Background Parkinson’s disease (PD) is a neurological disease that affects the motor system. The associated motor symptoms are muscle rigidity or stiffness, bradykinesia, tremors, and gait disturbances. The correct diagnosis, especially in the initial stages, is fundamental to the life quality of the individual with PD. However, the methods used for diagnosis of PD are still based on subjective criteria. As a result, the objective of this study is the proposal of a method for the discrimination of individuals with PD (in the initial stages of the disease) from healthy groups, based on the inertial sensor recordings. Methods A total of 27 participants were selected, 15 individuals previously diagnosed with PD and 12 healthy individuals. The data collection was performed using inertial sensors (positioned on the back of the hand and on the back of the forearm). Different numbers of features were used to compare the values of sensitivity, specificity, precision, and accuracy of the classifiers. For group classification, 4 classifiers were used and compared, those being [Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes (NB)]. Results When all individuals with PD were analyzed, the best performance for sensitivity and accuracy (0.875 and 0.800, respectively) was found in the SVM classifier, fed with 20% and 10% of the features, respectively, while the best performance for specificity and precision (0.933 and 0.917, respectively) was associated with the RF classifier fed with 20% of all the features. When only individuals with PD and score 1 on the Hoehn and Yahr scale (HY) were analyzed, the best performances for sensitivity, precision and accuracy (0.933, 0.778 and 0.848, respectively) were from the SVM classifier, fed with 40% of all features, and the best result for precision (0.800) was connected to the NB classifier, fed with 20% of all features. Conclusion Through an analysis of all individuals in this study with PD, the best classifier for the detection of PD (sensitivity) was the SVM fed with 20% of the features and the best classifier for ruling out PD (specificity) was the RF classifier fed with 20% of the features. When analyzing individuals with PD and score HY = 1, the SVM classifier was superior across the sensitivity, precision, and accuracy, and the NB classifier was superior in the specificity. The obtained result indicates that objective methods can be applied to help in the evaluation of PD.

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Eliminating facial electromyographic (EMG) signal from the electroencephalogram (EEG) is crucial for the accuracy of applications such as brain computer interfaces (BCIs) and brain functionality measurement. Facial electromyography typically corrupts the electroencephalogram. Although it is possible to find in the literature a number of multi-channel approaches for filtering corrupted EEG, studies employing single-channel approaches are scarce. In this context, this study proposed a single-channel method for attenuating facial EMG noise from contaminated EEG. The architecture of the method allows for the evaluation and incorporation of multiple decomposition and adaptive filtering techniques. The decomposition method was responsible for generating EEG or EMG reference signals for the adaptive filtering stage. In this study, the decomposition techniques CiSSA, EMD, EEMD, EMD-PCA, SSA, and Wavelet were evaluated. The adaptive filtering methods RLS, Wiener, LMS, and NLMS were investigated. A time and frequency domain set of features were estimated from experimental signals to evaluate the performance of the single channel method. This set of characteristics permitted the characterization of the contamination of distinct facial muscles, namely Masseter, Frontalis, Zygomatic, Orbicularis Oris, and Orbicularis Oculi. Data were collected from ten healthy subjects executing an experimental protocol that introduced the necessary variability to evaluate the filtering performance. The largest level of contamination was produced by the Masseter muscle, as determined by statistical analysis of the set of features and visualization of topological maps. Regarding the decomposition method, the SSA method allowed for the generation of more suitable reference signals, whereas the RLS and NLMS methods were more suitable when the reference signal was derived from the EEG. In addition, the LMS and RLS methods were more appropriate when the reference signal was the EMG. This study has a number of practical implications, including the use of filtering techniques to reduce EEG contamination caused by the activation of facial muscles required by distinct types of studies. All the developed code, including examples, is available to facilitate a more accurate reproduction and improvement of the results of this study.

Resumo: Estudos são realizados a fim de avaliar a severidade de doenças neurodegenerativas que apresentam como sintoma o tremor patológico. Estes utilizam escalas qualitativas para a análise do estado do indivíduo. De forma a tornar a avaliação mais objetiva, esta pesquisa apresenta resultados quantitativos, estimados por meio do valor médio quadrático (rms, do inglês, root mean square) de sinais registrados por acelerômetros, para quantificar o grau da severidade do tremor em indivíduos com a doença de Parkinson (DP). 27 indivíduos participaram do estudo, sendo, 15 com DP e 12 hígidos, e agrupados da seguinte forma: GI -DP sem carga e com carga; GII -hígidos sem carga e com carga. IntroduçãoO tremor humano é caracterizado por movimento involuntário de caráter oscilatório e rítmico, de qualquer parte do corpo [1]. O tremor pode ser uma função motora humana normal (fisiológico) presente em todos seres humanos ou anormal (patológico) em consequência de distúrbios da saúde ou envelhecimento [2]. Estudos e estatísticas governamentais mostram que a população senil é a mais afetada pelo tremor [3]. Essa manifestação pode causar incapacidade funcional considerável e leva o indivíduo ao afastamento social pela interferência nas atividades de vida diária (AVDs) [3].O presente estudo avaliou o tremor advindo da Doença de Parkinson (DP). A DP pode causar tremor patológico, sendo uma desordem crônica, progressiva e neurodegenerativa do sistema nervoso central (SNC), com implicações profundas para o indivíduo, como déficits em funções motoras, diminuição da amplitude de movimento (ADM) e força muscular [5]. O diagnóstico da DP ainda é primariamente clínico e a experiência do avaliador é um dos principais fatores para o diagnóstico correto. A avaliação da severidade da DP é feita por meio de uma escala denominada Unified Parkinson's Disease Rating Scale (UPDRS). Mesmo com os avanços em tecnologias aplicadas a saúde, a UPDRS avalia o tremor de forma qualitativa. Como os sintomas dos pacientes flutuam ao longo do dia, o momento em que o diagnóstico é dado, pode não representar o grau da severidade do tremor daquele paciente. Estudos que comparam diferentes métodos de tratamento da DP também usam a escala UPDRS para avaliar os resultados dos diferentes tratamentos, sejam circúrgicos ou farmacológicos [6].Diversas maneiras de tratamento são indicadas para a