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Smart Consumer Wearables as Digital Diagnostic Tools: A Review. Diagnostics (Basel) 2022; 12:diagnostics12092110. [PMID: 36140511 PMCID: PMC9498278 DOI: 10.3390/diagnostics12092110] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 11/17/2022] Open
Abstract
The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.
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2
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Kotfis K, van Diem-Zaal I, Williams Roberson S, Sietnicki M, van den Boogaard M, Shehabi Y, Ely EW. The future of intensive care: delirium should no longer be an issue. Crit Care 2022; 26:200. [PMID: 35790979 PMCID: PMC9254432 DOI: 10.1186/s13054-022-04077-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 06/30/2022] [Indexed: 01/02/2023] Open
Abstract
In the ideal intensive care unit (ICU) of the future, all patients are free from delirium, a syndrome of brain dysfunction frequently observed in critical illness and associated with worse ICU-related outcomes and long-term cognitive impairment. Although screening for delirium requires limited time and effort, this devastating disorder remains underestimated during routine ICU care. The COVID-19 pandemic brought a catastrophic reduction in delirium monitoring, prevention, and patient care due to organizational issues, lack of personnel, increased use of benzodiazepines and restricted family visitation. These limitations led to increases in delirium incidence, a situation that should never be repeated. Good sedation practices should be complemented by novel ICU design and connectivity, which will facilitate non-pharmacological sedation, anxiolysis and comfort that can be supplemented by balanced pharmacological interventions when necessary. Improvements in the ICU sound, light control, floor planning, and room arrangement can facilitate a healing environment that minimizes stressors and aids delirium prevention and management. The fundamental prerequisite to realize the delirium-free ICU, is an awake non-sedated, pain-free comfortable patient whose management follows the A to F (A-F) bundle. Moreover, the bundle should be expanded with three additional letters, incorporating humanitarian care: gaining (G) insight into patient needs, delivering holistic care with a 'home-like' (H) environment, and redefining ICU architectural design (I). Above all, the delirium-free world relies upon people, with personal challenges for critical care teams to optimize design, environmental factors, management, time spent with the patient and family and to humanize ICU care.
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Affiliation(s)
- Katarzyna Kotfis
- Department of Anesthesiology, Intensive Therapy and Acute Intoxications, Pomeranian Medical University in Szczecin, Szczecin, Poland.
| | - Irene van Diem-Zaal
- Department of Intensive Care, Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands.,Department of Intensive Care Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Shawniqua Williams Roberson
- Critical Illness, Brain Dysfunction, and Survivorship (CIBS) Center, Center for Health Services Research, Nashville, TN, USA.,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Marek Sietnicki
- Department of Architecture, West Pomeranian University of Technology in Szczecin, Szczecin, Poland
| | - Mark van den Boogaard
- Department of Intensive Care, Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | - Yahya Shehabi
- Monash Health School of Clinical Sciences, Monash University, Melbourne, VIC, Australia.,School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia
| | - E Wesley Ely
- Critical Illness, Brain Dysfunction, and Survivorship (CIBS) Center, Center for Health Services Research, Nashville, TN, USA.,Division of Allergy, Department of Medicine, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.,Geriatric Research, Education and Clinical Center (GRECC) Service, Nashville Veterans Affairs Medical Center, Tennessee Valley Healthcare System, Nashville, TN, USA
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3
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Bongers J, Gutierrez-Quintana R, Stalin CE. The Prospects of Non-EEG Seizure Detection Devices in Dogs. Front Vet Sci 2022; 9:896030. [PMID: 35677934 PMCID: PMC9168902 DOI: 10.3389/fvets.2022.896030] [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: 03/14/2022] [Accepted: 04/28/2022] [Indexed: 11/16/2022] Open
Abstract
The unpredictable nature of seizures is challenging for caregivers of epileptic dogs, which calls the need for other management strategies such as seizure detection devices. Seizure detection devices are systems that rely on non-electroencephalographic (non-EEG) ictal changes, designed to detect seizures. The aim for its use in dogs would be to provide owners with a more complete history of their dog's seizures and to help install prompt (and potentially life-saving) intervention. Although seizure detection via wearable intracranial EEG recordings is associated with a higher sensitivity in humans, there is robust evidence for reliable detection of generalized tonic-clonic seizures (GTCS) using non-EEG devices. Promising non-EEG changes described in epileptic humans, include heart rate variability (HRV), accelerometry (ACM), electrodermal activity (EDA), and electromyography (EMG). Their sensitivity and false detection rate to detect seizures vary, however direct comparison of studies is nearly impossible, as there are many differences in study design and standards for testing. A way to improve sensitivity and decrease false-positive alarms is to combine the different parameters thereby profiting from the strengths of each one. Given the challenges of using EEG in veterinary clinical practice, non-EEG ictal changes could be a promising alternative to monitor seizures more objectively. This review summarizes various seizure detection devices described in the human literature, discusses their potential use and limitations in veterinary medicine and describes what is currently known in the veterinary literature.
