Self-Reporting Technologies for Supporting Epilepsy Treatment

Epilepsy diagnosis and treatment relies heavily on patient self-reporting for informing clinical decision-making. These self-reports are traditionally collected from handwritten patient journals and tend to be either incomplete or inaccurate. Recent mobile and wearable health tracking developments stand to dramatically impact clinical practice through supporting patient and caregiver data collection activities. However, the specific types and characteristics of the data that clinicians need for patient care are not well known. In this study, we conducted interviews, a literature review, an expert panel, and online surveys to assess the availability and quality of patient-reported data that is useful but reported as being unavailable, difficult for patients to collect, or unreliable during epilepsy diagnosis and treatment, respectively. The results highlight important yet underexplored data collection and design opportunities for supporting the diagnosis, treatment, and self-management of epilepsy and expose notable gaps between clinical data needs and current patient practices

Characterizing Unstructured Motor Behaviors in the Epilepsy Monitoring Unit

Key advancements in recording hardware, data computation, clinical care, and cognitive science continue to drive new possibilities in how humans and machines can interact directly through thought. Neural data analyses with these advancements has progressed neuroscience research in functional brain mapping and brain-computer interfaces (BCIs). Much of our knowledge about BCIs is informed by data collected through carefully controlled experiments. Constraining BCI experiments with structured paradigms allows researchers to collect a high number of consistent data in a short amount of time, while also controlling for external confounds. Very little is currently known about how well these task-based relationships extend to daily life, in part because collecting data outside of the lab is challenging. To further understand natural brain activity, we must study more complex behaviors in more environmentally relevant settings. The results of this dissertation address three general challenges to studying neural correlates to unstructured behaviors. First, we continuously monitored unstructured human movements in the epilepsy monitoring unit using a video sensor synchronized to clinical intracortical electrodes. Second, we annotated unstructured behaviors from these video using both manual and computer vision methods. Finally, analyzed neural features with respect to unstructured human movements, and evaluated the performance of features identified in previous task-based studies. The preliminary nature of this work means that a majority of our demonstrations are whether the continuous paradigm can be leveraged, how one might go about leveraging it, and evaluations that tie our results back to earlier task-based studies. Our advances here motivate future works that focus more intently on what types of behaviors and neural signal features to explore

Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Camera

Conventional sleep monitoring is time-consuming, expensive and uncomfortable, requiring a large number of contact sensors to be attached to the patient. Video data is commonly recorded as part of a sleep laboratory assessment. If accurate sleep staging could be achieved solely from video, this would overcome many of the problems of traditional methods. In this work we use heart rate, breathing rate and activity measures, all derived from a near-infrared video camera, to perform sleep stage classification. We use a deep transfer learning approach to overcome data scarcity, by using an existing contact-sensor dataset to learn effective representations from the heart and breathing rate time series. Using a dataset of 50 healthy volunteers, we achieve an accuracy of 73.4\% and a Cohen's kappa of 0.61 in four-class sleep stage classification, establishing a new state-of-the-art for video-based sleep staging.Comment: Accepted to the 6th International Workshop on Computer Vision for Physiological Measurement (CVPM) at CVPR 2023. 10 pages, 12 figures, 5 table

Brain Tumour Classification Using Deep Features from Structural MRI

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