scholarly journals Predicting Parkinson’s Disease Medication Regimen Using Sensor Technology

2021 ◽  
Author(s):  
Jeremy Watts ◽  
Anahita Khojandi ◽  
Rama Vasudevan ◽  
Fatta B. Nahab ◽  
Ritesh Ramdhani
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Classification Model ◽  
Sensor Data ◽  
Sensor Technology ◽  
Medication Regimen ◽  
Levodopa Dose ◽  
Subjective Data ◽  
Random Forest Classification

Abstract Parkinson’s disease (PD) medication treatment planning is generally based on subjective data through in-office, physicianpatient interactions. The Personal KinetiGraphTM (PKG) has shown promise in enabling objective, continuous remote health monitoring for Parkinson’s patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to subtype patients based on levodopa regimens and response. We apply k-means clustering to a dataset of with-in-subject Parkinson’s medication changes—clinically assessed by the PKG and Hoehn & Yahr (H&Y) staging. A random forest classification model was then used to predict patients’ cluster allocation based on their respective PKG data and demographic information. Clinically relevant clusters were developed based on longitudinal dopaminergic regimens—partitioned by levodopa dose, administration frequency, and total levodopa equivalent daily dose—with the PKG increasing cluster granularity compared to the H&Y staging. A random forest classifier was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 87:9 ±1:3

scholarly journals Improving Medication Regimen Recommendation for Parkinson’s Disease Using Sensor Technology

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3553
Author(s):  
Jeremy Watts ◽  
Anahita Khojandi ◽  
Rama Vasudevan ◽  
Fatta B. Nahab ◽  
Ritesh A. Ramdhani
Keyword(s):  
Parkinson’S Disease ◽  
Time Series ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Treatment Planning ◽  
Time Series Data ◽  
Classification Model ◽  
Series Data ◽  
Demographic Information ◽  
Subjective Data

Parkinson’s disease medication treatment planning is generally based on subjective data obtained through clinical, physician-patient interactions. The Personal KinetiGraph™ (PKG) and similar wearable sensors have shown promise in enabling objective, continuous remote health monitoring for Parkinson’s patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to cluster patients based on levodopa regimens and response. The resulting clusters are then used to enhance treatment planning by providing improved initial treatment estimates to supplement a physician’s initial assessment. We apply k-means clustering to a dataset of within-subject Parkinson’s medication changes—clinically assessed by the MDS-Unified Parkinson’s Disease Rating Scale-III (MDS-UPDRS-III) and the PKG sensor for movement staging. A random forest classification model was then used to predict patients’ cluster allocation based on their respective demographic information, MDS-UPDRS-III scores, and PKG time-series data. Clinically relevant clusters were partitioned by levodopa dose, medication administration frequency, and total levodopa equivalent daily dose—with the PKG providing similar symptomatic assessments to physician MDS-UPDRS-III scores. A random forest classifier trained on demographic information, MDS-UPDRS-III scores, and PKG time-series data was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 86.9%, an F1 score of 90.7%, and an AUC of 0.871. A model that relied solely on demographic information and PKG time-series data provided the next best performance with an accuracy of 83.8%, an F1 score of 88.5%, and an AUC of 0.831, hence further enabling fully remote assessments. These computational methods demonstrate the feasibility of using sensor-based data to cluster patients based on their medication responses with further potential to assist with medication recommendations.

scholarly journals Hybrid Feature Extraction for Detection of Degree of Motor Fluctuation Severity in Parkinson’s Disease Patients

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 137 ◽  
Author(s):  
Murtadha D. Hssayeni ◽  
Joohi Jimenez-Shahed ◽  
Behnaz Ghoraani
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Extraction ◽  
Motor Function ◽  
Motor Fluctuation ◽  
Classification Model ◽  
Sensor Data ◽  
Body Movements ◽  
Hybrid Feature Extraction ◽  
Free Body

