Validation of a smartphone-based EEG among people with epilepsy: A prospective study

New wearable and portable EEG modalities in epilepsy: The views of hospital-based healthcare professionals

BACKGROUND

Novel mobile and portable EEG solutions, designed for short and long-term monitoring of individuals with epilepsy have been developed in recent years but, they are underutilized, lacking full integration into clinical routine. Exploring the opinions of hospital-based healthcare professionals regarding their potential application, technical requirements and value would be crucial for future device development and increase their clinical application.

PURPOSE

To evaluate professionals' opinions on novel EEG systems, focusing on their potential application in various clinical settings, professionals' interest in non-invasive solutions for ultra-long monitoring of people with epilepsy (PWE) and factors which could increase future use of novel EEG systems.

MATERIALS AND METHODS

We conducted an online survey where Hospital-based professionals shared opinions on potential advantages, clinical value, and key features of novel wearable EEG systems in five different clinical settings. Additionally, insights were gathered on the need for future research and, the need for additional information about devices from companies and researchers.

RESULTS

Respondents (n = 40) prioritized high performance, data quality, easy patient mobility, and comfort as crucial features for novel devices. Advantages were highlighted, including more natural settings, reduced application time, earlier epilepsy diagnosis, and decreased support requirements. Novel EEG devices were seen as valuable for epilepsy diagnosis, seizure monitoring, automatic seizure documentation, seizure alarms, and seizure forecasting. Interest in integrating these new systems into clinical practice was high, particularly for supervising drug-resistant epilepsy, reducing SUDEP, and detecting nocturnal seizures. Professionals emphasized the need for more research studies and highlighted the need for increased information from companies and researchers.

CONCLUSIONS

Professionals underscore specific technical and practical features, along with potential clinical advantages and value of novel EEG devices that could drive their development. While interest in integrating these solutions in clinical practice exists, further validation studies and enhanced communication between researchers, companies, and clinicians are crucial for overcoming potential scepticism and facilitating widespread adoption.

Open access EEG dataset of repeated measurements from a single subject for microstate analysis

Electroencephalography (EEG) microstate analysis is a neuroimaging analytical method that has received considerable attention in recent years and is widely used for analysing EEG signals. EEG is easily influenced by internal and external factors, which can affect the repeatability and stability of EEG microstate analysis. However, there have been few reports and publicly available datasets on the repeatability of EEG microstate analysis. In the current study, a 39-year-old healthy male underwent a total of 60 simultaneous electroencephalography and electrocardiogram measurements over a period of three months. After the EEG recording was completed, magnetic resonance imaging (MRI) was also conducted. To date, this EEG dataset has the highest number of repeated measurements for one individual. The dataset can be used to assess the stability and repeatability of EEG microstates and other analytical methods, to decode resting EEG states among subjects with open eyes, and to explore the stability and repeatability of cortical spatiotemporal dynamics through source analysis with individual MRI.

Developing Systems of Emergency and Inpatient Neurologic Care in Resource-Limited Settings

Neurologic diseases represent a significant global health challenge, leading to disability and mortality worldwide. Healthcare systems in low- and middle-income countries are disproportionally affected. In these resource-limited settings, numerous barriers hinder the effective delivery of emergency and inpatient neurologic care, including shortages of trained personnel, limited access to diagnostics and essential medications, inadequate facilities, and absence of rehabilitation services. Disparities in the neurology workforce, limited access to neuroimaging, and availability of acute interventions further exacerbate the problem. This article explores strategies to enhance global capacity for inpatient neurologic care, emphasizing the importance of workforce development, context-specific protocols, telehealth solutions, advocacy efforts, and collaborations.

A systematic review of the literature reporting on remote monitoring epileptic seizure detection devices

BACKGROUND

Early detection and alert notification of an impending seizure for people with epilepsy have the potential to reduce Sudden Unexpected Death in Epilepsy (SUDEP). Current remote monitoring seizure detection devices for people with epilepsy are designed to support real-time monitoring of their vital health parameters linked to seizure alert notification. An understanding of the rapidly growing literature on remote seizure detection devices is essential to address the needs of people with epilepsy and their carers.

AIM

This review aims to examine the technical characteristics, device performance, user preference, and effectiveness of remote monitoring seizure detection devices.

METHODOLOGY

A systematic review referenced to PRISMA guidelines was used.

RESULTS

A total of 1095 papers were identified from the initial search with 30 papers included in the review. Sixteen non-invasive remote monitoring seizure detection devices are currently available. Such seizure detection devices were found to have inbuilt intelligent sensor functionality to monitor electroencephalography, muscle movement, and accelerometer-based motion movement for detecting seizures remotely. Current challenges of these devices for people with epilepsy include skin irritation due to the type of patch electrode used and false alarm notifications, particularly during physical activity. The tight-fitted accelerometer-type devices are reported as uncomfortable from a wearability perspective for long-term monitoring. Also, continuous recording of physiological signals and triggering alert notifications significantly reduce the battery life of the devices. The literature highlights that 3.2 out of 5 people with epilepsy are not using seizure detection devices because of the cost and appearance of the device.

