Freezing of gait in Parkinson's disease: Classification using computational intelligence

Parkinson's disease (PD) is a neurodegenerative disease represented by the progressive loss of dopamine producing neurons, with motor and non-motor symptoms that may be hard to distinguish from other disorders. Affecting millions of people across the world, its symptoms include bradykinesia, tremors, depression, rigidity, postural instability, cognitive decline, and falls. Furthermore, changes in gait can be used as a primary diagnosis factor. A dataset is described that records data on healthy individuals and on PD patients, including those who experience freezing of gait, in both the ON and OFF-medication states. The dataset is comprised of data for four separate tasks: voluntary stop, timed up and go, simple motor task, and dual motor and cognitive task. Seven different classifiers are applied to two problems relating to this data. The first problem is to distinguish PD patients from healthy individuals, both overall and per task. The second problem is to determine the effectiveness of medication. A thorough analysis on the classifiers and their results is performed. Overall, multilayer perceptron and decision tree provide the most consistent results.

A Novel Physical Mobility Task to Assess Freezers in Parkinson's Disease

Freezing of gait (FOG), one of the most disabling features of Parkinson's disease (PD), is a brief episodic absence or marked reduction in stride progression despite the intention to walk. Progressively more people who experience FOG restrict their walking and reduce their level of physical activity. The purpose of this study is to develop and validate a physical mobility task that induces freezing of gait in a controlled environment, employing known triggers of FOG episodes according to the literature. To validate the physical mobility tasks, we recruited 10 volunteers that suffered PD-associated freezing (60.6 ± 7.29 years-old) with new FOG-Q ranging from 12 to 26. The validation of the proposed method was carried out using inertial sensors and video recordings. All subjects were assessed during the OFF and ON medication states. The total number of FOG occurrences during data collection was 144. The proposed tasks were able to trigger 120 FOG episodes, while the TUG test caused 24. The Inertial Measurement Unit (IMU) with accelerometer and gyroscope could not only detect FOG episodes but also allowed us to visualize the three types of FOG: akinesia, festination and trembling in place.

Wrist Movement Variability Assessment in Individuals with Parkinson's Disease

(1) Background: Parkinson’s disease (PD) is a neurodegenerative disorder represented by the progressive loss of dopamine-producing neurons, it decreases the individual’s motor functions and affects the execution of movements. There is a real need to include quantitative techniques and reliable methods to assess the evolution of PD. (2) Methods: This cross-sectional study assessed the variability of wrist RUD (radial and ulnar deviation) and FE (flexion and extension) movements measured by two pairs of capacitive sensors (PS25454 EPIC). The hypothesis was that PD patients have less variability in wrist movement execution than healthy individuals. The data was collected from 29 participants (age: 62.13 ± 9.7) with PD and 29 healthy individuals (60.70 ± 8). Subjects performed the experimental tasks at normal and fast speeds. Six features that captured the amplitude of the hand movements around two axes were estimated from the collected signals. (3) Results: The movement variability was greater for healthy individuals than for PD patients (p < 0.05). (4) Conclusion: The low variability seen in the PD group may indicate they execute wrist RUD and FE in a more restricted way. The variability analysis proposed here could be used as an indicator of patient progress in therapeutic programs and required changes in medication dosage.

Non-pharmacological treatments for COVID-19: current status and consensus

Purpose

COVID-19 is a disease caused by SARS-CoV-2 (coronavirus type 2 of the severe acute respiratory syndrome), isolated in China, in December 2019. The strategy currently used by physicians is to control disease and to treat symptoms, including non-pharmacological treatments, as there is still no specific treatment for COVID-19. Thus, the aim of this article is to carry out a systematic review about non-pharmacological treatments used for COVID-19, addressing current status and consensus found in the literature.

Methods

Three databases were consulted for evidence referring to the drugs indicated for COVID-19 (Cochrane Central, MEDLINE and Embase). The following terms and combinations were used: ((“2019-nCoV” OR 2019nCoV OR nCoV2019 OR “nCoV-2019” OR “COVID-19” OR COVID19 OR “HCoV-19” OR HCoV19 OR CoV OR “2019 novel*” OR Ncov OR “n-cov” OR “SARS-CoV-2” OR “SARSCoV-2” OR “SARSCoV2” OR “SARSCoV2” OR SARSCov19 OR “SARS-Cov19” OR “SARS-Cov-19”) OR “severe acute respiratory syndrome*” OR ((corona* OR corono*) AND (virus* OR viral* OR virinae*)) AND ((“lung injury”) OR (“ventilation use”) OR (“respiratory injuries” OR prone)) AND (treatment)) NOT Drugs NOT medicines NOT antivirals.

Results

A total of 28 articles were selected. These articles adopted one or more treatment methods for patients with severe cases of COVID-19, i.e., oxygen therapy, prone position, inhaled nitric oxide, intravenous infusion, passive immunotherapy, mesenchymal stem cells (MSC).

Conclusion

There is still no specific treatment approved for patients with COVID-19. The available evidence is not able yet to indicate the benefits or harms of non-pharmacological treatments, but some studies show that some treatments can play an important role in relation to COVID-19. The current consensus among researchers is that several studies using a randomized clinical trial should be carried out to provide evidence of safety and efficacy of the proposed treatments.