The success of medication adjustment in Parkinson’s disease (PD) patients with motor fluctuation relies on the knowledge about their fluctuation severity. However, because of the temporal and spatial variability in motor fluctuations, a single clinical examination often fails to capture the spectrum of motor impairment experienced in routine daily life. In this study, we developed an algorithm to estimate the degree of motor fluctuation severity from two wearable sensors’ data during subjects’ free body movements. Specifically, we developed a new hybrid feature extraction method to represent the longitudinal changes of motor function from the sensor data. Next, we developed a classification model based on random forest to learn the changes in the patterns of the sensor data as the severity of the motor function changes. We evaluated our algorithm using data from 24 subjects with idiopathic PD as they performed a variety of daily routine activities. A leave-one-subject-out assessment of the algorithm resulted in 83.33% accuracy, indicating that our approach holds a great promise to passively detect degree of motor fluctuation severity from continuous monitoring of an individual’s free body movements. Such a sensor-based assessment system and algorithm combination could provide the objective and comprehensive information about the fluctuation severity that can be used by the treating physician to effectively adjust therapy for PD patients with troublesome motor fluctuation.

User-Independent Detection for Freezing of Gait in Parkinson's Disease Using Random Forest Classification

2019 ◽  
Vol 4 (1) ◽  
pp. 57-72
Author(s):  
Amruta Meshram ◽  
Bharatendra Rai
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Block Design ◽  
Phase 1 ◽  
Freezing Of Gait ◽  
Random Forest Classification ◽  
Randomized Block Design ◽  
Risk Of Falls ◽  
Significant Factors

Freezing of gait (FOG) is a gait impairment which occurs in Parkinson's disease (PD) patients. As PD progresses, the patient is unable to perform locomotion normally. This increases the risk of falls and adversely affects the patient's quality of life. In this article, a user-independent method has been proposed to detect FOG events in PD patients. The proposed method is divided into three phases. Phase-1 extracts the statistical features from a FOG dataset. Phase-2 divides the data into two clusters based on FOG events. Phase-3 selects significant factors, using a randomized block design with replication. A Random Forest model is built using a combination of significant factors obtained from the design of experiments. The proposed method classifies FOG events with an average sensitivity up to 94.33% and specificity up to 92.77%. This model can be integrated along with non-pharmaceutical treatments to generate sensory-motor feedback at the onset of a FOG event.

User-Independent Detection for Freezing of Gait in Parkinson's Disease Using Random Forest Classification

2021 ◽  
pp. 456-472
Author(s):  
Amruta Meshram ◽  
Bharatendra Rai
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Block Design ◽  
Phase 1 ◽  
Freezing Of Gait ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Randomized Block Design ◽  
Significant Factors

Freezing of gait (FOG) is a gait impairment which occurs in Parkinson's disease (PD) patients. As PD progresses, the patient is unable to perform locomotion normally. This increases the risk of falls and adversely affects the patient's quality of life. In this article, a user-independent method has been proposed to detect FOG events in PD patients. The proposed method is divided into three phases. Phase-1 extracts the statistical features from a FOG dataset. Phase-2 divides the data into two clusters based on FOG events. Phase-3 selects significant factors, using a randomized block design with replication. A Random Forest model is built using a combination of significant factors obtained from the design of experiments. The proposed method classifies FOG events with an average sensitivity up to 94.33% and specificity up to 92.77%. This model can be integrated along with non-pharmaceutical treatments to generate sensory-motor feedback at the onset of a FOG event.

scholarly journals Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson’s disease

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Gloria Vergara-Diaz ◽  
Jean-Francois Daneault ◽  
Federico Parisi ◽  
Chen Admati ◽  
Christina Alfonso ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Symptom Severity ◽  
Wearable Sensors ◽  
Motor Fluctuations ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Motor Tasks ◽  
Specific Symptom ◽  
At Home

AbstractParkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. Dyskinesia and motor fluctuations are complications of PD medications. An objective measure of on/off time with/without dyskinesia has been sought for some time because it would facilitate the titration of medications. The objective of the dataset herein presented is to assess if wearable sensor data can be used to generate accurate estimates of limb-specific symptom severity. Nineteen subjects with PD experiencing motor fluctuations were asked to wear a total of five wearable sensors on both forearms and shanks, as well as on the lower back. Accelerometer data was collected for four days, including two laboratory visits lasting 3 to 4 hours each while the remainder of the time was spent at home and in the community. During the laboratory visits, subjects performed a battery of motor tasks while clinicians rated limb-specific symptom severity. At home, subjects were instructed to use a smartphone app that guided the periodic performance of a set of motor tasks.

