| | | | - Yin Guo
- Hunan Normal University, Changsha, Hunan, China
| | - Chunxia Lu
- Hunan Normal University, Changsha, Hunan, China
| | - Wanke Tang
- Hunan Normal University, Changsha, Hunan, China
| | - Lan Zheng
- Hunan Normal University, Changsha, Hunan, China
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Staffini A, Svensson T, Chung UI, Svensson AK. A Disentangled VAE-BiLSTM Model for Heart Rate Anomaly Detection. Bioengineering (Basel) 2023; 10:683. [PMID: 37370614 DOI: 10.3390/bioengineering10060683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/19/2023] [Accepted: 06/01/2023] [Indexed: 06/29/2023] Open
Abstract
Cardiovascular diseases (CVDs) remain a leading cause of death globally. According to the American Heart Association, approximately 19.1 million deaths were attributed to CVDs in 2020, in particular, ischemic heart disease and stroke. Several known risk factors for CVDs include smoking, alcohol consumption, lack of regular physical activity, and diabetes. The last decade has been characterized by widespread diffusion in the use of wristband-style wearable devices which can monitor and collect heart rate data, among other information. Wearable devices allow the analysis and interpretation of physiological and activity data obtained from the wearer and can therefore be used to monitor and prevent potential CVDs. However, these data are often provided in a manner that does not allow the general user to immediately comprehend possible health risks, and often require further analytics to draw meaningful conclusions. In this paper, we propose a disentangled variational autoencoder (β-VAE) with a bidirectional long short-term memory network (BiLSTM) backend to detect in an unsupervised manner anomalies in heart rate data collected during sleep time with a wearable device from eight heterogeneous participants. Testing was performed on the mean heart rate sampled both at 30 s and 1 min intervals. We compared the performance of our model with other well-known anomaly detection algorithms, and we found that our model outperformed them in almost all considered scenarios and for all considered participants. We also suggest that wearable devices may benefit from the integration of anomaly detection algorithms, in an effort to provide users more processed and straightforward information.
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Affiliation(s)
- Alessio Staffini
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
- Advanced Technology Department, ALBERT Inc., Shinjuku Front Tower 15F, 2-21-1, Kita-Shinjuku, Shinjuku-ku, Tokyo 169-0074, Japan
- Department of Economics and Finance, Catholic University of Milan, Largo Gemelli 1, 20123 Milan, Italy
| | - Thomas Svensson
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
- Graduate School of Health Innovation, Kanagawa University of Human Services, Research Gate Building Tonomachi 2-A 2, 3F, 3-25-10 Tonomachi, Kawasaki-ku, Kawasaki-shi 210-0821, Japan
- Department of Clinical Sciences, Skåne University Hospital, Lund University, 205 02 Malmö, Sweden
| | - Ung-Il Chung
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
- Graduate School of Health Innovation, Kanagawa University of Human Services, Research Gate Building Tonomachi 2-A 2, 3F, 3-25-10 Tonomachi, Kawasaki-ku, Kawasaki-shi 210-0821, Japan
- Clinical Biotechnology, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Akiko Kishi Svensson
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
- Department of Clinical Sciences, Skåne University Hospital, Lund University, 205 02 Malmö, Sweden
- Department of Diabetes and Metabolic Diseases, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
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Deka B, Deka D. Nonlinear analysis of heart rate variability signals in meditative state: a review and perspective. Biomed Eng Online 2023; 22:35. [PMID: 37055770 PMCID: PMC10103447 DOI: 10.1186/s12938-023-01100-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 04/03/2023] [Indexed: 04/15/2023] Open
Abstract
INTRODUCTION In recent times, an upsurge in the investigation related to the effects of meditation in reconditioning various cardiovascular and psychological disorders is seen. In majority of these studies, heart rate variability (HRV) signal is used, probably for its ease of acquisition and low cost. Although understanding the dynamical complexity of HRV is not an easy task, the advances in nonlinear analysis has significantly helped in analyzing the impact of meditation of heart regulations. In this review, we intend to present the various nonlinear approaches, scientific findings and their limitations to develop deeper insights to carry out further research on this topic. RESULTS Literature have shown that research focus on nonlinear domain is mainly concentrated on assessing predictability, fractality, and entropy-based dynamical complexity of HRV signal. Although there were some conflicting results, most of the studies observed a reduced dynamical complexity, reduced fractal dimension, and decimated long-range correlation behavior during meditation. However, techniques, such as multiscale entropy (MSE) and multifractal analysis (MFA) of HRV can be more effective in analyzing non-stationary HRV signal, which were hardly used in the existing research works on meditation. CONCLUSIONS After going through the literature, it is realized that there is a requirement of a more rigorous research to get consistent and new findings about the changes in HRV dynamics due to the practice of meditation. The lack of adequate standard open access database is a concern in drawing statistically reliable results. Albeit, data augmentation technique is an alternative option to deal with this problem, data from adequate number of subjects can be more effective. Multiscale entropy analysis is scantily employed in studying the effect of meditation, which probably need more attention along with multifractal analysis. METHODS Scientific databases, namely PubMed, Google Scholar, Web of Science, Scopus were searched to obtain the literature on "HRV analysis during meditation by nonlinear methods". Following an exclusion criteria, 26 articles were selected to carry out this scientific analysis.
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Affiliation(s)
- Bhabesh Deka
- Department of ECE, School of Engineering, Tezpur University, Assam, India.
| | - Dipen Deka
- Department of ECE, School of Engineering, Tezpur University, Assam, India
- Department of Instrumentation Engineering, Central Institute of Technology, Kokrajhar, India
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Paragliola G. A federated learning-based approach to recognize subjects at a high risk of hypertension in a non-stationary scenario. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2022.11.126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Asai Y, Tashiro T, Kondo Y, Hayashi M, Arihara H, Omote S, Tanio E, Yamashita S, Higuchi T, Hashimoto E, Yamada M, Tsuji H, Hayakawa Y, Suzuki R, Muro H, Yamamoto Y. Machine Learning-Based Prediction of Digoxin Toxicity in Heart Failure: A Multicenter Retrospective Study. Biol Pharm Bull 2023; 46:614-620. [PMID: 37005306 DOI: 10.1248/bpb.b22-00823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
Digoxin toxicity (plasma digoxin concentration ≥0.9 ng/mL) is associated with worsening heart failure (HF). Decision tree (DT) analysis, a machine learning method, has a flowchart-like model where users can easily predict the risk of adverse drug reactions. The present study aimed to construct a flowchart using DT analysis that can be used by medical staff to predict digoxin toxicity. We conducted a multicenter retrospective study involving 333 adult patients with HF who received oral digoxin treatment. In this study, we employed a chi-squared automatic interaction detection algorithm to construct DT models. The dependent variable was set as the plasma digoxin concentration (≥ 0.9 ng/mL) in the trough during the steady state, and factors with p < 0.2 in the univariate analysis were set as the explanatory variables. Multivariate logistic regression analysis was conducted to validate the DT model. The accuracy and misclassification rates of the model were evaluated. In the DT analysis, patients with creatinine clearance <32 mL/min, daily digoxin dose ≥1.6 µg/kg, and left ventricular ejection fraction ≥50% showed a high incidence of digoxin toxicity (91.8%; 45/49). Multivariate logistic regression analysis revealed that creatinine clearance <32 mL/min and daily digoxin dose ≥1.6 µg/kg were independent risk factors. The accuracy and misclassification rates of the DT model were 88.2 and 46.2 ± 2.7%, respectively. Although the flowchart created in this study needs further validation, it is straightforward and potentially useful for medical staff in determining the initial dose of digoxin in patients with HF.
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Affiliation(s)
- Yuki Asai
- Pharmacy, National Hospital Organization Mie Chuo Medical Center
| | - Takumi Tashiro
- Pharmacy, National Hospital Organization Shizuoka Medical Center
| | - Yoshihiro Kondo
- Pharmacy, National Hospital Organization Nagoya Medical Center
| | - Makoto Hayashi
- Pharmacy, National Hospital Organization Nagoya Medical Center
| | - Hiroki Arihara
- Pharmacy, National Hospital Organization Kanazawa Medical Center
| | - Saki Omote
- Pharmacy, National Hospital Organization Kanazawa Medical Center
| | - Ena Tanio
- Pharmacy, National Hospital Organization Kanazawa Medical Center
| | - Saena Yamashita
- Pharmacy, National Hospital Organization Kanazawa Medical Center
| | - Takashi Higuchi
- Pharmacy, National Hospital Organization Kanazawa Medical Center
| | - Ei Hashimoto
- Pharmacy, National Hospital Organization Kanazawa Medical Center
| | - Momoko Yamada
- Pharmacy, National Hospital Organization Kanazawa Medical Center
| | - Hinako Tsuji
- Pharmacy, National Hospital Organization Kanazawa Medical Center
| | - Yuji Hayakawa
- Pharmacy, National Center for Geriatrics and Gerontology
| | - Ryohei Suzuki
- Pharmacy, National Hospital Organization Higashinagoya National Hospital
| | - Hiroya Muro
- Pharmacy, National Hospital Organization Toyohashi Medical Center
| | - Yoshiaki Yamamoto
- Department of Clinical Research, National Hospital Organization Shizuoka Institute of Epilepsy and Neurological Disorders
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Tang L, Yang J, Wang Y, Deng R. Recent Advances in Cardiovascular Disease Biosensors and Monitoring Technologies. ACS Sens 2023; 8:956-973. [PMID: 36892106 DOI: 10.1021/acssensors.2c02311] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
Cardiovascular disease (CVD) causes significant mortality and remains the leading cause of death globally. Thus, to reduce mortality, early diagnosis by measurement of cardiac biomarkers and heartbeat signals presents fundamental importance. Traditional CVD examination requires bulky hospital instruments to conduct electrocardiography recording and immunoassay analysis, which are both time-consuming and inconvenient. Recently, development of biosensing technologies for rapid CVD marker screening attracted great attention. Thanks to the advancement in nanotechnology and bioelectronics, novel biosensor platforms are developed to achieve rapid detection, accurate quantification, and continuous monitoring throughout disease progression. A variety of sensing methodologies using chemical, electrochemical, optical, and electromechanical means are explored. This review first discusses the prevalence and common categories of CVD. Then, heartbeat signals and cardiac blood-based biomarkers that are widely employed in clinic, as well as their utilizations for disease prognosis, are summarized. Emerging CVD wearable and implantable biosensors and monitoring bioelectronics, allowing these cardiac markers to be continuously measured are introduced. Finally, comparisons of the pros and cons of these biosensing devices along with perspectives on future CVD biosensor research are presented.
