Discrimination between healthy and patients with Parkinson’s disease from hand resting activity using inertial measurement unit

Abstract Background Parkinson’s disease (PD) is a neurological disease that affects the motor system. The associated motor symptoms are muscle rigidity or stiffness, bradykinesia, tremors, and gait disturbances. The correct diagnosis, especially in the initial stages, is fundamental to the life quality of the individual with PD. However, the methods used for diagnosis of PD are still based on subjective criteria. As a result, the objective of this study is the proposal of a method for the discrimination of individuals with PD (in the initial stages of the disease) from healthy groups, based on the inertial sensor recordings. Methods A total of 27 participants were selected, 15 individuals previously diagnosed with PD and 12 healthy individuals. The data collection was performed using inertial sensors (positioned on the back of the hand and on the back of the forearm). Different numbers of features were used to compare the values of sensitivity, specificity, precision, and accuracy of the classifiers. For group classification, 4 classifiers were used and compared, those being [Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes (NB)]. Results When all individuals with PD were analyzed, the best performance for sensitivity and accuracy (0.875 and 0.800, respectively) was found in the SVM classifier, fed with 20% and 10% of the features, respectively, while the best performance for specificity and precision (0.933 and 0.917, respectively) was associated with the RF classifier fed with 20% of all the features. When only individuals with PD and score 1 on the Hoehn and Yahr scale (HY) were analyzed, the best performances for sensitivity, precision and accuracy (0.933, 0.778 and 0.848, respectively) were from the SVM classifier, fed with 40% of all features, and the best result for precision (0.800) was connected to the NB classifier, fed with 20% of all features. Conclusion Through an analysis of all individuals in this study with PD, the best classifier for the detection of PD (sensitivity) was the SVM fed with 20% of the features and the best classifier for ruling out PD (specificity) was the RF classifier fed with 20% of the features. When analyzing individuals with PD and score HY = 1, the SVM classifier was superior across the sensitivity, precision, and accuracy, and the NB classifier was superior in the specificity. The obtained result indicates that objective methods can be applied to help in the evaluation of PD.

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Predicting Axial Impairment in Parkinson’s Disease through a Single Inertial Sensor

Sensors ◽

10.3390/s22020412 ◽

2022 ◽

Vol 22 (2) ◽

pp. 412

Author(s):

Luigi Borzì ◽

Ivan Mazzetta ◽

Alessandro Zampogna ◽

Antonio Suppa ◽

Fernanda Irrera ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Regression Model ◽

Support Vector Regression ◽

Regression Models ◽

Inertial Sensor ◽

Measurement Unit ◽

Support Vector ◽

Model Parameters ◽

The Impact

Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson’s disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. Methods: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. Results: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). Conclusions: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients.

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Cigarette Smoking Detection with An Inertial Sensor and A Smart Lighter

Sensors ◽

10.3390/s19030570 ◽

2019 ◽

Vol 19 (3) ◽

pp. 570 ◽

Cited By ~ 15

Author(s):

Volkan Senyurek ◽

Masudul Imtiaz ◽

Prajakta Belsare ◽

Stephen Tiffany ◽

Edward Sazonov

Keyword(s):