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Affiliation(s)
| | | | - Catherine Elizabeth Stalin
- Neurology and Neurosurgery Service, The School of Veterinary Medicine, College of Medicine, Veterinary Medicine and Life Sciences, University of Glasgow, Glasgow, United Kingdom
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4
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Pale U, Teijeiro T, Atienza D. Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection. Front Neurol 2022; 13:816294. [PMID: 35432152 PMCID: PMC9008228 DOI: 10.3389/fneur.2022.816294] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
Long-term monitoring of patients with epilepsy presents a challenging problem from the engineering perspective of real-time detection and wearable devices design. It requires new solutions that allow continuous unobstructed monitoring and reliable detection and prediction of seizures. A high variability in the electroencephalogram (EEG) patterns exists among people, brain states, and time instances during seizures, but also during non-seizure periods. This makes epileptic seizure detection very challenging, especially if data is grouped under only seizure (ictal) and non-seizure (inter-ictal) labels. Hyperdimensional (HD) computing, a novel machine learning approach, comes in as a promising tool. However, it has certain limitations when the data shows a high intra-class variability. Therefore, in this work, we propose a novel semi-supervised learning approach based on a multi-centroid HD computing. The multi-centroid approach allows to have several prototype vectors representing seizure and non-seizure states, which leads to significantly improved performance when compared to a simple single-centroid HD model. Further, real-life data imbalance poses an additional challenge and the performance reported on balanced subsets of data is likely to be overestimated. Thus, we test our multi-centroid approach with three different dataset balancing scenarios, showing that performance improvement is higher for the less balanced dataset. More specifically, up to 14% improvement is achieved on an unbalanced test set with 10 times more non-seizure than seizure data. At the same time, the total number of sub-classes is not significantly increased compared to the balanced dataset. Thus, the proposed multi-centroid approach can be an important element in achieving a high performance of epilepsy detection with real-life data balance or during online learning, where seizures are infrequent.
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5
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Schach S, Rings T, Bregulla M, Witt JA, Bröhl T, Surges R, von Wrede R, Lehnertz K, Helmstaedter C. Electrodermal Activity Biofeedback Alters Evolving Functional Brain Networks in People With Epilepsy, but in a Non-specific Manner. Front Neurosci 2022; 16:828283. [PMID: 35310086 PMCID: PMC8927283 DOI: 10.3389/fnins.2022.828283] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/10/2022] [Indexed: 11/13/2022] Open
Abstract
There is evidence that biofeedback of electrodermal activity (EDA) can reduce seizure frequency in people with epilepsy. Prior studies have linked EDA biofeedback to a diffuse brain activation as a potential functional mechanism. Here, we investigated whether short-term EDA biofeedback alters EEG-derived large-scale functional brain networks in people with epilepsy. In this prospective controlled trial, thirty participants were quasi-randomly assigned to one of three biofeedback conditions (arousal, sham, or relaxation) and performed a single, 30-min biofeedback training while undergoing continuous EEG recordings. Based on the EEG, we derived evolving functional brain networks and examined their topological, robustness, and stability properties over time. Potential effects on attentional-executive functions and mood were monitored via a neuropsychological assessment and subjective self-ratings. Participants assigned to the relaxation group seemed to be most successful in meeting the task requirements for this specific control condition (i.e., decreasing EDA). Participants in the sham group were more successful in increasing EDA than participants in the arousal group. However, only the arousal biofeedback training was associated with a prolonged robustness-enhancing effect on networks. Effects on other network properties were mostly unspecific for the different groups. None of the biofeedback conditions affected attentional-executive functions or subjective behavioral measures. Our results suggest that global characteristics of evolving functional brain networks are modified by EDA biofeedback. Some alterations persisted after the single training session; however, the effects were largely unspecific across the different biofeedback protocols. Further research should address changes of local network characteristics and whether multiple training sessions will result in more specific network modifications.
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Affiliation(s)
- Sophia Schach
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- *Correspondence: Sophia Schach,
| | - Thorsten Rings
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | | | | | - Timo Bröhl
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Rainer Surges
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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6
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Tronstad C, Amini M, Bach DR, Martinsen OG. Current trends and opportunities in the methodology of electrodermal activity measurement. Physiol Meas 2022; 43. [PMID: 35090148 DOI: 10.1088/1361-6579/ac5007] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/28/2022] [Indexed: 11/12/2022]
Abstract
Electrodermal activity (EDA) has been measured in the laboratory since the late 1800s. Although the influence of sudomotor nerve activity and the sympathetic nervous system on EDA is well established, the mechanisms underlying EDA signal generation are not completely understood. Owing to simplicity of instrumentation and modern electronics, these measurements have recently seen a transfer from the laboratory to wearable devices, sparking numerous novel applications while bringing along both challenges and new opportunities. In addition to developments in electronics and miniaturization, current trends in material technology and manufacturing have sparked innovations in electrode technologies, and trends in data science such as machine learning and sensor fusion are expanding the ways that measurement data can be processed and utilized. Although challenges remain for the quality of wearable EDA measurement, ongoing research and developments may shorten the quality gap between wearable EDA and standardized recordings in the laboratory. In this topical review, we provide an overview of the basics of EDA measurement, discuss the challenges and opportunities of wearable EDA, and review recent developments in instrumentation, material technology, signal processing, modeling and data science tools that may advance the field of EDA research and applications over the coming years.
