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Publications

2019

Automated measures of gait dynamics and camptocormia angle in Parkinson's disease before and after subthalamic deep brain stimulation

Authors
Soares, C; Vilas Boas, MDC; Lopes, EM; Choupina, H; Soares Dos Reis, R; Fitas, D; Cunha, JPS; Monteiro, P; Linhares, P; Rosas, MJSL;

Publication
EUROPEAN JOURNAL OF NEUROLOGY

Abstract

2019

iHandU: Towards the Validation of a Wrist Rigidity Estimation for Intraoperative DBS Electrode Position Optimization

Authors
Lopes, EM; Sevilla, A; Vilas Boas, MD; Choupina, HMP; Nunes, DP; Rosas, MJ; Oliveira, A; Massano, J; Vaz, R; Cunha, JPS;

Publication
2019 9TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER)

Abstract
DBS surgery is considered for Parkinson's Disease patients when motor complications and consequent quality of life is no longer acceptable on optimal medical therapy prescribed by neurologists. Within the operating room, the electrode placement with the best clinical outcome for the patient is quantitatively assessed via the wrist rigidity assessment. A subjective scale is used, influenced by the neurologists' perception and experience. Our research group has previously designed a novel, comfortable and wireless system aiming to tackle this subjectivity. This system comprised a gyroscope sensor in a textile band, placed in the patients' hand, which communicated its measurement to a Smartphone via Bluetooth. During the wrist rigidity evaluation exam, a signal descriptor was computed from angular velocity (omega) and a polynomial mathematical model was used to classify the signals using a quantitative scale of rigidity improvement. In this present work, we aim to develop models that consider the 3-gyroscope-axes to acquire the omega and the cogwheel rigidity. Our results showed that y-gyroscope-axis remains the best way to classify the rigidity reduction, showing an accuracy of 78% and a mean error of 3.5%. According to previous results, the performance was similar and the decrease of samples to extract the omega features did not compromise system performance. The cogwheel rigidity did not improve the previous model and other gyroscope-axis beyond the y-axis decreased system performance.

2016

Synchronization in the random-field Kuramoto model on complex networks

Authors
Lopes, MA; Lopes, EM; Yoon, S; Mendes, JFF; Goltsev, AV;

Publication
PHYSICAL REVIEW E

Abstract
We study the impact of random pinning fields on the emergence of synchrony in the Kuramoto model on complete graphs and uncorrelated random complex networks. We consider random fields with uniformly distributed directions and homogeneous and heterogeneous (Gaussian) field magnitude distribution. In our analysis, we apply the Ott-Antonsen method and the annealed-network approximation to find the critical behavior of the order parameter. In the case of homogeneous fields, we find a tricritical point above which a second-order phase transition gives place to a first-order phase transition when the network is either fully connected or scale-free with the degree exponent gamma > 5. Interestingly, for scale-free networks with 2 < gamma <= 5, the phase transition is of second-order at any field magnitude, except for degree distributions with gamma = 3 when the transition is of infinite order at K-c = 0 independent of the random fields. Contrary to the Ising model, even strong Gaussian random fields do not suppress the second-order phase transition in both complete graphs and scale-free networks, although the fields increase the critical coupling for gamma > 3. Our simulations support these analytical results.