Cigarette Smoking ◽

Inertial Sensor ◽

Smoking Behavior ◽

Measurement Unit ◽

Support Vector ◽

Svm Classifier ◽

Free Living ◽

Objective Monitoring ◽

In The Wild ◽

Detection Of Smoking

In recent years, a number of wearable approaches have been introduced for objective monitoring of cigarette smoking based on monitoring of hand gestures, breathing or cigarette lighting events. However, non-reactive, objective and accurate measurement of everyday cigarette consumption in the wild remains a challenge. This study utilizes a wearable sensor system (Personal Automatic Cigarette Tracker 2.0, PACT2.0) and proposes a method that integrates information from an instrumented lighter and a 6-axis Inertial Measurement Unit (IMU) on the wrist for accurate detection of smoking events. The PACT2.0 was utilized in a study of 35 moderate to heavy smokers in both controlled (1.5–2 h) and unconstrained free-living conditions (~24 h). The collected dataset contained approximately 871 h of IMU data, 463 lighting events, and 443 cigarettes. The proposed method identified smoking events from the cigarette lighter data and estimated puff counts by detecting hand-to-mouth gestures (HMG) in the IMU data by a Support Vector Machine (SVM) classifier. The leave-one-subject-out (LOSO) cross-validation on the data from the controlled portion of the study achieved high accuracy and F1-score of smoking event detection and estimation of puff counts (97%/98% and 93%/86%, respectively). The results of validation in free-living demonstrate 84.9% agreement with self-reported cigarettes. These results suggest that an IMU and instrumented lighter may potentially be used in studies of smoking behavior under natural conditions.

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A Deep Learning Approach for Classification and Diagnosis of Parkinson’s Disease

10.21203/rs.3.rs-254647/v1 ◽

2021 ◽

Author(s):

Monika Jyotiyana ◽

Nishtha Kesswani ◽

Munish Kumar

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Deep Learning ◽

Nearest Neighbor ◽

Health Sector ◽

Classification Model ◽

Support Vector ◽

Learning Approaches ◽

K Nearest Neighbor ◽

Linear Discriminant

Abstract Deep learning techniques are playing an important role in the classification and prediction of diseases. Undoubtedly deep learning has a promising future in the health sector, especially in medical imaging. The popularity of deep learning approaches is because of their ability to handle a large amount of data related to the patients with accuracy, reliability in a short span of time. However, the practitioners may take time in analyzing and generating reports. In this paper, we have proposed a Deep Neural Network-based classification model for Parkinson’s disease. Our proposed method is one such good example giving faster and more accurate results for the classification of Parkinson’s disease patients with excellent accuracy of 94.87%. Based on the attributes of the dataset of the patient, the model can be used for the identification of Parkinsonism's. We have also compared the results with other existing approaches like Linear Discriminant Analysis, Support Vector Machine, K-Nearest Neighbor, Decision Tree, Classification and Regression Trees, Random Forest, Linear Regression, Logistic Regression, Multi-Layer Perceptron, and Naive Bayes.

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Parkinson’s Disease Diagnosis in Cepstral Domain Using MFCC and Dimensionality Reduction with SVM Classifier

Mobile Information Systems ◽

10.1155/2021/8822069 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Atiqur Rahman ◽

Sanam Shahla Rizvi ◽

Aurangzeb Khan ◽

Aaqif Afzaal Abbasi ◽

Shafqat Ullah Khan ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Dimensionality Reduction ◽

Intelligent System ◽

Neurological Diseases ◽

Binary Classification ◽

Disease Diagnosis ◽

Support Vector ◽

Svm Classifier ◽

Linear Discriminant

Parkinson’s disease (PD) is one of the most common and serious neurological diseases. Impairments in voice have been reported to be the early biomarkers of the disease. Hence, development of PD diagnostic tool will help early diagnosis of the disease. Additionally, intelligent system developed for binary classification of PD and healthy controls can also be exploited in future as an instrument for prodromal diagnosis. Notably, patients with rapid eye movement (REM) sleep behaviour disorder (RBD) represent a good model as they develop PD with a high probability. It has been shown that slight speech and voice impairment may be a sensitive marker of preclinical PD. In this study, we propose PD detection by extracting cepstral features from the voice signals collected from people with PD and healthy subjects. To classify the extracted features, we propose to use dimensionality reduction through linear discriminant analysis and classification through support vector machine. In order to validate the effectiveness of the proposed method, we also developed ten different machine learning models. It was observed that the proposed method yield area under the curve (AUC) of 88%, sensitivity of 73.33%, and specificity of 84%. Moreover, the proposed intelligent system was simulated using publicly available multiple types of voice database. Additionally, the data were collected from patients under on-state. The obtained results on the public database are promising compared to the previously published work.

