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Gao B, Jiang J, Zhou S, Li J, Zhou Q, Li X. Toward the Next Generation Human-Machine Interaction: Headworn Wearable Devices. Anal Chem 2024; 96:10477-10487. [PMID: 38888091 DOI: 10.1021/acs.analchem.4c01190] [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: 06/20/2024]
Abstract
Wearable devices are lightweight and portable devices worn directly on the body or integrated into the user's clothing or accessories. They are usually connected to the Internet and combined with various software applications to monitor the user's physical conditions. The latest research shows that wearable head devices, particularly those incorporating microfluidic technology, enable the monitoring of bodily fluids and physiological states. Here, we summarize the main forms, functions, and applications of head wearable devices through innovative researches in recent years. The main functions of wearable head devices are sensor monitoring, diagnosis, and even therapeutic interventions. Through this application, real-time monitoring of human physiological conditions and noninvasive treatment can be realized. Furthermore, microfluidics can realize real-time monitoring of body fluids and skin interstitial fluid, which is highly significant in medical diagnosis and has broad medical application prospects. However, despite the progress made, significant challenges persist in the integration of microfluidics into wearable devices at the current technological level. Herein, we focus on summarizing the cutting-edge applications of microfluidic contact lenses and offer insights into the burgeoning intersection between microfluidics and head-worn wearables, providing a glimpse into their future prospects.
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Affiliation(s)
- Bingbing Gao
- School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing 211816, P. R. China
| | - Jingwen Jiang
- School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing 211816, P. R. China
| | - Shu Zhou
- School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing 211816, P. R. China
| | - Jun Li
- School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing 211816, P. R. China
| | - Qian Zhou
- School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing 211816, P. R. China
| | - Xin Li
- School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing 211816, P. R. China
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Hammour G, Davies H, Atzori G, Della Monica C, Ravindran KKG, Revell V, Dijk DJ, Mandic DP. From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2024; 12:448-456. [PMID: 38765887 PMCID: PMC11100860 DOI: 10.1109/jtehm.2024.3388852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 03/21/2024] [Accepted: 04/10/2024] [Indexed: 05/22/2024]
Abstract
OBJECTIVE Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG. METHODS AND PROCEDURES The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work. Unlike previous studies, our research uniquely focuses on an older cohort (17 subjects aged 65-83, mean age 71.8 years, some with health conditions), and employs LightGBM for transfer learning, diverging from previous deep learning approaches. RESULTS Results show that the initial accuracy of the pre-trained model on ear-EEG was 70.1%, but fine-tuning the model with ear-EEG data improved its classification accuracy to 73.7%. The fine-tuned model exhibited a statistically significant improvement (p < 0.05, dependent t-test) for 10 out of the 13 participants, as reflected by an enhanced average Cohen's kappa score (a statistical measure of inter-rater agreement for categorical items) of 0.639, indicating a stronger agreement between automated and expert classifications of sleep stages. Comparative SHAP value analysis revealed a shift in feature importance for the N3 sleep stage, underscoring the effectiveness of the fine-tuning process. CONCLUSION Our findings underscore the potential of fine-tuning pre-trained scalp EEG models on ear-EEG data to enhance classification accuracy, particularly within an older population and using feature-based methods for transfer learning. This approach presents a promising avenue for ear-EEG analysis in sleep studies, offering new insights into the applicability of transfer learning across different populations and computational techniques. CLINICAL IMPACT An enhanced ear-EEG method could be pivotal in remote monitoring settings, allowing for continuous, non-invasive sleep quality assessment in elderly patients with conditions like dementia or sleep apnea.