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Affiliation(s)
- Christian Tronstad
- Department of Clinical and Biomedical Engineering, Oslo University Hospital, Sognsvannsveien 20, Oslo, 0372, NORWAY
| | - Maryam Amini
- Physics, University of Oslo Faculty of Mathematics and Natural Sciences, Sem Sælands vei 24, Oslo, 0371, NORWAY
| | - Dominik R Bach
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, London, WC1N 3AZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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Posada-Quintero HF, Landon CS, Stavitzski NM, Dean JB, Chon KH. Seizures Caused by Exposure to Hyperbaric Oxygen in Rats Can Be Predicted by Early Changes in Electrodermal Activity. Front Physiol 2022; 12:767386. [PMID: 35069238 PMCID: PMC8767060 DOI: 10.3389/fphys.2021.767386] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 11/26/2021] [Indexed: 11/13/2022] Open
Abstract
Hyperbaric oxygen (HBO2) is breathed during undersea operations and in hyperbaric medicine. However, breathing HBO2 by divers and patients increases the risk of central nervous system oxygen toxicity (CNS-OT), which ultimately manifests as sympathetic stimulation producing tachycardia and hypertension, hyperventilation, and ultimately generalized seizures and cardiogenic pulmonary edema. In this study, we have tested the hypothesis that changes in electrodermal activity (EDA), a measure of sympathetic nervous system activation, precedes seizures in rats breathing 5 atmospheres absolute (ATA) HBO2. Radio telemetry and a rodent tether apparatus were adapted for use inside a sealed hyperbaric chamber. The tethered rat was free to move inside a ventilated animal chamber that was flushed with air or 100% O2. The animal chamber and hyperbaric chamber (air) were pressurized in parallel at ~1 atmosphere/min. EDA activity was recorded simultaneously with cortical electroencephalogram (EEG) activity, core body temperature, and ambient pressure. We have captured the dynamics of EDA using time-varying spectral analysis of raw EDA (TVSymp), previously developed as a tool for sympathetic tone assessment in humans, adjusted to detect the dynamic changes of EDA in rats that occur prior to onset of CNS-OT seizures. The results show that a significant increase in the amplitude of TVSymp values derived from EDA recordings occurs on average (±SD) 1.9 ± 1.6 min before HBO2-induced seizures. These results, if corroborated in humans, support the use of changes in TVSymp activity as an early "physio-marker" of impending and potentially fatal seizures in divers and patients.
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Affiliation(s)
- Hugo F Posada-Quintero
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Carol S Landon
- Department of Molecular Pharmacology and Physiology, Morsani College of Medicine, University of South Florida, Tampa, FL, United States
| | - Nicole M Stavitzski
- Department of Molecular Pharmacology and Physiology, Morsani College of Medicine, University of South Florida, Tampa, FL, United States
| | - Jay B Dean
- Department of Molecular Pharmacology and Physiology, Morsani College of Medicine, University of South Florida, Tampa, FL, United States
| | - Ki H Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
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Deutsch CK, Patnaik PP, Greco FA. Is There a Characteristic Autonomic Response During Outbursts of Combative Behavior in Dementia Patients? J Alzheimers Dis Rep 2021; 5:389-394. [PMID: 34189410 PMCID: PMC8203282 DOI: 10.3233/adr-210007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
We sought to determine whether skin conductance level could warn of outbursts of combative behavior in dementia patients by using a wristband device. Two outbursts were captured and are reported here. Although no physiologic parameter measured by the wristband gave advance warning, there is a common pattern of parasympathetic withdrawal (increased heart rate) followed approximately 30 seconds later by sympathetic activation (increased skin conductance). In the literature, a similar pattern occurs in psychogenic non-epileptic seizures. We hypothesize that similar autonomic responses reflect similarities in pathophysiology and that physical activity may partially account for the time course of skin conductance.
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Affiliation(s)
- Curtis K Deutsch
- Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA, USA
| | - Pooja P Patnaik
- Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Frank A Greco
- VA Bedford Healthcare System, Medical Research Service, Bedford, MA, USA
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9
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Gabeff V, Teijeiro T, Zapater M, Cammoun L, Rheims S, Ryvlin P, Atienza D. Interpreting deep learning models for epileptic seizure detection on EEG signals. Artif Intell Med 2021; 117:102084. [PMID: 34127231 DOI: 10.1016/j.artmed.2021.102084] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 04/27/2021] [Accepted: 04/29/2021] [Indexed: 11/28/2022]
Abstract
While Deep Learning (DL) is often considered the state-of-the art for Artificial Intel-ligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability of neural network models. We have approached this issue in the context of online detection of epileptic seizures by developing a DL model from EEG signals, and associating certain properties of the model behavior with the expert medical knowledge. This has conditioned the preparation of the input signals, the network architecture, and the post-processing of the output in line with the domain knowledge. Specifically, we focused the discussion on three main aspects: (1) how to aggregate the classification results on signal segments provided by the DL model into a larger time scale, at the seizure-level; (2) what are the relevant frequency patterns learned in the first convolutional layer of different models, and their relation with the delta, theta, alpha, beta and gamma frequency bands on which the visual interpretation of EEG is based; and (3) the identification of the signal waveforms with larger contribution towards the ictal class, according to the activation differences highlighted using the DeepLIFT method. Results show that the kernel size in the first layer determines the interpretability of the extracted features and the sensitivity of the trained models, even though the final performance is very similar after post-processing. Also, we found that amplitude is the main feature leading to an ictal prediction, suggesting that a larger patient population would be required to learn more complex frequency patterns. Still, our methodology was successfully able to generalize patient inter-variability for the majority of the studied population with a classification F1-score of 0.873 and detecting 90% of the seizures.
