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1. Seizure occurrence is linked to multiday cycles in diverse physiological signals

   https://doi.org/10.1111/epi.17607  

   Gregg, Nicholas M. ; Pal Attia, Tal ; Nasseri, Mona ; Joseph, Boney ; Karoly, Philippa ; Cui, Jie ; Stirling, Rachel E. ; Viana, Pedro F. ; Richner, Thomas J. ; Nurse, Ewan S. ; et al ( April 2023 , Epilepsia)

   The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing.

   In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist‐worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter–Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology.

   Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal.

   Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time‐varying approaches to epilepsy care.

    

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2. A 65nm Resonant Electro-Quasistatic 5-240uW Human Whole-Body Powering and 2.19uW Communication SoC with Automatic Maximum Resonant Power Tracking

   https://doi.org/10.1109/CICC51472.2021.9431456  

   Modak, Nirmoy ; Das, Debayan ; Nath, Mayukh ; Chatterjee, Baibhab ; Gaurav Kumar, K ; Maity, Shovan ; Sen, Shreyas ( April 2021 , 2021 IEEE Custom Integrated Circuits Conference (CICC))

   Applications like Connected Healthcare through physiological signal monitoring and Secure Authentication using wearable keys can benefit greatly from battery-less operation. Low power communication along with energy harvesting is critical to sustain such perpetual battery-less operation. Previous studies have used techniques such as Tribo-Electric, Piezo-Electric, RF energy harvesting for Body Area Network devices, but they are restricted to on-body node placements. Human body channel is known to be a promising alternative to wireless radio wave communication for low power operation [1-4], through Human Body Communication, as well as very recently as a medium for power transfer through body coupled power transfer [5]. However, channel length (L) dependency of the received power makes it inefficient for L>40cm. There have also been a few studies for low power communication through the human body, but none of them could provide sustainable battery-less operation. In this paper, we utilize Resonant Electro Quasi-Static Human Body Communication (Res-EQS HBC) with Maximum Resonance Power Tracking (MRPT) to enable channel length independent whole-body communication and powering (Fig.1). We design the first system to simultaneously transfer Power and Data between a HUB device and a wearable through the human body to enable battery-less operation. Measurement results show 240uW, 28uW and 5uW power transfer through the body in a MachineMachine (large devices with strong ground connection) Tabletop (small devices kept on a table, as in [5]) and Wearable-Wearable (small form factor battery operated devices) scenario respectively, independent of body channel length, while enabling communication with a power consumption of only 2.19uW. This enables >25x more power transfer with >100x more efficiency compared to [5] for Tabletop and 100cm Body distance by utilizing the benefits of EQS. The MRPT loop automatically tracks device and posture dependent resonance point changes to maximize power transfer in all cases. 

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3. A “Two‐Part” Resonance Circuit based Detachable Sweat Patch for Noninvasive Biochemical and Biophysical Sensing

   https://doi.org/10.1002/adfm.202210136  

   Dong, Yan ; Liu, Tzu‐Li ; Chen, Shulin ; Nithianandam, Prasad ; Matar, Keyan ; Li, Jinghua ( November 2022 , Advanced Functional Materials)

   Wearable electronics play important roles in noninvasive, continuous, and personalized monitoring of multiple biosignals generated by the body. To unleash their full potential for the next‐generation human‐centered bio‐integrated electronics, wireless sensing capability is a desirable feature. However, state‐of‐the‐art wireless sensing technologies exploit rigid and bulky electronic modules for power supply, signal generation, and data transmission. This study reports a battery‐free device technology based on a “two‐part” resonance circuit model with modularized, physically separated, and detachable functional units for magnetic coupling and biosensing. The resulting platform combines advantages of electronics and microfluidics with low cost, minimized form factors, and improved performance stability. Demonstration of a detachable sweat patch capable of simultaneous recording of cortisol concentration, pH value, and temperature highlights the potential of the “two‐part” circuit for advanced, transformative biosensing. The resulting wireless sensors provide a new engineering solution to monitoring biosignals through intimate and seamless integration with skin surfaces.

    

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4. SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables

   https://doi.org/10.3390/bioengineering10080918  

   Al-Hussaini, Irfan ; Mitchell, Cassie S. ( August 2023 , Bioengineering)

   This work presents SeizFt—a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by interpretable sleep staging, our novel approach employs a unique combination of data augmentation, meaningful feature extraction, and an ensemble of decision trees to improve resilience to variations in EEG and to increase the capacity to generalize to unseen data. Fourier Transform (FT) Surrogates were utilized to increase sample size and improve the class balance between labeled non-seizure and seizure epochs. To enhance model stability and accuracy, SeizFt utilizes an ensemble of decision trees through the CatBoost classifier to classify each second of EEG recording as seizure or non-seizure. The SeizIt1 dataset was used for training, and the SeizIt2 dataset for validation and testing. Model performance for seizure detection was evaluated using two primary metrics: sensitivity using the any-overlap method (OVLP) and False Alarm (FA) rate using epoch-based scoring (EPOCH). Notably, SeizFt placed first among an array of state-of-the-art seizure detection algorithms as part of the Seizure Detection Grand Challenge at the 2023 International Conference on Acoustics, Speech, and Signal Processing (ICASSP). SeizFt outperformed state-of-the-art black-box models in accurate seizure detection and minimized false alarms, obtaining a total score of 40.15, combining OVLP and EPOCH across two tasks and representing an improvement of ~30% from the next best approach. The interpretability of SeizFt is a key advantage, as it fosters trust and accountability among healthcare professionals. The most predictive seizure detection features extracted from SeizFt were: delta wave, interquartile range, standard deviation, total absolute power, theta wave, the ratio of delta to theta, binned entropy, Hjorth complexity, delta + theta, and Higuchi fractal dimension. In conclusion, the successful application of SeizFt to wearable SensorDot data suggests its potential for real-time, continuous monitoring to improve personalized medicine for epilepsy.

    

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5. A Computationally Assisted Approach for Designing Wearable Biosensors toward Non‐Invasive Personalized Molecular Analysis

   https://doi.org/10.1002/adma.202212161  

   Mukasa, Daniel ; Wang, Minqiang ; Min, Jihong ; Yang, Yiran ; Solomon, Samuel A. ; Han, Hong ; Ye, Cui ; Gao, Wei ( July 2023 , Advanced Materials)

   Wearable sweat sensors have the potential to revolutionize precision medicine as they can non‐invasively collect molecular information closely associated with an individual's health status. However, the majority of clinically relevant biomarkers cannot be continuously detected in situ using existing wearable approaches. Molecularly imprinted polymers (MIPs) are a promising candidate to address this challenge but haven't yet gained widespread use due to their complex design and optimization process yielding variable selectivity. Here, QuantumDock is introduced, an automated computational framework for universal MIP development toward wearable applications. QuantumDock utilizes density functional theory to probe molecular interactions between monomers and the target/interferent molecules to optimize selectivity, a fundamentally limiting factor for MIP development toward wearable sensing. A molecular docking approach is employed to explore a wide range of known and unknown monomers, and to identify the optimal monomer/cross‐linker choice for subsequent MIP fabrication. Using an essential amino acid phenylalanine as the exemplar, experimental validation of QuantumDock is performed successfully using solution‐synthesized MIP nanoparticles coupled with ultraviolet–visible spectroscopy. Moreover, a QuantumDock‐optimized graphene‐based wearable device is designed that can perform autonomous sweat induction, sampling, and sensing. For the first time, wearable non‐invasive phenylalanine monitoring is demonstrated in human subjects toward personalized healthcare applications.

    

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