CONCLUSION

Seizure detection devices can potentially reduce morbidity and mortality for people with epilepsy. Therefore, further collaboration of clinicians, technical experts, and researchers is needed for the future development of these devices. Finally, it is important to always take into consideration the expectations and requirements of people with epilepsy and their carers to facilitate the next generation of remote monitoring seizure detection devices.

Review on the current long-term, limited lead electroencephalograms

In the last century, 10-20 lead EEG recordings became the gold standard of surface EEG recordings, and the 10-20 system provided comparability between international studies. With the emergence of advanced EEG sensors, that may be able to record and process signals in much more compact units, this additional sensor technology now opens up opportunities to revisit current ambulatory EEG recording practices and specific patient populations, and even electrodes that are embedded into the head surface. Here, we aim to provide an overview of current limited sensor long-term EEG systems. We performed a literature review using Pubmed as a database and included the relevant articles. The review identified several systems for recording long-term ambulatory EEGs. In general, EEGs recorded with these modalities can be acquired in ambulatory and home settings, achieve good sensitivity with low false detection rates, are used for automatic seizure detection as well as seizure forecasting, and are well tolerated by patients, but each of them has advantages and disadvantages. Subcutaneous, subgaleal, and subscalp electrodes are minimally invasive and provide stable signals that can record ultra--long-term EEG and are in general less noisy than scalp EEG, but they have limited spatial coverage and require anesthesia, a surgical procedure and a trained surgeon to be placed. Behind and in the ear electrodes are discrete, unobtrusive with a good sensitivity mainly for temporal seizures but might miss extratemporal seizures, recordings could be obscured by muscle artifacts and bilateral ictal patterns might be difficult to register. Finally, recording systems using electrodes in a headband can be easily and quickly placed by the patient or caregiver, but have less spatial coverage and are more prone to movement because electrodes are not attached. Overall, limited EEG recording systems offer a promising opportunity to potentially record targeted EEG with focused indications for prolonged periods, but further validation work is needed.

Remote collection of electrophysiological data with brain wearables: opportunities and challenges

Collection of electroencephalographic (EEG) data provides an opportunity to non-invasively study human brain plasticity, learning and the evolution of various neuropsychiatric disorders. Traditionally, due to sophisticated hardware, EEG studies have been largely limited to research centers which restrict both testing contexts and repeated longitudinal measures. The emergence of low-cost "wearable" EEG devices now provides the prospect of frequent and remote monitoring of the human brain for a variety of physiological and pathological brain states. In this manuscript, we survey evidence that EEG wearables provide high-quality data and review various software used for remote data collection. We then discuss the growing body of evidence supporting the feasibility of remote and longitudinal EEG data collection using wearables including a discussion of potential biomedical applications of these protocols. Lastly, we discuss some additional challenges needed for EEG wearable research to gain further widespread adoption.

The Power of ECG in Semi-Automated Seizure Detection in Addition to Two-Channel behind-the-Ear EEG

Long-term home monitoring of people living with epilepsy cannot be achieved using the standard full-scalp electroencephalography (EEG) coupled with video. Wearable seizure detection devices, such as behind-the-ear EEG (bte-EEG), offer an unobtrusive method for ambulatory follow-up of this population. Combining bte-EEG with electrocardiography (ECG) can enhance automated seizure detection performance. However, such frameworks produce high false alarm rates, making visual review necessary. This study aimed to evaluate a semi-automated multimodal wearable seizure detection framework using bte-EEG and ECG. Using the SeizeIT1 dataset of 42 patients with focal epilepsy, an automated multimodal seizure detection algorithm was used to produce seizure alarms. Two reviewers evaluated the algorithm's detections twice: (1) using only bte-EEG data and (2) using bte-EEG, ECG, and heart rate signals. The readers achieved a mean sensitivity of 59.1% in the bte-EEG visual experiment, with a false detection rate of 6.5 false detections per day. Adding ECG resulted in a higher mean sensitivity (62.2%) and a largely reduced false detection rate (mean of 2.4 false detections per day), as well as an increased inter-rater agreement. The multimodal framework allows for efficient review time, making it beneficial for both clinicians and patients.

Epilepsy diagnosis using a clinical decision tool and artificially intelligent electroencephalography

OBJECTIVE

To construct a tool for non-experts to calculate the probability of epilepsy based on easily obtained clinical information combined with an artificial intelligence readout of the electroencephalogram (AI-EEG).

