A review of mobile apps for epilepsy self-management.

Mobile health app developers increasingly are interested in supporting the daily self-care of people with chronic conditions. The purpose of this study was to review mobile applications (apps) to promote epilepsy self-management. It investigates the following: 1) the available mobile apps for epilepsy, 2) how these apps support patient education and self-management (SM), and 3) their usefulness in supporting management of epilepsy. We conducted the review in Fall 2017 and assessed apps on the Apple App Store that related to the terms "epilepsy" and "seizure". Inclusion criteria included apps (adult and pediatric) that, as follows, were: 1) developed for patients or the community; 2) made available in English, and 3) less than $5.00. Exclusion criteria included apps that were designed for dissemination of publications, focused on healthcare providers, or were available in other languages. The search resulted in 149 apps, of which 20 met the selection criteria. A team reviewed each app in terms of three sets of criteria: 1) epilepsy-specific descriptions and SM categories employed by the apps and 2) Mobile App Rating Scale (MARS) subdomain scores for reviewing engagement, functionality, esthetics, and information; and 3) behavioral change techniques. Most apps were for adults and free. Common SM domains for the apps were treatment, seizure tracking, response, and safety. A number of epilepsy apps existed, but many offered similar functionalities and incorporated few SM domains. The findings underline the need for mobile apps to cover broader domains of SM and behavioral change techniques and to be evaluated for outcomes.

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors.

OBJECTIVE: New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive and automated, and provide false alarm rates (FARs) bearable in everyday life. This study quantifies the performance of new multimodal wrist-worn convulsive seizure detectors. METHODS: Hand-annotated video-electroencephalographic seizure events were collected from 69 patients at six clinical sites. Three different wristbands were used to record electrodermal activity (EDA) and accelerometer (ACM) signals, obtaining 5,928 h of data, including 55 convulsive epileptic seizures (six focal tonic-clonic seizures and 49 focal to bilateral tonic-clonic seizures) from 22 patients. Recordings were analyzed offline to train and test two new machine learning classifiers and a published classifier based on EDA and ACM. Moreover, wristband data were analyzed to estimate seizure-motion duration and autonomic responses. RESULTS: The two novel classifiers consistently outperformed the previous detector. The most efficient (Classifier III) yielded sensitivity of 94.55%, and an FAR of 0.2 events/day. No nocturnal seizures were missed. Most patients had <1 false alarm every 4 days, with an FAR below their seizure frequency. When increasing the sensitivity to 100% (no missed seizures), the FAR is up to 13 times lower than with the previous detector. Furthermore, all detections occurred before the seizure ended, providing reasonable latency (median = 29.3 s, range = 14.8-151 s). Automatically estimated seizure durations were correlated with true durations, enabling reliable annotations. Finally, EDA measurements confirmed the presence of postictal autonomic dysfunction, exhibiting a significant rise in 73% of the convulsive seizures. SIGNIFICANCE: The proposed multimodal wrist-worn convulsive seizure detectors provide seizure counts that are more accurate than previous automated detectors and typical patient self-reports, while maintaining a tolerable FAR for ambulatory monitoring. Furthermore, the multimodal system provides an objective description of motor behavior and autonomic dysfunction, aimed at enriching seizure characterization, with potential utility for SUDEP warning.

Enhancing nasal endoscopy: Classification, detection, and segmentation of anatomic landmarks using a convolutional neural network.

KEY POINTS: A convolutional neural network (CNN)-based model can accurately localize and segment turbinates in images obtained during nasal endoscopy (NE). This model represents a starting point for algorithms that comprehensively interpret NE findings.

Features of Importance in Nasal Endoscopy: Deriving a Meaningful Framework.