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Affiliation(s)
- Lichao Tang
- Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, 60208, Illinois, United States
| | - Jiyuan Yang
- Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, 47906, Indiana, United States
| | - Yuxi Wang
- Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
- Precision Medicine Key Laboratory of Sichuan Province & Precision Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Ruijie Deng
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu, 610064, Sichuan, China
- Precision Medicine Key Laboratory of Sichuan Province & Precision Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
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Asai Y, Ooi H, Sato Y. Risk evaluation of carbapenem-induced liver injury based on machine learning analysis. J Infect Chemother 2023; 29:660-666. [PMID: 36914094 DOI: 10.1016/j.jiac.2023.03.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/06/2023] [Accepted: 03/09/2023] [Indexed: 03/13/2023]
Abstract
INTRODUCTION Information regarding carbapenem-induced liver injury is limited, and the rate of liver injury caused by meropenem (MEPM) and doripenem (DRPM) remains unknown. Decision tree (DT) analysis, a machine learning method, has a flowchart-like model where users can easily predict the risk of liver injury. Thus, we aimed to compare the rate of liver injury between MEPM and DRPM and construct a flowchart that can be used to predict carbapenem-induced liver injury. METHODS We investigated patients treated with MEPM (n = 310) or DRPM (n = 320) and confirmed liver injury as the primary outcome. We used a chi-square automatic interaction detection algorithm to construct DT models. The dependent variable was set as liver injury from a carbapenem (MEPM or DRPM), and factors including alanine aminotransferase (ALT), albumin-bilirubin (ALBI) score, and concomitant use of acetaminophen were used as explanatory variables. RESULTS The rates of liver injury were 22.9% (71/310) and 17.5% (56/320) in the MEPM and DRPM groups, respectively; no significant differences in the rate were observed (95% confidence interval: 0.710-1.017). Although the DT model of MEPM could not be constructed, DT analysis showed that the incidence of introducing DRPM in patients with ALT >22 IU/L and ALBI scores > -1.87 might be high-risk. CONCLUSIONS The risk of developing liver injury did not differ significantly between the MEPM and DRPM groups. Since ALT and ALBI score are evaluated in clinical settings, this DT model is convenient and potentially useful for medical staff in assessing liver injury before DRPM administration.
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Affiliation(s)
- Yuki Asai
- Pharmacy, National Hospital Organization Mie Chuo Medical Center, 2158-5 Hisaimyojin, Tsu, Mie, 514-1101, Japan.
| | - Hayahide Ooi
- Pharmacy, National Hospital Organization Mie Chuo Medical Center, 2158-5 Hisaimyojin, Tsu, Mie, 514-1101, Japan
| | - Yoshiharu Sato
- Pharmacy, National Hospital Organization Mie Chuo Medical Center, 2158-5 Hisaimyojin, Tsu, Mie, 514-1101, Japan
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12
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Tian Y, Pan Y, Wang M, Meng X, Zhao X, Liu L, Wang Y, Wang Y. The combination of heart rate variability and ABCD 2 score portends adverse outcomes after minor stroke or transient ischemic attack. J Neurol Sci 2023; 445:120522. [PMID: 36634579 DOI: 10.1016/j.jns.2022.120522] [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: 07/29/2022] [Revised: 11/06/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND PURPOSE The residual recurrent risk of stroke, which cannot be entirely explained by the traditional ABCD2 score, still existed. Heart rate variability (HRV), a method for reflecting the function of automatic nervous system (ANS), was a novel predictor of secondary stroke events. We aimed to investigate the relationships of combined HRV and ABCD2 score with adverse outcomes after acute minor stroke (MS) or transient ischemic attack (TIA), and further investigate the independent associations between HRV and adverse outcomes after MS/TIA stratified by ABCD2 score. METHODS Data were obtained from the Third China National Stroke Registry (CNSR-III) study. We assessed the activity of ANS using standard deviation of NN intervals (SDNN), a time domain index of HRV. Trained investigators collected clinical characteristics and estimated ABCD2 score for each participant. All enrolled patients were categorized into different risk groups based on SDNN level and ABCD2 score. The clinial outcomes included recurrent stroke, recurrent ischemic stroke, and disability within 1-year follow-up. We evaluated whether combined SDNN and ABCD2 score were associated with recurrent events using multivariable Cox regression models, and those with disability using multivariable logistic regression models. The independent associations between SDNN and diverse outcomes stratified by ABCD2 score were explored using multivariable Cox and logistic regression analyses. RESULTS A total of 5,743 participants [3,316 (70.02) males, 62.0 (54.0-69.0) years] were included. Patients with low SDNN and ABCD2 ≥ 4 were associated with higher risk of recurrent stroke within 1 year (10.8% versus 4.9%; [HR] 1.31, 95% [CI] 0.92-1.88, P = 0.14) compared to patients with high SDNN with ABCD2 < 4. Lower SDNN was associated with higher recurrent stroke in patients with ABCD2 0-3 score ([HR] 0.73, 95% [CI] 0.57-0.947, P = 0.01) and ABCD2 4-5 score ([HR] 0.85, 95% [CI] 0.74-0.97, P = 0.02), but not in patients with ABCD2 6-7 score. CONCLUSION The combination of HRV and ABCD2 score might efficiently stratify the risk of 1-year recurrent stroke after MS/TIA. Moreover, lower SDNN was independently related to recurrent stroke in patients with MS/TIA, especially for those with low-to-moderate traditional vascular risk factors.
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Affiliation(s)
- Yu Tian
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Chinese Institute for Brain Research, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China; Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Yuesong Pan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Chinese Institute for Brain Research, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China; Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Mengxing Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Chinese Institute for Brain Research, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China; Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Xia Meng
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Chinese Institute for Brain Research, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China; Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Xingquan Zhao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Chinese Institute for Brain Research, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China; Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Liping Liu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Chinese Institute for Brain Research, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China; Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Chinese Institute for Brain Research, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China; Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Yilong Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Chinese Institute for Brain Research, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China; Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China.
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13
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Nawaz M, Ahmed J. Cloud-based healthcare framework for real-time anomaly detection and classification of 1-D ECG signals. PLoS One 2022; 17:e0279305. [PMID: 36574391 PMCID: PMC9794080 DOI: 10.1371/journal.pone.0279305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 12/05/2022] [Indexed: 12/28/2022] Open
Abstract
Real-time data collection and pre-processing have enabled the recognition, realization, and prediction of diseases by extracting and analysing the important features of physiological data. In this research, an intelligent end-to-end system for anomaly detection and classification of raw, one-dimensional (1D) electrocardiogram (ECG) signals is given to assess cardiovascular activity automatically. The acquired raw ECG data is pre-processed carefully before storing it in the cloud, and then deeply analyzed for anomaly detection. A deep learning-based auto-encoder(AE) algorithm is applied for the anomaly detection of 1D ECG time-series signals. As a next step, the implemented system identifies it by a multi-label classification algorithm. To improve the classification accuracy and model robustness the improved feature-engineered parameters of the large and diverse datasets have been incorporated. The training has been done using the amazon web service (AWS) machine learning services and cloud-based storage for a unified solution. Multi-class classification of raw ECG signals is challenging due to a large number of possible label combinations and noise susceptibility. To overcome this problem, a performance comparison of a large set of machine algorithms in terms of classification accuracy is presented on an improved feature-engineered dataset. The proposed system reduces the raw signal size up to 95% using wavelet time scattering features to make it less compute-intensive. The results show that among several state-of-the-art techniques, the long short-term memory (LSTM) method has shown 100% classification accuracy, and an F1 score on the three-class test dataset. The ECG signal anomaly detection algorithm shows 98% accuracy using deep LSTM auto-encoders with a reconstructed error threshold of 0.02 in terms of absolute error loss. Our approach provides performance and predictive improvement with an average mean absolute error loss of 0.0072 for normal signals and 0.078 for anomalous signals.