MATERIALS AND METHODS

We performed a chart review of 205 consecutive patients aged 18 years or older who underwent routine EEG. We created a point system to calculate the pre-EEG probability of epilepsy in a pilot study cohort. We also computed a post-test probability based on AI-EEG results.

RESULTS

One hundred and four (50.7%) patients were female, the mean age was 46 years, and 110 (53.7%) were diagnosed with epilepsy. Findings favoring epilepsy included developmental delay (12.6% vs 1.1%), prior neurological injury (51.4% vs 30.9%), childhood febrile seizures (4.6% vs 0.0%), postictal confusion (43.6% vs 20.0%), and witnessed convulsions (63.6% vs 21.1%); findings favoring alternative diagnoses were lightheadedness (3.6% vs 15.8%) or onset after prolonged sitting or standing (0.9% vs 7.4%). The final point system included 6 predictors: Presyncope (-3 points), cardiac history (-1), convulsion or forced head turn (+3), neurological disease history (+2), multiple prior spells (+1), postictal confusion (+2). Total scores of ≤1 point predicted <5% probability of epilepsy, while cumulative scores ≥7 predicted >95%. The model showed excellent discrimination (AUROC: 0.86). A positive AI-EEG substantially increases the probability of epilepsy. The impact is greatest when the pre-EEG probability is near 30%.

SIGNIFICANCE

A decision tool using a small number of historical clinical features accurately predicts the probability of epilepsy. In indeterminate cases, AI-assisted EEG helps resolve uncertainty. This tool holds promise for use by healthcare workers without specialty epilepsy training if validated in an independent cohort.

Challenges in epilepsy diagnosis and management in a low-resource setting: An experience from Bhutan

Epilepsy is an important cause of morbidity and mortality especially in low- and middle-income countries. People with epilepsy (PWE) face difficulties in access to healthcare, appropriate diagnostic tests and anti-seizure medications (ASM). Bhutan is one such country in the Himalayas that has reported doubling of the prevalence of epilepsy from 155.7 per 100,000 population in 2017 to 312.4 in 2021. The country has one centre for electroencephalography and magnetic resonance imaging for a population of 0.7 million and does not have any neurologists as of 2023. There are 16 ASMs registered in the country but only selected medications are available at the primary level hospitals (phenobarbital, phenytoin and diazepam). There are challenges in the availability of these medicines all time round the year in all levels of hospitals. Neurosurgical treatment options are limited by the lack of adequate pre-surgical evaluation facilities and lack of trained human resources. The majority of PWE have reported facing societal stigma with significant impact on the overall quality of life. It is important to screen for psychiatric comorbidities and provide psychological support wherever possible. There is a need for a comprehensive national guideline that will cater to the needs of PWE and their caregivers within the resources available in the country. A special focus on the institutional and human resource capacity development for the study and care of epilepsy is recommended.

A Scoring Framework and Apparatus for Epilepsy Seizure Detection Using a Wearable Belt

To develop a wearable device that can detect epilepsy seizures. In particular, due to their prevalence, attention is focused on detecting the generalized tonic-clonic seizure (GTCS) type. When a seizure is detected, an alert phone call is initiated and an alarm SMS sent to the nearest health-care provider (and/or a predesignated family member), including the patient's location as global positioning system (GPS) coordinates. A wearable belt is developed including an Arduino processor that constantly acquires data from four different sensing modalities and monitors the acquired signal patterns for abnormalities. The sensors are a heart rate sensor, electromyography sensor, blood oxygen level (oxygen saturation) sensor, and an accelerometer to detect sudden falls. Higher-than-normal threshold levels are established for each sensor's signal. If two or more signal measurements exceed the corresponding threshold value for a predetermined time interval, then the seizure alarm is triggered. Clinical trials were not pursued in this study as this is the initial phase of system development (phase 0). Instead, the instrumented belt seizure detection prototype was tested on nine healthy individuals mimicking, to some degree, seizure symptoms. A total of eighteen trials took place of which half had <2 sensor thresholds exceeded and no alarm, whereas the other half resulted in activating the alarm when two or more sensor thresholds were exceeded for at least the predetermined time interval corresponding to each of the higher-than-normal sensor readings. For each trial that triggered the alarm when a seizure was detected, the on-board GPS and global system for mobile communication (GSM) units successfully initiated an alert phone call to a predesignated number in addition to sending an SMS message, including GPS location coordinates. Continuous real-time monitoring of signals from the four different sensors allows the developed wearable belt to detect GTCS while reducing false alarms. The proposed device produces an important alarm that may save a patient's life.

Teleneurology and Health Disparities

The disparate access to, and use of, telemedicine reflects those of in-person health. These disparities are perpetuated as a result of individual, social, and structural factors like low digital literacy, unequal availability of broadband services, and systemic racism. This review focuses on extant literature on disparities in teleneurology, including racial or ethnic disparities, language inequities, and the global context. Understanding social and structural barriers to equitable access to quality teleneurology is critical to addressing and preventing health disparities, ensuring effective and equitable neurological care for all patients.

