Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration.

Objective: To remove signal contamination in electroencephalogram (EEG) traces coming from ocular, motion, and muscular artifacts which degrade signal quality. To do this in real-time, with low computational overhead, on a mobile platform in a channel count independent manner to enable portable Brain-Computer Interface (BCI) applications.

Methods: We propose a Deep AutoEncoder (DAE) neural network for single-channel EEG artifact removal, and implement it on a smartphone via TensorFlow Lite.

A systematic review of quantitative EEG findings in Fibromyalgia, Chronic Fatigue Syndrome and Long COVID.

Fibromyalgia Syndrome (FMS), Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Long COVID (LC) are similar multisymptom clinical syndromes but with difference in dominant symptoms in each individual. There is existing and emerging literature on possible functional alterations of the central nervous system in these conditions. This review aims to synthesise and appraise the literature on resting-state quantitative EEG (qEEG) in FMS, ME/CFS and LC, drawing on previous research on FMS and ME/CFS to help understand neuropathophysiology of the new condition LC.

A Wrist-Worn Internet of Things Sensor Node for Wearable Equivalent Daylight Illuminance Monitoring.

Light exposure is a vital regulator of physiology and behavior in humans. However, monitoring of light exposure is not included in current wearable Internet of Things (IoT) devices, and only recently have international standards defined [Formula: see text] -optic equivalent daylight illuminance (EDI) measures for how the eye responds to light. This article reports a wearable light sensor node that can be incorporated into the IoT to provide monitoring of EDI exposure in real-world settings.

Applying Team Science to Collaborative Digital Health Research: Learnings from the Wearable Clinic.

Collaboration across disciplinary boundaries is vital to address the complex challenges and opportunities in Digital Health. We present findings and experiences of applying the principles of Team Science to a digital health research project called 'The Wearable Clinic'. Challenges faced were a lack of shared understanding of key terminology and concepts, and differences in publication cultures between disciplines.

Organisms as sensors in biohybrid entities as a novel tool for in-field aquatic monitoring.

Rapidly intensifying global warming and water pollution calls for more efficient and continuous environmental monitoring methods. Biohybrid systems connect mechatronic components to living organisms and this approach can be used to extract data from the organisms. Compared to conventional monitoring methods, they allow for a broader data collection over long periods, minimizing the need for sampling processes and human labour.

Associations between light exposure and sleep timing and sleepiness while awake in a sample of UK adults in everyday life.

Experimental and interventional studies show that light can regulate sleep timing and sleepiness while awake by setting the phase of circadian rhythms and supporting alertness. The extent to which differences in light exposure explain variations in sleep and sleepiness within and between individuals in everyday life remains less clear. Here, we establish a method to address this deficit, incorporating an open-source wearable wrist-worn light logger (SpectraWear) and smartphone-based online data collection.

The Complexity of Transferring Remote Monitoring and Virtual Care Technology Between Countries: Lessons From an International Workshop.

International deployment of remote monitoring and virtual care (RMVC) technologies would efficiently harness their positive impact on outcomes. Since Canada and the United Kingdom have similar populations, health care systems, and digital health landscapes, transferring digital health innovations between them should be relatively straightforward. Yet examples of successful attempts are scarce.

User experiences of pre-sleep sensory alpha brainwave entrainment for people with chronic pain and sleep disturbance.

To explore the user experiences of pre-sleep alpha entrainment via a smartphone-enabled audio or visual stimulation program for people with chronic pain and sleep disturbance. Semi-structured interviews were held with 27 participants completing a feasibility study of pre-sleep entrainment use for 4 weeks. Transcriptions were subject to template analysis.

A feasibility study of pre-sleep audio and visual alpha brain entrainment for people with chronic pain and sleep disturbance.

Introduction: Chronic pain and sleep disturbance are bi-directionally related. Cortical electrical activity in the alpha frequency band can be enhanced with sensory stimulation the phenomenon of entrainment, and may reduce pain perception. A smartphone based programme which delivers 10 Hz stimulation through flickering light or binaural beats was developed for use at night, pre-sleep, with the aim of improving night time pain and sleep and thereby subsequent pain and related daytime symptoms.

HEART rate variability biofeedback for long COVID symptoms (HEARTLOC): protocol for a feasibility study.

Introduction: Long COVID (LC), also known as post-COVID-19 syndrome, refers to symptoms persisting 12 weeks after COVID-19 infection. It affects up to one in seven people contracting the illness and causes a wide range of symptoms, including fatigue, breathlessness, palpitations, dizziness, pain and brain fog. Many of these symptoms can be linked to dysautonomia or dysregulation of the autonomic nervous system after SARS-CoV2 infection.

Optimising sounds for the driving of sleep oscillations by closed-loop auditory stimulation.

Recent studies have shown that slow oscillations (SOs) can be driven by rhythmic auditory stimulation, which deepens slow-wave sleep (SWS) and improves memory and the immune-supportive hormonal milieu related to this sleep stage. While different attempts have been made to optimise the driving of the SOs by changing the number of click stimulations, no study has yet investigated the impact of applying more than five clicks in a row. Likewise, the importance of the type of sounds in eliciting brain responses is presently unclear.

LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS (LOCOMOTION): protocol for a mixed-methods study in the UK.

Introduction: Long COVID, a new condition whose origins and natural history are not yet fully established, currently affects 1.5 million people in the UK. Most do not have access to specialist long COVID services.