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Affiliation(s)
- Valentin Gabeff
- Embedded Systems Laboratory (ESL), EPFL, Lausanne, Switzerland.
| | - Tomas Teijeiro
- Embedded Systems Laboratory (ESL), EPFL, Lausanne, Switzerland
| | - Marina Zapater
- Embedded Systems Laboratory (ESL), EPFL, Lausanne, Switzerland; REDS Institute, University of Applied Sciences Western Switzerland (HEIG-VD/HES-SO), Yverdon-les-Bains, Switzerland
| | - Leila Cammoun
- Department of Clinical Neurosciences, Neurology Service, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Sylvain Rheims
- Department of Functional Neurology and Epileptology, Hospices Civils de Lyon and University of Lyon, Lyon, France; Lyon's Neurosciences Research Center (INSERM U1028/CNRS UMR 5292), Lyon, France
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Neurology Service, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - David Atienza
- Embedded Systems Laboratory (ESL), EPFL, Lausanne, Switzerland
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Wong CK, Ho DTY, Tam AR, Zhou M, Lau YM, Tang MOY, Tong RCF, Rajput KS, Chen G, Chan SC, Siu CW, Hung IFN. Artificial intelligence mobile health platform for early detection of COVID-19 in quarantine subjects using a wearable biosensor: protocol for a randomised controlled trial. BMJ Open 2020; 10:e038555. [PMID: 32699167 PMCID: PMC7380847 DOI: 10.1136/bmjopen-2020-038555] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 06/24/2020] [Accepted: 06/26/2020] [Indexed: 12/12/2022] Open
Abstract
INTRODUCTION There is an outbreak of COVID-19 worldwide. As there is no effective therapy or vaccine yet, rigorous implementation of traditional public health measures such as isolation and quarantine remains the most effective tool to control the outbreak. When an asymptomatic individual with COVID-19 exposure is being quarantined, it is necessary to perform temperature and symptom surveillance. As such surveillance is intermittent in nature and highly dependent on self-discipline, it has limited effectiveness. Advances in biosensor technologies made it possible to continuously monitor physiological parameters using wearable biosensors with a variety of form factors. OBJECTIVE To explore the potential of using wearable biosensors to continuously monitor multidimensional physiological parameters for early detection of COVID-19 clinical progression. METHOD This randomised controlled open-labelled trial will involve 200-1000 asymptomatic subjects with close COVID-19 contact under mandatory quarantine at designated facilities in Hong Kong. Subjects will be randomised to receive a remote monitoring strategy (intervention group) or standard strategy (control group) in a 1:1 ratio during the 14 day-quarantine period. In addition to fever and symptom surveillance in the control group, subjects in the intervention group will wear wearable biosensors on their arms to continuously monitor skin temperature, respiratory rate, blood pressure, pulse rate, blood oxygen saturation and daily activities. These physiological parameters will be transferred in real time to a smartphone application called Biovitals Sentinel. These data will then be processed using a cloud-based multivariate physiology analytics engine called Biovitals to detect subtle physiological changes. The results will be displayed on a web-based dashboard for clinicians' review. The primary outcome is the time to diagnosis of COVID-19. ETHICS AND DISSEMINATION Ethical approval has been obtained from institutional review boards at the study sites. Results will be published in peer-reviewed journals.