Noninvasive mobile EEG as a tool for seizure monitoring and management: A systematic review

In the last two decades new noninvasive mobile electroencephalography (EEG) solutions have been developed to overcome limitations of conventional clinical EEG and to improve monitoring of patients with long-term conditions. Despite the availability of mobile innovations, their adoption is still very limited. The aim of this study is to review the current state-of-the-art and highlight the main advantages of adopting noninvasive mobile EEG solutions in clinical trials and research studies of people with epilepsy or suspected seizures. Device characteristics are described, and their evaluation is presented. Two authors independently performed a literature review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A combination of different digital libraries was used (Embase, MEDLINE, Global Health, PsycINFO and https://clinicaltrials.gov/). Twenty-three full-text, six conference abstracts, and eight webpages were included, where a total of 14 noninvasive mobile solutions were identified. Published studies demonstrated at different levels how EEG recorded via mobile EEG can be used for visual detection of EEG abnormalities and for the application of automatic-detection algorithms with acceptable specificity and sensitivity. When the quality of the signal was compared with scalp EEG, many similarities were found in the background activities and power spectrum. Several studies indicated that the experience of patients and health care providers using mobile EEG was positive in different settings. Ongoing trials are focused mostly on improving seizure-detection accuracy and also on testing and assessing feasibility and acceptability of noninvasive devices in the hospital and at home. This review supports the potential clinical value of noninvasive mobile EEG systems and their advantages in terms of time, technical support, cost, usability, and reliability when applied to seizure detection and management. On the other hand, the limitations of the studies confirmed that future research is needed to provide more evidence regarding feasibility and acceptability in different settings, as well as the data quality and detection accuracy of new noninvasive mobile EEG solutions.

Pocketable Labs for Everyone: Synchronized Multi-Sensor Data Streaming and Recording on Smartphones with the Lab Streaming Layer

The streaming and recording of smartphone sensor signals is desirable for mHealth, telemedicine, environmental monitoring and other applications. Time series data gathered in these fields typically benefit from the time-synchronized integration of different sensor signals. However, solutions required for this synchronization are mostly available for stationary setups. We hope to contribute to the important emerging field of portable data acquisition by presenting open-source Android applications both for the synchronized streaming (Send-a) and recording (Record-a) of multiple sensor data streams. We validate the applications in terms of functionality, flexibility and precision in fully mobile setups and in hybrid setups combining mobile and desktop hardware. Our results show that the fully mobile solution is equivalent to well-established desktop versions. With the streaming application Send-a and the recording application Record-a, purely smartphone-based setups for mobile research and personal health settings can be realized on off-the-shelf Android devices.

Monitoring the frequency and duration of epileptic seizures: "A journey through time"

Seizure monitoring plays an undeniably important role in diagnosing and managing epileptic seizures. Establishing the frequency and duration of seizures is crucial for assessing the burden of this chronic neurological disease, selecting treatment methods, determining how frequently these methods are applied, and informing short and long-term therapeutic decisions. Over the years, seizure monitoring tools and methods have evolved and become increasingly sophisticated; from home seizure diaries to EEG monitoring to cutting-edge responsive neurostimulation systems. In this article, the various methods of seizure monitoring are reviewed.

Efficacy of Smart EEG Monitoring Amidst the COVID-19 Pandemic

Ever since the COVID-19 pandemic has majorly altered diagnosis and prognosis practices, the need for telemedicine and mobile/electronic health has never been more appreciated. Drastic complications of the pandemic such as burdens on the social and employment status resulting from extended quarantine and physical distancing, has also negatively impacted mental health. Doctors and healthcare workers have seen more than just the lungs affected by COVID-19. Neurological complications including stroke, headache, and seizures have been reported for populations of patients. Most mental conditions can be detected using the Electroencephalogram (EEG) signal. Brain disorders, neurodegenerative diseases, seizure/epilepsy, sleep/fatigue, stress, and depression have certain characteristics in the EEG wave, which clearly differentiate them from normal conditions. Smartphone apps analyzing the EEG signal have been introduced in the market. However, the efficacy of such apps has not been thoroughly investigated. Factors and their inter-relationships impacting efficacy can be studied through a causal model. This short communications/perspective paper outlines the initial premises of a system dynamics approach to assess the efficacy of smart EEG monitoring apps amid the pandemic, that could be revolutionary for patient well-being and care policies.

Patterns and Perceptions of Smartphone Use Among Academic Neurologists in the United States: Questionnaire Survey

Background

Smartphone technology is ubiquitous throughout neurologic practices, and numerous apps relevant to a neurologist’s clinical practice are now available. Data from other medical specialties suggest high utilization of smartphones in routine clinical care. However, the ways in which these devices are used by neurologists for patient care–related activities are not well defined.