OBJECTIVE: Critical components of the nasal endoscopic examination have not been definitively established for either the normal examination or for clinical disorders. This study aimed to identify concordance among rhinologists regarding the importance of examination findings for various nasal pathologies. STUDY DESIGN: A consortium of 19 expert rhinologists across the United States was asked to rank the importance of findings on nasal endoscopy for 5 different sinonasal symptom presentations. SETTING: An online questionnaire was distributed in July 2023. METHODS: The questionnaire utilized JotForm® software and featured 5 cases with a set of 4 identical questions per case, each covering a common indication for nasal endoscopy. Rankings were synthesized into Normalized Attention Scores (NASs) and Weighted Normalized Attention Scores (W-NASs) to represent the perceived importance of each feature, scaled from 0 to 1. RESULTS: General concordance was found for examination findings on nasal endoscopy within each case. The perceived features of importance differed between cases based on clinical presentation. For instance, in evaluating postnasal drip, the middle meatus was selected as the most important structure to examine (NAS, 0.73), with mucus selected as the most important abnormal finding (W-NAS, 0.66). The primary feature of interest for mucus was whether it was purulent or not (W-NAS, 0.67). Similar analyses were performed for features in each case. CONCLUSION: The implicit framework existing among rhinologists may help standardize examinations and improve diagnostic accuracy, augment the instruction of trainees, and inform the development of artificially intelligent algorithms to enhance clinical decision-making during nasal endoscopy.

Ochsner Emergency Department Overcrowding Scale (OEDOCS): predicting Emergency Department crowding among diverse academic hospital settings.

We developed the Ochsner Emergency Department Overcrowding Scale (OEDOCS) to help us measure and respond to crowding among diverse-sized Emergency Departments (ED) within our network. Not satisfied with our current Emergency Department (ED) crowding score, we first surveyed our ED staff to report perceived crowding and then developed models to predict perceived crowding from our Electronic Health Record (EHR) data. Staff at two ED locations, one large and one small, were asked to report a perceived crowding level between 0-200 every four hours for over 3 months. In addition, we collected Electronic Health Record (EHR) data during the same period. Next, we investigated models for predicting perceived crowding. Linear regression performed the best with an RMSE of 41.77 and 41.98% RMSE improvement over our previous crowding score. We have made OEDOCS publicly available.

A Contextual Inquiry: FDA Investigational New Drug Clinical Review.

The U.S. Food and Drug Administration (FDA) is modernizing IT infrastructure and investigating software requirements for addressing increased regulator workload and complexity requirements during Investigational New Drug (IND) reviews. We conducted a mixed-method, Contextual Inquiry (CI) study for establishing a detailed understanding of daily IND-related research, writing, and decision-making tasks. Individual reviewers faced notable challenges while attempting to search, transfer, compare, consolidate and reference content between multiple documents. The review process would likely benefit from the development of software tools for both addressing these problems and fostering existing knowledge sharing behaviors within individual and group settings.

Evaluation and recommendations for effective data visualization for seizure forecasting algorithms.

OBJECTIVE: Seizure forecasting algorithms have become increasingly accurate and may reduce the morbidity and mortality caused by seizure unpredictability. Translating these benefits into meaningful health outcomes for people with epilepsy requires effective data visualization of algorithm outputs. To date, no studies have investigated patient and physician perspectives on effective translation of algorithm outputs into data visualizations through health information technology. MATERIALS AND METHODS: We developed front-end data visualizations as part of a Seizure Forecast Visualization Toolkit. We surveyed 627 people living with epilepsy and caregivers, and 28 epilepsy healthcare providers. Respondents scored each visualization in terms of international standardized software quality criteria for functionality, appropriateness, and usability. RESULTS: People with epilepsy and caregivers ranked hourly radar charts highest for protecting against errors in interpreting forecasts, reducing anxiety from seizure unpredictability, and understanding seizure patterns. Accuracy in interpreting visuals, such as a risk gauge, was dependent on seizure frequency. Visuals showing hourly/daily forecasts were more useful for patients who experienced seizure cycling than those who did not. Hourly line graphs and monthly heat maps were rated highest among clinicians for ease of understanding, anticipated integration into clinical practice, and the likelihood of clinical usage. Epilepsy providers indicated that daily heat maps, daily line graphs, and hourly line graphs were most useful for interpreting seizure diary patterns, assessing therapy impact, and counseling on seizure safety. DISCUSSION: The choice of data visualization impacts the effective translation of seizure forecast algorithms into meaningful health outcomes. CONCLUSION: This effort underlines the importance of incorporating standardized, quantitative methods for assessing the effectiveness of data visualization to translate seizure forecast algorithms into clinical practice.

Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit.