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Affiliation(s)
- Ghena Hammour
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BTLondonU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Harry Davies
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BTLondonU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Giuseppe Atzori
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Ciro Della Monica
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Kiran K. G. Ravindran
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Victoria Revell
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
| | - Derk-Jan Dijk
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Danilo P. Mandic
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BTLondonU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
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Tian T, Aaron RE, DuNova AY, Jendle JH, Kerr D, Cengiz E, Drincic A, Pickup JC, Chen KY, Schwartz N, Muchmore DB, Akturk HK, Levy CJ, Schmidt S, Bellazzi R, Wu AHB, Spanakis EK, Najafi B, Chase JG, Seley JJ, Klonoff DC. Diabetes Technology Meeting 2023. J Diabetes Sci Technol 2024:19322968241235205. [PMID: 38528741 DOI: 10.1177/19322968241235205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 1 to November 4, 2023. Meeting topics included digital health; metrics of glycemia; the integration of glucose and insulin data into the electronic health record; technologies for insulin pumps, blood glucose monitors, and continuous glucose monitors; diabetes drugs and analytes; skin physiology; regulation of diabetes devices and drugs; and data science, artificial intelligence, and machine learning. A live demonstration of a personalized carbohydrate dispenser for people with diabetes was presented.
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Affiliation(s)
- Tiffany Tian
- Diabetes Technology Society, Burlingame, CA, USA
| | | | | | - Johan H Jendle
- School of Medicine and Health, Institute of Medical Sciences, Örebro University, Örebro, Sweden
| | | | - Eda Cengiz
- School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | | | - John C Pickup
- King's College London School of Medicine, London, UK
| | - Kong Y Chen
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | | | | | - Halis K Akturk
- Barbara Davis Center for Diabetes, University of Colorado, Aurora, CO, USA
| | - Carol J Levy
- Division of Endocrinology, Diabetes, and Metabolism, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | | | | | - Alan H B Wu
- University of California, San Francisco, San Francisco, CA, USA
| | - Elias K Spanakis
- Baltimore VA Medical Center and School of Medicine, University of Maryland, Baltimore, MD, USA
| | | | | | - Jane Jeffrie Seley
- Division of Endocrinology, Diabetes & Metabolism, Weill Cornell Medicine, New York City, NY, USA
| | - David C Klonoff
- Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, CA, USA
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Di Filippo D, Sunstrum FN, Khan JU, Welsh AW. Non-Invasive Glucose Sensing Technologies and Products: A Comprehensive Review for Researchers and Clinicians. SENSORS (BASEL, SWITZERLAND) 2023; 23:9130. [PMID: 38005523 PMCID: PMC10674292 DOI: 10.3390/s23229130] [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: 10/06/2023] [Revised: 11/01/2023] [Accepted: 11/08/2023] [Indexed: 11/26/2023]
Abstract
Diabetes Mellitus incidence and its negative outcomes have dramatically increased worldwide and are expected to further increase in the future due to a combination of environmental and social factors. Several methods of measuring glucose concentration in various body compartments have been described in the literature over the years. Continuous advances in technology open the road to novel measuring methods and innovative measurement sites. The aim of this comprehensive review is to report all the methods and products for non-invasive glucose measurement described in the literature over the past five years that have been tested on both human subjects/samples and tissue models. A literature review was performed in the MDPI database, with 243 articles reviewed and 124 included in a narrative summary. Different comparisons of techniques focused on the mechanism of action, measurement site, and machine learning application, outlining the main advantages and disadvantages described/expected so far. This review represents a comprehensive guide for clinicians and industrial designers to sum the most recent results in non-invasive glucose sensing techniques' research and production to aid the progress in this promising field.
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Affiliation(s)
- Daria Di Filippo
- Discipline of Women’s Health, School of Clinical Medicine, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia;
| | - Frédérique N. Sunstrum
- Product Design, School of Design, Faculty of Design, Architecture and Built Environment, University of Technology Sydney, Sydney, NSW 2007, Australia;
| | - Jawairia U. Khan
- Institute for Biomedical Materials and Devices, School of Mathematical and Physical Sciences, Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia;
| | - Alec W. Welsh
- Discipline of Women’s Health, School of Clinical Medicine, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia;
- Department of Maternal-Fetal Medicine, Royal Hospital for Women, Randwick, NSW 2031, Australia
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Hammour G, Atzori G, Monica CD, Ravindran KKG, Revell V, Dijk DJ, Mandic DP. Hearables: Automatic Sleep Scoring from Single-Channel Ear-EEG in Older Adults. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083340 DOI: 10.1109/embc40787.2023.10340253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Sleep disorders are a prevalent problem among older adults, yet obtaining an accurate and reliable assessment of sleep quality can be challenging. Traditional polysomnography (PSG) is the gold standard for sleep staging, but is obtrusive, expensive, and requires expert assistance. To this end, we propose a minimally invasive single-channel single ear-EEG automatic sleep staging method for older adults. The method employs features from the frequency, time, and structural complexity domains, which provide a robust classification of sleep stages from a standardised viscoelastic earpiece. Our method is verified on a dataset of older adults and achieves a kappa value of at least 0.61, indicating substantial agreement. This paves the way for a non-invasive, cost-effective, and portable alternative to traditional PSG for sleep staging.
