scholarly journals Detection of Parkinsonian Motor Symptoms Using Non-Invasive Motion Sensing Technology

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
John Sakaleros ◽  
Farzin Shamloo ◽  
Aditya Shanghavi ◽  
Anne Sereno
Keyword(s):  
Rating Scale ◽  
Movement Speed ◽  
Svm Classifier ◽  
Muscle Rigidity ◽  
Subjective Time ◽  
Rotational Acceleration ◽  
Acceleration Signal ◽  
Non Invasive ◽  
Resting Tremor ◽  
Frequency Components

Parkinson’s Disease (PD) is characterized by impaired movement, resting tremor, and muscle rigidity. The Unified PD Rating Scale (UPDRS) is a standardized protocol used by neurologists to measure progression of disease as well as evaluation of treatments. However, this examination is subjective, time consuming, and results can be affected by stress, diet, or sleep. Our goal is to develop a non-invasive device that can record objective clinically-relevant measurements during subtasks of the UPDRS to allow for remote evaluations, which would be beneficial considering the frequency of clinical visits for medication adjustments. Five healthy individuals (ages 21-59) completed UPDRS tasks 3.6 (pronation/supination of hands) and 3.17 (rest tremor amplitude). Participants performed these tasks twice, first normally and second simulating PD patients (tremor, bradykinesia, reduction of movement amplitude) after viewing example videos. Motion data including linear and angular accelerations in 3 dimensions was acquired using a lightweight wrist-mounted motion sensor. Three features were extracted: (1) Power of higher frequency components of the linear acceleration signal (rest task), as a measure of resting tremor amplitude. (2) Power of higher frequency components of the rotational acceleration signal (pronation/supination task), as a measure of bradykinesia. (3) Standard deviation of the local maxima of the rotational acceleration (pronation/supination task), as a measure of reduction in movement speed and amplitude. These features were used to correctly differentiate trials completed with and without simulated PD symptoms, using an SVM classifier with leave-one-out cross validation accuracy of 95%. These findings suggest it is possible to capture clinical features of PD using motion sensors. Future work in PD patients will examine how these measures correlate with UPDRS evaluations and whether they will be helpful in providing a quick, objective telehealth measure of progression and treatment response that can supplement current tools. 

Comprehensive analysis of resting tremor based on acceleration signals of patients with Parkinson’s disease

2021 ◽  
pp. 1-13
Author(s):  
Sen Liu ◽  
Han Yuan ◽  
Jiali Liu ◽  
Hai Lin ◽  
Cuiwei Yang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Statistical Tests ◽  
Real Life ◽  
Feature Analysis ◽  
Support Vector ◽  
Monitoring Methods ◽  
Resting Tremor ◽  
Acceleration Signals

BACKGROUND: Resting tremor is an essential characteristic in patients suffering from Parkinson’s disease (PD). OBJECTIVE: Quantification and monitoring of tremor severity is clinically important to help achieve medication or rehabilitation guidance in daily monitoring. METHODS: Wrist-worn tri-axial accelerometers were utilized to record the long-term acceleration signals of PD patients with different tremor severities rated by Unified Parkinson’s Disease Rating Scale (UPDRS). Based on the extracted features, three kinds of classifiers were used to identify different tremor severities. Statistical tests were further designed for the feature analysis. RESULTS: The support vector machine (SVM) achieved the best performance with an overall accuracy of 94.84%. Additional feature analysis indicated the validity of the proposed feature combination and revealed the importance of different features in differentiating tremor severities. CONCLUSION: The present work obtains a high-accuracy classification in tremor severity, which is expected to play a crucial role in PD treatment and symptom monitoring in real life.

scholarly journals 0794 Reduction in Restless Legs Syndrome Symptoms with Non-Invasive Peripheral Nerve Stimulation

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A302-A302
Author(s):  
J D Charlesworth ◽  
F C Baker ◽  
V Kolotovska ◽  
B Adlou ◽  
M de Zambotti ◽  
...  
Keyword(s):  
Medical Device ◽  
Restless Legs Syndrome ◽  
Therapeutic Efficacy ◽  
Rating Scale ◽  
Pharmacological Intervention ◽  
Controlled Study ◽  
Promising Alternative ◽  
Restless Legs ◽  
Non Invasive ◽  
Clinically Significant