Objective

This paper aims to characterize current patterns of smartphone use and perceptions of the utility of smartphones for patient care–related activities among academic neurology trainees and attending physicians. We also seek to characterize areas of need for future app development.

Methods

We developed a 31-item electronic questionnaire to address these questions and invited neurology trainees and attendings of all residency programs based in the United States to participate. We summarized descriptive statistics for respondents and specifically compared responses between trainees and attending physicians.

Results

We received 213 responses, including 112 trainee and 87 attending neurologist responses. Neurology trainees reported more frequent use of their smartphone for patient care–related activities than attending neurologists (several times per day: 84/112, 75.0% of trainees; 52/87, 59.8% of attendings; P=.03). The most frequently reported activities were internet use, calendar use, communication with other physicians, personal education, and health care–specific app use. Both groups also reported regular smartphone use for the physical examination, with trainees again reporting more frequent usage compared with attendings (more than once per week: 35/96, 36.5% of trainees; 8/58, 13.8% of attendings; P=.03). Respondents used their devices most commonly for the vision, cranial nerve, and language portions of the neurologic examination. The majority of respondents in both groups reported their smartphones as “very useful” or “essential” for the completion of patient care–related activities (81/108, 75.0% of trainees; 50/83, 60.2% of attendings; P=.12). Neurology trainees reported a greater likelihood of using their smartphones in the future than attending neurologists (“very likely”: 73/102, 71.6% of trainees; 40/82, 48.8% of attendings; P=.005). The groups differed in their frequencies of device usage for specific patient care–related activities, with trainees reporting higher usage for most activities. Despite high levels of use, only 12 of 184 (6.5%) respondents reported ever having had any training on how to use their device for clinical care. Regarding future app development, respondents rated vision, language, mental status, and cranial nerve testing as potentially being the most useful to aid in the performance of the neurologic examination.

Conclusions

Smartphones are used frequently and are subjectively perceived to be highly useful by academic neurologists. Trainees tended to use their devices more frequently than attendings. Our results suggest specific avenues for future technological development to improve smartphone use for patient care–related activities. They also suggest an unmet need for education on effectively using smartphone technology for clinical care.

Wearable seizure detection devices in refractory epilepsy

Epilepsy affects 50 million patients and their caregivers worldwide. Devices that facilitate the detection of seizures can have a large influence on a patient's quality of life, therapeutic decisions and the conduct of clinical trials with anti-epileptic drugs. This article provides an up-to-date overview and comparison between wearable seizure detection devices (WSDDs), taking into account the newly proposed standards for testing and clinical validation of devices. 16 devices were included in our comparison. The F1-score, combining the device's accurate recall and precision, was calculated for each of these devices and used to evaluate their performance. The devices were separated by development phase and ranked by F1-score from highest to lowest. We describe 16 WSDDs: 6 of which were accelerometry (ACM)-based, 3 surface electromyography-based, 1 was a wearable application of EEG, 4 had multimodal sensors and 2 other types of sensors. We observed a significant inconsistency in the description of performance measures. The devices in the most advanced development phase with the highest F1-scores incorporated ACM- and sEMG-based sensors to detect tonic-clonic seizures. This review highlights the importance of implementing standards for an optimal comparison and, therefore, improving the research and development of WSDDs. WSDDs can improve the patient's care and quality of life, decrease seizure underreporting and they could potentially prevent sudden-unexpected-death in epilepsy. We discuss the central role of the neurologist in the use of WSDDs, and why a business to business to consumer model is better than the current business to consumer model of most WSDDs.

Tablet-based electroencephalography diagnostics for patients with epilepsy in the West African Republic of Guinea

BACKGROUND AND PURPOSE

Epilepsy is most common in lower-income settings where access to electroencephalography (EEG) is generally poor. A low-cost tablet-based EEG device may be valuable, but the quality and reproducibility of the EEG output are not established.

METHODS

Tablet-based EEG was deployed in a heterogeneous epilepsy cohort in the Republic of Guinea (2018-2019), consisting of a tablet wirelessly connected to a 14-electrode cap. Participants underwent EEG twice (EEG1 and EEG2), separated by a variable time interval. Recordings were scored remotely by experts in clinical neurophysiology as to data quality and clinical utility.