Background: Using machine learning to combine wrist accelerometer (ACM) and electrodermal activity (EDA) has been shown effective to detect primarily and secondarily generalized tonic-clonic seizures, here termed as convulsive seizures (CS). A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm. Here we present its performance on pediatric and adult patients in epilepsy monitoring units (EMUs). Methods: Patients diagnosed with epilepsy participated in a prospective multi-center clinical study. Three board-certified neurologists independently labeled CS from video-EEG. The Detection Algorithm was evaluated in terms of Sensitivity and false alarm rate per 24 h-worn (FAR) on all the data and on only periods of rest. Performance were analyzed also applying the Detection Algorithm offline, with a less sensitive but more specific parameters configuration ("Active mode"). Results: Data from 152 patients (429 days) were used for performance evaluation (85 pediatric aged 6-20 years, and 67 adult aged 21-63 years). Thirty-six patients (18 pediatric) experienced a total of 66 CS (35 pediatric). The Sensitivity (corrected for clustered data) was 0.92, with a 95% confidence interval (CI) of [0.85-1.00] for the pediatric population, not significantly different (p > 0.05) from the adult population's Sensitivity (0.94, CI: [0.89-1.00]). The FAR on the pediatric population was 1.26 (CI: [0.87-1.73]), higher (p < 0.001) than in the adult population (0.57, CI: [0.36-0.81]). Using the Active mode, the FAR decreased by 68% while reducing Sensitivity to 0.95 across the population. During rest periods, the FAR's were 0 for all patients, lower than during activity periods (p < 0.001). Conclusions: Performance complies with FDA's requirements of a lower bound of CI for Sensitivity higher than 0.7 and of a FAR lower than 2, for both age groups. The pediatric FAR was higher than the adult FAR, likely due to higher pediatric activity. The high Sensitivity and precision (having no false alarms) during sleep might help mitigate SUDEP risk by summoning caregiver intervention. The Active mode may be advantageous for some patients, reducing the impact of the FAR on daily life. Future work will examine the performance and usability outside of EMUs.

Seizure detection devices for use in antiseizure medication clinical trials: A systematic review.

OBJECTIVE: This study characterizes the current capabilities of seizure detection device (SDD) technology and evaluates the fitness of these devices for use in anti-seizure medication (ASM) clinical trials. METHODS: Through a systematic literature review, 36 wireless SDDs featured in published device validation studies were identified. Each device's seizure detection capabilities that addressed ASM clinical trial primary endpoint measurement needs were cataloged. RESULTS: The two most common types of seizures targeted by ASMs in clinical trials are generalized tonic-clonic (GTC) seizures and focal with impaired awareness (FIA) seizures. The Brain Sentinel SPEAC achieved the highest performance for the detection of GTC seizures (F1-score = 0.95). A non-commercial wireless EEG device achieved the highest performance for the detection of FIA seizures (F1-score = 0.88). DISCUSSION: A preliminary assessment of device capabilities for measuring selected ASM clinical trial secondary endpoints was performed. The need to address key limitations in validation studies is highlighted in order to support future assessments of SDD fitness for ASM clinical trial use. In tandem, a stepwise framework to streamline device testing is put forth. These suggestions provide a starting point for establishing SDD reporting requirements before device integration into ASM clinical trials.

Seizure reporting technologies for epilepsy treatment: A review of clinical information needs and supporting technologies.

This review surveys current seizure detection and classification technologies as they relate to aiding clinical decision-making during epilepsy treatment. Interviews and data collected from neurologists and a literature review highlighted a strong need for better distinguishing between patients exhibiting generalized and partial seizure types as well as achieving more accurate seizure counts. This information is critical for enabling neurologists to select the correct class of antiepileptic drugs (AED) for their patients and evaluating AED efficiency during long-term treatment. In our questionnaire, 100% of neurologists reported they would like to have video from patients prior to selecting an AED during an initial consultation. Presently, only 30% have access to video. In our technology review we identified that only a subset of available technologies surpassed patient self-reporting performance due to high false positive rates. Inertial seizure detection devices coupled with video capture for recording seizures at night could stand to address collecting seizure counts that are more accurate than current patient self-reporting during day and night time use.