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Affiliation(s)
- Menaa Nawaz
- Department of electrical engineering, Riphah International University, Islamabad, Pakistan
- * E-mail:
| | - Jameel Ahmed
- Department of electrical engineering, Riphah International University, Islamabad, Pakistan
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14
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Khan Mamun MMR, Sherif A. Advancement in the Cuffless and Noninvasive Measurement of Blood Pressure: A Review of the Literature and Open Challenges. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 10:bioengineering10010027. [PMID: 36671599 PMCID: PMC9854981 DOI: 10.3390/bioengineering10010027] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 12/20/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022]
Abstract
Hypertension is a chronic condition that is one of the prominent reasons behind cardiovascular disease, brain stroke, and organ failure. Left unnoticed and untreated, the deterioration in a health condition could even result in mortality. If it can be detected early, with proper treatment, undesirable outcomes can be avoided. Until now, the gold standard is the invasive way of measuring blood pressure (BP) using a catheter. Additionally, the cuff-based and noninvasive methods are too cumbersome or inconvenient for frequent measurement of BP. With the advancement of sensor technology, signal processing techniques, and machine learning algorithms, researchers are trying to find the perfect relationships between biomedical signals and changes in BP. This paper is a literature review of the studies conducted on the cuffless noninvasive measurement of BP using biomedical signals. Relevant articles were selected using specific criteria, then traditional techniques for BP measurement were discussed along with a motivation for cuffless measurement use of biomedical signals and machine learning algorithms. The review focused on the progression of different noninvasive cuffless techniques rather than comparing performance among different studies. The literature survey concluded that the use of deep learning proved to be the most accurate among all the cuffless measurement techniques. On the other side, this accuracy has several disadvantages, such as lack of interpretability, computationally extensive, standard validation protocol, and lack of collaboration with health professionals. Additionally, the continuing work by researchers is progressing with a potential solution for these challenges. Finally, future research directions have been provided to encounter the challenges.
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Affiliation(s)
| | - Ahmed Sherif
- School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, MS 39406, USA
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15
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Moshawrab M, Adda M, Bouzouane A, Ibrahim H, Raad A. Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22197472. [PMID: 36236570 PMCID: PMC9573761 DOI: 10.3390/s22197472] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/28/2022] [Accepted: 09/29/2022] [Indexed: 05/13/2023]
Abstract
Today's world is changing dramatically due to the influence of various factors. Whether due to the rapid development of technological tools, advances in telecommunication methods, global economic and social events, or other reasons, almost everything is changing. As a result, the concepts of a "job" or work have changed as well, with new work shifts being introduced and the office no longer being the only place where work is done. In addition, our non-stop active society has increased the stress and pressure at work, causing fatigue to spread worldwide and becoming a global problem. Moreover, it is medically proven that persistent fatigue is a cause of serious diseases and health problems. Therefore, monitoring and detecting fatigue in the workplace is essential to improve worker safety in the long term. In this paper, we provide an overview of the use of smart wearable devices to monitor and detect occupational physical fatigue. In addition, we present and discuss the challenges that hinder this field and highlight what can be done to advance the use of smart wearables in workplace fatigue detection.
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Affiliation(s)
- Mohammad Moshawrab
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
- Correspondence: ; Tel.: +1-(581)624-9394
| | - Mehdi Adda
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
| | - Abdenour Bouzouane
- Département d’Informatique et de Mathématique, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Chicoutimi, QC G7H 2B1, Canada
| | - Hussein Ibrahim
- Institut Technologique de Maintenance Industrielle, 175 Rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada
| | - Ali Raad
- Faculty of Arts & Sciences, Islamic University of Lebanon, Wardaniyeh P.O. Box 30014, Lebanon
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Huhn S, Matzke I, Koch M, Gunga HC, Maggioni MA, Sié A, Boudo V, Ouedraogo WA, Compaoré G, Bunker A, Sauerborn R, Bärnighausen T, Barteit S. Using wearable devices to generate real-world, individual-level data in rural, low-resource contexts in Burkina Faso, Africa: A case study. Front Public Health 2022; 10:972177. [PMID: 36249225 PMCID: PMC9561896 DOI: 10.3389/fpubh.2022.972177] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/30/2022] [Indexed: 01/25/2023] Open
Abstract
Background Wearable devices may generate valuable data for global health research for low- and middle-income countries (LMICs). However, wearable studies in LMICs are scarce. This study aims to investigate the use of consumer-grade wearables to generate individual-level data in vulnerable populations in LMICs, focusing on the acceptability (quality of the devices being accepted or even liked) and feasibility (the state of being workable, realizable, and practical, including aspects of data completeness and plausibility). Methods We utilized a mixed-methods approach within the health and demographic surveillance system (HDSS) to conduct a case study in Nouna, Burkina Faso (BF). All HDSS residents older than 6 years were eligible. N = 150 participants were randomly selected from the HDSS database to wear a wristband tracker (Withings Pulse HR) and n = 69 also a thermometer patch (Tucky thermometer) for 3 weeks. Every 4 days, a trained field worker conducted an acceptability questionnaire with participants, which included questions for the field workers as well. Descriptive and qualitative thematic analyses were used to analyze the responses of study participants and field workers. Results In total, n = 148 participants were included (and n = 9 field workers). Participant's acceptability ranged from 94 to 100% throughout the questionnaire. In 95% of the cases (n = 140), participants reported no challenges with the wearable. Most participants were not affected by the wearable in their daily activities (n = 122, 83%) and even enjoyed wearing them (n = 30, 20%). Some were concerned about damage to the wearables (n = 7, 5%). Total data coverage (i.e., the proportion of the whole 3-week study duration covered by data) was 43% for accelerometer (activity), 3% for heart rate, and 4% for body shell temperature. Field workers reported technical issues like faulty synchronization (n = 6, 1%). On average, participants slept 7 h (SD 3.2 h) and walked 8,000 steps per day (SD 5573.6 steps). Acceptability and data completeness were comparable across sex, age, and study arms. Conclusion Wearable devices were well-accepted and were able to produce continuous measurements, highlighting the potential for wearables to generate large datasets in LMICs. Challenges constituted data missingness mainly of technical nature. To our knowledge, this is the first study to use consumer-focused wearables to generate objective datasets in rural BF.
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Affiliation(s)
- Sophie Huhn
- Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany,*Correspondence: Sophie Huhn
| | - Ina Matzke
- Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany
| | - Mara Koch
- Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany
| | - Hanns-Christian Gunga
- Charité – Universitätsmedizin Berlin, Institute of Physiology, Center for Space Medicine and Extreme Environments Berlin, Berlin, Germany
| | - Martina Anna Maggioni
- Charité – Universitätsmedizin Berlin, Institute of Physiology, Center for Space Medicine and Extreme Environments Berlin, Berlin, Germany,Department of Biomedical Sciences for Health, Università Degli Studi di Milano, Milano, Italy
| | - Ali Sié
- Centre de Recherche en Santé, Nouna, Burkina Faso
| | | | | | | | - Aditi Bunker
- Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany
| | - Rainer Sauerborn
- Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany
| | - Till Bärnighausen
- Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany,Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, United States,Africa Health Research Institute (AHRI), KwaZulu-Natal, South Africa
| | - Sandra Barteit
- Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany
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17
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Kim RB, Alge OP, Liu G, Biesterveld BE, Wakam G, Williams AM, Mathis MR, Najarian K, Gryak J. Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system. Sci Rep 2022; 12:11347. [PMID: 35790802 PMCID: PMC9256604 DOI: 10.1038/s41598-022-15496-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 06/24/2022] [Indexed: 12/01/2022] Open
Abstract
Postoperative patients are at risk of life-threatening complications such as hemodynamic decompensation or arrhythmia. Automated detection of patients with such risks via a real-time clinical decision support system may provide opportunities for early and timely interventions that can significantly improve patient outcomes. We utilize multimodal features derived from digital signal processing techniques and tensor formation, as well as the electronic health record (EHR), to create machine learning models that predict the occurrence of several life-threatening complications up to 4 hours prior to the event. In order to ensure that our models are generalizable across different surgical cohorts, we trained the models on a cardiac surgery cohort and tested them on vascular and non-cardiac acute surgery cohorts. The best performing models achieved an area under the receiver operating characteristic curve (AUROC) of 0.94 on training and 0.94 and 0.82, respectively, on testing for the 0.5-hour interval. The AUROCs only slightly dropped to 0.93, 0.92, and 0.77, respectively, for the 4-hour interval. This study serves as a proof-of-concept that EHR data and physiologic waveform data can be combined to enable the early detection of postoperative deterioration events.
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Affiliation(s)
- Renaid B Kim
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Olivia P Alge
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Gang Liu
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Ben E Biesterveld
- Department of Surgery, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Glenn Wakam
- Department of Surgery, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Aaron M Williams
- Department of Surgery, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Michael R Mathis
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 48109, USA.,Michigan Center for Integrative Research in Critical Care (MCIRCC), University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jonathan Gryak
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA. .,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 48109, USA.
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Gupta K, Bajaj V, Ansari IA, Rajendra Acharya U. Hyp-Net: Automated detection of hypertension using deep convolutional neural network and Gabor transform techniques with ballistocardiogram signals. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.06.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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19
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ECG signal based automated hypertension detection using fourier decomposition method and cosine modulated filter banks. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103629] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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20
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Park HS, Seo K, Kim HS, Im SI, Kim BJ, Kim BK, Heo JH. Postoperative effects of bariatric surgery on heart rate recovery and heart rate variability. KOSIN MEDICAL JOURNAL 2022. [DOI: 10.7180/kmj.22.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Background: Several studies have reported associations between obesity and autonomic dysfunction. However, little research has investigated the effect of bariatric surgery on heart rate recovery (HRR) in the treadmill test and heart rate variability (HRV) in 24-hour Holter monitoring. We investigated the effects of bariatric surgery on HRR and HRV, which are parameters related to autonomic dysfunction. Methods: We retrospectively investigated patients who underwent bariatric surgery in 2019. The treadmill test, 24-hour Holter monitoring, and echocardiography were performed before and 6 months after surgery. We compared the changes in HRR in the treadmill test and HRV parameters such as the time domain and spectral domain in 24-hour Holter monitoring before and after surgery. Results: Of the 40 patients who underwent bariatric surgery, 25 patients had the treadmill test or 24-hour Holter monitoring both before and after surgery. Body weight and body mass index significantly decreased after surgery (112.86±24.37 kg vs. 89.10±20.26 kg, p<0.001; 39.22±5.69 kg/m2 vs. 31.00±5.09 kg/m2, p<0.001, respectively). HRR significantly increased (n=23; 43.00±20.97 vs. 64.29±18.49, p=0.001). The time domain of HRV parameters increased (n=21; standard deviation of the N-N interval 123.57±28.05 vs. 152.57±39.49, p=0.002 and mean N-N interval 791.57±88.84 vs. 869.05±126.31, p=0.002).Conclusions: Our data showed that HRR after exercise and HRV during 24-hour Holter monitoring improved after weight reduction with bariatric surgery through improved cardiac autonomic function.