RESULTS

There were 149 participants (41% female; median age 17.9 years; 66.6% ≤21 years of age; mean seizures per month 5.7 ± SD 15.5). The mean duration of EEG1 was 53 ± 12.3 min and that of EEG2 was 29.6 ± 12.8 min. The mean quality scores of EEG1 and EEG2 were 6.4 [range, 1 (low) to 10 (high); both medians 7.0]. A total of 44 (29.5%) participants had epileptiform discharges (EDs) at EEG1 and 25 (16.8%) had EDs at EEG2. EDs were focal/multifocal (rather than generalized) in 70.1% of EEG1 and 72.5% of EEG2 interpretations. A total of 39 (26.2%) were recommended for neuroimaging after EEG1 and 22 (14.8%) after EEG2. Of participants without EDs at EEG1 (n = 53, 55.8%), seven (13.2%) had EDs at EEG2. Of participants with detectable EDs on EEG1 (n = 23, 24.2%), 12 (52.1%) did not have EDs at EEG2.

CONCLUSIONS

Tablet-based EEG had a reproducible quality level on repeat testing and was useful for the detection of EDs. The incremental yield of a second EEG in this setting was ~13%. The need for neuroimaging access was evident.

Comparison of mobile and clinical EEG sensors through resting state simultaneous data collection

Development of mobile sensors brings new opportunities to medical research. In particular, mobile electroencephalography (EEG) devices can be potentially used in low cost screening for epilepsy and other neurological and psychiatric disorders. The necessary condition for such applications is thoughtful validation in the specific medical context. As part of validation and quality assurance, we developed a computer-based analysis pipeline, which aims to compare the EEG signal acquired by a mobile EEG device to the one collected by a medically approved clinical-grade EEG device. Both signals are recorded simultaneously during 30 min long sessions in resting state. The data are collected from 22 patients with epileptiform abnormalities in EEG. In order to compare two multichannel EEG signals with differently placed references and electrodes, a novel data processing pipeline is proposed. It allows deriving matching pairs of time series which are suitable for similarity assessment through Pearson correlation. The average correlation of 0.64 is achieved on a test dataset, which can be considered a promising result, taking the positions shift due to the simultaneous electrode placement into account.

Smartphone Apps in the Context of Tinnitus: Systematic Review

Smartphones containing sophisticated high-end hardware and offering high computational capabilities at extremely manageable costs have become mainstream and an integral part of users' lives. Widespread adoption of smartphone devices has encouraged the development of many smartphone applications, resulting in a well-established ecosystem, which is easily discoverable and accessible via respective marketplaces of differing mobile platforms. These smartphone applications are no longer exclusively limited to entertainment purposes but are increasingly established in the scientific and medical field. In the context of tinnitus, the ringing in the ear, these smartphone apps range from relief, management, self-help, all the way to interfacing external sensors to better understand the phenomenon. In this paper, we aim to bring forth the smartphone applications in and around tinnitus. Based on the PRISMA guidelines, we systematically analyze and investigate the current state of smartphone apps, that are directly applied in the context of tinnitus. In particular, we explore Google Scholar, CiteSeerX, Microsoft Academics, Semantic Scholar for the identification of scientific contributions. Additionally, we search and explore Google's Play and Apple's App Stores to identify relevant smartphone apps and their respective properties. This review work gives (1) an up-to-date overview of existing apps, and (2) lists and discusses scientific literature pertaining to the smartphone apps used within the context of tinnitus.

Biosensors for Personal Mobile Health: A System Architecture Perspective

Advances in mobile biosensors, integrating developments in materials science and instrumentation, are fueling an expansion in health data being collected and analyzed in decentralized settings. For example, semiconductor-based sensors are enabling measurement of vital signs, and microfluidic-based sensors are enabling measurement of biochemical markers. As biosensors for mobile health are becoming increasingly paired with smart devices, it will become critical for researchers to design biosensors - with appropriate functionalities and specifications - to work seamlessly with accompanying connected hardware and software. This article describes recent research in biosensors, as well as current mobile health devices in use, as classified into four distinct system architectures that take into account the biosensing and data processing functions required in personal mobile health devices. We also discuss the path forward for integrating biosensors into smartphone-based mobile health devices.

Can commercially available wearable EEG devices be used for diagnostic purposes? An explorative pilot study

Electroencephalography (EEG) is a core element in the diagnosis of epilepsy syndromes and can help to monitor antiseizure treatment. Mobile EEG (mEEG) devices are increasingly available on the consumer market and may offer easier access to EEG recordings especially in rural or resource-poor areas. The usefulness of consumer-grade devices for clinical purposes is still underinvestigated. Here, we compared EEG traces of a commercially available mEEG device (Emotiv EPOC) to a simultaneously recorded clinical video EEG (vEEG). Twenty-two adult patients (11 female, mean age 40.2 years) undergoing noninvasive vEEG monitoring for clinical purposes were prospectively enrolled. The EEG recordings were evaluated by 10 independent raters with unmodifiable view settings. The individual evaluations were compared with respect to the presence of abnormal EEG findings (regional slowing, epileptiform potentials, seizure pattern). Video EEG yielded a sensitivity of 56% and specificity of 88% for abnormal EEG findings, whereas mEEG reached 39% and 85%, respectively. Interrater reliability coefficients were better in vEEG as compared to mEEG (ϰ = 0.50 vs. 0.30), corresponding to a moderate and fair agreement. Intrarater reliability between mEEG and vEEG evaluations of simultaneous recordings of a given participant was moderate (ϰ = 0.48). Given the limitations of our exploratory pilot study, our results suggest that vEEG is superior to mEEG, but that mEEG can be helpful for diagnostic purposes. We present the first quantitative comparison of simultaneously acquired clinical and mobile consumer-grade EEG for a clinical use-case.