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Affiliation(s)
- Chun Ka Wong
- Division of Cardiology, Department of Medicine, University of Hong Kong, Hong Kong, Hong Kong
| | - Deborah Tip Yin Ho
- Division of Infectious Diseases, Department of Medicine, University of Hong Kong, Hong Kong, Hong Kong
| | - Anthony Raymond Tam
- Division of Infectious Diseases, Department of Medicine, University of Hong Kong, Hong Kong, Hong Kong
| | - Mi Zhou
- Division of Cardiology, Department of Medicine, University of Hong Kong, Hong Kong, Hong Kong
| | - Yuk Ming Lau
- Division of Cardiology, Department of Medicine, University of Hong Kong, Hong Kong, Hong Kong
| | - Milky Oi Yan Tang
- Division of Infectious Diseases, Department of Medicine, University of Hong Kong, Hong Kong, Hong Kong
| | | | | | - Gengbo Chen
- Research and Development, Biofourmis, Singapore
| | | | - Chung Wah Siu
- Division of Cardiology, Department of Medicine, University of Hong Kong, Hong Kong, Hong Kong
| | - Ivan Fan Ngai Hung
- Division of Infectious Diseases, Department of Medicine, University of Hong Kong, Hong Kong, Hong Kong
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11
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Ryvlin P, Cammoun L, Hubbard I, Ravey F, Beniczky S, Atienza D. Noninvasive detection of focal seizures in ambulatory patients. Epilepsia 2020; 61 Suppl 1:S47-S54. [PMID: 32484920 PMCID: PMC7754288 DOI: 10.1111/epi.16538] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 04/20/2020] [Accepted: 04/26/2020] [Indexed: 02/02/2023]
Abstract
Reliably detecting focal seizures without secondary generalization during daily life activities, chronically, using convenient portable or wearable devices, would offer patients with active epilepsy a number of potential benefits, such as providing more reliable seizure count to optimize treatment and seizure forecasting, and triggering alarms to promote safeguarding interventions. However, no generic solution is currently available to reach these objectives. A number of biosignals are sensitive to specific forms of focal seizures, in particular heart rate and its variability for seizures affecting the neurovegetative system, and accelerometry for those responsible for prominent motor activity. However, most studies demonstrate high rates of false detection or poor sensitivity, with only a minority of patients benefiting from acceptable levels of accuracy. To tackle this challenging issue, several lines of technological progress are envisioned, including multimodal biosensing with cross‐modal analytics, a combination of embedded and distributed self‐aware machine learning, and ultra–low‐power design to enable appropriate autonomy of such sophisticated portable solutions.
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Affiliation(s)
- Philippe Ryvlin
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland
| | - Leila Cammoun
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland
| | - Ilona Hubbard
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland
| | - France Ravey
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland
| | - Sandor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark.,Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - David Atienza
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland.,Embedded Systems Laboratory, Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland
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12
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Patel UK, Anwar A, Saleem S, Malik P, Rasul B, Patel K, Yao R, Seshadri A, Yousufuddin M, Arumaithurai K. Artificial intelligence as an emerging technology in the current care of neurological disorders. J Neurol 2019; 268:1623-1642. [PMID: 31451912 DOI: 10.1007/s00415-019-09518-3] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 08/14/2019] [Accepted: 08/17/2019] [Indexed: 01/06/2023]
Abstract
BACKGROUND Artificial intelligence (AI) has influenced all aspects of human life and neurology is no exception to this growing trend. The aim of this paper is to guide medical practitioners on the relevant aspects of artificial intelligence, i.e., machine learning, and deep learning, to review the development of technological advancement equipped with AI, and to elucidate how machine learning can revolutionize the management of neurological diseases. This review focuses on unsupervised aspects of machine learning, and how these aspects could be applied to precision neurology to improve patient outcomes. We have mentioned various forms of available AI, prior research, outcomes, benefits and limitations of AI, effective accessibility and future of AI, keeping the current burden of neurological disorders in mind. DISCUSSION The smart device system to monitor tremors and to recognize its phenotypes for better outcomes of deep brain stimulation, applications evaluating fine motor functions, AI integrated electroencephalogram learning to diagnose epilepsy and psychological non-epileptic seizure, predict outcome of seizure surgeries, recognize patterns of autonomic instability to prevent sudden unexpected death in epilepsy (SUDEP), identify the pattern of complex algorithm in neuroimaging classifying cognitive impairment, differentiating and classifying concussion phenotypes, smartwatches monitoring atrial fibrillation to prevent strokes, and prediction of prognosis in dementia are unique examples of experimental utilizations of AI in the field of neurology. Though there are obvious limitations of AI, the general consensus among several nationwide studies is that this new technology has the ability to improve the prognosis of neurological disorders and as a result should become a staple in the medical community. CONCLUSION AI not only helps to analyze medical data in disease prevention, diagnosis, patient monitoring, and development of new protocols, but can also assist clinicians in dealing with voluminous data in a more accurate and efficient manner.
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Affiliation(s)
- Urvish K Patel
- Department of Neurology and Public Health, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY, 10029, USA.
