Bakgrund: Hjärnskakning är en vanlig diagnos och vissa patienter upplever att fysisk ansträngning utlöser symtom lång tid efter hjärnskakningen. En förändring i reglering av cerebralt blodflöde (CBF) har visats vara en potentiell orsak bakom detta. Konditionsträning under tröskeln för symtomexacerbation kan förkorta återhämtningstiden för patienterna. På Neurorehab vid Norrlands universitetsjukhus i Umeå identifieras tröskeln med ett stegrat arbetsprov på ergometercykel. Det finns inte någon studie där transkraniell doppler (TCD) använts för att mäta förändringar i cerebralt blodflöde (CBF) under detta arbetsprov. Syfte: Att undersöka genomförbarhet och klinisk relevans av att använda TCD för mätning av blodflödeshastighet i arteria cerebri media (ACMh), hos friska män, under stegrat arbetsprov. Metod: Sex friska och regelbundet aktiva män genomförde ett stegrat arbetsprov på ergometercykel under samtidig mätning av hjärtfrekvens, blodtryck, partialtryck end-tidal CO2 (PetCO2) och blodflödeshastighet i arteria cerebri media (ACMh, mätt med TCD). Smärta från TCD-utrustning och upplevd ansträngning skattades. Tidsåtgången för TCD-tillägget samt eventuell signalförlust noterades. Resultat: Fem studiedeltagare rapporterade ökad smärta (huvudvärk), skattad med Borg CR10 skala, från TCD-utrustningen. Total tidsåtgång för TCD-tillägget var 7 minuter och 40 sekunder i median (IQR, 5 minuter och 32 sekunder). Signalförlust uppstod för en studiedeltagare på vänster sida. PetCO2 och ACMh följdes åt under arbetsprovet bortsett från avvikelser vid två tillfällen. Slutsatser: Studien visar att mätning av ACMh med TCD är genomförbart och ger relevant information om hur CBF ter sig under genomförandet av stegrat arbetsprov. TCD-utrustningen orsakade smärta vilket kan vara problematiskt vid genomförande för personer med postkontusionellt syndrom.

There has in recent years been interdisciplinary research on utilizing machine learning for detecting and classifying neurodegenerative disorders with the sole goal of outperforming state-of-the-art models in terms of metrics such as accuracy, specificity, and sensitivity. Specifically, these studies have been conducted using existing networks on ”novel” methods of pre-processing data or by developing new convolutional neural networks. As of now, no work has looked into how different normalization techniques affect a deep or shallow convolutional neural network in terms of numerical stability, its performance, explainability, and interpretability. This work delves into what normalization technique is most suitable for deep and shallow convolutional neural networks. Two baselines were created, one shallow and one deep, and applied eight different normalization techniques to these model architectures. Conclusions were drawn based on our analysis of numerical stability, performance (metrics), and methods of Explainable Artificial Intelligence. Our findings indicate that normalization techniques affect models differently regarding the mentioned aspects of our analysis, especially numerical stability and explainability. Moreover, we show that there should indeed be a preference to select one method over the other in future studies of this interdisciplinary field.

Background: Stroke is a collective name for a disease caused by circulatory disorders in the brain. The patients who are affected often have complications that are experienced as a major life change. The complications mean that rehabilitative care is necessary, which is based on a good caring relationship. Aim: To describe nurses' experiences of caring for patients in rehabilitation after a stroke. Method: Systematic literature study for analysis of ten qualitative studies. Results: Nurses experienced that the rehabilitative care in the stroke wards meant helping the patients find their ‘self’, by seeing the importance of the caring relationship, relatives and the patients' participation. The nurses also experienced that the rehabilitative care involved working in a team, where the nurses experienced the importance of their extensive role and the teamwork. Conclusion: The rehabilitative care after a stroke meant that the nurses help patients find their 'self'. They perceived it as important for patients to be able to achieve good health after their illness. The nurses also felt that their professional role in the team was extensive because they had a rehabilitative responsibility in addition to the general nurse duties. It was something that the nurses felt required a teamwork in the rehabilitation team. Keyword: Caring, nurse-perspective, rehabilitative care, stroke-care, systematic literature review.

Epilepsy affects more than 50 million people and is one of the most prevalent neurological disorders and has a high impact on the quality of life of those suffering from it. However, 70% of epilepsy patients can live seizure free with proper diagnosis and treatment. Patients are evaluated using scalp EEG recordings which is cheap and non-invasive. Diagnostic yield is however low and qualified personnel need to process large amounts of data in order to accurately assess patients. MindReader is an unsupervised classifier which detects spectral anomalies and generates a hypothesis of the underlying patient state over time. The aim is to highlight abnormal, potentially epileptiform states, which could expedite analysis of patients and let qualified personnel attest the results. It was used to evaluate 95 scalp EEG recordings from healthy adults and adult patients with epilepsy. Interictal Epileptiform discharges (IED) occurring in the samples had been retroactively annotated, along with the patient state and maneuvers performed by personnel, to enable characterization of the classifier’s detection performance. The performance was slightly worse than previous benchmarks on pediatric scalp EEG recordings, with a 7% and 33% drop in specificity and sensitivity, respectively. Electrode positioning and partial spatial extent of events saw notable impact on performance. However, no correlation between annotated disturbances and reduction in performance could be found. Additional explorative analysis was performed on serialized intermediate data to evaluate the analysis design. Hyperparameters and electrode montage options were exposed to optimize for the average Mathew’s correlation coefficient (MCC) per electrode per patient, on a subset of the patients with epilepsy. An increased window length and lowered amount of training along with an common average montage proved most successful. The Euclidean distance of cumulative spectra (ECS), a metric suitable for spectral analysis, and homologous L2 and L1 loss function were implemented, of which the ECS further improved the average performance for all samples. Four additional analyses, featuring new time-frequency transforms and multichannel convolutional autoencoders were evaluated and an analysis using the continuous wavelet transform (CWT) and a convolutional autoencoder (CNN) performed the best, with an average MCC score of 0.19 and 56.9% sensitivity with approximately 13.9 false positives per minute.