In-ear-EEG - a portable platform for home monitoring

There are many useful medical treatment devices today, which are indispensable in health care. However, in some emergency situations and in prehospital care mobile, easy-to-use devices could further improve the patient-centred care. For example, a mobile, easy-to-use home-monitoring EEG-system would be useful for monitoring diseases like epilepsy and for treating diseases like attention deficit disorder (ADHD) using biofeedback. Such a device should be equipped with the ability to start self-performed by user recordings and provide high signal quality, while having an affordable price. Here, we used in-ear-EEG technology and state of the art electronic components to develop such a system. This paper presents a portable, all-in-one EEG-system, capable to record biosignals on the external ear. An amplifier was developed with ADS1299 and optimised to be coupled with a smartphone. The system has a low price and at the same time provides high signal quality, has very effective common-mode-rejection, performs a fast cold start and shows low power consumptions which ensures a long time of operation. The system is easy to use and could be self-mounted and controlled by unskilled users as well. Results of test measurements are compared to a conventional EEG-System and show comparable records results quality.

Managing Epilepsy by Telemedicine in Resource-Poor Settings

Epilepsy is a common and treatable disease; in rich countries the expectation is that two-thirds of people will have their seizure episodes controlled on medication. In low- and middle-income countries (LMICs) however most people are not on treatment either because no doctors live near them or the logistics of affordable drug supply is absent. People with epilepsy then are prone to the bad effects of this disease—death, disfigurement from accidents and burns, and social problems due to the stigma with which the disease is associated. So this represents a failure of conventional face-to-face medicine. Might a telemedicine approach do better? The World Health Organization has suggested that non-physician health workers are empowered to diagnose and manage epilepsy; to do this they will need considerable medical support, which might be provided by telemedicine through the telephone, smartphone applications or a combination. This paper sets out what telemedicine does at present for people with epilepsy in LMICs and suggests how it might be developed in the future.

A deep learning approach for real-time detection of sleep spindles

OBJECTIVE

Sleep spindles have been implicated in memory consolidation and synaptic plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection are critical for real-time applications.

APPROACH

Here we propose a novel deep learning strategy (SpindleNet) to detect sleep spindles based on a single EEG channel. While the majority of spindle detection methods are used for off-line applications, our method is well suited for online applications.

MAIN RESULTS

Compared with other spindle detection methods, SpindleNet achieves superior detection accuracy and speed, as demonstrated in two publicly available expert-validated EEG sleep spindle datasets. Our real-time detection of spindle onset achieves detection latencies of 150-350 ms (~two-three spindle cycles) and retains excellent performance under low EEG sampling frequencies and low signal-to-noise ratios. SpindleNet has good generalization across different sleep datasets from various subject groups of different ages and species.

SIGNIFICANCE

SpindleNet is ultra-fast and scalable to multichannel EEG recordings, with an accuracy level comparable to human experts, making it appealing for long-term sleep monitoring and closed-loop neuroscience experiments.

Smartphone EEG and remote online interpretation for children with epilepsy in the Republic of Guinea: Quality, characteristics, and practice implications

PURPOSE

Children with epilepsy in low-income countries often go undiagnosed and untreated. We examine a portable, low-cost smartphone-based EEG technology in a heterogeneous pediatric epilepsy cohort in the West African Republic of Guinea.

METHODS

Children with epilepsy were recruited at the Ignace Deen Hospital in Conakry, 2017. Participants underwent sequential EEG recordings with an app-based EEG, the Smartphone Brain Scanner-2 (SBS2) and a standard Xltek EEG. Raw EEG data were transmitted via Bluetooth^™ connection to an Android^™ tablet and uploaded for remote EEG specialist review and reporting via a new, secure web-based reading platform, crowdEEG. The results were compared to same-visit Xltek 10-20 EEG recordings for identification of epileptiform and non-epileptiform abnormalities.

