The development of mobile health for epilepsy has grown in the last years, bringing new applications (apps) to the market and improving already existing ones. In this systematic review, we analyse the scope of mobile apps for seizure detection and epilepsy self-management, with two research questions in mind: what are the characteristics of current solutions and do they meet users’ requirements? What should be considered when designing mobile health for epilepsy? We used PRISMA methodology to search within App Store and Google Play Store from February to April of 2021, reaching 55 potential apps. A more thorough analysis regarding particular features was performed on 26 of those apps. The content of these apps was evaluated in five categories, regarding if there was personalisable content; features related to medication management; what aspects of seizure log were present; what type of communication prevailed; and if there was any content related to seizure alarm or seizure action plans. Moreover, the 26 apps were evaluated through using MARS by six raters, including two neurologists. The analysis of MARS categories was performed for the top and bottom apps, to understand the core differences. Overall, the lowest MARS scores were related to engagement and information, which play a big part in long-term use, and previous studies raised the concern of assuring continuous use, especially in younger audiences. With that in mind, we identified conceptual improvement points, which were divided in three main topics: customisation, simplicity and healthcare connection. Moreover, we summarised some ideas to improve m-health apps catered around long-term adherence. We hope this work contributes to a better understanding of the current scope in mobile epilepsy management, endorsing healthcare professionals and developers to provide off-the-shelf solutions that engage patients and allows them to better manage their condition.

Objective: This work explores Hall effect sensing paired with a permanent magnet, in the context of pulmonary rehabilitation exercise training. Methods: Experimental evaluation was performed considering as reference the gold-standard of respiratory monitoring, an airflow transducer, and performance was compared to another wearable device with analogous usability - a piezoelectric sensor. A total of 16 healthy participants performed 15 activities, representative of pulmonary rehabilitation exercises, simultaneously using all devices. Evaluation was performed based on detection of flow reversal events and key respiratory parameters. Results: Overall, the proposed sensor outperformed the piezoelectric sensor with a mean ratio, precision, and recall of 0.97, 0.97, and 0.95, respectively, against 0.98, 0.90, and 0.88. Evaluation regarding the respiratory parameters indicates an adequate accuracy when it comes to breath cycle, inspiration, and expiration times, with mean relative errors around 4% for breath cycle and 8% for inspiration/expiration times, despite some variability. Bland-Altman analysis indicates no systematic biases. Conclusion: Characterization of the proposed sensor shows adequate monitoring capabilities for exercises that do not rely heavily on torso mobility, but may present a limitation when it comes to activities such as side stretches. Significance: This work provides a comprehensive characterization of a magnetic field-based respiration sensor, including a discussion on its robustness to different algorithm thresholds. It proves the viability of the sensor in a range of exercises, expanding the applicability of Hall effect sensors as a feasible wearable approach to real-time respiratory monitoring.

This study aims to review the proposed methodologies and reported performances of automated algorithms for seizure forecast. A systematic review was conducted on studies reported up to May 10, 2024. Four databases and registers were searched, and studies were included when they proposed an original algorithm for automatic human epileptic seizure forecast that was patient specific, based on intraindividual cyclic distribution of events and/or surrogate measures of the preictal state and provided an evaluation of the performance. Two meta-analyses were performed, one evaluating area under the ROC curve (AUC) and another Brier Skill Score (BSS). Eighteen studies met the eligibility criteria, totaling 43 included algorithms. A total of 419 patients participated in the studies, and 19442 seizures were reported across studies. Of the analyzed algorithms, 23 were eligible for the meta-analysis with AUC and 12 with BSS. The overall mean AUC was 0.71, which was similar between the studies that relied solely on surrogate measures of the preictal state, on cyclic distributions of events, and on a combination of these. BSS was also similar for the three types of input data, with an overall mean BSS of 0.13. This study provides a characterization of the state of the art in seizure forecast algorithms along with their performances, setting a benchmark for future developments. It identified a considerable lack of standardization across study design and evaluation, leading to the proposal of guidelines for the design of seizure forecast solutions.

Biosignals represent a first-line source of information to understand the behavior and state of human biological systems, often used in machine learning problems. However, the development of healthcare-related algorithms that are both personalized and robust requires the collection of large volumes of data to capture representative instances of all possible states. While the rise of flexible biosignal acquisition solutions has enabled the expedition of data collection, they often require complicated frameworks or do not provide the customization required in some research contexts. As such, EpiBOX was developed as an open-source, standalone, and automated platform that enables the long-term acquisition of biosignals, passable to be operated by individuals with low technological proficiency. In particular, in this paper, we present an in-depth explanation of the framework, methods for the evaluation of its performance, and the corresponding findings regarding the perspective of the end-user. The impact of the network connection on data transfer latency was studied, demonstrating innocuous latency values for reasonable signal strengths and manageable latency values even when the connection was unstable. Moreover, performance profiling of the EpiBOX user interface (mobile application) indicates a suitable performance in all aspects, providing an encouraging outlook on adherence to the system. Finally, the experience of our research group is described as a use case, indicating a promising outlook regarding the use of the EpiBOX framework within similar contexts. As a byproduct of these features, our hope is that by empowering physicians, technicians, and monitored subjects to supervise the biosignal collection process, we enable researchers to scale biosignal collection.

The task of automatic text summarization produces a concise and fluent text summary while preserving key information and overall meaning. Recent approaches to document-level summarization have seen significant improvements in recent years by using models based on the Transformer architecture. However, the quadratic memory and time complexities with respect to the sequence length make them very expensive to use, especially with long sequences, as required by document-level summarization. Our work addresses the problem of document-level summarization by studying how efficient Transformer techniques can be used to improve the automatic summarization of very long texts. In particular, we will use the arXiv dataset, consisting of several scientific papers and the corresponding abstracts, as baselines for this work. Then, we propose a novel retrieval-enhanced approach based on the architecture which reduces the cost of generating a summary of the entire document by processing smaller chunks. The results were below the baselines but suggest a more efficient memory a consumption and truthfulness.

Objective: In the context of pulmonary rehabilitation exercise training, wearable real-time monitoring of respiratory patterns may represent a valuable tool in increasing accessibility to treatment, as well as expanding the opportunities of treatment automation and locomotor-respiratory coupling. This work explores Hall effect sensing, paired with a permanent magnet, embedded in a chest strap. Methods: Experimental evaluation was performed considering as reference the gold-standard of respiratory monitoring, an airflow transducer, and performance was compared to another wearable device with analogous usability but with a different working principle - a piezoelectric sensor, also embedded in a chest strap. A total of 16 healthy participants performed 15 different activities, representative of pulmonary rehabilitation exercises, simultaneously using the three devices. Evaluation was performed based on detection of flow reversal events, as well as fiducials detection latency. Results: The proposed sensor shows comparable performance to the piezoelectric sensor with a mean ratio, precision, and recall of 1.10, 0.89, and 0.98, respectively, against 1.35, 0.71, and 0.96 of the piezoelectric sensor, overall also presenting consistently smaller latencies. Conclusion: The characterization of the proposed sensor also shows adequate monitoring capabilities for exercises that do not rely heavily on torso mobility, but may present a limitation when it comes to activities such as torso rotations and side stretches. Significance: This work expands the applicability of Hall effect sensors, demonstrating their use in the context of real-time respiratory monitoring.