This thesis consists of four research papers presenting a microdata analysis approach to assess and evaluate the Parkinson’s disease (PD) motor symptoms using smartphone-based systems. PD is a progressive neurological disorder that is characterized by motor symptoms. It is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Both patients’ perception regarding common symptom and their motor function need to be related to the repeated and time-stamped assessment; with this, the full extent of patient’s condition could be revealed. The smartphone enables and facilitates the remote, long-term and repeated assessment of PD symptoms. Two types of collected data from smartphone were used, one during a three year, and another during one-day clinical study. The data were collected from series of tests consisting of tapping and spiral motor tests. During the second time scale data collection, along smartphone-based measurements patients were video recorded while performing standardized motor tasks according to Unified Parkinson’s disease rating scales (UPDRS). At first, the objective of this thesis was to elaborate the state of the art, sensor systems, and measures that were used to detect, assess and quantify the four cardinal and dyskinetic motor symptoms. This was done through a review study. The review showed that smartphones as the new generation of sensing devices are preferred since they are considered as part of patients’ daily accessories, they are available and they include high-resolution activity data. Smartphones can capture important measures such as forces, acceleration and radial displacements that are useful for assessing PD motor symptoms. Through the obtained insights from the review study, the second objective of this thesis was to investigate whether a combination of tapping and spiral drawing tests could be useful to quantify dexterity in PD. More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD. The results from this study showed that tapping and spiral drawing tests that were collected by smartphone can detect movements reasonably well related to under- and over-medication. The thesis continued by developing an Approximate Entropy (ApEn)-based method, which aimed to measure the amount of temporal irregularity during spiral drawing tests. One of the disabilities associated with PD is the impaired ability to accurately time movements. The increase in timing variability among patients when compared to healthy subjects, suggests that the Basal Ganglia (BG) has a role in interval timing. ApEn method was used to measure temporal irregularity score (TIS) which could significantly differentiate the healthy subjects and patients at different stages of the disease. This method was compared to two other methods which were used to measure the overall drawing impairment and shakiness. TIS had better reliability and responsiveness compared to the other methods. However, in contrast to other methods, the mean scores of the ApEn-based method improved significantly during a 3-year clinical study, indicating a possible impact of pathological BG oscillations in temporal control during spiral drawing tasks. In addition, due to the data collection scheme, the study was limited to have no gold standard for validating the TIS. However, the study continued to further investigate the findings using another screen resolution, new dataset, new patient groups, and for shorter term measurements. The new dataset included the clinical assessments of patients while they performed tests according to UPDRS. The results of this study confirmed the findings in the previous study. Further investigation when assessing the correlation of TIS to clinical ratings showed the amount of temporal irregularity present in the spiral drawing cannot be detected during clinical assessment since TIS is an upper limb high frequency-based measure.

Essa dissertação apresenta o desenvolvimento métodos para caracterização da conectividade entre séries temporais neurofisiológicas. Utilizam-se metodologias provenientes da Teoria da Informação Entropias Aproximada e Amostral para representar a complexidade da série no tempo, o que permite inferir como sua variabilidade se transfere a outras sequências, através do uso da coerência parcial direcionada. Para cada sistema analisado: (1) Faz-se uma transformação em outro, relacionando-o às medidas de entropia, (2) Estima-se a conectividade pela coerência parcial direcionada e (3) Avalia-se a robustez do procedimento via simulações de Monte Carlo e análise de sensibilidade. Para os exemplos simulados, a técnica proposta é capaz de oferecer resultados plausíveis, através da correta inferência da direção de conectividade em casos de acoplamento não-linear (quadrático), com número reduzido de amostras temporais dos sinais, em que outras abordagens falham. Embora de simples implementação, conclui-se que o processo mostra-se como uma extensão da causalidade de Granger para o caso não-linear. / The purpose of this work is to present the development of methods for characterizing the connectivity between nonlinear neurophysiological time series. Methodologies from Information Theory Approximate and Sample Entropies are used to represent the complexity of the series in a period of time, which allows inferring on how its variability is transferred to other sequences, using partial directed coherence. Methods: For each system under consideration, (1) It is done a transformation in another, relating it to measures of entropy, (2) The connectivity is estimated by the use of partial directed coherence and (3) The robustness of the procedure is analyzed via Monte Carlo simulations and sensitivity analysis. Results: For the simulated examples, the proposed technique is able to offer plausible results, through the correct inference of the connectivity direction, in cases of nonlinear coupling (quadratic), with a reduced number of signals samples, where other approaches fail. Conclusion: The process proves to be an extension of the Granger causality to the nonlinear case.

