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Zhuo S, Wu Z, Williams C, Sundaresan C, Ameri SK. In-Ear Electronics with Mechanical Adaptability for Physiological Sensing. Adv Healthc Mater 2024:e2404296. [PMID: 39663718 DOI: 10.1002/adhm.202404296] [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: 11/01/2024] [Indexed: 12/13/2024]
Abstract
Significant developments have been made in the field of wearable healthcare by utilizing soft materials for the construction of electronic sensors. However, the lack of adaptability to complex topologies, such as ear canal, results in inadequate sensing performance. Here, we report an in-ear physiological sensor with mechanical adaptability, which softens upon contact with the ear canal's skin, thus reducing the sensor-skin mechanical mismatch and interface impedance. An efficient strategy of mechanical adjustment and switching is exploited to increase the softness of the device, leading to a significant decrease in Young's modulus from 30.5 MPa of thermoplastic polyurethane (TPU) to 0.86 MPa of TPU/Ecoflex foam (TEF).The mechanical adaptability at body temperature endows the in-ear device improved device-canal contact area and interface stability. As a result, the TEF-based in-ear device demonstrates reliable sensing, low motion artifact, and high comfort in electroencephalography (EEG) and core body temperature sensing. High quality EEG signals of alpha, beta, delta, and gamma are measured during different activities. Moreover, the TEF-based in-ear device exhibits high reusability for over 4 months, which makes it suitable for long-term healthcare monitoring.
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Affiliation(s)
- Shuyun Zhuo
- Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, K7L 3N6, Canada
| | - Zihuan Wu
- Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, K7L 3N6, Canada
| | - Chris Williams
- Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, K7L 3N6, Canada
| | - Chithiravel Sundaresan
- Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, K7L 3N6, Canada
| | - Shideh Kabiri Ameri
- Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, K7L 3N6, Canada
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, K7L 3N6, Canada
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Sauvage E, Matta J, Dang CT, Fan J, Cruzado G, Cicoira F, Merle G. Electroconductive cardiac patch based on bioactive PEDOT:PSS hydrogels. J Biomed Mater Res A 2024; 112:1817-1826. [PMID: 38689450 DOI: 10.1002/jbm.a.37729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 04/13/2024] [Accepted: 04/22/2024] [Indexed: 05/02/2024]
Abstract
Engineering cardiac implants for treating myocardial infarction (MI) has advanced, but challenges persist in mimicking the structural properties and variability of cardiac tissues using traditional bioconstructs and conventional engineering methods. This study introduces a synthetic patch with a bioactive surface designed to swiftly restore functionality to the damaged myocardium. The patch combines a composite, soft, and conductive hydrogel-based on (3,4-ethylenedioxythiophene):polystyrene-sulfonate (PEDOT:PSS) and polyvinyl alcohol (PVA). This cardiac patch exhibits a reasonably high electrical conductivity (40 S/cm) and a stretchability up to 50% of its original length. Our findings reveal its resilience to 10% cyclic stretching at 1 Hz with no loss of conductivity over time. To mediate a strong cell-scaffold adhesion, we biofunctionalize the hydrogel with a N-cadherin mimic peptide, providing the cardiac patch with a bioactive surface. This modification promote increased adherence and proliferation of cardiac fibroblasts (CFbs) while effectively mitigating the formation of bacterial biofilm, particularly against Staphylococcus aureus, a common pathogen responsible for surgical site infections (SSIs). Our study demonstrates the successful development of a structurally validated cardiac patch possessing the desired mechanical, electrical, and biofunctional attributes for effective cardiac recovery. Consequently, this research holds significant promise in alleviating the burden imposed by myocardial infarctions.
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Affiliation(s)
- Erwan Sauvage
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Quebec, Canada
| | - Justin Matta
- Department of Experimental Surgery, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Cat-Thy Dang
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Quebec, Canada
| | - Jiaxin Fan
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Quebec, Canada
| | - Graziele Cruzado
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Quebec, Canada
| | - Fabio Cicoira
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Quebec, Canada
| | - Géraldine Merle
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Quebec, Canada
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Awuah WA, Ahluwalia A, Darko K, Sanker V, Tan JK, Tenkorang PO, Ben-Jaafar A, Ranganathan S, Aderinto N, Mehta A, Shah MH, Lee Boon Chun K, Abdul-Rahman T, Atallah O. Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications. World Neurosurg 2024; 189:138-153. [PMID: 38789029 DOI: 10.1016/j.wneu.2024.05.104] [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: 01/22/2024] [Revised: 05/16/2024] [Accepted: 05/17/2024] [Indexed: 05/26/2024]
Abstract
Brain-computer interfaces (BCIs), a remarkable technological advancement in neurology and neurosurgery, mark a significant leap since the inception of electroencephalography in 1924. These interfaces effectively convert central nervous system signals into commands for external devices, offering revolutionary benefits to patients with severe communication and motor impairments due to a myriad of neurological conditions like stroke, spinal cord injuries, and neurodegenerative disorders. BCIs enable these individuals to communicate and interact with their environment, using their brain signals to operate interfaces for communication and environmental control. This technology is especially crucial for those completely locked in, providing a communication lifeline where other methods fall short. The advantages of BCIs are profound, offering autonomy and an improved quality of life for patients with severe disabilities. They allow for direct interaction with various devices and prostheses, bypassing damaged or nonfunctional neural pathways. However, challenges persist, including the complexity of accurately interpreting brain signals, the need for individual calibration, and ensuring reliable, long-term use. Additionally, ethical considerations arise regarding autonomy, consent, and the potential for dependence on technology. Despite these challenges, BCIs represent a transformative development in neurotechnology, promising enhanced patient outcomes and a deeper understanding of brain-machine interfaces.
