Integrated Flexible Hybrid Silicone-Textile Dual-Resonant Sensors and Switching Circuit for Wearable Neurodegeneration Monitoring Systems

Saied, Imran; Chandran, Siddharthan; Arslan, Tughrul

doi:10.1109/tbcas.2019.2951500

Cited by 31 publications

(27 citation statements)

References 20 publications

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“…This study demonstrated the potential of using wearable devices to monitor dementia. Saied et al demonstrated a wearable system for monitoring neurodegenerative diseases using flexible silicone-textile sensors and flexible switching circuits [70]. Wearable systems can monitor physiological changes in the brain from neurodegenerative diseases, including brain atrophy and lateral ventricle enlargement.…”

Section: Monitoring Systemmentioning

confidence: 99%

Personalized Healthcare for Dementia

2021

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Dementia is one of the most common health problems affecting older adults, and the population with dementia is growing. Dementia refers to a comprehensive syndrome rather than a specific disease and is characterized by the loss of cognitive abilities. Many factors are related to dementia, such as aging, genetic profile, systemic vascular disease, unhealthy diet, and physical inactivity. As the causes and types of dementia are diverse, personalized healthcare is required. In this review, we first summarize various diagnostic approaches associated with dementia. Particularly, clinical diagnosis methods, biomarkers, neuroimaging, and digital biomarkers based on advances in data science and wearable devices are comprehensively reviewed. We then discuss three effective approaches to treating dementia, including engineering design, exercise, and diet. In the engineering design section, recent advances in monitoring and drug delivery systems for dementia are introduced. Additionally, we describe the effects of exercise on the treatment of dementia, especially focusing on the effects of aerobic and resistance training on cognitive function, and the effects of diets such as the Mediterranean diet and ketogenic diet on dementia.

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Section: Monitoring Systemmentioning

confidence: 99%

Personalized Healthcare for Dementia

2021

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“…Several studies have been conducted on investigating and designing such devices for stroke and breast cancer detection. The authors have also previously investigated RF sensing for detecting brain atrophy and lateral ventricle enlargement [20]- [21]. These devices have the advantage of being low-cost, low-profile, and non-ionising; however, the translation into clinical use is still in its infancy.…”

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confidence: 99%

Non-Invasive RF Technique for Detecting Different Stages of Alzheimer’s Disease and Imaging Beta-Amyloid Plaques and Tau Tangles in the Brain

Saied

Arslan

Chandran

et al. 2020

IEEE Trans. Med. Imaging

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This paper describes a novel approach of detecting different stages of Alzheimer's disease (AD) and imaging betaamyloid plaques and tau tangles in the brain using RF sensors. Dielectric measurements were obtained from grey matter and white matter regions of brain tissues with severe AD pathology at a frequency range of 200 MHz to 3 GHz using a vector network analyzer and dielectric probe. Computational models were created on CST Microwave Suite using a realistic head model and the measured dielectric properties to represent affected brain regions at different stages of AD. Simulations were carried out to test the performance of the RF sensors. Experiments were performed using textile-based RF sensors on fabricated phantoms, representing a human brain with different volumes of AD-affected brain tissues. Experimental data was collected from the sensors and processed in an imaging algorithm to reconstruct images of the affected areas in the brain. Measured dielectric properties in brain tissues with AD pathology were found to be different from healthy human brain tissues. Simulation and experimental results indicated a correlated shift in the captured reflection coefficient data from RF sensors as the amount of affected brain regions increased. Finally, images reconstructed from the imaging algorithm successfully highlighted areas of the brain affected by plaques and tangles as a result of AD. The results from this study show that RF sensing can be used to identify areas of the brain affected by AD pathology. This provides a promising new non-invasive technique for monitoring the progression of AD.

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“…Future research will focus on investigating DL techniques to classify AD traits from image data generated from the authors' previous work in [19]. In addition, the authors also plan to utiluse ML to classify AD based on other physiological changes in the brain that can be detected by RF sensors [32], [33]. Depending on these results, the next and final goal is to combine the different studies together to develop an AI solution that will take the captured RF data and predict the progression rate of AD in a patient.…”

Section: Discussionmentioning

confidence: 99%

Classification of Alzheimer's Disease Using RF Signals and Machine Learning

Saied

Arslan

Chandran

2022

IEEE J. Electromagn. RF Microw. Med. Biol.

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Objectives: Alzheimer's disease is one of the most fastest growing and costly diseases in the world today. It affects the livelihood of not just patients, but those who take care of them, including care givers, nurses, and close family members. Current progression monitoring techniques are based on MRI and PET scans which are inconvenient for patients to use. In addition, more intelligent and efficient methods are needed to predict what the current stage of the disease is and strategies on how to slow down its progress over time. Technology or Method: In this paper, machine learning was used with S-parameter data obtained from 6 antennas that were placed around the head to noninvasively capture changes in the brain in the presence of Alzheimer's disease pathology. Measurements were conducted for 9 different human models that varied in head sizes. The data was processed in several machine learning algorithms. Each algorithm's prediction and accuracy score were generated and the results were compared to determine which machine learning algorithm could be used to efficiently classify different stages of Alzheimer's disease. Results: Results from the study showed that overall, the logistic regression model had the best accuracy of 98.97% and efficiency in differentiating between 4 different stages of Alzheimer's disease. Clinical or Biological Impact: The results obtained here provide a transformative approach to clinics and monitoring systems where machine learning can be integrated with noninvasive microwave medical sensors and systems to intelligently predict the stage of Alzheimer's disease in the brain.

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Integrated Flexible Hybrid Silicone-Textile Dual-Resonant Sensors and Switching Circuit for Wearable Neurodegeneration Monitoring Systems

Cited by 31 publications

References 20 publications

Personalized Healthcare for Dementia

Personalized Healthcare for Dementia

Non-Invasive RF Technique for Detecting Different Stages of Alzheimer’s Disease and Imaging Beta-Amyloid Plaques and Tau Tangles in the Brain

Classification of Alzheimer's Disease Using RF Signals and Machine Learning

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