scholarly journals Unobtrusive detection of Parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniques

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexandros Papadopoulos ◽  
Dimitrios Iakovakis ◽  
Lisa Klingelhoefer ◽  
Sevasti Bostantjopoulou ◽  
K. Ray Chaudhuri ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Disease Onset ◽  
Sensor Data ◽  
Validation Dataset ◽  
Fine Motor ◽  
Motor Symptoms ◽  
Total Contribution ◽  
Sensitivity Specificity

AbstractParkinson’s Disease (PD) is the second most common neurodegenerative disorder, affecting more than 1% of the population above 60 years old with both motor and non-motor symptoms of escalating severity as it progresses. Since it cannot be cured, treatment options focus on the improvement of PD symptoms. In fact, evidence suggests that early PD intervention has the potential to slow down symptom progression and improve the general quality of life in the long term. However, the initial motor symptoms are usually very subtle and, as a result, patients seek medical assistance only when their condition has substantially deteriorated; thus, missing the opportunity for an improved clinical outcome. This situation highlights the need for accessible tools that can screen for early motor PD symptoms and alert individuals to act accordingly. Here we show that PD and its motor symptoms can unobtrusively be detected from the combination of accelerometer and touchscreen typing data that are passively captured during natural user-smartphone interaction. To this end, we introduce a deep learning framework that analyses such data to simultaneously predict tremor, fine-motor impairment and PD. In a validation dataset from 22 clinically-assessed subjects (8 Healthy Controls (HC)/14 PD patients with a total data contribution of 18.305 accelerometer and 2.922 typing sessions), the proposed approach achieved 0.86/0.93 sensitivity/specificity for the binary classification task of HC versus PD. Additional validation on data from 157 subjects (131 HC/26 PD with a total contribution of 76.528 accelerometer and 18.069 typing sessions) with self-reported health status (HC or PD), resulted in area under curve of 0.87, with sensitivity/specificity of 0.92/0.69 and 0.60/0.92 at the operating points of highest sensitivity or specificity, respectively. Our findings suggest that the proposed method can be used as a stepping stone towards the development of an accessible PD screening tool that will passively monitor the subject-smartphone interaction for signs of PD and which could be used to reduce the critical gap between disease onset and start of treatment.

scholarly journals Levodopa improves handwriting and instrumental tasks in previously treated patients with Parkinson’s disease

2020 ◽  
Vol 127 (10) ◽  
pp. 1369-1376
Author(s):  
Thomas Müller ◽  
Ali Harati
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Motor Behavior ◽  
Fine Motor ◽  
Motor Symptoms ◽  
Execution Speed ◽  
Previously Treated Patients ◽  
Previously Treated ◽  
And Performance ◽  
The Individual

Abstract Motor symptoms in patients with Parkinson’s disease may be determined with instrumental tests and rating procedures. Their outcomes reflect the functioning and the impairment of the individual patient when patients are tested off and on dopamine substituting drugs. Objectives were to investigate whether the execution speed of a handwriting task, instrumentally assessed fine motor behavior, and rating scores improve after soluble levodopa application. 38 right-handed patients were taken off their regular drug therapy for at least 12 h before scoring, handwriting, and performance of instrumental devices before and 1 h after 100 mg levodopa intake. The outcomes of all performed procedures improved. The easy-to-perform handwriting task and the instrumental tests demand for fast and precise execution of movement sequences with considerable cognitive load in the domains' attention and concentration. These investigations may serve as additional tools for the testing of the dopaminergic response.

scholarly journals Non–motor Symptoms in Parkinson’s Disease: It’s Relationship with Age of Onset, Duration and Stage of Disease and the Dose and Duration of Levodopa Usage

1970 ◽  
Vol 21 (1) ◽  
pp. 12-17
Author(s):  
M Ahmed Ali ◽  
Anisul Haque ◽  
AKM Anwarulla ◽  
Quamruddin Ahmad
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Psychiatric Symptoms ◽  
Disease Onset ◽  
Motor Symptoms ◽  
Autonomic Symptoms ◽  
Prolonged Duration ◽  
Relationship Of ◽  
The Relationship ◽  
Non Motor Symptoms

Parkinson's disease is a disease of motor manifestations but non-motor symptoms are also common in Parkinson's disease. Little emphasis is put on non-motor symptoms of PD and there is little data on the relationship of non-motor symptoms to different aspects of the patient and the disease. In this study the relationship of non-motor symptoms to age at onset, duration and stage of the disease, and dose and duration of levodopa use are studied.128 patients of PD were studied for non-motor symptoms. 111 patients had different types of sensory, autonomic or psychiatric symptoms. Sensory and autonomic symptoms were significantly more common in patients with early age of disease onset and more prolonged duration of the disease, but psychiatric symptoms had no relationship with these factors. In this study it was also found that the frequencies of non-motor symptoms were related to the stage of the disease, longer the duration of the disease more and more non-motor symptoms appear so that 100% patients in stage 5 of the disease had non-motor symptoms. Also sensory and autonomic symptoms were significantly more common in patients with longer duration and higher dose of levodopa use but psychiatric symptoms were significantly commoner in patients with prolonged duration of levodopa use but not to dose of levodopa used.   doi: 10.3329/taj.v21i1.3211 TAJ 2008; 21(1): 12-17

scholarly journals Assessment of real life eating difficulties in Parkinson’s disease patients by measuring plate to mouth movement elongation with inertial sensors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Konstantinos Kyritsis ◽  
Petter Fagerberg ◽  
Ioannis Ioakimidis ◽  
K. Ray Chaudhuri ◽  
Heinz Reichmann ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Eating Behavior ◽  
Neurodegenerative Disorder ◽  
Real Life ◽  
Sensor Data ◽  
Measurement Unit ◽  
Motor Symptoms ◽  
Drug Induced ◽  
In The Wild