Abstract Introduction Restless Legs Syndrome (RLS) is a sensorimotor neurological condition characterized by an uncontrollable urge to move the legs that interferes with falling and staying asleep. For the over 5 million Americans with clinically significant RLS, these symptoms occur multiple nights per week, significantly impair quality of life, increase the prevalence of depression and anxiety, and increase suicide risk. FDA-approved medications for RLS are associated with progressively worsening RLS symptoms and numerous adverse events, whereas existing medical device treatments have limited efficacy. Methods We evaluated a novel neurostimulation intervention for RLS developed by Noctrix Health; electrical stimulation was applied non-invasively and bilaterally to the peroneal nerve of patients with moderate-to-severe primary RLS. Stimulation parameters were engineered to maximize therapeutic efficacy while minimizing interference with sleep. To assess the therapeutic efficacy of this technique, we conducted a multi-site randomized patient-blinded crossover trial comparing active neurostimulation treatment to a sham device. Following a lab visit for calibration, optimization, and training, each patient was instructed to self-administer each treatment - active and sham - for 14 consecutive nights at home. Results Active neurostimulation treatment resulted in a clinically significant reduction in RLS severity of 4.2 points on the International RLS Rating Scale (IRLS) relative to sham (P<0.01), comparable to FDA-approved medications. Moreover, 79% of patients demonstrated a clinically significant improvement on the Clinical Global Impressions-Improvement scale (CGI-I) compared to 7% for sham (P<0.01). Conclusion To our knowledge, this is the first sham-controlled study demonstrating a clinically significant reduction in RLS severity resulting from a non-pharmacological intervention. This therapeutic effect was sustained over 2-weeks of in-home patient-administered usage, indicating consistent efficacy. A medical device based on this technology could be a promising alternative or complement to medications. Support Funding was provided by Noctrix Health, Inc.

scholarly journals A clinical rating scale of speech dysfunction on Parkinson's disease

1978 ◽  
Vol 25 (1) ◽  
Author(s):  
Alison K. Thompson
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Muscle Rigidity ◽  
Clinical Use ◽  
Clinical Notes ◽  
Clinical Rating ◽  
Research Findings ◽  
Clinical Rating Scale

The speech dysfunction of Parkinson's disease is complex and individually variable owing to the interaction of muscle rigidity, tremor and disturbance of movement. Eight speech dimensions which are characteristically disturbed in Parkinson's disease are discussed with reference to available research findings. In order to provide a more detailed description of the speech than could be obtained by clinical notes alone, a speech rating scale has been developed, and is presented in summarized form for clinical use. Incidence and progression of the speech dysfunction are considered in addition to the problems of assessment peculiar to the patient with Parkinson's disease.

scholarly journals An Open Source Classifier for Bed Mattress Signal in Infant Sleep Monitoring

2021 ◽  
Vol 14 ◽  
Author(s):  
Jukka Ranta ◽  
Manu Airaksinen ◽  
Turkka Kirjavainen ◽  
Sampsa Vanhatalo ◽  
Nathan J. Stevenson
Keyword(s):  
Neural Network ◽  
Open Source ◽  
Short Term Memory ◽  
Support Vector ◽  
Svm Classifier ◽  
Infant Sleep ◽  
Deep Sleep ◽  
Non Invasive ◽  
Piezo Element ◽  
Comparable Accuracy

ObjectiveTo develop a non-invasive and clinically practical method for a long-term monitoring of infant sleep cycling in the intensive care unit.MethodsForty three infant polysomnography recordings were performed at 1–18 weeks of age, including a piezo element bed mattress sensor to record respiratory and gross-body movements. The hypnogram scored from polysomnography signals was used as the ground truth in training sleep classifiers based on 20,022 epochs of movement and/or electrocardiography signals. Three classifier designs were evaluated in the detection of deep sleep (N3 state): support vector machine (SVM), Long Short-Term Memory neural network, and convolutional neural network (CNN).ResultsDeep sleep was accurately identified from other states with all classifier variants. The SVM classifier based on a combination of movement and electrocardiography features had the highest performance (AUC 97.6%). A SVM classifier based on only movement features had comparable accuracy (AUC 95.0%). The feature-independent CNN resulted in roughly comparable accuracy (AUC 93.3%).ConclusionAutomated non-invasive tracking of sleep state cycling is technically feasible using measurements from a piezo element situated under a bed mattress.SignificanceAn open source infant deep sleep detector of this kind allows quantitative, continuous bedside assessment of infant’s sleep cycling.

scholarly journals Remote Evaluation of Parkinson's Disease Using a Conventional Webcam and Artificial Intelligence

2021 ◽  
Vol 12 ◽  
Author(s):  
Mariana H. G. Monje ◽  
Sergio Domínguez ◽  
Javier Vera-Olmos ◽  
Angelo Antonini ◽  
Tiago A. Mestre ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Upper Limb ◽  
Rating Scale ◽  
Hand Movement ◽  
Movement Speed ◽  
Clinical Scales ◽  
Video Frames ◽  
Disease Rating