| | - Arsalan Anwar
- Department of Neurology, UH Cleveland Medical Center, Cleveland, OH, USA
| | - Sidra Saleem
- Department of Neurology, University of Toledo, Toledo, OH, USA
| | - Preeti Malik
- Department of Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bakhtiar Rasul
- Department of Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Karan Patel
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA
| | - Robert Yao
- Department of Biomedical Informatics, Arizona State University and Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Ashok Seshadri
- Department of Psychiatry, Mayo Clinic Health System, Rochester, MN, USA
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Breathable Dry Silver/Silver Chloride Electronic Textile Electrodes for Electrodermal Activity Monitoring. BIOSENSORS-BASEL 2018; 8:bios8030079. [PMID: 30149594 PMCID: PMC6163896 DOI: 10.3390/bios8030079] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2018] [Revised: 08/15/2018] [Accepted: 08/21/2018] [Indexed: 01/14/2023]
Abstract
The focus of this study is to design and integrate silver/silver chloride (Ag/AgCl) electronic textile (e-textile) electrodes into different textile substrates to evaluate their ability to monitor electrodermal activity (EDA). Ag/AgCl e-textiles were stitched into woven textiles of cotton, nylon, and polyester to function as EDA monitoring electrodes. EDA stimulus responses detected by dry e-textile electrodes at various locations on the hand were compared to the EDA signals collected by dry solid Ag/AgCl electrodes. 4-h EDA data with e-textile and clinically conventional rigid electrodes were compared in relation to skin surface temperature. The woven cotton textile substrate with e-textile electrodes (0.12 cm2 surface area, 0.40 cm distance) was the optimal material to detect the EDA stimulus responses with the highest average Pearson correlation coefficient of 0.913 ± 0.041 when placed on the distal phalanx of the middle finger. In addition, differences with EDA waveforms recorded on various fingers were observed. Trends of long-term measurements showed that skin surface temperature affected EDA signals recorded by non-breathable electrodes more than when e-textile electrodes were used. The effective design criteria outlined for e-textile electrodes can promote the development of comfortable and unobtrusive EDA monitoring systems, which can help improve our knowledge of the human neurological system.
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14
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Shu L, Xie J, Yang M, Li Z, Li Z, Liao D, Xu X, Yang X. A Review of Emotion Recognition Using Physiological Signals. SENSORS (BASEL, SWITZERLAND) 2018; 18:E2074. [PMID: 29958457 PMCID: PMC6069143 DOI: 10.3390/s18072074] [Citation(s) in RCA: 167] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 05/26/2018] [Accepted: 06/12/2018] [Indexed: 01/30/2023]
Abstract
Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed.
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Affiliation(s)
- Lin Shu
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.
| | - Jinyan Xie
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.
| | - Mingyue Yang
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.
| | - Ziyi Li
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.
| | - Zhenqi Li
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.
| | - Dan Liao
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.
| | - Xiangmin Xu
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.
| | - Xinyi Yang
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.
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15
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Zhao X, Lhatoo SD. Seizure detection: do current devices work? And when can they be useful? Curr Neurol Neurosci Rep 2018; 18:40. [PMID: 29796939 DOI: 10.1007/s11910-018-0849-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW The unpredictability and apparent randomness of epileptic seizures is one of the most vexing aspects of epilepsy. Methods or devices capable of detecting seizures may help prevent injury or even death and significantly improve quality of life. Here, we summarize and evaluate currently available, unimodal, or polymodal detection systems for epileptic seizures, mainly in the ambulatory setting. RECENT FINDINGS There are two broad categories of detection devices: EEG-based and non-EEG-based systems. Wireless wearable EEG devices are now available both in research and commercial arenas. Neuro-stimulation devices are currently evolving and initial experiences of these show potential promise. As for non-EEG devices, different detecting systems show different sensitivity according to the different patient and seizure types. Regardless, when used in combination, these modalities may complement each other to increase positive predictive value. Although some devices with high sensitivity are promising, practical widespread use of such detection systems is still some way away. More research and experience are needed to evaluate the most efficient and integrated systems, to allow for better approaches to detection and prediction of seizures. The concept of closed-loop systems and prompt intervention may substantially improve quality of life for patients and carers.
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Affiliation(s)
- Xiuhe Zhao
- Epilepsy Center, University Hospitals Cleveland Medical Center, 11100 Euclid Avenue, Cleveland, OH, 44106, USA.,Neurology Department, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China
| | - Samden D Lhatoo
- Epilepsy Center, University Hospitals Cleveland Medical Center, 11100 Euclid Avenue, Cleveland, OH, 44106, USA. .,NIH/NINDS Center for SUDEP Research, Boston, MA, USA.
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16
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Ulate-Campos A, Tsuboyama M, Loddenkemper T. Devices for Ambulatory Monitoring of Sleep-Associated Disorders in Children with Neurological Diseases. CHILDREN (BASEL, SWITZERLAND) 2017; 5:E3. [PMID: 29295578 PMCID: PMC5789285 DOI: 10.3390/children5010003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/18/2017] [Revised: 11/18/2017] [Accepted: 12/18/2017] [Indexed: 12/30/2022]
Abstract
Good sleep quality is essential for a child's wellbeing. Early sleep problems have been linked to the later development of emotional and behavioral disorders and can negatively impact the quality of life of the child and his or her family. Sleep-associated conditions are frequent in the pediatric population, and even more so in children with neurological problems. Monitoring devices can help to better characterize sleep efficiency and sleep quality. They can also be helpful to better characterize paroxysmal nocturnal events and differentiate between nocturnal seizures, parasomnias, and obstructive sleep apnea, each of which has a different management. Overnight ambulatory detection devices allow for a tolerable, low cost, objective assessment of sleep quality in the patient's natural environment. They can also be used as a notification system to allow for rapid recognition and prompt intervention of events like seizures. Optimal monitoring devices will be patient- and diagnosis-specific, but may include a combination of modalities such as ambulatory electroencephalograms, actigraphy, and pulse oximetry. We will summarize the current literature on ambulatory sleep devices for detecting sleep disorders in children with neurological diseases.
