Систем за подршку одлучивању, евалуацију и праћење стања пацијената оболелих од неуродегенеративних болести

Системи за подршку клиничком одлучивању представљају рачунарске алате који применом напредних технологија могу утицати на доношење одлука у вези са пацијентима. У овој дисертацији представљени су истраживање и развој новог система за подршку одлучивању, евалуацију и праћење стања пацијената оболелих од неуродегенеративних болести. Анализа клинички релевантних и свакодневних покрета чини основу овог система. Обрасци ових покрета снимљени су помоћу бежичних, носивих сензора малих димензија и тежине, који не захтевају компликовану поставку и могу се једноставно применити у било ком окружењу. Први део система намењен је (раном) препознавању Паркинсонове болести (ПБ) на основу анализе хода и алгоритама дубоког учења. Резултати су показали да је ПБ пацијенте могуће препознати са високом тачношћу. Други део система посвећен је праћењу симптома ПБ брадикинезије применом резоновања који се базира на знању. Представљена је метода за анализу покрета који се користе за евалуацију брадикинезије. Поред тога, применом различитих метода обраде сигнала развијена је нова метрика за квантификацију важних карактеристика ових покрета. Предикција степена развоја симптома се заснива на новом експертском систему који у потпуности објективизује клиничке евалуационе критеријуме. Валидација је урађена на примеру покрета тапкања прстију, који је снимљен на пацијенатима са типичним и атипичним паркинсонизимом. Показана је висока усаглашеност у поређењу са клиничким подацима. Развијени систем је објективан, аутоматизован, једноставно се користи, садржи интуитиван графички и параметарски приказ резултата и значајно доприноси унапређењу клиничких процедура за евалуацију и праћење стања пацијената са неуродегенеративним болестима.Clinical decision support system represents a computer-aided tool that utilizes advanced technologies for influencing clinical decisions about patients. This dissertation presents research and development of a new decision support system for the assessment of patients with neurodegenerative diseases. The analysis of movements that are part of standard clinical scales or everyday activities represents the basis of the system. These movements are recorded using small and lightweight wearable, wireless sensors, which do not require complicated setup and can be easily applied in any environment. The first part of system is dedicated to the (early) recognition of Parkinson’s disease (PD) based on gait analysis and deep learning algorithms. PD patients could be identified with a high accuracy. The other part of the system is dedicated to the assessment of PD symptoms, more specifically, bradykinesia, utilizing the knowledge-based reasoning. A method for analysis of bradykinesia related movements is defined and presented. Moreover, by applying different signal processing techniques, new metrics have been developed to quantify the essential characteristics of these movements. The prediction of symptom severity was performed using new expert system that completely objectified the clinical evaluation criteria. Validation was performed on the example of the finger-tapping movement of patients with typical and atypical parkinsonism. A high compliance rate was obtained compared to clinical data. The developed system is objective, automated, easy to use, contains an intuitive graphical and parametric presentation of results, and significantly contributes to the improvement of clinical assessment of patients with neurodegenerative diseases

Linguistic- and Acoustic-based Automatic Dementia Detection using Deep Learning Methods

Dementia can affect a person's speech and language abilities, even in the early stages. Dementia is incurable, but early detection can enable treatment that can slow down and maintain mental function. Therefore, early diagnosis of dementia is of great importance. However, current dementia detection procedures in clinical practice are expensive, invasive, and sometimes inaccurate. In comparison, computational tools based on the automatic analysis of spoken language have the potential to be applied as a cheap, easy-to-use, and objective clinical assistance tool for dementia detection. In recent years, several studies have shown promise in this area. However, most studies focus heavily on the machine learning aspects and, as a consequence, often lack sufficient incorporation of clinical knowledge. Many studies also concentrate on clinically less relevant tasks such as the distinction between HC and people with AD which is relatively easy and therefore less interesting both in terms of the machine learning and the clinical application. The studies in this thesis concentrate on automatically identifying signs of neurodegenerative dementia in the early stages and distinguishing them from other clinical, diagnostic categories related to memory problems: (FMD, MCI, and HC). A key focus, when designing the proposed systems has been to better consider (and incorporate) currently used clinical knowledge and also to bear in mind how these machine-learning based systems could be translated for use in real clinical settings. Firstly, a state-of-the-art end-to-end system is constructed for extracting linguistic information from automatically transcribed spontaneous speech. The system's architecture is based on hierarchical principles thereby mimicking those used in clinical practice where information at both word-, sentence- and paragraph-level is used when extracting information to be used for diagnosis. Secondly, hand-crafted features are designed that are based on clinical knowledge of the importance of pausing and rhythm. These are successfully joined with features extracted from the end-to-end system. Thirdly, different classification tasks are explored, each set up so as to represent the types of diagnostic decision-making that is relevant in clinical practice. Finally, experiments are conducted to explore how to better deal with the known problem of confounding and overlapping symptoms on speech and language from age and cognitive decline. A multi-task system is constructed that takes age into account while predicting cognitive decline. The studies use the publicly available DementiaBank dataset as well as the IVA dataset, which has been collected by our collaborators at the Royal Hallamshire Hospital, UK. In conclusion, this thesis proposes multiple methods of using speech and language information for dementia detection with state-of-the-art deep learning technologies, confirming the automatic system's potential for dementia detection

Measuring Behavior 2018 Conference Proceedings

These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions

Libro de actas. XXXV Congreso Anual de la Sociedad Española de Ingeniería Biomédica

596 p.CASEIB2017 vuelve a ser el foro de referencia a nivel nacional para el intercambio científico de conocimiento, experiencias y promoción de la I D i en Ingeniería Biomédica. Un punto de encuentro de científicos, profesionales de la industria, ingenieros biomédicos y profesionales clínicos interesados en las últimas novedades en investigación, educación y aplicación industrial y clínica de la ingeniería biomédica. En la presente edición, más de 160 trabajos de alto nivel científico serán presentados en áreas relevantes de la ingeniería biomédica, tales como: procesado de señal e imagen, instrumentación biomédica, telemedicina, modelado de sistemas biomédicos, sistemas inteligentes y sensores, robótica, planificación y simulación quirúrgica, biofotónica y biomateriales. Cabe destacar las sesiones dedicadas a la competición por el Premio José María Ferrero Corral, y la sesión de competición de alumnos de Grado en Ingeniería biomédica, que persiguen fomentar la participación de jóvenes estudiantes e investigadores