scholarly journals Configurable Offline Sensor Placement Identification for a Medical Device Monitoring Parkinson’s Disease

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7801
Author(s):  
Nicholas Kostikis ◽  
George Rigas ◽  
Spyridon Konitsiotis ◽  
Dimitrios I. Fotiadis
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Medical Device ◽  
Sensor Placement ◽  
Body Sensor Networks ◽  
Research Orientation ◽  
Average Accuracy ◽  
Fitness And Health ◽  
Term Data

Sensor placement identification in body sensor networks is an important feature, which could render such a system more robust, transparent to the user, and easy to wear for long term data collection. It can be considered an active measure to avoid the misuse of a sensing system, specifically as these platforms become more ubiquitous and, apart from their research orientation, start to enter industries, such as fitness and health. In this work we discuss the offline, fixed class, sensor placement identification method implemented in PDMonitor®, a medical device for long-term Parkinson’s disease monitoring at home. We analyze the stepwise procedure used to accurately identify the wearables depending on how many are used, from two to five, given five predefined body positions. Finally, we present the results of evaluating the method in 88 subjects, 61 Parkinson’s disease patients and 27 healthy subjects, when the overall average accuracy reached 99.1%.

scholarly journals PhoneMD: Learning to Diagnose Parkinson’s Disease from Smartphone Data

2019 ◽  
Vol 33 ◽  
pp. 1118-1125 ◽  
Author(s):  
Patrick Schwab ◽  
Walter Karlen
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Assessment ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Term Data

Parkinson’s disease is a neurodegenerative disease that can affect a person’s movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson’s disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson’s disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson’s disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson’s disease.

scholarly journals A New Implantable Closed-Loop Clinical Neural Interface: First Application in Parkinson’s Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Mattia Arlotti ◽  
Matteo Colombo ◽  
Andrea Bonfanti ◽  
Tomasz Mandat ◽  
Michele Maria Lanotte ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Closed Loop ◽  
Distributed Data ◽  
Brain Disorders ◽  
Neural Signals ◽  
Clinical Benefits ◽  
Term Data ◽  
Deep Brain

Deep brain stimulation (DBS) is used for the treatment of movement disorders, including Parkinson’s disease, dystonia, and essential tremor, and has shown clinical benefits in other brain disorders. A natural path for the improvement of this technique is to continuously observe the stimulation effects on patient symptoms and neurophysiological markers. This requires the evolution of conventional deep brain stimulators to bidirectional interfaces, able to record, process, store, and wirelessly communicate neural signals in a robust and reliable fashion. Here, we present the architecture, design, and first use of an implantable stimulation and sensing interface (AlphaDBSR System) characterized by artifact-free recording and distributed data management protocols. Its application in three patients with Parkinson’s disease (clinical trial n. NCT04681534) is shown as a proof of functioning of a clinically viable implanted brain-computer interface (BCI) for adaptive DBS. Reliable artifact free-recordings, and chronic long-term data and neural signal management are in place.

Neurobehavioral Consequences Associated with Long Term Tramadol Utilization and Pathological Mechanisms

2020 ◽  
Vol 18 (10) ◽  
pp. 758-768 ◽  
Author(s):  
Khadga Raj ◽  
Pooja Chawla ◽  
Shamsher Singh
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Serotonin Reuptake ◽  
Selective Serotonin Reuptake Inhibitors ◽  
Serotonin Syndrome ◽  
Dopamine Synthesis ◽  
Synthetic Analog

: Tramadol is a synthetic analog of codeine used to treat pain of moderate to severe intensity and is reported to have neurotoxic potential. At therapeutic dose, tramadol does not cause major side effects in comparison to other opioid analgesics, and is useful for the management of neurological problems like anxiety and depression. Long term utilization of tramadol is associated with various neurological disorders like seizures, serotonin syndrome, Alzheimer’s disease and Parkinson’s disease. Tramadol produces seizures through inhibition of nitric oxide, serotonin reuptake and inhibitory effects on GABA receptors. Extensive tramadol intake alters redox balance through elevating lipid peroxidation and free radical leading to neurotoxicity and produces neurobehavioral deficits. During Alzheimer’s disease progression, low level of intracellular signalling molecules like cGMP, cAMP, PKC and PKA affect both learning and memory. Pharmacologically tramadol produces actions similar to Selective Serotonin Reuptake Inhibitors (SSRIs), increasing the concentration of serotonin, which causes serotonin syndrome. In addition, tramadol also inhibits GABAA receptors in the CNS has been evidenced to interfere with dopamine synthesis and release, responsible for motor symptoms. The reduced level of dopamine may produce bradykinesia and tremors which are chief motor abnormalities in Parkinson’s Disease (PD).