Objective: This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease (PD) remotely using an accessible standard webcam.Methods: We consecutively enrolled a cohort of 42 patients with PD and healthy subjects (HSs). The participants were recorded performing MDS-UPDRS III bradykinesia upper limb tasks with a computer webcam. The video frames were processed using the artificial intelligence algorithms tracking the movements of the hands. The video extracted features were correlated with clinical rating using the Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale and inertial measurement units (IMUs). The developed classifiers were validated on an independent dataset.Results: We found significant differences in the motor performance of the patients with PD and HSs in all the bradykinesia upper limb motor tasks. The best performing classifiers were unilateral finger tapping and hand movement speed. The model correlated both with the IMUs for quantitative assessment of motor function and the clinical scales, hence demonstrating concurrent validity with the existing methods.Conclusions: We present here the proof-of-concept of a novel webcam-based technology to remotely detect the parkinsonian features using artificial intelligence. This method has preliminarily achieved a very high diagnostic accuracy and could be easily expanded to other disease manifestations to support PD management.

QUALITY CONTROL OF THERAPY WITH THE USE OF CAPILLARIUM BULBAR CONJUNCTION

2019 ◽  
pp. 189-194
Author(s):  
G.B. Nazarenko ◽  
T.S. Heilo
Keyword(s):  
Blood Flow ◽  
General Condition ◽  
Video Recording ◽  
Physical Exertion ◽  
The State ◽  
Movement Speed ◽  
Clinical Use ◽  
Non Invasive ◽  
Special Software ◽  
The Right

A method for diagnosing and monitoring the patient’s general condition has been developed, including the use of the specially developed and registered for clinical use of the device OKO Capillaroscope for non-invasive, non-contact video recording under a large increase in blood flow in the human bulbar conjunctival microvessels using special software that allows: measuring movement speed erythrocytes in individual arterioles, venules, and in the main preferred channels; to fix in microvessels the possible presence of sludge syndrome, stasis of the erythrocyte movement; to assess the number and shape of capillaries, the presence of arteriolar-venular anastomoses, the identity of blood flow in the microvessels of the conjunctiva in the right and left eye; measure arteriole diameters and venules. The method is aimed at a clinical study of the parameters of the microcirculation of the conjunctiva at rest, which allows extrapolating the recorded parameters of the intravascular (rheological) part of the microcirculation system to the state of the microhemodynamics of the whole organism, which reflects the state of general homeostasis. The method can be applied to control and monitor the general condition of a person or patient in any research in experimental, sports or therapeutic purposes, since the determined digital parameters of microcirculation allow to assess the general condition of a person, the response to physical exertion and evaluate the completeness and completeness of therapeutic treatment in various pathologies due to somatic or local degenerative changes in the patient’s body.

scholarly journals Effect of Trihexiphenidyl and Procyclidine for the management of resting tremor

2016 ◽  
Vol 44 (2) ◽  
pp. 72-75
Author(s):  
Md Ahsan Habib ◽  
ASM Alamgir ◽  
Subash Kanti Dey ◽  
Afroja Alam ◽  
Ahmed Asafuddoula ◽  
...  
Keyword(s):  
Essential Tremor ◽  
Rating Scale ◽  
Anticholinergic Agent ◽  
Interventional Study ◽  
Resting Tremor ◽  
Associated Symptoms ◽  
Disease Rating ◽  
The Difference ◽  
Medical University ◽  
To Receive

Parkinson’s disease is the main etiology of resting tremor but may also rarely occur in Essential Tremor, Multiple System Atrophy & Progressive Suprneuclear Palsy. Levodopa improves bradykinesia, rigidity and other commonly associated symptoms. When resting tremor is the predominant presenting symptom of Parkinson's disease or when tremor persists despite adequate control of other parkinsonian symptoms with low dosages of levodopa, an anticholinergic agent such as trihexyphenidyl or Procyclidine may be the treatment of choice. This prospective interventional study was carried out in the department of Neurology, Bangabandhu Sheikh Mujib Medical University, Dhaka from March, 2014 to June, 2014 with the intention to outline effectiveness, similarities and differences between Trihexyphenidyl and Procyclidine in alleviating resting tremor. For Parkinson’s disease, patients presenting with predominant tremor but minimal bradykinesia and rigidity were purposively selected for the study. Resting tremor was assessed by united parkinson’s disease rating scale (UPDRS). A total of 30 consecutive patients, both male and female, having resting tremor due to different etiology & attending both indoor and outpatient department of Neurology, BSMMU were randomized to receive either Trihexyphenidyl or Procyclidine for two weeks. For most of the patients (93%) resting tremor were due to Parkinson’s disease and only 7% were due to Essential tremor. In case of Trihexiphenidyl, constancy and amplitude of resting tremor were improved in 60% and 80% respectively. In case of Procyclidine, constancy and amplitude of resting tremor were imoroved 87% and 67% respectively. The difference of improvement between Trihexiphenidyl group and Procyclidine group was not statistically significant.Bangladesh Med J. 2015 May; 44 (2): 72-75

scholarly journals Using Tractography to Distinguish SWEDD from Parkinson’s Disease Patients Based on Connectivity

