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The increasing age of our society is connected to a rising number of people suffering from disorders. One such disorder is Parkinson’s disease (PD). Predictions indicate that the number of individuals affected by PD will more, than double in the future. Neurologists and data scientists consider handwriting as one of the motor symptoms of PD and recognize it as a valuable resource for detecting this disorder. Within this framework, we introduce an innovative system for Parkinson’s disease detection, which encompasses several key stages. The process commences with data augmentation and preprocessing, subsequently leading to the segmentation of online handwriting into Beta strokes. Following that, feature extraction is carried out utilizing the Beta-elliptical approach and the fuzzy perceptual detector. Finally, we employ bidirectional long short-term memory (BLSTM) for the classification task. To assess the performance of our system, we created a new online Arabic handwriting dataset designed for detecting Parkinson’s disease. The results we obtained affirm the efficacy of our proposed system. Through comprehensive evaluations conducted on the PaHaW dataset, we achieved good accuracy, thereby highlighting that our system surpasses the performance of existing systems.

Introduction: Impaired sit-to-stand and stand-to-sit movements (postural transitions, PTs) in patients with Parkinson's disease (PD) and older adults (OA) are associated with risk of falling and reduced quality of life. Inertial measurement units (IMUs, also called “wearables”) are powerful tools to monitor PT kinematics. The purpose of this study was to develop and validate an algorithm, based on a single IMU positioned at the lower back, for PT detection and description in the above-mentioned groups in a home-like environment.Methods: Four PD patients (two with dyskinesia) and one OA served as algorithm training group, and 21 PD patients (16 without and 5 with dyskinesia) and 11 OA served as test group. All wore an IMU on the lower back and were videotaped while performing everyday activities for 90–180 min in a non-standardized home-like environment. Accelerometer and gyroscope signals were analyzed using discrete wavelet transformation (DWT), a six degrees-of-freedom (DOF) fusion algorithm and vertical displacement estimation.Results: From the test group, 1,001 PTs, defined by video reference, were analyzed. The accuracy of the algorithm for the detection of PTs against video observation was 82% for PD patients without dyskinesia, 47% for PD patients with dyskinesia and 85% for OA. The overall accuracy of the PT direction detection was comparable across groups and yielded 98%. Mean PT duration values were 1.96 s for PD patients and 1.74 s for OA based on the algorithm (p < 0.001) and 1.77 s for PD patients and 1.51 s for OA based on clinical observation (p < 0.001).Conclusion: Validation of the PT detection algorithm in a home-like environment shows acceptable accuracy against the video reference in PD patients without dyskinesia and controls. Current limitations are the PT detection in PD patients with dyskinesia and the use of video observation as the video reference. Potential reasons are discussed.

Introduction: Impaired sit-to-stand and stand-to-sit movements (postural transitions, PTs) in patients with Parkinson's disease (PD) and older adults (OA) are associated with risk of falling and reduced quality of life. Inertial measurement units (IMUs, also called “wearables”) are powerful tools to monitor PT kinematics. The purpose of this study was to develop and validate an algorithm, based on a single IMU positioned at the lower back, for PT detection and description in the above-mentioned groups in a home-like environment. Methods: Four PD patients (two with dyskinesia) and one OA served as algorithm training group, and 21 PD patients (16 without and 5 with dyskinesia) and 11 OA served as test group. All wore an IMU on the lower back and were videotaped while performing everyday activities for 90–180 min in a non-standardized home-like environment. Accelerometer and gyroscope signals were analyzed using discrete wavelet transformation (DWT), a six degrees-of-freedom (DOF) fusion algorithm and vertical displacement estimation. Results: From the test group, 1,001 PTs, defined by video reference, were analyzed. The accuracy of the algorithm for the detection of PTs against video observation was 82% for PD patients without dyskinesia, 47% for PD patients with dyskinesia and 85% for OA. The overall accuracy of the PT direction detection was comparable across groups and yielded 98%. Mean PT duration values were 1.96 s for PD patients and 1.74 s for OA based on the algorithm (p < 0.001) and 1.77 s for PD patients and 1.51 s for OA based on clinical observation (p < 0.001). Conclusion: Validation of the PT detection algorithm in a home-like environment shows acceptable accuracy against the video reference in PD patients without dyskinesia and controls. Current limitations are the PT detection in PD patients with dyskinesia and the use of video observation as the video reference. Potential reasons are discussed. [ABSTRACT FROM AUTHOR]

ObjectivesTo evaluate the effect of deep brain stimulation of the subthalamic nucleus (STN-DBS) on alexithymia, a deficit in affective regulation, comparing patients with Parkinson’s disease (PD) submitted to STN-DBS (DBS group) to PD patients not yet treated with STN-DBS (pre-DBS group) and to healthy participants (C group). MethodsWe recruited 27 consecutive STN-DBS PD patients, 38 consecutive pre-DBS patients and 27 healthy participants. Patients were assessed for alexithymia (Toronto Alexithymia Scale), depression (Beck Depression Inventory) and cognitive functions (reasoning, memory, attentional and executive tests). ResultsThe DBS patients performed worse than the pre-DBS patients in the Corsi’s Block-Tapping Test, in the phonemic fluency task and in the Frontal Assessment Battery. 29.6% DBS and 31.6% pre-DBS patients resulted alexithymic, compared with14.8% in the C group. Results pointed out significantly higher alexithymia scores in both the DBS and pre-DBS groups compared with the C group, while no difference emerged between the DBS and pre-DBS groups. Pre-DBS group showed a significantly higher BDI score than the C group, while DBS group did not. ConclusionAlthough the results suggest that STN-DBS does not affect alexithymia, both the DBS and pre-DBS patients reported higher prevalence (about 30%) of alexithymia than did healthy subjects (14.8%).