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Faust O, Hong W, Loh HW, Xu S, Tan RS, Chakraborty S, Barua PD, Molinari F, Acharya UR. Heart rate variability for medical decision support systems: A review. Comput Biol Med 2022; 145:105407. [DOI: 10.1016/j.compbiomed.2022.105407] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/09/2022] [Accepted: 03/12/2022] [Indexed: 12/22/2022]
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22
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Leveraging Continuous Vital Sign Measurements for Real-Time Assessment of Autonomic Nervous System Dysfunction After Brain Injury: A Narrative Review of Current and Future Applications. Neurocrit Care 2022; 37:206-219. [PMID: 35411542 DOI: 10.1007/s12028-022-01491-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 03/14/2022] [Indexed: 02/03/2023]
Abstract
Subtle and profound changes in autonomic nervous system (ANS) function affecting sympathetic and parasympathetic homeostasis occur as a result of critical illness. Changes in ANS function are particularly salient in neurocritical illness, when direct structural and functional perturbations to autonomic network pathways occur and may herald impending clinical deterioration or intervenable evolving mechanisms of secondary injury. Sympathetic and parasympathetic balance can be measured quantitatively at the bedside using multiple methods, most readily by extracting data from electrocardiographic or photoplethysmography waveforms. Work from our group and others has demonstrated that data-analytic techniques can identify quantitative physiologic changes that precede clinical detection of meaningful events, and therefore may provide an important window for time-sensitive therapies. Here, we review data-analytic approaches to measuring ANS dysfunction from routine bedside physiologic data streams and integrating this data into multimodal machine learning-based model development to better understand phenotypical expression of pathophysiologic mechanisms and perhaps even serve as early detection signals. Attention will be given to examples from our work in acute traumatic brain injury on detection and monitoring of paroxysmal sympathetic hyperactivity and prediction of neurologic deterioration, and in large hemispheric infarction on prediction of malignant cerebral edema. We also discuss future clinical applications and data-analytic challenges and future directions.
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Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, Katsaggelos AK, Maglaveras N. State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint). JMIR Med Inform 2022; 10:e38454. [PMID: 35969441 PMCID: PMC9425174 DOI: 10.2196/38454] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/03/2022] [Accepted: 07/03/2022] [Indexed: 11/13/2022] Open
Abstract
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.
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Affiliation(s)
- Georgios Petmezas
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Leandros Stefanopoulos
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vassilis Kilintzis
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreas Tzavelis
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
| | - John A Rogers
- Department of Material Science, Northwestern University, Evanston, IL, United States
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Nicos Maglaveras
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Ma R, Gao J, Mao S, Wang Z. Association between heart rate and cardiovascular death in patients with coronary heart disease: A NHANES-based cohort study. Clin Cardiol 2022; 45:574-582. [PMID: 35352385 PMCID: PMC9045079 DOI: 10.1002/clc.23818] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 03/08/2022] [Accepted: 03/11/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Due to the lack of research, this study aimed to assess the association between the specific range of heart rate and cardiovascular (CV) death in coronary heart disease (CHD) patients. HYPOTHESIS Heart rate of 70-79 bpm may be associated with reduced risk of CV death in CHD patients. METHODS This retrospective cohort study collected the data of CHD patients from the eight cycles of the Health and Nutrition Examination Survey (NHANES). The included patients were divided into four groups: <60, 60-69, 70-79, and ≥80 bpm. The start of follow-up date was the mobile examination center date, the last follow-up date was December 31, 2015. The average follow-up time was 81.70 months, and the longest follow-up time was 200 months. Competing risk models were developed to evaluate the association between heart rate and CV death, with hazard ratios (HRs) and 95% confidence intervals (CIs) calculated. RESULTS A total of 1648 patients with CHD were included in this study. CHD patients at heart rate of <60 (HR, 1.35; 95% CI, 1.34-1.36), 60-69 (HR, 1.05; 95% CI, 1.04-1.06) or ≥80 (HR, 1.39; 95% CI, 1.38-1.41) bpm had a higher risk of CV death than those at heart rate of 70-79 bpm. CONCLUSIONS Heart rate of <70 or ≥80 bpm was associated with an elevated risk of CV death among CHD patients. Continuous monitoring of heart rate may help to screen for health risks and offer early interventions to corresponding patients.
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Affiliation(s)
- Ruicong Ma
- Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Jianbo Gao
- Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Shiyuan Mao
- Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Zhirong Wang
- Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
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Rajput JS, Sharma M, Kumar TS, Acharya UR. Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19074014. [PMID: 35409698 PMCID: PMC8997686 DOI: 10.3390/ijerph19074014] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/19/2022] [Accepted: 03/22/2022] [Indexed: 01/27/2023]
Abstract
Managing hypertension (HPT) remains a significant challenge for humanity. Despite advancements in blood pressure (BP)-measuring systems and the accessibility of effective and safe anti-hypertensive medicines, HPT is a major public health concern. Headaches, dizziness and fainting are common symptoms of HPT. In HPT patients, normalcy may be observed at one instant and abnormality may prevail during a long duration of 24 h ambulatory BP. This may cause difficulty in identifying patients with HPT, and hence there is a possibility that individuals may be untreated or administered insufficiently. Most importantly, uncontrolled HPT can lead to severe complications (stroke, heart attack, kidney disease, and heart failure), mainly ignoring the signs in nascent stages. HPT in the beginning stages may not present distinct symptoms and may be difficult to diagnose from standard physiological signals. Hence, ballistocardiography (BCG) signal was used in this study to detect HPT automatically. The processed signals from BCG were converted into scalogram images using a continuous wavelet transform (CWT) and were then fed into a 2-D convolutional neural network model (2D-CNN). The model was trained to learn and recognize BCG patterns of healthy controls (HC) and HPT classes. Our proposed model obtained a high classification accuracy of 86.14% with a ten-fold cross-validation (CV) strategy. Hence, this is the first use of a 2D-CNN model (deep-learning algorithm) to detect HPT employing BCG signals.
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Affiliation(s)
- Jaypal Singh Rajput
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India; (J.S.R.); (T.S.K.)
| | - Manish Sharma
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India; (J.S.R.); (T.S.K.)
- Correspondence:
| | - T. Sudheer Kumar
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India; (J.S.R.); (T.S.K.)
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 639798, Singapore;
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- Department of Biomedical Engineering, School of Science and Technology, Singapore 639798, Singapore
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26
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Chen Z, Shi J, Pommier T, Cottin Y, Salomon M, Decourselle T, Lalande A, Couturier R. Prediction of Myocardial Infarction From Patient Features With Machine Learning. Front Cardiovasc Med 2022; 9:754609. [PMID: 35369326 PMCID: PMC8964399 DOI: 10.3389/fcvm.2022.754609] [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] [Received: 08/06/2021] [Accepted: 02/02/2022] [Indexed: 11/24/2022] Open
Abstract
This study proposes machine learning-based models to automatically evaluate the severity of myocardial infarction (MI) from physiological, clinical, and paraclinical features. Two types of machine learning models are investigated for the MI assessment: the classification models classify the presence of the infarct and the persistent microvascular obstruction (PMO), and the regression models quantify the Percentage of Infarcted Myocardium (PIM) of patients suspected of having an acute MI during their reception in the emergency department. The ground truth labels for these supervised models are derived from the corresponding Delayed Enhancement MRI (DE-MRI) exams and manual annotations of the myocardium and scar tissues. Experiments were conducted on 150 cases and evaluated with cross-validation. Results showed that for the MI (PMO inclusive) and the PMO (infarct exclusive), the best models obtained respectively a mean error of 0.056 and 0.012 for the quantification, and 88.67 and 77.33% for the classification accuracy of the state of the myocardium. The study of the features' importance also revealed that the troponin value had the strongest correlation to the severity of the MI among the 12 selected features. For the proposal's translational perspective, in cardiac emergencies, qualitative and quantitative analysis can be obtained prior to the achievement of MRI by relying only on conventional tests and patient features, thus, providing an objective reference for further treatment by physicians.