scholarly journals Correlations between Motor Symptoms across Different Motor Tasks, Quantified via Random Forest Feature Classification in Parkinson’s Disease

2017 ◽  
Vol 8 ◽  
Author(s):  
Andreas Kuhner ◽  
Tobias Schubert ◽  
Massimo Cenciarini ◽  
Isabella Katharina Wiesmeier ◽  
Volker Arnd Coenen ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Motor Symptoms ◽  
Motor Tasks ◽  
Feature Classification

CONCURRENCE OF FRAILTY AND PARKINSON’S DISEASE

2012 ◽  
pp. 1-5
Author(s):  
K.P. ROLAND ◽  
K.M.D. CORNETT ◽  
O. THEOU ◽  
J.M. JAKOBI ◽  
G.R. JONES
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cardiovascular Health ◽  
Community Dwelling ◽  
Health Study ◽  
Cardiovascular Health Study ◽  
Physical Frailty ◽  
Levodopa Dose ◽  
Frailty Phenotype ◽  
Age Related

Background: Females with Parkinson’s disease (PD) are at greater risk of frailty than males. Little is known about how age and disease-related characteristics influence frailty in females with PD because frailty studies often exclude persons with underlying neurological pathologies. Objective: To determine age and diseaserelated characteristics that best explain physical frailty in community-dwelling females with and without PD. Design & Measurement: Correlation coefficients described relationships between PD-related characteristics and physical frailty phenotype criteria (Cardiovascular Health Study). Regression analysis identified associations between disease-related characteristics and frailty in non-PD and PD females. Setting: Community-dwelling. Participants: Females with mild to moderate PD (n = 17, mean age = 66 ± 8.5 years) and non-PD (n = 18, mean age = 72 ± 13.2 years) participated. Results: Daily carbidopa-levodopa dose best explained frailty in PD females (β = 0.5), whereas in non-PD females, age (β = 0.7) and comorbidity (β = 0.5) were most associated with frailty. Conclusions: Dopaminergic medication explained frailty in PD and not measures of disease progression (i.e. severity, duration). In females without PD age-related accumulation of comorbidities resulted in greater risk of frailty. This indicates dopaminergic management of PD symptoms may better reflect frailty in females with PD than disease severity or duration. These data suggest the influence of underlying frailty should be considered when managing neurological conditions. Understanding how frailty concurrently exists with PD and how these conditions progress within the aging female will facilitate future care management.

scholarly journals Nondestructive Inspection of Reinforced Concrete Utility Poles with ISOMAP and Random Forest

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3463 ◽  
Author(s):  
Saeed Ullah ◽  
Minjoong Jeong ◽  
Woosang Lee
Keyword(s):  
Reinforced Concrete ◽  
Random Forest ◽  
Transmission Lines ◽  
Structural Integrity ◽  
Sensor Data ◽  
Structural Safety ◽  
Portable Devices ◽  
Random Forest Classification ◽  
Safety Issues ◽  
Forest Classification

Reinforced concrete poles are very popular in transmission lines due to their economic efficiency. However, these poles have structural safety issues in their service terms that are caused by cracks, corrosion, deterioration, and short-circuiting of internal reinforcing steel wires. Therefore, they must be periodically inspected to evaluate their structural safety. There are many methods of performing external inspection after installation at an actual site. However, on-site nondestructive safety inspection of steel reinforcement wires inside poles is very difficult. In this study, we developed an application that classifies the magnetic field signals of multiple channels, as measured from the actual poles. Initially, the signal data were gathered by inserting sensors into the poles, and these data were then used to learn the patterns of safe and damaged features. These features were then processed with the isometric feature mapping (ISOMAP) dimensionality reduction algorithm. Subsequently, the resulting reduced data were processed with a random forest classification algorithm. The proposed method could elucidate whether the internal wires of the poles were broken or not according to actual sensor data. This method can be applied for evaluating the structural integrity of concrete poles in combination with portable devices for signal measurement (under development).

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