We have developed and evaluated several dynamical machine-learning algorithms that were designed to track the presence and severity of tremor and dyskinesia with 1-s resolution by analyzing signals collected from Parkinson's disease (PD) patients wearing small numbers of hybrid sensors with both 3-D accelerometeric and surface-electromyographic modalities. We tested the algorithms on a 44-h signal database built from hybrid sensors worn by eight PD patients and four healthy subjects who carried out unscripted and unconstrained activities of daily living in an apartment-like environment. Comparison of the performance of our machine-learning algorithms against independent clinical annotations of disorder presence and severity demonstrates that, despite their differing approaches to dynamic pattern classification, dynamic neural networks, dynamic support vector machines, and hidden Markov models were equally effective in keeping error rates of the dynamic tracking well below 10%. A common set of experimentally derived signal features were used to train the algorithm without the need for subject-specific learning. We also found that error rates below 10% are achievable even when our algorithms are tested on data from a sensor location that is different from those used in algorithm training. [ABSTRACT FROM AUTHOR]

In this study, the use of an instrumented balance test based on inertial sensors was evaluated in patients with Parkinson's disease (PD). We aimed to objectively characterize motor subtypes of PD [tremor dominant (TD) and postural instability gait difficulty (PIGD)], to help to quantitatively classify the PD subjects into motor subtypes. Subjects were studied performing postural tests, while wearing a device including a tri-axial accelerometer on the lower back, in four different experimental conditions that depended on feet position (feet-together or semi-tandem) and vision (eyes open or closed). Postural measures, after a reliability check, were tested to identify their sensitivity to the disease, to the PD subtypes, and to the experimental conditions. The results highlight the possibility of distinguishing between the TD and PIGD subtypes by means of objective postural measures that are able to detect tremor and PIGD features and are able to classify a subject as TD or PIGD with good accuracy. Feet position influences frequency measures, whereas eyes closure influences the displacement measures and enhances differences between PD and control subjects, suggesting that postural displacement measures may be capable of detecting different adaptation processes to external sensory conditions between patients with PD and control subjects. [ABSTRACT FROM AUTHOR]

The microbiome is the ecological community of commensal, symbiotic, and pathogenic microorganisms that share our body space (Medical and Health Genomics, 2016, page 15-28). The human gut is the location where the maximum number of microorganisms can be found. Among the different microorganisms they can be broadly classified into two groups: the beneficial and harmful. In the human gut there is always a balance between the beneficial and the opportunistic microorganism which maintains human health. However, if the balance is not maintained and homeostasis is disturbed, with an increase in opportunistic microorganisms, it may result in various diseases like inflammatory bowel disease, irritable bowel disease, ulcerative colitis, Crohn's disease, colorectal cancer, metabolic disorders and neurodegenerative diseases including motor neuron diseases. In the present chapter we discuss the role of gut bacteria in motor neuron diseases like multiple sclerosis, Parkinson's disease and amyotrophic lateral sclerosis., (© 2020 Elsevier Inc. All rights reserved.)

Objectives: To evaluate the effect of deep brain stimulation of the subthalamic nucleus (STN-DBS) on alexithymia, a deficit in affective regulation, comparing patients with Parkinson's disease (PD) submitted to STN-DBS (DBS group) to PD patients not yet treated with STN-DBS (pre-DBS group) and to healthy participants (C group)., Methods: We recruited 27 consecutive STN-DBS PD patients, 38 consecutive pre-DBS patients and 27 healthy participants. Patients were assessed for alexithymia (Toronto Alexithymia Scale), depression, [beck depression inventory (BDI)], and cognitive functions (reasoning, memory, attentional, and executive tests)., Results: The DBS patients performed worse than the pre-DBS patients in the corsi's block-tapping test, in the phonemic fluency task and in the Frontal Assessment Battery. Around 30% of DBS (29.6%) and pre-DBS (31.6%) patients resulted alexithymic, compared with 14.8% in the C group. The results pointed out significantly higher alexithymia scores in both the DBS and pre-DBS groups compared with the C group, while no difference emerged between the DBS and pre-DBS groups. Pre-DBS group showed a significantly higher BDI score than the C group, while DBS group did not., Conclusion: Although the results suggest that STN-DBS does not affect alexithymia, both the DBS and pre-DBS patients reported higher prevalence (about 30%) of alexithymia than did healthy subjects (14.8%).