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Affiliation(s)
- Zhihao Chen
- FEMTO-ST Institute, UMR 6174 CNRS, Univ. Bourgogne Franche-Comté, Belfort, France
| | - Jixi Shi
- FEMTO-ST Institute, UMR 6174 CNRS, Univ. Bourgogne Franche-Comté, Belfort, France
- IRSEEM, EA 4353, ESIGELEC, Univ. Normandie, Saint-Étienne-du-Rouvray, France
| | - Thibaut Pommier
- Department of Cardiology, University Hospital of Dijon, Dijon, France
| | - Yves Cottin
- Department of Cardiology, University Hospital of Dijon, Dijon, France
| | - Michel Salomon
- FEMTO-ST Institute, UMR 6174 CNRS, Univ. Bourgogne Franche-Comté, Belfort, France
| | | | - Alain Lalande
- Department of Medical Imaging, University Hospital of Dijon, Dijon, France
- ImViA Laboratory, EA 7535, Univ. Bourgogne Franche-Comté, Dijon, France
| | - Raphaël Couturier
- FEMTO-ST Institute, UMR 6174 CNRS, Univ. Bourgogne Franche-Comté, Belfort, France
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27
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Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and Prospects. SENSORS 2022; 22:s22030756. [PMID: 35161502 PMCID: PMC8840097 DOI: 10.3390/s22030756] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/11/2022] [Accepted: 01/15/2022] [Indexed: 12/23/2022]
Abstract
Wearable devices use sensors to evaluate physiological parameters, such as the heart rate, pulse rate, number of steps taken, body fat and diet. The continuous monitoring of physiological parameters offers a potential solution to assess personal healthcare. Identifying outliers or anomalies in heart rates and other features can help identify patterns that can play a significant role in understanding the underlying cause of disease states. Since anomalies are present within the vast amount of data generated by wearable device sensors, identifying anomalies requires accurate automated techniques. Given the clinical significance of anomalies and their impact on diagnosis and treatment, a wide range of detection methods have been proposed to detect anomalies. Much of what is reported herein is based on previously published literature. Clinical studies employing wearable devices are also increasing. In this article, we review the nature of the wearables-associated data and the downstream processing methods for detecting anomalies. In addition, we also review supervised and un-supervised techniques as well as semi-supervised methods that overcome the challenges of missing and un-annotated healthcare data.
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28
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von Rennenberg R, Krause T, Herm J, Hellwig S, Scheitz JF, Endres M, Haeusler KG, Nolte CH. Heart Rate Variability and Recurrent Stroke and Myocardial Infarction in Patients With Acute Mild to Moderate Stroke. Front Neurol 2022; 12:772674. [PMID: 35002927 PMCID: PMC8733333 DOI: 10.3389/fneur.2021.772674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 12/08/2021] [Indexed: 11/13/2022] Open
Abstract
Objectives: In patients with acute ischemic stroke, reduced heart rate variability (HRV) may indicate poor outcome. We tested whether HRV in the acute phase of stroke is associated with higher rates of mortality, recurrent stroke, myocardial infarction (MI) or functional outcome. Materials and Methods: Patients with acute mild to moderate ischemic stroke without known atrial fibrillation were prospectively enrolled to the investigator-initiated Heart and Brain interfaces in Acute Ischemic Stroke (HEBRAS) study (NCT 02142413). HRV parameters were assessed during the in-hospital stay using a 10-min section of each patient's ECG recording at day- and nighttime, calculating time and frequency domain HRV parameters. Frequency of a combined endpoint of recurrent stroke, MI or death of any cause and the respective individual events were assessed 12 months after the index stroke. Patients' functional outcome was measured by the modified Rankin Scale (mRS) at 12 months. Results: We included 308 patients (37% female, median NIHSS = 2 on admission, median age 69 years). Complete follow-up was achieved in 286/308 (93%) patients. At 12 months, 32 (9.5%), 5 (1.7%) and 13 (3.7%) patients had suffered a recurrent stroke, MI or death, respectively. After adjustment for age, sex, stroke severity and vascular risk factors, there was no significant association between HRV and recurrent stroke, MI, death or the combined endpoint. We did not find a significant impact of HRV on a mRS ≥ 2 12 months after the index stroke. Conclusion: HRV did not predict recurrent vascular events in patients with acute mild to moderate ischemic stroke.
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Affiliation(s)
- Regina von Rennenberg
- Klinik und Hochschulambulanz für Neurologie, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), Berlin, Germany.,Center for Stroke Research Berlin (CSB), Charité - Universitätsmedizin Berlin, Berlin, Germany.,German Center for Neurodegenerative Diseases (Deutsches Zentrum für Neurodegenerative Erkrankungen), Berlin, Germany
| | - Thomas Krause
- Department of Neurology, Jüdisches Krankenhaus Berlin, Berlin, Germany
| | - Juliane Herm
- Klinik und Hochschulambulanz für Neurologie, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), Berlin, Germany.,Center for Stroke Research Berlin (CSB), Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Simon Hellwig
- Klinik und Hochschulambulanz für Neurologie, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), Berlin, Germany.,Center for Stroke Research Berlin (CSB), Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jan F Scheitz
- Klinik und Hochschulambulanz für Neurologie, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), Berlin, Germany.,Center for Stroke Research Berlin (CSB), Charité - Universitätsmedizin Berlin, Berlin, Germany.,German Center for Cardiovascular Research (Deutsches Zentrum für Herz-Kreislaufforschung), Berlin, Germany
| | - Matthias Endres
- Klinik und Hochschulambulanz für Neurologie, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), Berlin, Germany.,Center for Stroke Research Berlin (CSB), Charité - Universitätsmedizin Berlin, Berlin, Germany.,German Center for Neurodegenerative Diseases (Deutsches Zentrum für Neurodegenerative Erkrankungen), Berlin, Germany.,German Center for Cardiovascular Research (Deutsches Zentrum für Herz-Kreislaufforschung), Berlin, Germany
| | | | - Christian H Nolte
- Klinik und Hochschulambulanz für Neurologie, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), Berlin, Germany.,Center for Stroke Research Berlin (CSB), Charité - Universitätsmedizin Berlin, Berlin, Germany.,German Center for Neurodegenerative Diseases (Deutsches Zentrum für Neurodegenerative Erkrankungen), Berlin, Germany.,German Center for Cardiovascular Research (Deutsches Zentrum für Herz-Kreislaufforschung), Berlin, Germany
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29
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Oliveira Júnior FD, Pereira R, Silva A, Brito Alves JD, Costa-Silva J, Braga V, Balarini C. Different acquisition systems for heart rate variability analysis may lead to diverse outcomes. Braz J Med Biol Res 2022; 55:e11720. [PMID: 35137854 PMCID: PMC8852161 DOI: 10.1590/1414-431x2021e11720] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/26/2021] [Indexed: 12/14/2022] Open
Abstract
Heart rate variability (HRV) is a relevant physiological variable for the estimation of cardiac autonomic function. Although the gold standard for HRV registration is the electrocardiogram (ECG), several applications (APPs) have been increasingly developed. The evaluation carried out by these devices must be compatible with ECG standards. The aim of this study was to compare the data obtained simultaneously with ECG and APP with chest heart rate transmitters. Fifty-six healthy individuals (28 men and 28 women) were evaluated at rest through a short simultaneous HRV measurement with both devices. Data from both acquisition systems were analyzed separately using their own analysis software and exported and analyzed using a validated software. Signal recordings were compatible between the two acquisition systems (Pearson r=0.99; P<0.0001). Although a high correlation was found for the HRV variables obtained in the time domain (Spearman r=0.99; P<0.0001), the correlation decreased in the frequency domain (Pearson r=0.85; P<0.0001) when two software programs were used. Comparison of the averages of spectral analysis parameters also showed differences when HRV data were analyzed separately in each device for low-frequency (LF) and high-frequency (HF) bands. Although the portability of these mobile devices allows for optimal HRV evaluation, the direct analysis obtained from these devices must be carefully evaluated with respect to frequency domain parameters.
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30
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Early recognition of neonatal sepsis using a bioinformatic vital sign monitoring tool. Pediatr Res 2022; 91:270-272. [PMID: 34716420 DOI: 10.1038/s41390-021-01829-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 10/15/2021] [Accepted: 10/20/2021] [Indexed: 12/31/2022]
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31
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Liu XX, Wang S. Effect of aerobic exercise on executive function in individuals with methamphetamine use disorder: Modulation by the autonomic nervous system. Psychiatry Res 2021; 306:114241. [PMID: 34688059 DOI: 10.1016/j.psychres.2021.114241] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 09/06/2021] [Accepted: 10/10/2021] [Indexed: 12/30/2022]
Abstract
This study assessed the effects of aerobic exercise on cardiac autonomic nervous system function (based on heart rate variability [HRV]) and executive function among individuals with methamphetamine use disorder (MUD). We further examine the role of autonomic nervous system control in aerobic exercise (assessed via cardiopulmonary fitness) and executive function. A total of 330 individuals with MUD were randomly divided into exercise (n = 165) and control (n = 165) groups, who underwent eight-week aerobic exercise/health education program consisting of five 60 min sessions a week. The outcome measures included cardiopulmonary fitness, HRV time-domain and frequency-domain parameters, and executive function. Our statistical analyses comprised repeated-measures analyses of variance, correlation analyses, and mediation and moderation effect tests. The results indicated that aerobic exercise could simultaneously improve autonomic nervous system function and executive function among individuals with MUD. Moreover, the changes in cardiopulmonary fitness, high frequency HRV, and executive function were positively correlated. HRV did not significantly mediate the relationship between aerobic exercise and executive function; however, it did have a moderating effect, which was eliminated after adjusting for demographic and drug-use covariates. Among the covariates, age was the greatest confounder and was inversely proportional to cardiopulmonary function, HRV, and executive function. Cardiac autonomic nervous system function exerted a moderating, rather than a mediating, effect on the relationship between aerobic exercise and executive function. However, this potential effect was largely influenced by covariates, particularly age.