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Affiliation(s)
- Adriana Ulate-Campos
- Department of Neurology, National Children's Hospital Dr. Carlos Saenz Herrera, 10103 San José, Costa Rica.
| | - Melissa Tsuboyama
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.
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17
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Tharayil JJ, Chiang S, Moss R, Stern JM, Theodore WH, Goldenholz DM. A big data approach to the development of mixed-effects models for seizure count data. Epilepsia 2017; 58:835-844. [PMID: 28369781 DOI: 10.1111/epi.13727] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/13/2017] [Indexed: 12/23/2022]
Abstract
OBJECTIVE Our objective was to develop a generalized linear mixed model for predicting seizure count that is useful in the design and analysis of clinical trials. This model also may benefit the design and interpretation of seizure-recording paradigms. Most existing seizure count models do not include children, and there is currently no consensus regarding the most suitable model that can be applied to children and adults. Therefore, an additional objective was to develop a model that accounts for both adult and pediatric epilepsy. METHODS Using data from SeizureTracker.com, a patient-reported seizure diary tool with >1.2 million recorded seizures across 8 years, we evaluated the appropriateness of Poisson, negative binomial, zero-inflated negative binomial, and modified negative binomial models for seizure count data based on minimization of the Bayesian information criterion. Generalized linear mixed-effects models were used to account for demographic and etiologic covariates and for autocorrelation structure. Holdout cross-validation was used to evaluate predictive accuracy in simulating seizure frequencies. RESULTS For both adults and children, we found that a negative binomial model with autocorrelation over 1 day was optimal. Using holdout cross-validation, the proposed model was found to provide accurate simulation of seizure counts for patients with up to four seizures per day. SIGNIFICANCE The optimal model can be used to generate more realistic simulated patient data with very few input parameters. The availability of a parsimonious, realistic virtual patient model can be of great utility in simulations of phase II/III clinical trials, epilepsy monitoring units, outpatient biosensors, and mobile Health (mHealth) applications.
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Affiliation(s)
- Joseph J Tharayil
- Clinical Epilepsy Section, NINDS, NIH, Bethesda, Maryland, U.S.A.,Department of Biomedical Engineering, Duke University, Durham, North Carolina, U.S.A
| | - Sharon Chiang
- Department of Statistics, Rice University, Houston, Texas, U.S.A.,Baylor College of Medicine, Houston, Texas, U.S.A
| | | | - John M Stern
- University of California Los Angeles Medical Center, Los Angeles, California, U.S.A
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18
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Stiles-Shields C, Ho J, Mohr DC. A review of design characteristics of cognitive behavioral therapy-informed behavioral intervention technologies for youth with depression and anxiety. Digit Health 2016; 2:2055207616675706. [PMID: 29942571 PMCID: PMC6001244 DOI: 10.1177/2055207616675706] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Accepted: 09/23/2016] [Indexed: 11/19/2022] Open
Abstract
Objective Cognitive behavioral therapy (CBT) has the strongest evidence base for the prevention and treatment of depression and anxiety in youth. Behavioral intervention technologies (BITs) provide an opportunity to overcome access barriers to traditional delivery of CBT. The present review evaluates the design characteristics of CBT-informed BITs for depression and anxiety designed for and tested with youth. Methods A state-of-the-art review of three library databases (PubMed, Scopus, and Web of Science) was conducted to identify papers that evaluated the use of CBT-informed BITs for the prevention and/or treatment of depression and anxiety among youth. Narrative results of design characteristics were organized using the BIT model, which provides a framework for design and evaluation. Results 219 unique results were retrieved through the search. After review, 14 papers (4 prevention and 10 treatment) met the selection criteria. A broad diversity occurred in reporting the design and methodology of CBT delivered to youth through BITs. Psychoeducation was overwhelmingly utilized as the primary change strategy throughout the interventions, with a heavy use of content delivery elements and linear workflows. The reporting of sample characteristics was minimal and varied. Conclusions Providing psychoeducation via content delivery was the most utilized BIT change strategy in the interventions, likely limiting the use of multiple BIT elements or flexible workflows. While characterizations could be inferred from the current reports, the high level of variability in reporting is problematic. Generalizability becomes increasingly more difficult to carry out effectively without clear descriptions of the design for evaluated BITs.
