Medical Technology: A Systematic Review on Medical Devices Utilized for Epilepsy Prediction and Management

User involvement in the design and development of medical devices in epilepsy: A systematic review

OBJECTIVE

This systematic review aims to describe the involvement of persons with epilepsy (PWE), healthcare professionals (HP) and caregivers (CG) in the design and development of medical devices is epilepsy.

METHODS

A systematic review was conducted, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligibility criteria included peer-reviewed research focusing on medical devices for epilepsy management, involving users (PWE, CG, and HP) during the MDD process. Searches were performed on PubMed, Web of Science, and Scopus, and a total of 55 relevant articles were identified and reviewed.

RESULTS

From 1999 to 2023, there was a gradual increase in the number of publications related to user involvement in epilepsy medical device development (MDD), highlighting the growing interest in this field. The medical devices involved in these studies encompassed a range of seizure detection tools, healthcare information systems, vagus nerve stimulation (VNS) and electroencephalogram (EEG) technologies reflecting the emphasis on seizure detection, prediction, and prevention. PWE and CG were the primary users involved, underscoring the importance of their perspectives. Surveys, usability testing, interviews, and focus groups were the methods used for capturing user perspectives. User involvement occurs in four out of the five stages of MDD, with production being the exception.

SIGNIFICANCE

User involvement in the MDD process for epilepsy management is an emerging area of interest holding a significant promise for improving device quality and patient outcomes. This review highlights the need for broader and more effective user involvement, as it currently lags in the development of commercially available medical devices for epilepsy management. Future research should explore the benefits and barriers of user involvement to enhance medical device technologies for epilepsy.

PLAIN LANGUAGE SUMMARY

This review covers studies that have involved users in the development process of medical devices for epilepsy. The studies reported here have focused on getting input from people with epilepsy, their caregivers, and healthcare providers. These devices include tools for detecting seizures, stimulating nerves, and tracking brain activity. Most user feedback was gathered through surveys, usability tests, interviews, and focus groups. Users were involved in nearly every stage of device development except production. The review highlights that involving users can improve device quality and patient outcomes, but more effective involvement is needed in commercial device development. Future research should focus on the benefits and challenges of user involvement.

Towards user-centered design of medical devices for SUDEP prediction and prevention: Insights from persons with epilepsy and caregivers

OBJECTIVES

As epilepsy management medical devices emerge as potential technological solutions for prediction and prevention of sudden death in epilepsy (SUDEP), there is a gap in understanding the features and priorities that should be included in the design of these devices. This study aims to bridge the gap between current technology and emerging needs by leveraging insights from persons with epilepsy (PWE) and caregivers (CG) on current epilepsy management devices and understanding how SUDEP awareness influences preferences and design considerations for potential future solutions.

METHODS

Two cross-sectional surveys were designed to survey PWE and CG on medical device design features, SUDEP awareness, and participation in medical device research. Data analysis included both qualitative thematic analysis and quantitative statistical analysis.

RESULTS

The survey revealed that among 284 responses, CG were more aware of SUDEP than PWE. Comfort was identified as the primary concern regarding wearable medical devices for epilepsy management with significant differences between PWE and CG regarding acceptance and continuous use preferences. The thematic analysis identified integration with daily life, aesthetic and emotional resonance, adaptability to seizure characteristics, and user-centric design specifications as crucial factors to be considered for enhanced medical device adoption. The integration of a companion app is seen as an important tool to enhance communication and data sharing.

DISCUSSION

This study reveals that while SUDEP awareness can promote the development of future SUDEP predictive and preventive medical devices, these should be designed to mitigate its impact on daily life and anxiety of both PWE and CG. Comfort and acceptance are seen as key priorities to support continuous use and are seen as a technical requirement of future medical devices for SUDEP prediction and prevention. Widespread adoption requires these technologies to be customizable to adapt to different lifestyles and social situations. A holistic approach should be used in the design of future medical devices to capture several dimensions of PWE and CG epilepsy management journey and uphold communication between healthcare professionals, PWE and CG.

CONCLUSION

Data from this study highlight the importance of considering user preferences and experiences in the design of epilepsy management medical devices with potential applicability for SUDEP prediction and prevention. By employing user-centered design methods this research provides valuable insights to inform the development of future SUDEP prediction and prevention devices.

Fostering patient engagement through the co-design of seizure detection and monitoring technologies: A roadmap for collaboration between research and development

The large number of technological developments suggests that patients with epilepsy can be better supported in the management of their seizures, especially when their condition is drug resistant. Patients and their caregivers, who are generally supportive of seizure detection and monitoring technologies, can provide relevant information to improve their effectiveness. We propose a comprehensive co-design approach to support more efficient development of seizure detection and monitoring technologies. Such an approach should follow the steps of the research and development process, take into account the temporal requirements characteristic of seizure management, focus on the themes of autonomy and self-management, and be guided by disease experts. If co-design practices are to continue to contribute to their development, they must also meet the scientific requirements of validity and reproducibility.

Advancements in Wearable Digital Health Technology: A Review of Epilepsy Management

This review explores recent advancements in wearable digital health technology specifically designed to manage epilepsy. Epilepsy presents unique challenges in monitoring and management due to the unpredictable nature of seizures. Wearable devices offer continuous monitoring and real-time data collection, providing insights into seizure patterns and trends. Wearable technology is important in epilepsy management because it enables early detection, prediction, and personalized intervention, empowering patients and healthcare providers. Key findings highlight the potential of wearable devices to improve seizure detection accuracy, enhance patient empowerment through real-time monitoring, and facilitate data-driven decision-making in clinical practice. However, further research is needed to validate the accuracy and reliability of these devices across diverse patient populations and clinical settings. Collaborative efforts between researchers, clinicians, technology developers, and patients are essential to drive innovation and advancements in wearable digital health technology for epilepsy management, ultimately improving outcomes and quality of life for individuals with this neurological condition.

Discovering Themes in Deep Brain Stimulation Research Using Explainable Artificial Intelligence

Deep brain stimulation is a treatment that controls symptoms by changing brain activity. The complexity of how to best treat brain dysfunction with deep brain stimulation has spawned research into artificial intelligence approaches. Machine learning is a subset of artificial intelligence that uses computers to learn patterns in data and has many healthcare applications, such as an aid in diagnosis, personalized medicine, and clinical decision support. Yet, how machine learning models make decisions is often opaque. The spirit of explainable artificial intelligence is to use machine learning models that produce interpretable solutions. Here, we use topic modeling to synthesize recent literature on explainable artificial intelligence approaches to extracting domain knowledge from machine learning models relevant to deep brain stimulation. The results show that patient classification (i.e., diagnostic models, precision medicine) is the most common problem in deep brain stimulation studies that employ explainable artificial intelligence. Other topics concern attempts to optimize stimulation strategies and the importance of explainable methods. Overall, this review supports the potential for artificial intelligence to revolutionize deep brain stimulation by personalizing stimulation protocols and adapting stimulation in real time.

A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases

Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson's disease, while tremors predominate in epilepsy and Alzheimer's disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them.