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Affiliation(s)
- Xiao-Xia Liu
- School of Physical Education and Sport Science, Fujian Normal University, 1 Keji Road, Minhou District, Fuzhou, Fujian 350117, China
| | - Shen Wang
- School of Physical Education and Sport Science, Fujian Normal University, 1 Keji Road, Minhou District, Fuzhou, Fujian 350117, China.
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32
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Boljanić T, Miljković N, Lazarevic LB, Knezevic G, Milašinović G. Relationship between electrocardiogram-based features and personality traits: Machine learning approach. Ann Noninvasive Electrocardiol 2021; 27:e12919. [PMID: 34837662 PMCID: PMC8739611 DOI: 10.1111/anec.12919] [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: 09/10/2021] [Revised: 11/08/2021] [Accepted: 11/12/2021] [Indexed: 11/27/2022] Open
Abstract
Background Based on the known relationship between the human emotion and standard surface electrocardiogram (ECG), we explored the relationship between features extracted from standard ECG recorded during relaxation and seven personality traits (Honesty/humility, Emotionality, eXtraversion, Agreeableness, Conscientiousness, Openness, and Disintegration) by using the machine learning (ML) approach which learns from the ECG‐based features and predicts the appropriate personality trait by adopting an automated software algorithm. Methods A total of 71 healthy university students participated in the study. For quantification of 62 ECG‐based parameters (heart rate variability, as well as temporal and amplitude‐based parameters) for each ECG record, we used computation procedures together with publicly available data and code. Among 62 parameters, 34 were segregated into separate features according to their diagnostic relevance in clinical practice. To examine the feature influence on personality trait classification and to perform classification, we used random forest ML algorithm. Results Classification accuracy when clinically relevant ECG features were employed was high for Disintegration (81.3%) and Honesty/humility (75.0%) and moderate to high for Openness (73.3%) and Conscientiousness (70%), while it was low for Agreeableness (56.3%), eXtraversion (47.1%), and Emotionality (43.8%). When all calculated features were used, the classification accuracies were the same or lower, except for the eXtraversion (52.9%). Correlation analysis for selected features is presented. Conclusions Results indicate that clinically relevant features might be applicable for personality traits prediction, although no remarkable differences were found among selected groups of parameters. Physiological associations of established relationships should be further explored.
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Affiliation(s)
- Tanja Boljanić
- School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.,Tecnalia Serbia Ltd., Belgrade, Serbia
| | - Nadica Miljković
- School of Electrical Engineering, University of Belgrade, Belgrade, Serbia
| | - Ljiljana B Lazarevic
- Institute of Psychology and Laboratory for Research of Individual Differences, University of Belgrade, Belgrade, Serbia
| | - Goran Knezevic
- Department of Psychology and Laboratory for Research of Individual Differences, Faculty of Philosophy, University of Belgrade, Belgrade, Serbia
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Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects. J Clin Med 2021; 10:jcm10225330. [PMID: 34830612 PMCID: PMC8623430 DOI: 10.3390/jcm10225330] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 11/08/2021] [Accepted: 11/10/2021] [Indexed: 12/21/2022] Open
Abstract
The human right ventricle is barely monitored during open-chest surgery due to the absence of intraoperative imaging techniques capable of elaborating its complex function. Accordingly, artificial intelligence could not be adopted for this specific task. We recently proposed a video-based approach for the real-time evaluation of the epicardial kinematics to support medical decisions. Here, we employed two supervised machine learning algorithms based on our technique to predict the patients’ outcomes before chest closure. Videos of the beating hearts were acquired before and after pulmonary valve replacement in twelve Tetralogy of Fallot patients and recordings were properly labeled as the “unhealthy” and “healthy” classes. We extracted frequency-domain-related features to train different supervised machine learning models and selected their best characteristics via 10-fold cross-validation and optimization processes. Decision surfaces were built to classify two additional patients having good and unfavorable clinical outcomes. The k-nearest neighbors and support vector machine showed the highest prediction accuracy; the patients’ class was identified with a true positive rate ≥95% and the decision surfaces correctly classified the additional patients in the “healthy” (good outcome) or “unhealthy” (unfavorable outcome) classes. We demonstrated that classifiers employed with our video-based technique may aid cardiac surgeons in decision making before chest closure.
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Erdoğan YE, Narin A. COVID-19 detection with traditional and deep features on cough acoustic signals. Comput Biol Med 2021; 136:104765. [PMID: 34416571 PMCID: PMC8364172 DOI: 10.1016/j.compbiomed.2021.104765] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 08/09/2021] [Accepted: 08/10/2021] [Indexed: 12/23/2022]
Abstract
The COVID-19 epidemic, in which millions of people suffer, has affected the whole world in a short time. This virus, which has a high rate of transmission, directly affects the respiratory system of people. While symptoms such as difficulty in breathing, cough, and fever are common, hospitalization and fatal consequences can be seen in progressive situations. For this reason, the most important issue in combating the epidemic is to detect COVID-19(+) early and isolate those with COVID-19(+) from other people. In addition to the RT-PCR test, those with COVID-19(+) can be detected with imaging methods. In this study, it was aimed to detect COVID-19(+) patients with cough acoustic data, which is one of the important symptoms. Based on these data, features were obtained from traditional feature extraction methods using empirical mode decomposition (EMD) and discrete wavelet transform (DWT). Deep features were also obtained using pre-trained ResNet50 and pre-trained MobileNet models. Feature selection was applied to all obtained features with the ReliefF algorithm. In this case, the highest 98.4% accuracy and 98.6% F1-score values were obtained by selecting the EMD + DWT features using ReliefF. In another study in which deep features were used, features obtained from ResNet50 and MobileNet using scalogram images were used. For the features selected using the ReliefF algorithm, the highest performance was found with support vector machines-cubic as 97.8% accuracy and 98.0% F1-score. It has been determined that the features obtained by traditional feature approaches show higher performance than deep features. Among the chaotic measurements, the approximate entropy measurement was determined to be the highest distinguishing feature. According to the results, a highly successful study is presented with cough acoustic data that can easily be obtained from mobile and computer-based applications. We anticipate that this study will be useful as a decision support system in this epidemic period, when it is important to correctly identify even one person.
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Affiliation(s)
- Yunus Emre Erdoğan
- Zonguldak Bulent Ecevit University, Faculty of Engineering, Department of Electrical and Electronics Engineering, Zonguldak, Turkey; Eregli Iron and Steel Works Co., Electronics Automation Department, Zonguldak, Turkey.
| | - Ali Narin
- Zonguldak Bulent Ecevit University, Faculty of Engineering, Department of Electrical and Electronics Engineering, Zonguldak, Turkey.
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Sharma M, Rajput JS, Tan RS, Acharya UR. Automated Detection of Hypertension Using Physiological Signals: A Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:5838. [PMID: 34072304 PMCID: PMC8198170 DOI: 10.3390/ijerph18115838] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 05/10/2021] [Accepted: 05/24/2021] [Indexed: 01/09/2023]
Abstract
Arterial hypertension (HT) is a chronic condition of elevated blood pressure (BP), which may cause increased incidence of cardiovascular disease, stroke, kidney failure and mortality. If the HT is diagnosed early, effective treatment can control the BP and avert adverse outcomes. Physiological signals like electrocardiography (ECG), photoplethysmography (PPG), heart rate variability (HRV), and ballistocardiography (BCG) can be used to monitor health status but are not directly correlated with BP measurements. The manual detection of HT using these physiological signals is time consuming and prone to human errors. Hence, many computer-aided diagnosis systems have been developed. This paper is a systematic review of studies conducted on the automated detection of HT using ECG, HRV, PPG and BCG signals. In this review, we have identified 23 studies out of 250 screened papers, which fulfilled our eligibility criteria. Details of the study methods, physiological signal studied, database used, various nonlinear techniques employed, feature extraction, and diagnostic performance parameters are discussed. The machine learning and deep learning based methods based on ECG and HRV signals have yielded the best performance and can be used for the development of computer-aided diagnosis of HT. This work provides insights that may be useful for the development of wearable for continuous cuffless remote monitoring of BP based on ECG and HRV signals.
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Affiliation(s)
- Manish Sharma
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India;
| | - Jaypal Singh Rajput
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India;
| | - Ru San Tan
- National Heart Centre, Singapore 639798, Singapore;
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 639798, Singapore;
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- Department of Biomedical Engineering, School of Science and Technology, SUSS, Singapore 599494, Singapore
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Jeong DU, Taye GT, Hwang HJ, Lim KM. Optimal Length of Heart Rate Variability Data and Forecasting Time for Ventricular Fibrillation Prediction Using Machine Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:6663996. [PMID: 37601811 PMCID: PMC10435312 DOI: 10.1155/2021/6663996] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 02/01/2021] [Accepted: 03/04/2021] [Indexed: 08/22/2023]
Abstract
Ventricular fibrillation (VF) is a cardiovascular disease that is one of the major causes of mortality worldwide, according to the World Health Organization. Heart rate variability (HRV) is a biomarker that is used for detecting and predicting life-threatening arrhythmias. Predicting the occurrence of VF in advance is important for saving patients from sudden death. We extracted features from seven HRV data lengths to predict the onset of VF before nine different forecast times and observed the prediction accuracies. By using only five features, an artificial neural network classifier was trained and validated based on 10-fold cross-validation. Maximum prediction accuracies of 88.18% and 88.64% were observed at HRV data lengths of 10 and 20 s, respectively, at a forecast time of 0 s. The worst prediction accuracy was recorded at an HRV data length of 70 s and a forecast time of 80 s. Our results showed that features extracted from HRV signals near the VF onset could yield relatively high VF prediction accuracies.
