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

Spectral parameters for finger tapping quantification

A miniature inertial sensor placed on fingertip of index finger while performing finger tapping test can be used for an objective quantification of finger tapping motion. Temporal and spatial parameters such as cadence, tapping duration, and tapping angle can be extracted for detailed analysis. However, the mentioned parameters, although intuitive and simple to interpret, do not always provide all the necessary information regarding the subject's motor performance. Analysis of frequency content of the finger tapping movement can provide crucial information about the patient's condition. In this paper, we present parameters extracted from spectral analysis that we found to be significant for finger tapping assessment. With these parameters, tapping's intra-variability, movement smoothness and anomalies that may occur within the tapping performance can be detected and described, providing significant information for further diagnostics and monitoring progress of the disease or response to therapy

Impact of depression on gait variability in Parkinson's disease

Objective The goal of this study was to analyze how depression associated with Parkinson’s disease (PD) affected gait variability in these patients using a dual-task paradigm. Additionally, the dependency of the executive functions and the impact of depression on gait variability were analyzed. Patients and Methods Three subject groups were included: patients with PD, but no depression (PD-NonDep; 14 patients), patients with both PD and depression (PD-Dep; 16 patients) and healthy controls (HC; 15 subjects). Gait was recorded using the wireless sensors. The participants walked under four conditions: single-task, motor dual- task, cognitive dual-task, and combined dual-task. Variability of stride length, stride duration, and swing time was calculated and analyzed using the statistical methods. Results Variability of stride duration and stride length were not significantly different between PD-Dep and PD-NonDep patients. The linear mixed model showed that swing time variability was statistically significantly higher in PD-Dep patients compared to controls (p = 0.001). Hamilton Disease Rating Scale scores were significantly correlated with the swing time variability (p = 0.01). Variability of all three parameters of gait was significantly higher while performing combined or cognitive task and this effect was more pronounced in PD-Dep group of patients. Conclusions Depression in PD was associated with swing time variability, and this effect was more prominent while performing a dual-task. Significance Diagnosing and treating depression might be important for gait improvement and fall reduction in PD patients

SPECTRAL PARAMETERS FOR FINGER TAPPING QUATIFICATION

A miniature inertial sensor placed on fingertip of index finger while performing finger tapping test can be used for an objective quantification of finger tapping motion. Temporal and spatial parameters such as cadence, tapping duration, and tapping angle can be extracted for detailed analysis. However, the mentioned parameters, although intuitive and simple to interpret, do not always provide all the necessary information regarding the subject’s motor performance. Analysis of frequency content of the finger tapping movement can provide crucial information about the patient's condition. In this paper, we present parameters extracted from spectral analysis that we found to be significant for finger tapping assessment. With these parameters, tapping’s intra-variability, movement smoothness and anomalies that may occur within the tapping performance can be detected and described, providing significant information for further diagnostics and monitoring progress of the disease or response to therapy

Quick computer aided differential diagnostics based on repetitive finger tapping in Parkinson’s disease and atypical parkinsonisms

Background: Parkinson's disease (PD) is the second most common neurodegenerative disorder whose prevalence rises with age, yet clinical diagnosis is still a challenging task due to similar manifestations of other neurodegenerative movement disorders. In untreated patients or those with unclear responses to medication, correct percentages of early diagnoses go as low as 26%. Technology has been used in various forms to facilitate discerning between persons with PD and healthy individuals, but much less work has been dedicated to separating PD and atypical parkinsonisms. Methods: A wearable system was developed based on inertial sensors that capture the movements of fingers during repetitive finger tapping. A k-nearest-neighbor classifier was used on features extracted from gyroscope recordings for quick aid in differential diagnostics, discerning patients with PD, progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and healthy controls (HC). Results: The overall classification accuracy achieved was 85.18% in the multiclass setup. MSA and HC groups were the easiest to discern (100%), while PSP was the most elusive diagnosis, as some patients were incorrectly assigned to MSA and HC groups. Conclusions: The system shows potential for use as a tool for quick diagnostic aid, and in the era of big data, offers a means of standardization of data collection that could allow scientists to aggregate multi-center data for further research

Serious gaming based on Kinect technology for autistic children in Serbia

In this paper, a new, serious Kinect-based game for autistic children in Serbia is presented. The main purpose of this game is fine-tuning of motor skills, and performance improvement of basic cognitive tasks through practicing five game categories: Sorting, Math exercises, Catching, Imitation and Seeking. It is a user-friendly game, with colorful interface, approachable for autistic children. Therapists can monitor numerical scores and graphical representation of the results. This way, while the child plays and performs integrated task of motor and cognitive exercises, the therapists can quantitatively and objectively assess child's progress, based on gesture precision, smoothness, repeatability, reflexes, and results from mathematical tasks

An Expert System for Quantification of Bradykinesia Based on Wearable Inertial Sensors

Wearable sensors and advanced algorithms can provide significant decision support for clinical practice. Currently, the motor symptoms of patients with neurological disorders are often visually observed and evaluated, which may result in rough and subjective quantification. Using small inertial wearable sensors, fine repetitive and clinically important movements can be captured and objectively evaluated. In this paper, a new methodology is designed for objective evaluation and automatic scoring of bradykinesia in repetitive finger-tapping movements for patients with idiopathic Parkinson’s disease and atypical parkinsonism. The methodology comprises several simple and repeatable signal-processing techniques that are applied for the extraction of important movement features. The decision support system consists of simple rules designed to match universally defined criteria that are evaluated in clinical practice. The accuracy of the system is calculated based on the reference scores provided by two neurologists. The proposed expert system achieved an accuracy of 88.16% for files on which neurologists agreed with their scores. The introduced system is simple, repeatable, easy to implement, and can provide good assistance in clinical practice, providing a detailed analysis of finger-tapping performance and decision support for symptom evaluation

Challenges of Stride Segmentation and Their Implementation for Impaired Gait

Stride segmentation represents important but challenging part of the gait analysis. Different methods and sensor systems have been proposed for detection of markers for segmentation of gait sequences. This task is often performed with wearable sensors comprising force sensors and/or inertial sensors. In this paper, we have compared four different methods for stride segmentation based on signals collected from force sensing resistors, accelerometers and gyro sensors. The results were evaluated on 15 healthy and 15 patients with Parkinson's disease, and expressed in terms of number of imprecisely, missed or wrongly detected gait events, as well as temporal absolute error. It was established that the methods using the inertial data, provide results with up to 12% higher error rate comparing to detection from force sensing resistors

Implementation of continuous wavelet transformation in repetitive finger tapping analysis for patients with PD

In this paper we propose a methodology for objective evaluation and classification of repetitive finger tapping performance based solely on its spectral behavior. We used miniature sensor system with gyroscope placed over fingertip of index finger for finger tapping recording. The study included 20 subjects 10 patients with Parkinson's disease (PD) and 10 age and gender matched healthy controls. Acquired data were preprocessed using continuous wavelet transformation (CWT), and their coefficients were used in further analysis. Based on cross-sections of CWT in time and frequency, we introduced parameters describing characteristic tapping frequencies and vigor of the performed movements, its decrement and isolated characteristic frequency areas. These parameters were further used in classification for distinction between PD patients and controls, achieving 95% classification accuracy