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Affiliation(s)
| | - Arjun Ahluwalia
- School of Medicine, Queen's University Belfast, Belfast, United Kingdom
| | - Kwadwo Darko
- Department of Neurosurgery, Korle Bu Teaching Hospital, Accra, Ghana
| | - Vivek Sanker
- Department of Neurosurgery, Trivandrum Medical College, India
| | - Joecelyn Kirani Tan
- Faculty of Medicine, University of St Andrews, St. Andrews, Scotland, United Kingdom.
| | | | - Adam Ben-Jaafar
- University College Dublin, School of Medicine, Belfield, Dublin, Ireland
| | - Sruthi Ranganathan
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Nicholas Aderinto
- Internal Medicine Department, LAUTECH Teaching Hospital, Ogbomoso, Nigeria
| | - Aashna Mehta
- University of Debrecen-Faculty of Medicine, Debrecen, Hungary
| | | | | | | | - Oday Atallah
- Department of Neurosurgery, Hannover Medical School, Hannover, Germany
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van der Heijden P, Gilbert C, Jafari S, Lucchini MA. Multi-Channel Soft Dry Electrodes for Electrocardiography Acquisition in the Ear Region. SENSORS (BASEL, SWITZERLAND) 2024; 24:420. [PMID: 38257511 PMCID: PMC10819754 DOI: 10.3390/s24020420] [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: 10/27/2023] [Revised: 12/22/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024]
Abstract
In-ear acquisition of physiological signals, such as electromyography (EMG), electrooculography (EOG), electroencephalography (EEG), and electrocardiography (ECG), is a promising approach to mobile health (mHealth) due to its non-invasive and user-friendly nature. By providing a convenient and comfortable means of physiological signal monitoring, in-ear signal acquisition could potentially increase patient compliance and engagement with mHealth applications. The development of reliable and comfortable soft dry in-ear electrode systems could, therefore, have significant implications for both mHealth and human-machine interface (HMI) applications. This research evaluates the quality of the ECG signal obtained with soft dry electrodes inserted in the ear canal. An earplug with six soft dry electrodes distributed around its perimeter was designed for this study, allowing for the analysis of the signal coming from each electrode independently with respect to a common reference placed at different positions on the body of the participants. An analysis of the signals in comparison with a reference signal measured on the upper right chest (RA) and lower left chest (LL) was performed. The results show three typical behaviors for the in-ear electrodes. Some electrodes have a high correlation with the reference signal directly after inserting the earplug, other electrodes need a settling time of typically 1-3 min, and finally, others never have a high correlation. The SoftPulseTM electrodes used in this research have been proven to be perfectly capable of measuring physiological signals, paving the way for their use in mHealth or HMI applications. The use of multiple electrodes distributed in the ear canal has the advantage of allowing a more reliable acquisition by intelligently selecting the signal acquisition locations or allowing a better spatial resolution for certain applications by processing these signals independently.
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Affiliation(s)
| | | | - Samira Jafari
- Dätwyler Schweiz AG, 6467 Schattdorf, Switzerland (M.A.L.)
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Lorenz EA, Su X, Skjæret-Maroni N. A review of combined functional neuroimaging and motion capture for motor rehabilitation. J Neuroeng Rehabil 2024; 21:3. [PMID: 38172799 PMCID: PMC10765727 DOI: 10.1186/s12984-023-01294-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Technological advancements in functional neuroimaging and motion capture have led to the development of novel methods that facilitate the diagnosis and rehabilitation of motor deficits. These advancements allow for the synchronous acquisition and analysis of complex signal streams of neurophysiological data (e.g., EEG, fNIRS) and behavioral data (e.g., motion capture). The fusion of those data streams has the potential to provide new insights into cortical mechanisms during movement, guide the development of rehabilitation practices, and become a tool for assessment and therapy in neurorehabilitation. RESEARCH OBJECTIVE This paper aims to review the existing literature on the combined use of motion capture and functional neuroimaging in motor rehabilitation. The objective is to understand the diversity and maturity of technological solutions employed and explore the clinical advantages of this multimodal approach. METHODS This paper reviews literature related to the combined use of functional neuroimaging and motion capture for motor rehabilitation following the PRISMA guidelines. Besides study and participant characteristics, technological aspects of the used systems, signal processing methods, and the nature of multimodal feature synchronization and fusion were extracted. RESULTS Out of 908 publications, 19 were included in the final review. Basic or translation studies were mainly represented and based predominantly on healthy participants or stroke patients. EEG and mechanical motion capture technologies were most used for biomechanical data acquisition, and their subsequent processing is based mainly on traditional methods. The system synchronization techniques at large were underreported. The fusion of multimodal features mainly supported the identification of movement-related cortical activity, and statistical methods were occasionally employed to examine cortico-kinematic relationships. CONCLUSION The fusion of motion capture and functional neuroimaging might offer advantages for motor rehabilitation in the future. Besides facilitating the assessment of cognitive processes in real-world settings, it could also improve rehabilitative devices' usability in clinical environments. Further, by better understanding cortico-peripheral coupling, new neuro-rehabilitation methods can be developed, such as personalized proprioceptive training. However, further research is needed to advance our knowledge of cortical-peripheral coupling, evaluate the validity and reliability of multimodal parameters, and enhance user-friendly technologies for clinical adaptation.
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Affiliation(s)
- Emanuel A Lorenz
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Xiaomeng Su
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Nina Skjæret-Maroni
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
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