RESULTS

97 children meeting the International League Against Epilepsy's definition of epilepsy (49 male; mean age 10.3 years, 29 untreated with an antiepileptic drug; 0 with a prior EEG) were enrolled. Epileptiform discharges were detected on 21 (25.3%) SBS2 and 31 (37.3%) standard EEG recordings. The SBS2 had a sensitivity of 51.6% (95%CI 32.4%, 70.8%) and a specificity of 90.4% (95%CI 81.4%, 94.4%) for all types of epileptiform discharges, with positive and negative predictive values of 76.2% and 75.8% respectively. For generalized discharges, the SBS2 had a sensitivity of 43.5% with a specificity of 96.2%.

CONCLUSIONS

The SBS2 has a moderate sensitivity and high specificity for the detection of epileptiform abnormalities in children with epilepsy in this low-income setting. Use of the SBS2+crowdEEG platform permits specialist input for patients with previously poor access to clinical neurophysiology expertise.

The Digital Outcome Measure

Improving clinical outcomes remains the gold standard in advancing healthcare. Focusing on outcomes holds the potential to unite all clinical stakeholders including payers, industry, providers, and patients. Yet, the dominant ways in which outcomes are captured, provider-collected or patient-reported, have significant limitations. The emerging field of biosensors and wearables, which aims to capture many types of health data, holds promise to specifically capture outcomes while complementing existing outcome collection methods. A digital outcome measure, unlike a traditional provider-collected or patient-reported outcome measure, depends less on active patient or provider participation. Thus, digital outcome measures may be more amenable to standardization as well as greater collection consistency, frequency, and accuracy.

Teleneurology and mobile technologies: the future of neurological care

Neurological disorders are the leading cause of global disability. However, for most people around the world, current neurological care is poor. In low-income countries, most individuals lack access to proper neurological care, and in high-income countries, distance and disability limit access. With the global proliferation of smartphones, teleneurology - the use of technology to provide neurological care and education remotely - has the potential to improve and increase access to care for billions of people. Telestroke has already fulfilled this promise, but teleneurology applications for chronic conditions are still in their infancy. Similarly, few studies have explored the capabilities of mobile technologies such as smartphones and wearable sensors, which can guide care by providing objective, frequent, real-world assessments of patients. In low-income settings, teleneurology can increase the capacity of local care systems through professional development, diagnostic support and consultative services. In high-income settings, teleneurology is likely to promote the expansion and migration of neurological care away from institutions, incorporate systems of asynchronous communication (such as e-mail), integrate clinicians with diverse skill sets and reach new populations. Inertia, outdated policies and social barriers - especially the digital divide - will slow this progress at considerable cost. However, a future increasingly will be possible in which neurological care can be accessed by anyone, anywhere. Here, we examine the emerging evidence regarding the benefits of teleneurology for chronic conditions, its role and risks in low-income countries and the promise of mobile technologies to measure disease status and deliver care. We conclude by discussing the future trends, barriers and timing for the adoption of teleneurology.

Community-based rehabilitation offers cost-effective epilepsy treatment in rural Guinea-Bissau

Treatment of epilepsy in low-income countries is a challenge considering the lack of resources, availability of antiepileptic drugs, and cultural beliefs. We used a community-based rehabilitation (CBR) service for the detection, monitoring, and treatment of epilepsy. A local network of trained community volunteers provided education, good quality antiepileptic drugs, and clinical follow-up for people with epilepsy (PWE). In a period of 2years, approximately 22,500 people were screened in central Guinea-Bissau, and 112 PWE were identified and registered. Monthly check-ups were offered to monitor treatment effect and increase compliance. Retrospective analysis on 81 records of patients under treatment in June 2016 showed a decrease of seizure frequency in 88.8% after treatment initiation and was maintained throughout the clinical follow-up of 15months. A conservative estimation of the treatment and monitoring of a single person with epilepsy revealed a daily cost of $0.73. Despite acknowledging epilepsy as a neglected condition by the World Health Organization (WHO), most PWE still lack appropriate treatment. Although CBR service has been suggested as efficient strategy to reduce the treatment gap, little information is available on the efficacy of the programs. Our experiences show that CBR service is a cost-effective approach to monitor treatment and increase compliance in PWE. This experience may be of value for other resource-poor settings.

Physiological Informatics: Collection and Analyses of Data from Wearable Sensors and Smartphone for Healthcare

Physiological data from wearable sensors and smartphone are accumulating rapidly, and this provides us the chance to collect dynamic and personalized information as phenotype to be integrated to genotype for the holistic understanding of complex diseases. This integration can be applied to early prediction and prevention of disease, therefore promoting the shifting of disease care tradition to the healthcare paradigm. In this chapter, we summarize the physiological signals which can be detected by wearable sensors, the sharing of the physiological big data, and the mining methods for the discovery of disease-associated patterns for personalized diagnosis and treatment. We discuss the challenges of physiological informatics about the storage, the standardization, the analyses, and the applications of the physiological data from the wearable sensors and smartphone. At last, we present our perspectives on the models for disentangling the complex relationship between early disease prediction and the mining of physiological phenotype data.