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Gorbachev I, Smirnov A, Ivanov GR, Venelinov T, Amova A, Datsuk E, Anisimkin V, Kuznetsova I, Kolesov V. Langmuir-Blodgett Films with Immobilized Glucose Oxidase Enzyme Molecules for Acoustic Glucose Sensor Application. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115290. [PMID: 37300021 DOI: 10.3390/s23115290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/26/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023]
Abstract
In this work, a sensitive coating based on Langmuir-Blodgett (LB) films containing monolayers of 1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine (DPPE) with an immobilized glucose oxidase (GOx) enzyme was created. The immobilization of the enzyme in the LB film occurred during the formation of the monolayer. The effect of the immobilization of GOx enzyme molecules on the surface properties of a Langmuir DPPE monolayer was investigated. The sensory properties of the resulting LB DPPE film with an immobilized GOx enzyme in a glucose solution of various concentrations were studied. It has shown that the immobilization of GOx enzyme molecules into the LB DPPE film leads to a rising LB film conductivity with an increasing glucose concentration. Such an effect made it possible to conclude that acoustic methods can be used to determine the concentration of glucose molecules in an aqueous solution. It was found that for an aqueous glucose solution in the concentration range from 0 to 0.8 mg/mL the phase response of the acoustic mode at a frequency of 42.7 MHz has a linear form, and its maximum change is 55°. The maximum change in the insertion loss for this mode was 18 dB for a glucose concentration in the working solution of 0.4 mg/mL. The range of glucose concentrations measured using this method, from 0 to 0.9 mg/mL, corresponds to the corresponding range in the blood. The possibility of changing the conductivity range of a glucose solution depending on the concentration of the GOx enzyme in the LB film will make it possible to develop glucose sensors for higher concentrations. Such technological sensors would be in demand in the food and pharmaceutical industries. The developed technology can become the basis for creating a new generation of acoustoelectronic biosensors in the case of using other enzymatic reactions.
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Affiliation(s)
- Ilya Gorbachev
- Kotelnikov Institute of Radio Engineering and Electronics of RAS, 125009 Moscow, Russia
| | - Andrey Smirnov
- Kotelnikov Institute of Radio Engineering and Electronics of RAS, 125009 Moscow, Russia
| | - George R Ivanov
- University Laboratory "Nanoscience and Nanotechnology", University of Architecture, Civil Engineering and Geodesy, 1164 Sofia, Bulgaria
| | - Tony Venelinov
- University Laboratory "Nanoscience and Nanotechnology", University of Architecture, Civil Engineering and Geodesy, 1164 Sofia, Bulgaria
| | - Anna Amova
- University Laboratory "Nanoscience and Nanotechnology", University of Architecture, Civil Engineering and Geodesy, 1164 Sofia, Bulgaria
| | - Elizaveta Datsuk
- Kotelnikov Institute of Radio Engineering and Electronics of RAS, 125009 Moscow, Russia
| | - Vladimir Anisimkin
- Kotelnikov Institute of Radio Engineering and Electronics of RAS, 125009 Moscow, Russia
| | - Iren Kuznetsova
- Kotelnikov Institute of Radio Engineering and Electronics of RAS, 125009 Moscow, Russia
| | - Vladimir Kolesov
- Kotelnikov Institute of Radio Engineering and Electronics of RAS, 125009 Moscow, Russia
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