Fully printed and multifunctional graphene-based wearable e-textiles for personalized healthcare applications.

Wearable e-textiles have gained huge tractions due to their potential for non-invasive health monitoring. However, manufacturing of multifunctional wearable e-textiles remains challenging, due to poor performance, comfortability, scalability, and cost. Here, we report a fully printed, highly conductive, flexible, and machine-washable e-textiles platform that stores energy and monitor physiological conditions including bio-signals.

Design and optimization of a TensorFlow Lite deep learning neural network for human activity recognition on a smartphone.

Human Activity Recognition (HAR), using machine learning to identify times spent (for example) walking, sitting, and standing, is widely used in health and wellness wearable devices, in ambient assistant living devices, and in rehabilitation. In this paper, a stacked Long Short-Term Memory (LSTM) structure is designed for HAR to be implemented on a smartphone. The use of an edge device for the processing means that the raw collected data does not need to be passed to the cloud for processing, mitigating potential bandwidth, power consumption, and privacy concerns.

Impact of shift working on the potential for self-powering via kinetic energy harvesting in wearable devices.

Wearable devices are having a transformative impact on personalised monitoring and care. However, they frequently have limited battery life, requiring charging every few days; a major source of user frustration. Kinetic energy harvesting may help overcome this, collecting energy from the user's motion to allow the device to self-charge.

Electronic and electrochemical viral detection for point-of-care use: A systematic review.

Detecting viruses, which have significant impact on health and the economy, is essential for controlling and combating viral infections. In recent years there has been a focus towards simpler and faster detection methods, specifically through the use of electronic-based detection at the point-of-care. Point-of-care sensors play a particularly important role in the detection of viruses.

Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering.

This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on inertial measurement units to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, inertial measurement units are attached to each individual EEG or ECG electrode to collect local movement data. This data is then used to remove the motion artefact by using normalised least mean square adaptive filtering.

Monitoring of Dynamic Plantar Foot Temperatures in Diabetes with Personalised 3D-Printed Wearables.

Diabetic foot ulcers (DFUs) are a life-changing complication of diabetes that can lead to amputation. There is increasing evidence that long-term management with wearables can reduce incidence and recurrence of this condition. Temperature asymmetry measurements can alert to DFU development, but measurements of dynamic information, such as rate of temperature change, are under investigated.

Using EEG Alpha States to Understand Learning During Alpha Neurofeedback Training for Chronic Pain.

Objective: Alpha-neurofeedback (α-NFB) is a novel therapy which trains individuals to volitionally increase their alpha power to improve pain. Learning during NFB is commonly measured using static parameters such as mean alpha power. Considering the biphasic nature of alpha rhythm (high and low alpha), dynamic parameters describing the time spent by individuals in high alpha state and the pattern of transitioning between states might be more useful.

Alpha entrainment drives pain relief using visual stimulation in a sample of chronic pain patients: a proof-of-concept controlled study.

One-third of the population in the UK and worldwide struggle with chronic pain. Entraining brain alpha activity through noninvasive visual stimulation has been shown to reduce experimental pain in healthy volunteers. Neural oscillations entrainment offers a potential noninvasive and nonpharmacological intervention for patients with chronic pain, which can be delivered in the home setting and has the potential to reduce use of medications.

Adaptive Symptom Monitoring Using Hidden Markov Models - An Application in Ecological Momentary Assessment.

Wearable and mobile technology provides new opportunities to manage health conditions remotely and unobtrusively. For example, healthcare providers can repeatedly sample a person's condition to monitor progression of symptoms and intervene if necessary. There is usually a utility-tolerability trade-off between collecting information at sufficient frequencies and quantities to be useful, and over-burdening the user or the underlying technology, particularly when active input is required from the user.

A smartphone based platform for portable non-invasive light and sound neuromodulation.

This paper presents a smartphone based system for presenting light and sound stimulation to a user for neuromodulation experiments. A smartphone platform was used to increase ease of use and enable out-of-the-clinic experiments. The created Android app provides both visual and auditory entrainment stimuli, along with a real-time extraction of ongoing electroencephalogram (EEG) phase using a Phase Locked Loop (PLL) for enabling closed loop simulations.

Entraining Alpha Activity Using Visual Stimulation in Patients With Chronic Musculoskeletal Pain: A Feasibility Study.

Entraining alpha activity with rhythmic visual, auditory, and electrical stimulation can reduce experimentally induced pain. However, evidence for alpha entrainment and pain reduction in patients with chronic pain is limited. This feasibility study investigated whether visual alpha stimulation can increase alpha power in patients with chronic musculoskeletal pain and, secondarily, if chronic pain was reduced following stimulation.

Effects of neurofeedback in the management of chronic pain: A systematic review and meta-analysis of clinical trials.

Background And Objective: Neurofeedback (NFB) provides real-time feedback about neurophysiological signals to patients, thereby encouraging modulation of pain-associated brain activity. This review aims to evaluate the effectiveness and safety of NFB in alleviating pain and pain-associated symptoms in chronic pain patients.

Methods: MEDLINE, PUBMED, Web of Science and PsycINFO databases were searched using the strategy: ("Neurofeedback" OR "EEG Biofeedback" OR "fMRI Biofeedback") AND ("Pain" or "Chronic Pain").