AbstractParkinson’s disease (PD) is a neurodegenerative disorder with both motor and non-motor symptoms. Despite the progressive nature of PD, early diagnosis, tracking the disease’s natural history and measuring the drug response are factors that play a major role in determining the quality of life of the affected individual. Apart from the common motor symptoms, i.e., tremor at rest, rigidity and bradykinesia, studies suggest that PD is associated with disturbances in eating behavior and energy intake. Specifically, PD is associated with drug-induced impulsive eating disorders such as binge eating, appetite-related non-motor issues such as weight loss and/or gain as well as dysphagia—factors that correlate with difficulties in completing day-to-day eating-related tasks. In this work we introduce Plate-to-Mouth (PtM), an indicator that relates with the time spent for the hand operating the utensil to transfer a quantity of food from the plate into the mouth during the course of a meal. We propose a two-step approach towards the objective calculation of PtM. Initially, we use the 3D acceleration and orientation velocity signals from an off-the-shelf smartwatch to detect the bite moments and upwards wrist micromovements that occur during a meal session. Afterwards, we process the upwards hand micromovements that appear prior to every detected bite during the meal in order to estimate the bite’s PtM duration. Finally, we use a density-based scheme to estimate the PtM durations distribution and form the in-meal eating behavior profile of the subject. In the results section, we provide validation for every step of the process independently, as well as showcase our findings using a total of three datasets, one collected in a controlled clinical setting using standardized meals (with a total of 28 meal sessions from 7 Healthy Controls (HC) and 21 PD patients) and two collected in-the-wild under free living conditions (37 meals from 4 HC/10 PD patients and 629 meals from 3 HC/3 PD patients, respectively). Experimental results reveal an Area Under the Curve (AUC) of 0.748 for the clinical dataset and 0.775/1.000 for the in-the-wild datasets towards the classification of in-meal eating behavior profiles to the PD or HC group. This is the first work that attempts to use wearable Inertial Measurement Unit (IMU) sensor data, collected both in clinical and in-the-wild settings, towards the extraction of an objective eating behavior indicator for PD.

scholarly journals Impulse control disorders in Parkinson’s disease

2021 ◽  
Vol 21 (1) ◽  
pp. 30-35
Author(s):  
Mateusz Toś
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Impulse Control ◽  
Atypical Antipsychotics ◽  
Disease Onset ◽  
Impulse Control Disorders ◽  
Motor Symptoms ◽  
Mesolimbic Pathway ◽  
Non Motor Symptoms ◽  
Antiparkinsonian Drugs

Parkinson’s disease is a neurodegenerative disease characterised by typical motor symptoms and a range of non-motor symptoms, among which impulse control disorders, defined by an inability to resist temptations, impulses or urges, despite them being potentially harmful to the patient or caregivers, are gaining an increasing research interest. The most common compulsive activities include pathological gambling, hyper-sexuality, compulsive buying, and binge eating. The prevalence of impulse control disorders varies greatly depending on the country where the study was conducted, probably due to cultural and socioeconomic factors or the research methods used. Non-ergotamine dopamine agonists, and to a lesser extent highdose L-dopa and other antiparkinsonian drugs, are considered to be major risk factors for the development of impulse control disorders. Young age of patients, male gender, and early age of disease onset also increase the risk of developing this type of disorder. A probable cause of impulse control disorders is a state of dopaminergic overstimulation within the mesolimbic pathway and frontal-striatal circuit. The management of impulse control disorders is particularly challenging in view of the possible worsening of motor symptoms. The primary strategy remains dose reduction, discontinuation or switching from a dopamine agonist to another drug. If this type of intervention has failed, it is advisable to add atypical antipsychotics or antiepileptic drugs. Because of the low detection rate of impulse control disorders and their potentially devastating impact on patients’ personal and family lives, every clinician managing patients with Parkinson’s disease should be particularly vigilant for the presence of such disorders.

scholarly journals Finger Tapping Measures for Parkinson’s Disease: Preliminary Evaluation of an Android Application for Data Collection in Australia

2021 ◽  
Author(s):  
Hanna Suominen ◽  
Mehika Manocha ◽  
Jane Desborough ◽  
Anne Parkinson ◽  
Deborah Apthorp
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Data Collection ◽  
Finger Tapping ◽  
Fine Motor ◽  
Preliminary Evaluation ◽  
Motor Symptoms ◽  
User Evaluation ◽  
Android Application ◽  
Chronic Disorder

Parkinson’s Disease (PD) is a progressive chronic disorder with a high misdiagnosis rate. Because finger-tapping tasks correlate with its fine-motor symptoms, they could be used to help diagnose and assess PD. We first designed and developed an Android application to perform finger-tapping tasks without trained supervision, which is not always feasible for patients. Then, we conducted a preliminary user evaluation in Australia with six patients clinically diagnosed with PD and sixteen controls without PD. The application could be used in research and healthcare for regular symptom and progression assessment and feedback.

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

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