The mechanistic role of thymoquinone in Parkinson's disease: focus on neuroprotection in pre-clinical studies

2021 ◽  
Vol 14 ◽  
Author(s):  
Mohammad Najim Uddin ◽  
Mohammad Injamul Hoq ◽  
Israt Jahan ◽  
Shafayet Ahmed Siddiqui ◽  
Chayan Dhar Clinton ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Studies ◽  
Nigella Sativa ◽  
Biological Effects ◽  
Neuroprotective Activity ◽  
Substantia Nigra Pars Compacta ◽  
Movement Difficulties

: Thymoquinone (TQ) is one of the leading phytochemicals, which is abundantly found in Nigella sativa L. seeds. TQ exhibited various biological effects such as antioxidant, anti-inflammatory, antimicrobial, and anti-tumoral in several pre-clinical studies. Parkinson's disease (PD) is a long-term neurodegenerative disease with movement difficulties, and the common feature of neurodegeneration in PD patients is caused by dopaminergic neural damage in the substantia nigra pars compacta. The neuroprotective activity of TQ has been studied in various neurological disorders. TQ-mediated neuroprotection against PD yet to be reported in a single frame; therefore, this review is intended to narrate the potentiality of TQ in the therapy of PD. TQ has been shown to protect against neurotoxins via amelioration of neuroinflammation, oxidative stress, apoptosis, thereby protects neurodegeneration in PD models. TQ could be an emerging therapeutic intervention in PD management, but mechanistic studies have been remained to be investigated to clarify its neuroprotective role.

scholarly journals Focal Subthalamic Atrophy After Long‐Term Deep Brain Stimulation in Parkinson's Disease

2021 ◽  
Author(s):  
Ellen Gelpi ◽  
Christine Haberler ◽  
Alexander Micko ◽  
Andrea Polt ◽  
Andreas Amon ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Deep Brain

Long term mortality of patients with Parkinson’s disease treated with deep brain stimulation in a reference center

2021 ◽  
Vol 202 ◽  
pp. 106486
Author(s):  
Ana Luísa Rocha ◽  
Ana Oliveira ◽  
Cláudia Sousa ◽  
Pedro Monteiro ◽  
Maria José Rosas ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Reference Center ◽  
Deep Brain ◽  
Long Term Mortality

scholarly journals Extradural Motor Cortex Stimulation in Parkinson’s Disease: Long-Term Clinical Outcome

2021 ◽  
Vol 11 (4) ◽  
pp. 416
Author(s):  
Carla Piano ◽  
Francesco Bove ◽  
Delia Mulas ◽  
Enrico Di Stasio ◽  
Alfonso Fasano ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Motor Cortex ◽  
Clinical Outcome ◽  
Motor Cortex Stimulation ◽  
Dopaminergic Therapy

Previous investigations have reported on the motor benefits and safety of chronic extradural motor cortex stimulation (EMCS) for patients with Parkinson’s disease (PD), but studies addressing the long-term clinical outcome are still lacking. In this study, nine consecutive PD patients who underwent EMCS were prospectively recruited, with a mean follow-up time of 5.1 ± 2.5 years. As compared to the preoperatory baseline, the Unified Parkinson’s Disease Rating Scale (UPDRS)-III in the off-medication condition significantly decreased by 13.8% at 12 months, 16.1% at 18 months, 18.4% at 24 months, 21% at 36 months, 15.6% at 60 months, and 8.6% at 72 months. The UPDRS-IV decreased by 30.8% at 12 months, 22.1% at 24 months, 25% at 60 months, and 36.5% at 72 months. Dopaminergic therapy showed a progressive reduction, significant at 60 months (11.8%). Quality of life improved by 18.0% at 12 months, and 22.4% at 60 months. No surgical complication, cognitive or behavioral change occurred. The only adverse event reported was an infection of the implantable pulse generator pocket. Even in the long-term follow-up, EMCS was shown to be a safe and effective treatment option in PD patients, resulting in improvements in motor symptoms and quality of life, and reductions in motor complications and dopaminergic therapy.

Evidence for Early and Regular Physical Therapy and Exercise in Parkinson's Disease

2021 ◽  
Author(s):  
Terry D. Ellis ◽  
Cristina Colón-Semenza ◽  
Tamara R. DeAngelis ◽  
Cathi A. Thomas ◽  
Marie-Hélène Saint Hilaire ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Physical Therapy ◽  
Secondary Prevention ◽  
Medical Management ◽  
Long Term Outcomes ◽  
High Prevalence ◽  
Growing Body ◽  
Prevention Model

AbstractAdvances in medical management of Parkinson's disease (PD) have resulted in living longer with disability. Although disability worsens over the course of the disease, there are signs of disability even in the early stages. Several studies reveal an early decline in gait and balance and a high prevalence of nonmotor signs in the prodromal period that contribute to early disability. There is a growing body of evidence revealing the benefits of physical therapy and exercise to mitigate motor and nonmotor signs while improving physical function and reducing disability. The presence of early disability coupled with the benefits of exercise suggests that physical therapy should be initiated earlier in the disease. In this review, we present the evidence revealing early disability in PD and the effectiveness of physical therapy and exercise, followed by a discussion of a secondary prevention model of rehabilitation to reduce early disability and optimize long-term outcomes.

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