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Affiliation(s)
- Da Un Jeong
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea
| | - Getu Tadele Taye
- Health Informatics Units, School of Public Health, Mekelle University, Mekelle, Ethiopia
| | - Han-Jeong Hwang
- Department of Electronics and Information Engineering, Korea University, Sejong, Republic of Korea
| | - Ki Moo Lim
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea
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Alkhodari M, Jelinek HF, Werghi N, Hadjileontiadis LJ, Khandoker AH. Estimating Left Ventricle Ejection Fraction Levels Using Circadian Heart Rate Variability Features and Support Vector Regression Models. IEEE J Biomed Health Inform 2021; 25:746-754. [PMID: 32750938 DOI: 10.1109/jbhi.2020.3002336] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVES The purpose of this study was to set an optimal fit of the estimated LVEF at hourly intervals from 24-hour ECG recordings and compare it with the fit based on two gold-standard guidelines. METHODS Support vector regression (SVR) models were applied to estimate LVEF from ECG derived heart rate variability (HRV) data in one-hour intervals from 24-hour ECG recordings of patients with either preserved, mid-range, or reduced LVEF, obtained from the Intercity Digital ECG Alliance (IDEAL) study. A step-wise feature selection approach was used to ensure the best possible estimations of LVEF levels. RESULTS The experimental results have shown that the lowest Root Mean Square Error (RMSE) between the original and estimated LVEF levels was during 3-4 am, 5-6 am and 6-7 pm. CONCLUSION The observations suggest these hours as possible times for intervention and optimal treatment outcomes. In addition, LVEF classifications following the ACCF/AHA guidelines leads to a more accurate assessment of mid-range LVEF. SIGNIFICANCE This study paves the way to explore the use of HRV features in the prediction of LVEF percentages as an indicator of disease progression, which may lead to an automated classification process for CAD patients.
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Targeting the Autonomic Nervous System for Risk Stratification, Outcome Prediction and Neuromodulation in Ischemic Stroke. Int J Mol Sci 2021; 22:ijms22052357. [PMID: 33652990 PMCID: PMC7956667 DOI: 10.3390/ijms22052357] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 02/22/2021] [Accepted: 02/24/2021] [Indexed: 02/07/2023] Open
Abstract
Ischemic stroke is a worldwide major cause of mortality and disability and has high costs in terms of health-related quality of life and expectancy as well as of social healthcare resources. In recent years, starting from the bidirectional relationship between autonomic nervous system (ANS) dysfunction and acute ischemic stroke (AIS), researchers have identified prognostic factors for risk stratification, prognosis of mid-term outcomes and response to recanalization therapy. In particular, the evaluation of the ANS function through the analysis of heart rate variability (HRV) appears to be a promising non-invasive and reliable tool for the management of patients with AIS. Furthermore, preclinical molecular studies on the pathophysiological mechanisms underlying the onset and progression of stroke damage have shown an extensive overlap with the activity of the vagus nerve. Evidence from the application of vagus nerve stimulation (VNS) on animal models of AIS and on patients with chronic ischemic stroke has highlighted the surprising therapeutic possibilities of neuromodulation. Preclinical molecular studies highlighted that the neuroprotective action of VNS results from anti-inflammatory, antioxidant and antiapoptotic mechanisms mediated by α7 nicotinic acetylcholine receptor. Given the proven safety of non-invasive VNS in the subacute phase, the ease of its use and its possible beneficial effect in hemorrhagic stroke as well, human studies with transcutaneous VNS should be less challenging than protocols that involve invasive VNS and could be the proof of concept that neuromodulation represents the very first therapeutic approach in the ultra-early management of stroke.
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Hernandez L, Kim R, Tokcan N, Derksen H, Biesterveld BE, Croteau A, Williams AM, Mathis M, Najarian K, Gryak J. Multimodal tensor-based method for integrative and continuous patient monitoring during postoperative cardiac care. Artif Intell Med 2021; 113:102032. [PMID: 33685593 DOI: 10.1016/j.artmed.2021.102032] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 01/06/2021] [Accepted: 02/08/2021] [Indexed: 11/26/2022]
Abstract
Patients recovering from cardiovascular surgeries may develop life-threatening complications such as hemodynamic decompensation, making the monitoring of patients for such complications an essential component of postoperative care. However, this need has given rise to an inexorable increase in the number and modalities of data points collected, making it challenging to effectively analyze in real time. While many algorithms exist to assist in monitoring these patients, they often lack accuracy and specificity, leading to alarm fatigue among healthcare practitioners. In this study we propose a multimodal approach that incorporates salient physiological signals and EHR data to predict the onset of hemodynamic decompensation. A retrospective dataset of patients recovering from cardiac surgery was created and used to train predictive models. Advanced signal processing techniques were employed to extract complex features from physiological waveforms, while a novel tensor-based dimensionality reduction method was used to reduce the size of the feature space. These methods were evaluated for predicting the onset of decompensation at varying time intervals, ranging from a half-hour to 12 h prior to a decompensation event. The best performing models achieved AUCs of 0.87 and 0.80 for the half-hour and 12-h intervals respectively. These analyses evince that a multimodal approach can be used to develop clinical decision support systems that predict adverse events several hours in advance.
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Affiliation(s)
- Larry Hernandez
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Renaid Kim
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Neriman Tokcan
- The Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA 02142, United States
| | - Harm Derksen
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Ben E Biesterveld
- Department of Surgery, University of Michigan, Ann Arbor, MI 48109, United States
| | - Alfred Croteau
- Hartford HealthCare Medical Group, Hartford, CT 06106, United States
| | - Aaron M Williams
- Department of Surgery, University of Michigan, Ann Arbor, MI 48109, United States
| | - Michael Mathis
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI 48109, United States
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, United States; Michigan Center for Integrative Research in Critical Care (MCIRCC), University of Michigan, Ann Arbor, MI 48109, United States; Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI 48109, United States
| | - Jonathan Gryak
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States; Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI 48109, United States.
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Predicting cardiovascular events with deep learning approach in the context of the internet of things. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05542-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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41
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Paragliola G, Coronato A. An hybrid ECG-based deep network for the early identification of high-risk to major cardiovascular events for hypertension patients. J Biomed Inform 2020; 113:103648. [PMID: 33276113 DOI: 10.1016/j.jbi.2020.103648] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 10/27/2020] [Accepted: 11/27/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND AND OBJECTIVE As the population becomes older and more overweight, the number of potential high-risk subjects with hypertension continues to increase. ICT technologies can provide valuable support for the early assessment of such cases since the practice of conducting medical examinations for the early recognition of high-risk subjects affected by hypertension is quite difficult, time-consuming, and expensive. METHODS This paper presents a novel time series-based approach for the early identification of increases in hypertension to discriminate between cardiovascular high-risk and low-risk hypertensive patients through the analyses of electrocardiographic holter signals. RESULTS The experimental results show that the proposed model achieves excellent results in terms of classification accuracy compared with the state-of-the-art. In terms of performances, our model reaches an average accuracy at 98%, Sensitivity and Specificity achieve both an average value at 97%. CONCLUSION The analysis of the whole time series shows promising results in terms of highlighting the tiny differences between subjects affected by hypertension.
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Affiliation(s)
- Giovanni Paragliola
- National Research Council (CNR) - Institute for High-Performance Computing and Networking (ICAR), Naples, Italy.
| | - Antonio Coronato
- National Research Council (CNR) - Institute for High-Performance Computing and Networking (ICAR), Naples, Italy.
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Soh DCK, Ng E, Jahmunah V, Oh SL, Tan RS, Acharya U. Automated diagnostic tool for hypertension using convolutional neural network. Comput Biol Med 2020; 126:103999. [DOI: 10.1016/j.compbiomed.2020.103999] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 08/29/2020] [Accepted: 08/29/2020] [Indexed: 12/13/2022]
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43
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Analysis of temporal correlation in heart rate variability through maximum entropy principle in a minimal pairwise glassy model. Sci Rep 2020; 10:15353. [PMID: 32948805 PMCID: PMC7501304 DOI: 10.1038/s41598-020-72183-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 07/28/2020] [Indexed: 02/05/2023] Open
Abstract
In this work we apply statistical mechanics tools to infer cardiac pathologies over a sample of M patients whose heart rate variability has been recorded via 24 h Holter device and that are divided in different classes according to their clinical status (providing a repository of labelled data). Considering the set of inter-beat interval sequences \documentclass[12pt]{minimal}
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\begin{document}$$i=1,\ldots ,M$$\end{document}i=1,…,M, we estimate their probability distribution \documentclass[12pt]{minimal}
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\begin{document}$$P(\mathbf {r})$$\end{document}P(r) exploiting the maximum entropy principle. By setting constraints on the first and on the second moment we obtain an effective pairwise \documentclass[12pt]{minimal}
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\begin{document}$$(r_n,r_m)$$\end{document}(rn,rm) model, whose parameters are shown to depend on the clinical status of the patient. In order to check this framework, we generate synthetic data from our model and we show that their distribution is in excellent agreement with the one obtained from experimental data. Further, our model can be related to a one-dimensional spin-glass with quenched long-range couplings decaying with the spin–spin distance as a power-law. This allows us to speculate that the 1/f noise typical of heart-rate variability may stem from the interplay between the parasympathetic and orthosympathetic systems.
