Soft Nanomembrane Sensors and Flexible Hybrid Bioelectronics for Wireless Quantification of Blepharospasm

Mahmood, Musa; Kwon, Shinjae; Berkmen, Gamze Kilic; Kim, Yun-Soung; Scorr, Laura; Jinnah, Hyder A.; Yeo, Woon‐Hong

doi:10.1109/tbme.2020.2975773

Cited by 22 publications

(31 citation statements)

References 27 publications

(37 reference statements)

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“…Expert recommendations for diagnosing cervical, OMD, and limb dystonia were also published ( 12 ). Moreover, new clinical evaluation tools were invented such as a soft, flexible hybrid bioelectronic system that offers highly conformal, gentle lamination on the skin, while enabling wireless, quantitative detection of electrophysiological signals ( 13 ). The wearable bioelectronics outperforms the conventional manual clinical rating for BSP patients ( 13 ).…”

Section: Relationship Between Bsp Omd and Msmentioning

confidence: 99%

Blepharospasm, Oromandibular Dystonia, and Meige Syndrome: Clinical and Genetic Update

Liang-jun

et al. 2021

Front. Neurol.

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Meige syndrome (MS) is cranial dystonia characterized by the combination of upper and lower cranial involvement and including binocular eyelid spasms (blepharospasm; BSP) and involuntary movements of the jaw muscles (oromandibular dystonia; OMD). The etiology and pathogenesis of this disorder of the extrapyramidal system are not well-understood. Neurologic and ophthalmic examinations often reveal no abnormalities, making diagnosis difficult and often resulting in misdiagnosis. A small proportion of patients have a family history of the disease, but to date no causative genes have been identified to date and no cure is available, although botulinum toxin A therapy effectively mitigates the symptoms and deep brain stimulation is gaining increasing attention as a viable alternative treatment option. Here we review the history and progress of research on MS, BSP, and OMD, as well as the etiology, pathology, diagnosis, and treatment.

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Section: Relationship Between Bsp Omd and Msmentioning

confidence: 99%

Blepharospasm, Oromandibular Dystonia, and Meige Syndrome: Clinical and Genetic Update

Liang-jun

et al. 2021

Front. Neurol.

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“…In recent years, great breakthroughs have been achieved in the development of ML‐stretchable sensing systems for human–machine interfaces and healthcare. [ 19,80–97 ] To date, these reported ML‐stretchable sensing systems can be generally divided into three types ( Table 1 ): ML‐stretchable electrode systems, ML‐stretchable tactile sensor systems, and ML‐multiple sensor systems, which are used for bioelectrical signal recognition, haptic perception, and multimodal information integration, respectively.…”

Section: Ml‐stretchable Sensing Systemsmentioning

confidence: 99%

Fusing Stretchable Sensing Technology with Machine Learning for Human–Machine Interfaces

Wang

Luo

et al. 2021

Adv Funct Materials

100

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Sensors and algorithms are two fundamental elements to construct intelligent systems. The recent progress in machine learning (ML) has produced great advancements in intelligent systems, owing to the powerful data analysis capability of ML algorithms. However, the performance of most systems is still hindered by sensing techniques that typically rely on rigid and bulky sensor devices, which cannot conform to irregularly curved and dynamic surfaces for high‐quality data acquisition. Skin‐like stretchable sensing technology with unique characteristics, such as high conformability, low modulus, and light weight, has been recently developed to solve this issue. Here, the recent progress in the fusion of emerging stretchable electronics and ML technology, for bioelectrical signal recognition, tactile perception, and multimodal integration is summarized, and the challenges and future developments are further discussed. These efforts aim to accelerate various perception and reasoning tasks for advanced intelligent applications, such as human–machine interfaces, healthcare, and robotics.

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“…A customized printed circuit board (PCB) with an nRF52 (Nordic Semiconductor, Trondheim, Norway) and an ADS 1299 (Texas Instruments, Dallas, TX, USA) was used to collect EEG and EOG signals at a sampling rate of 250 Hz and to transmit them to an Android mobile device via Bluetooth for data storage. The systems used to collect the lab datasets, the nanomembrane-based electrodes and the custom PCB with the nRF52 and ADS 1299 have been extensively studied and validated by comparison with wellestablished measurement systems by numerous related previous studies [17][18][19][20][21] Figure 1E shows representative multi-taper spectrograms of data measured by the standard PSG setup and the setup used for this study at each of the five sleep stages.…”

Section: Measured Lab Datasetmentioning

confidence: 99%

Automatic and Accurate Sleep Stage Classification via a Convolutional Deep Neural Network and Nanomembrane Electrodes

Kwon

Yeo

2022

Biosensors

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Sleep stage classification is an essential process of diagnosing sleep disorders and related diseases. Automatic sleep stage classification using machine learning has been widely studied due to its higher efficiency compared with manual scoring. Typically, a few polysomnography data are selected as input signals, and human experts label the corresponding sleep stages manually. However, the manual process includes human error and inconsistency in the scoring and stage classification. Here, we present a convolutional neural network (CNN)-based classification method that offers highly accurate, automatic sleep stage detection, validated by a public dataset and new data measured by wearable nanomembrane dry electrodes. First, our study makes a training and validation model using a public dataset with two brain signal and two eye signal channels. Then, we validate this model with a new dataset measured by a set of nanomembrane electrodes. The result of the automatic sleep stage classification shows that our CNN model with multi-taper spectrogram pre-processing achieved 88.85% training accuracy on the validation dataset and 81.52% prediction accuracy on our laboratory dataset. These results validate the reliability of our classification method on the standard polysomnography dataset and the transferability of our CNN model for other datasets measured with the wearable electrodes.

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Soft Nanomembrane Sensors and Flexible Hybrid Bioelectronics for Wireless Quantification of Blepharospasm

Cited by 22 publications

References 27 publications

Blepharospasm, Oromandibular Dystonia, and Meige Syndrome: Clinical and Genetic Update

Blepharospasm, Oromandibular Dystonia, and Meige Syndrome: Clinical and Genetic Update

Fusing Stretchable Sensing Technology with Machine Learning for Human–Machine Interfaces

Automatic and Accurate Sleep Stage Classification via a Convolutional Deep Neural Network and Nanomembrane Electrodes

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