Essa dissertação apresenta o desenvolvimento métodos para caracterização da conectividade entre séries temporais neurofisiológicas. Utilizam-se metodologias provenientes da Teoria da Informação Entropias Aproximada e Amostral para representar a complexidade da série no tempo, o que permite inferir como sua variabilidade se transfere a outras sequências, através do uso da coerência parcial direcionada. Para cada sistema analisado: (1) Faz-se uma transformação em outro, relacionando-o às medidas de entropia, (2) Estima-se a conectividade pela coerência parcial direcionada e (3) Avalia-se a robustez do procedimento via simulações de Monte Carlo e análise de sensibilidade. Para os exemplos simulados, a técnica proposta é capaz de oferecer resultados plausíveis, através da correta inferência da direção de conectividade em casos de acoplamento não-linear (quadrático), com número reduzido de amostras temporais dos sinais, em que outras abordagens falham. Embora de simples implementação, conclui-se que o processo mostra-se como uma extensão da causalidade de Granger para o caso não-linear. / The purpose of this work is to present the development of methods for characterizing the connectivity between nonlinear neurophysiological time series. Methodologies from Information Theory Approximate and Sample Entropies are used to represent the complexity of the series in a period of time, which allows inferring on how its variability is transferred to other sequences, using partial directed coherence. Methods: For each system under consideration, (1) It is done a transformation in another, relating it to measures of entropy, (2) The connectivity is estimated by the use of partial directed coherence and (3) The robustness of the procedure is analyzed via Monte Carlo simulations and sensitivity analysis. Results: For the simulated examples, the proposed technique is able to offer plausible results, through the correct inference of the connectivity direction, in cases of nonlinear coupling (quadratic), with a reduced number of signals samples, where other approaches fail. Conclusion: The process proves to be an extension of the Granger causality to the nonlinear case.

Nowadays, a significant proportion of the population in the world is affected by cerebral diseases like epilepsy. In this study, frequency domain features of electroencephalography (EEG) signals were studied and analyzed, with a view being able to detect epileptic seizures more easily. The power spectrum and spectrogram were determined by using fast fourier transform (FFT) and the scalogram was found by performing continuous wavelet transform (CWT) on the testing EEG signal. In addition, two schemes, i.e. method 1 and method 2, were implemented for detecting epileptic seizures and the applicability of the two methods to electrocardiogram (ECG) signals were tested. A third method for anomaly detection in ECG signals was tested. / En signifikant del av population påverkas idag av neurala sjukdomar som epilepsi. I denna studie studerades och analyserades egenskaper inom frekvensdomänen av elektroencefalografi (EEG), med sikte på att lättare kunna upptäcka epileptiska anfall. Effektspektrumet och spektrogramet bestämdes med hjälp av en snabb fouriertransform och skalogrammet hittades genom att genomföra en kontinuerlig wavelet transform (CWT) på testsignalen från EEGsignalen. I addition till detta skapades två system, metod 1 och metod 2, som implementerades för att upptäcka epileptiska anfall. Användbarheten av dessa två metoder inom elektrokardiogramsignaler (ECG) testades. En tredje metod för anomalidetektering i ECGsignaler testades.