Abstract
The gold standard methods for real-time core temperature (CT) monitoring are invasive and cost-inefficient. The application of Kalman filters for an indirect estimation of CT has been explored in the literature since 2010. This paper presents a comparative study between different state of the art Extended Kalman Filter (EKF) estimation algorithms and a new approach based on a biomimetic human body response pre-emptive mapping concept. In this new method, a mapping model of the physiological response of the heart rate (HR) change to CT increase is pre-applied to the input of the EKF estimation CT procedure in a near real-time manner. The algorithm was trained and tested using two datasets (total participants = 18). The best performing algorithm with this novel pre-emptive mapping achieved in an average Root Mean Squared Error (RMSE) of 0.34°C while the best state of the art EKF model (without pre-emptive mapping) resulted in a RMSE of 0.41°C, leading to a 17% improvement performance of our novel method. Given these favorable outcomes, it is compelling to assess its efficacy on a larger dataset in the near future.

Abstract
The human brain, with its vast network of billions of neurons and trillions of synapses (connections) between diverse cell types, remains one of the greatest mysteries in science and medicine. Despite extensive research, an understanding of the underlying mechanisms that drive normal behaviors and response to disease states is still limited. Advancement in the Neuroscience field and development of therapeutics for related pathologies requires innovative technologies that can provide a dynamic and systematic understanding of the interactions between neurons and neural circuits. In this work, we provide an up-to-date overview of the evolution of neurophotonic approaches in the last 10 years through a multi-source, literature analysis. From an initial corpus of 243 papers retrieved from Scopus, PubMed and WoS databases, we have followed the PRISMA approach to select 56 papers in the area. Following a full-text evaluation of these 56 scientific articles, six main areas of applied research were identified and discussed: (1) Advanced optogenetics, (2) Multimodal neural interfaces, (3) Innovative therapeutics, (4) Imaging devices and probes, (5) Remote operations, and (6) Microfluidic platforms. For each area, the main technologies selected are discussed according to the photonic principles applied, the neuroscience application evaluated and the more indicative results of efficiency and scientific potential. This detailed analysis is followed by an outlook of the main challenges tackled over the last 10 years in the Neurophotonics field, as well as the main technological advances regarding specificity, light delivery, multimodality, imaging, materials and system designs. We conclude with a discussion of considerable challenges for future innovation and translation in Neurophotonics, from light delivery within the brain to physical constraints and data management strategies.

Abstract

Abstract
Introduction and objectives: [ 99mTc] Tc-HMPAO, developed for brain imaging, is taken up by the heart, but never used to study it. We aimed to compare cardiac images with [99mTc] Tc-HMPAO and [ 99mTc] Tc-Tetrofosmin, using imaging techniques. Materials and methods: Cardiac gated SPECTs with [99mTc] Tc-HMPAO were compared with myocardial perfusion scintigraphies (MPS) with [99mTc] Tc-Tetrofosmin in three inpatients, from the vascular surgery ward. We developed algorithms in MATLAB R2016 to compare the [99mTc] Tc-Tetrofosmin/ [99mTc] Tc-HMPAO images. Pixel-wise correlations for slices, reversibility, and polar maps were obtained. Results: Correlations of both radiotracers’ myocardial images were as high as 0.93. Polar maps correlations were 0.93-0.95 (for both stress and rest) and 0.62-0.90 (reversibility). One of the patients (smoker) had significant lung [99mTc] Tc-HMPAO uptake. Conclusions: Cardiac SPECT with [99mTc] Tc-HMPAO might be a screening method for myocardial ischemia in non-smoking patients with epilepsy suspected of having heart changes, and who need to perform a brain perfusion SPECT. Keywords: myocardial perfusion scintigraphy; epilepsy; HMPAO; myocardial SPECT.

Abstract
<p>Many clinical applications involve in-bed patient activity monitoring, from intensive care and neuro-critical infirmary, to semiology-based epileptic seizure diagnosis support or sleep monitoring at home, which require accurate recognition of in-bed movement actions from video streams.</p> <p>The major challenges of clinical application arise from the domain gap between common in-the-lab and clinical scenery (e.g. viewpoint, occlusions, out-of-domain actions), the requirement of minimally intrusive monitoring to already existing clinical practices (e.g. non-contact monitoring), and the significantly limited amount of labeled clinical action data available.</p> <p>Focusing on one of the most demanding in-bed clinical scenarios - semiology-based epileptic seizure classification – this review explores the challenges of video-based clinical in-bed monitoring, reviews video-based action recognition trends, monocular 3D MoCap, and semiology-based automated seizure classification approaches. Moreover, provides a guideline to take full advantage of transfer learning for in-bed action recognition for quantified, evidence-based clinical diagnosis support.</p> <p>The review suggests that an approach based on 3D MoCap and skeleton-based action recognition, strongly relying on transfer learning, could be advantageous for these clinical in-bed action recognition problems. However, these still face several challenges, such as spatio-temporal stability, occlusion handling, and robustness before realizing the full potential of this technology for routine clinical usage.</p>

Abstract
Epileptic seizure classification based on seizure semiology requires automated, quantitative approaches to support the diagnosis of epilepsy, which affects 1 % of the world's population. Current approaches address the problem on a seizure level, neglecting the detailed evaluation of the classification of the underlying action features, also known as Movements of Interest (MOIs), which are critical for epileptologists in determining their classifications. Moreover, it hinders objective comparison of these approaches and attribution of performance differences due to datasets, intra-dataset MOI distribution, or architecture variations. Objective evaluation of action recognition techniques is crucial, with MOIs serving as foundational elements of semiology for clinical in-bed applications to facilitate epileptic seizure classification. However, until now, there were no MOI datasets available nor benchmarks comparing different action recognition approaches for this clinical problem. Therefore, as a pilot, we introduced a novel, simulated seizure semiology dataset carried out by 8 experienced epileptologists in an EMU bed, consisting of 7 MOI classes. We compare several computer vision methods for MOI classification, two image-based (I3D and Uniformerv2), and two skeleton-based (ST-GCN++ and PoseC3D) action recognition approaches. This study emphasizes the advantages of a 2-stage skeleton-based action recognition approach in a transfer learning setting (4 classes) and the multi-scale challenge of MOI classification (7 classes), advocating for the integration of skeleton-based methods with hand gesture recognition technologies in the future. The study's controlled MOI simulation dataset provides us with the opportunity to advance the development of automated epileptic seizure classification systems, paving the way for enhancing their performance and having the potential to contribute to improved patient care.