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Zhang R, Hua Z, Chen C, Liu G, Wen W. Analysis of autonomic nervous pattern in hypertension based on short-term heart rate variability. BIOMED ENG-BIOMED TE 2020; 66:/j/bmte.ahead-of-print/bmt-2019-0184/bmt-2019-0184.xml. [PMID: 32769220 DOI: 10.1515/bmt-2019-0184] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 06/30/2020] [Indexed: 11/15/2022]
Abstract
Physiological studies have found that the autonomic nervous system plays an important role in controlling blood pressure values. This paper, based on machine learning approaches, analysed short-term heart rate variability to determine differences in autonomic nervous function between hypertensive patients and normal population. The electrocardiogram (ECG) of hypertensive patients are 137 ECG recordings provided by Smart Health for Assessing the Risk of Events via ECG (SHAREE database). The RR intervals of healthy subjects include the data of 18 subjects from the MIT-BIH Normal Sinus Rhythm Database (nsrdb) and 54 subjects from the Normal Sinus Rhythm RR Interval Database (nsr2db). In this paper, each RR segment includes continuous 500 beats. Seventeen features were extracted to distinguish the hypertensive heart beat rhythms from the normal ones, and Kolmogorov-Smirnov test and sequential backward selection (SBS) were applied to get the best feature combinations. In addition, support vector machine (SVM), k-nearest neighbor (KNN) and random forest (RF) were applied as classifiers in the study. The performance of each classifier was evaluated independently using the leave-one-subject-out validation method. The best predictive model was based on RF and enabled to identify hypertensive patients by five features with an accuracy of 86.44%. The best five HRV features are sample entropy (SampEn), very low frequency spectral powers (VLF), root mean square of successful differences (RMSSD), ratio of low frequency spectral powers and high frequency spectral powers (LF/HF) and vector angle index (VAI). The results of the study show sympathetic overactivity and decreased parasympathetic tone in hypertensive patients.
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Affiliation(s)
- Ruiqi Zhang
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing, China
| | - Zhengchun Hua
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing, China
| | - Chen Chen
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing, China
| | - Guangyuan Liu
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing, China
| | - Wanhui Wen
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing, China
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45
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Rajput JS, Sharma M, Tan RS, Acharya UR. Automated detection of severity of hypertension ECG signals using an optimal bi-orthogonal wavelet filter bank. Comput Biol Med 2020; 123:103924. [DOI: 10.1016/j.compbiomed.2020.103924] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 07/18/2020] [Accepted: 07/18/2020] [Indexed: 12/18/2022]
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Abstract
The combination of pediatric cardiology being both a perceptual and a cognitive subspecialty demands a complex decision-making model which makes artificial intelligence a particularly attractive technology with great potential. The prototypical artificial intelligence system would autonomously impute patient data into a collaborative database that stores, syncs, interprets and ultimately classifies the patient's profile to specific disease phenotypes to compare against a large aggregate of shared peer health data and outcomes, the current medical body of literature and ongoing trials to offer morbidity and mortality prediction, drug therapy options targeted to each patient's genetic profile, tailored surgical plans and recommendations for timing of sequential imaging. The focus of this review paper is to offer a primer on artificial intelligence and paediatric cardiology by briefly discussing the history of artificial intelligence in medicine, modern and future applications in adult and paediatric cardiology across selected concentrations, and current barriers to implementation of these technologies.
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47
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Agliari E, Barra A, Barra OA, Fachechi A, Franceschi Vento L, Moretti L. Detecting cardiac pathologies via machine learning on heart-rate variability time series and related markers. Sci Rep 2020; 10:8845. [PMID: 32483156 PMCID: PMC7264331 DOI: 10.1038/s41598-020-64083-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 04/06/2020] [Indexed: 11/16/2022] Open
Abstract
In this paper we develop statistical algorithms to infer possible cardiac pathologies, based on data collected from 24 h Holter recording over a sample of 2829 labelled patients; labels highlight whether a patient is suffering from cardiac pathologies. In the first part of the work we analyze statistically the heart-beat series associated to each patient and we work them out to get a coarse-grained description of heart variability in terms of 49 markers well established in the reference community. These markers are then used as inputs for a multi-layer feed-forward neural network that we train in order to make it able to classify patients. However, before training the network, preliminary operations are in order to check the effective number of markers (via principal component analysis) and to achieve data augmentation (because of the broadness of the input data). With such groundwork, we finally train the network and show that it can classify with high accuracy (at most ~85% successful identifications) patients that are healthy from those displaying atrial fibrillation or congestive heart failure. In the second part of the work, we still start from raw data and we get a classification of pathologies in terms of their related networks: patients are associated to nodes and links are drawn according to a similarity measure between the related heart-beat series. We study the emergent properties of these networks looking for features (e.g., degree, clustering, clique proliferation) able to robustly discriminate between networks built over healthy patients or over patients suffering from cardiac pathologies. We find overall very good agreement among the two paved routes.
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Affiliation(s)
- Elena Agliari
- Dipartimento di Matematica "Guido Castelnuovo", Sapienza Università di Roma, P. le A. Moro, 00185, Roma, Italy
| | - Adriano Barra
- Dipartimento di Matematica e Fisica "Ennio De Giorgi", Università del Salento, Via per Arnesano, 73100, Lecce, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Campus Ecotekne, Via Monteroni, 73100, Lecce, Italy
| | - Orazio Antonio Barra
- Department of Environmental Engineering, University of Calabria (UNICAL-DIAM), 87035, Arcavacata, Cosenza, Italy.
- Politecnico Internazionale "Scientia et Ars" (POLISA), Largo Intendenza, 89900, Vibo Valentia, Italy.
| | - Alberto Fachechi
- Dipartimento di Matematica e Fisica "Ennio De Giorgi", Università del Salento, Via per Arnesano, 73100, Lecce, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Campus Ecotekne, Via Monteroni, 73100, Lecce, Italy
| | - Lorenzo Franceschi Vento
- Politecnico Internazionale "Scientia et Ars" (POLISA), Largo Intendenza, 89900, Vibo Valentia, Italy
| | - Luciano Moretti
- Department of Cardiology "C. & G. Mazzoni", Hospital (APH), Via degli Iris, 63100, Ascoli-Piceno, Italy
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Application of a convolutional neural network for predicting the occurrence of ventricular tachyarrhythmia using heart rate variability features. Sci Rep 2020; 10:6769. [PMID: 32317680 PMCID: PMC7174382 DOI: 10.1038/s41598-020-63566-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 03/26/2020] [Indexed: 02/01/2023] Open
Abstract
Predicting the occurrence of ventricular tachyarrhythmia (VTA) in advance is a matter of utmost importance for saving the lives of cardiac arrhythmia patients. Machine learning algorithms have been used to predict the occurrence of imminent VTA. In this study, we used a one-dimensional convolutional neural network (1-D CNN) to extract features from heart rate variability (HRV), thereby to predict the onset of VTA. We also compared the prediction performance of our CNN with other machine leaning (ML) algorithms such as an artificial neural network (ANN), a support vector machine (SVM), and a k-nearest neighbor (KNN), which used 11 HRV features extracted using traditional methods. The proposed CNN achieved relatively higher prediction accuracy of 84.6%, while the ANN, SVM, and KNN algorithms obtained prediction accuracies of 73.5%, 67.9%, and 65.9% using 11 HRV features, respectively. Our result showed that the proposed 1-D CNN could improve VTA prediction accuracy by integrating the data cleaning, preprocessing, feature extraction, and prediction.
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49
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Heart rate variability (HRV): From brain death to resonance breathing at 6 breaths per minute. Clin Neurophysiol 2020; 131:676-693. [DOI: 10.1016/j.clinph.2019.11.013] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 10/14/2019] [Accepted: 11/06/2019] [Indexed: 12/13/2022]
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50
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Soh DCK, Ng EYK, Jahmunah V, Oh SL, San TR, Acharya UR. A computational intelligence tool for the detection of hypertension using empirical mode decomposition. Comput Biol Med 2020; 118:103630. [PMID: 32174317 DOI: 10.1016/j.compbiomed.2020.103630] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 01/23/2020] [Accepted: 01/24/2020] [Indexed: 12/28/2022]
Abstract
Hypertension (HPT), also known as high blood pressure, is a precursor to heart, brain or kidney diseases. Some symptoms of HPT include headaches, dizziness and fainting. The potential diagnosis of masked hypertension is of specific interest in this study. In masked hypertension (MHPT), the instantaneous blood pressure appears normal, but the 24-h ambulatory blood pressure is abnormal. Hence patients with MHPT are difficult to identify and thus remain untreated or are treated insufficiently. Hence, a computational intelligence tool (CIT) using electrocardiograms (ECG) signals for HPT and possible MHPT detection is proposed in this work. Empirical mode decomposition (EMD) is employed to decompose the pre-processed signals up to five levels. Nonlinear features are extracted from the five intrinsic mode functions (IMFs) thereafter. Student's t-test is subsequently applied to select a set of highly discriminatory features. This feature set is then input to various classifiers, in which, the best accuracy of 97.70% is yielded by the k-nearest neighbor (k-NN) classifier. The developed tool is evaluated by the 10-fold cross validation technique. Our findings suggest that the developed system is useful for diagnostic computational intelligence tool in hospital settings, and that it enables the automatic classification of HPT versus normal ECG signals.
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Affiliation(s)
| | - E Y K Ng
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.
| | - V Jahmunah
- School of Engineering, Ngee Ann Polytechnic, Singapore
| | - Shu Lih Oh
- School of Engineering, Ngee Ann Polytechnic, Singapore
| | | | - U Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan
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