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Affiliation(s)
- Colleen Stiles-Shields
- Center for Behavioral Intervention Technologies, Northwestern University, Chicago, IL, USA
| | - Joyce Ho
- Center for Behavioral Intervention Technologies, Northwestern University, Chicago, IL, USA
| | - David C Mohr
- Center for Behavioral Intervention Technologies, Northwestern University, Chicago, IL, USA
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Chatterjee S, Hovsepian K, Sarker H, Saleheen N, al'Absi M, Atluri G, Ertin E, Lam C, Lemieux A, Nakajima M, Spring B, Wetter DW, Kumar S. mCrave: Continuous Estimation of Craving During Smoking Cessation. PROCEEDINGS OF THE ... ACM INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING . UBICOMP (CONFERENCE) 2016; 2016:863-874. [PMID: 27990501 PMCID: PMC5161415 DOI: 10.1145/2971648.2971672] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Craving usually precedes a lapse for impulsive behaviors such as overeating, drinking, smoking, and drug use. Passive estimation of craving from sensor data in the natural environment can be used to assist users in coping with craving. In this paper, we take the first steps towards developing a computational model to estimate cigarette craving (during smoking abstinence) at the minute-level using mobile sensor data. We use 2,012 hours of sensor data and 1,812 craving self-reports from 61 participants in a smoking cessation study. To estimate craving, we first obtain a continuous measure of stress from sensor data. We find that during hours of day when craving is high, stress associated with self-reported high craving is greater than stress associated with low craving. We use this and other insights to develop feature functions, and encode them as pattern detectors in a Conditional Random Field (CRF) based model to infer craving probabilities.
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20
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Chen W, Jaques N, Taylor S, Sano A, Fedor S, Picard RW. Wavelet-based motion artifact removal for electrodermal activity. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:6223-6. [PMID: 26737714 DOI: 10.1109/embc.2015.7319814] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Electrodermal activity (EDA) recording is a powerful, widely used tool for monitoring psychological or physiological arousal. However, analysis of EDA is hampered by its sensitivity to motion artifacts. We propose a method for removing motion artifacts from EDA, measured as skin conductance (SC), using a stationary wavelet transform (SWT). We modeled the wavelet coefficients as a Gaussian mixture distribution corresponding to the underlying skin conductance level (SCL) and skin conductance responses (SCRs). The goodness-of-fit of the model was validated on ambulatory SC data. We evaluated the proposed method in comparison with three previous approaches. Our method achieved a greater reduction of artifacts while retaining motion-artifact-free data.
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Patel S, Park H, Bonato P, Chan L, Rodgers M. A review of wearable sensors and systems with application in rehabilitation. J Neuroeng Rehabil 2012; 9:21. [PMID: 22520559 PMCID: PMC3354997 DOI: 10.1186/1743-0003-9-21] [Citation(s) in RCA: 661] [Impact Index Per Article: 55.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2011] [Accepted: 04/20/2012] [Indexed: 12/15/2022] Open
Abstract
The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed.
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Affiliation(s)
- Shyamal Patel
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA, USA
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
| | - Hyung Park
- Rehabilitation Medicine Department Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Paolo Bonato
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA
| | - Leighton Chan
- Rehabilitation Medicine Department Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Mary Rodgers
- Department of Physical Therapy and Rehabilitation Science, University of Maryland School of Medicine, Baltimore, MD, USA
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
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Bob P, Jasova D, Raboch J. Subclinical epileptiform process in patients with unipolar depression and its indirect psychophysiological manifestations. PLoS One 2011; 6:e28041. [PMID: 22132204 PMCID: PMC3222677 DOI: 10.1371/journal.pone.0028041] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2011] [Accepted: 10/31/2011] [Indexed: 11/25/2022] Open
Abstract
Background According to recent clinical findings epileptiform activity in temporolimbic structures may cause depressive and other psychiatric symptoms that may occur independently of any seizure in patient's history. In addition in these patients subclinical seizure-like activity with indirect clinical manifestations likely may occur in a form of various forms of cognitive, affective, memory, sensory, behavioral and somatic symptoms (the so-called complex partial seizure-like symptoms). A typical characteristic of epileptiform changes is increased neural synchrony related to spreading of epileptiform activity between hemispheres even in subclinical conditions i.e. without seizures. These findings suggest a hypothesis that measures reflecting a level of synchronization and information transfer between hemispheres could reflect spreading of epileptiform activity and might be related to complex partial seizure-like symptoms. Methods and Findings Suitable data for such analysis may provide various physiological signals reflecting brain laterality, as for example bilateral electrodermal activity (EDA) that is closely related to limbic modulation influences. With this purpose we have performed measurement and analysis of bilateral EDA and compared the results with psychometric measures of complex partial seizure-like symptoms, depression and actually experienced stress in 44 patients with unipolar depression and 35 healthy controls. The results in unipolar depressive patients show that during rest conditions the patients with higher level of complex partial seizure like symptoms (CPSI) display increased level of EDA transinformation (PTI) calculated between left and right EDA records (Spearman correlation between CPSI and PTI is r = 0.43, p = 0.004). Conclusions The result may present potentially useful clinical finding suggesting that increased EDA transinformation (PTI) could indirectly indicate increased neural synchrony as a possible indicator of epileptiform activity in unipolar depressive patients treated by serotoninergic antidepresants.
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Affiliation(s)
- Petr Bob
- Department of Psychiatry and UHSL, First Faculty of Medicine, Center for Neuropsychiatric Research of Traumatic Stress, Charles University, Prague, Czech Republic.
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