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Mansu Kim ◽  
Hyunjin Park
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Diffusion Tensor ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Support Vector ◽  
Svm Classifier ◽  
Research Database ◽  
Significant Group

Background. It is critical to distinguish between Parkinson’s disease (PD) and scans without evidence of dopaminergic deficit (SWEDD), because the two groups are different and require different therapeutic approaches.Objective. The aim of this study was to distinguish SWEDD patients from PD patients using connectivity information derived from diffusion tensor imaging tractography.Methods. Diffusion magnetic resonance images of SWEDD (n=37) and PD (n=40) were obtained from a research database. Tractography, the process of obtaining neural fiber information, was performed using custom software. Group-wise differences between PD and SWEDD patients were quantified using the number of connected fibers between two regions, and correlation analyses were performed based on clinical scores. A support vector machine classifier (SVM) was applied to distinguish PD and SWEDD based on group-wise differences.Results. Four connections showed significant group-wise differences and correlated with the Unified Parkinson’s Disease Rating Scale sponsored by the Movement Disorder Society. The SVM classifier attained 77.92% accuracy in distinguishing between SWEDD and PD using these identified connections.Conclusions. The connections and regions identified represent candidates for future research investigations.

Use of inhaled loxapine in acute psychiatric agitation

2016 ◽  
Vol 33 (S1) ◽  
pp. s228-s228
Author(s):  
S. Ovejero ◽  
M. Iza ◽  
S. Vallejo ◽  
C. Vera ◽  
A. Sedano ◽  
...  
Keyword(s):  
Rating Scale ◽  
Psychiatric Inpatient ◽  
Physical Restraint ◽  
Inpatient Unit ◽  
Young Mania ◽  
Non Invasive ◽  
Psychiatric Inpatient Unit ◽  
Syndrome Scale ◽  
Competing Interest ◽  
Negative Syndrome

ObjectivesThe aim of this work is to study the efficacy of loxapine inhalation powder on agitated patients in a psychiatric inpatient unit.MethodsNineteen patients sample, with an average age of 39.4 years old, diagnosed with schizophrenia, bipolar disorder or schizoaffective disorder. Patients inhaled loxapine 10 mg, using the staccato system, when they suffered a psychomotor agitation. The clinical efficacy was measured as a change from baseline in the Positive and Negative Syndrome Scale-Excited Component (PANSS-EC) and in the Young Mania Rating Scale (YMRS) one hour after the administration of loxapine.ResultsA mean of 9.8 points reduction (22.6 at baseline and 12.7 one hour after the administration) was found on the PANSS-EC (t-test, P < .001) and 68.4% of the patients were considered responders as they obtained a reduction of at least 40% of the basal score. On 10 of the total of the agitated patients showed an improvement of the psychomotor excitement, and this allowed the clinicians to remove the physical restraint; on 6 of the agitated patients the physical restraint could be avoided during the whole treatment; and 3 of the patients experienced a reduction of the excitement. The reduction on PANNS-EC on the latest group was not statistically significant (t-test, P = .121).ConclusionsInhaled loxapine was a non-invasive, rapid and effective alternative treatment for acute agitation in a psychiatric inpatient unit. It resulted more effective on mild and moderate cases; not been significantly effective on the severe cases of agitation.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Intelligent Classification Technique for Breast Cancer

2019 ◽  
Vol 8 (6) ◽  
pp. 2313-2316
Keyword(s):  
Breast Cancer ◽  
Developed Countries ◽  
Invasive Disease ◽  
Support Vector ◽  
Svm Classifier ◽  
Image Processing Algorithm ◽  
Non Invasive ◽  
Computer Aided ◽  
Aided Diagnosis

Breast cancer is also a leading cause of cancer death in the less developed countries of the world. This is partly because a shift in lifestyles is causing an increase in incidence. Breast cancer originates from the inner lining of milk ducts/lobes either in the form of invasive or non invasive disease in general. Mammography, particularly with Computer-Aided Detection (CAD), can now produce images detailed enough for diagnostic purposes, and digital mammography allows transmission of 3-dimensionssal images over long distances. The aim for the system is to design a Computer Aided Diagnosis systematic tool for perceiving non cancerous and perilous (cancer causing) mammogram. The aim of the research is proposed to develop an image processing algorithm for an automatic detection and classification of breast lesions accurately. CAD tool helped radiologist in expanding his assurance accuracy. Support vector machine (SVM) classifier is used to discriminate the tumors into benign or malignant. Incorporate best features of the find out that has significant responsibility in achieving the perfect turnout which are then designated and associated with ANN to train and classify.

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