Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.

The aim of this study was to analyze the efficacy of a cognitive training program on cognitive performance and quality of life in nondemented Parkinson's disease patients. Participants who met UK Brain Bank diagnosis criteria for Parkinson's disease, with I-III Hoehn & Yahr, aged 50-80, and nondemented (Mini-Mental State Examination ≥ 23) were recruited. Patient's cognitive performance and functional and quality-of-life measures were assessed with standardized neuropsychological tests and scales at baseline and after 4 weeks. Subjects were randomly and blindly allocated by age and premorbid intelligence (Vocabulary, Wechsler Adult Intelligence Scale-III) into 2 groups: an experimental group and a control group. The experimental group received 4 weeks of 3 weekly 45-minute sessions using multimedia software and paper-and-pencil cognitive exercises, and the control group received speech therapy. A total of 28 patients were analyzed. Compared with the control group participants (n = 12), the experimental group participants (n = 16) demonstrated improved performance in tests of attention, information processing speed, memory, visuospatial and visuoconstructive abilities, semantic verbal fluency, and executive functions. There were no observable benefits in self-reported quality of life or cognitive difficulties in activities of daily living. We concluded that intensive cognitive training may be a useful tool in the management of cognitive functions in Parkinson's disease. © 2011 Movement Disorder Society.

Among Parkinson’s disease (PD) motor symptoms, freezing of gait (FOG) may\ud be the most incapacitating. FOG episodes may result in falls and reduce patients’\ud quality of life. Accurate assessment of FOG would provide objective information\ud to neurologists about the patient’s condition and the symptom’s characteristics,\ud while it could enable non-pharmacologic support based on rhythmic\ud cues.\ud This paper is, to the best of our knowledge, the first study to propose a\ud deep learning method for detecting FOG episodes in PD patients. This model\ud is trained using a novel spectral data representation strategy which considers information from both the previous and current signal windows. Our approach\ud was evaluated using data collected by a waist-placed inertial measurement unit\ud from 21 PD patients who manifested FOG episodes. These data were also employed\ud to reproduce the state-of-the-art methodologies, which served to perform\ud a comparative study to our FOG monitoring system.\ud The results of this study demonstrate that our approach successfully outperforms\ud the state-of-the-art methods for automatic FOG detection. Precisely, the\ud deep learning model achieved 90% for the geometric mean between sensitivity\ud and specificity, whereas the state-of-the-art methods were unable to surpass the\ud 83% for the same metric.Peer ReviewedPostprint (published version

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Though antiretroviral therapy attenuates neurocognitive disruption, impairment is still observed. We studied the nadir CD4 cell count as a predictor of neurocognitive changes. This cross-sectional study assessed 64 HIV-infected patients in two groups: G1 (n = 26, nadir CD4 < or =200 cells/ml) and G2 (n = 38, nadir CD4 >200 cells/ml). Percentages of patients showing neurocognitive impairment were compared according to different nadir CD4 cutoffs (200, 250, 300, and 350 cells/ml). From G2, we also took the subgroup of patients receiving treatment (G3) and compared this group with G1, in which all patients were being treated. Demographic and clinical variables were evaluated, as were differences in neurocognitive function. Neurocognitive impairment tended to be more prevalent in G1 [19 patients (73.1%)] than in G2 [20 (52.6%), p = 0.123]. When nadir CD4 cutoffs were compared, there was a trend toward more impaired subjects as the CD4 nadir decreased. Significantly different functioning was found in attention/working memory (digit span backward, p = 0.032) and executive functions (trail making test, part B, p = 0.020), with better performance in G2. Comparison between G1 and G3 confirmed those findings. We found differences in neurocognitive functioning in relation to nadir CD4 count in HIV-infected patients. Attention should be given to this value in the management of neurocognitive protection in HIV infection.