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

Parkinson s Disease ◽

10.1155/2016/8704910 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 3

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.

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Real-Time Detection of Freezing Motions in Parkinson's Patients for Adaptive Gait Phase Synchronous Cueing

Frontiers in Neurology ◽

10.3389/fneur.2021.720516 ◽

2021 ◽

Vol 12 ◽

Author(s):

Ardit Dvorani ◽

Vivian Waldheim ◽

Magdalena C. E. Jochner ◽

Christina Salchow-Hömmen ◽

Jonas Meyer-Ohle ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Prediction Models ◽

Inertial Sensor ◽

Freezing Of Gait ◽

Area Under The Curve ◽

Support Vector ◽

Average Performance ◽

Gait Phase ◽

Fog Detection

Parkinson's disease is the second most common neurodegenerative disease worldwide reducing cognitive and motoric abilities of affected persons. Freezing of Gait (FoG) is one of the severe symptoms that is observed in the late stages of the disease and considerably impairs the mobility of the person and raises the risk of falls. Due to the pathology and heterogeneity of the Parkinsonian gait cycle, especially in the case of freezing episodes, the detection of the gait phases with wearables is challenging in Parkinson's disease. This is addressed by introducing a state-automaton-based algorithm for the detection of the foot's motion phases using a shoe-placed inertial sensor. Machine-learning-based methods are investigated to classify the actual motion phase as normal or FoG-affected and to predict the outcome for the next motion phase. For this purpose, spatio-temporal gait and signal parameters are determined from the segmented movement phases. In this context, inertial sensor fusion is applied to the foot's 3D acceleration and rate of turn. Support Vector Machine (SVM) and AdaBoost classifiers have been trained on the data of 16 Parkinson's patients who had shown FoG episodes during a clinical freezing-provoking assessment course. Two clinical experts rated the video-recorded trials and marked episodes with festination, shank trembling, shuffling, or akinesia. Motion phases inside such episodes were labeled as FoG-affected. The classifiers were evaluated using leave-one-patient-out cross-validation. No statistically significant differences could be observed between the different classifiers for FoG detection (p>0.05). An SVM model with 10 features of the actual and two preceding motion phases achieved the highest average performance with 88.5 ± 5.8% sensitivity, 83.3 ± 17.1% specificity, and 92.8 ± 5.9% Area Under the Curve (AUC). The performance of predicting the behavior of the next motion phase was significantly lower compared to the detection classifiers. No statistically significant differences were found between all prediction models. An SVM-predictor with features from the two preceding motion phases had with 81.6 ± 7.7% sensitivity, 70.3 ± 18.4% specificity, and 82.8 ± 7.1% AUC the best average performance. The developed methods enable motion-phase-based FoG detection and prediction and can be utilized for closed-loop systems that provide on-demand gait-phase-synchronous cueing to mitigate FoG symptoms and to prevent complete motoric blockades.

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Task-Oriented Intelligent Solution to Measure Parkinson’s Disease Tremor Severity

Journal of Healthcare Engineering ◽

10.1155/2021/9624386 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Ghayth AlMahadin ◽

Ahmad Lotfi ◽

Marie Mc Carthy ◽

Philip Breedon

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Data Collection ◽

Rating Scale ◽

Classification Performance ◽

Support Vector ◽

Svm Classifier ◽

Common Symptom ◽

Average Rule ◽

Novel Approach

Tremor is a common symptom of Parkinson’s disease (PD). Currently, tremor is evaluated clinically based on MDS-UPDRS Rating Scale, which is inaccurate, subjective, and unreliable. Precise assessment of tremor severity is the key to effective treatment to alleviate the symptom. Therefore, several objective methods have been proposed for measuring and quantifying PD tremor from data collected while patients performing scripted and unscripted tasks. However, up to now, the literature appears to focus on suggesting tremor severity classification methods without discrimination tasks effect on classification and tremor severity measurement. In this study, a novel approach to identify a recommended system is used to measure tremor severity, including the influence of tasks performed during data collection on classification performance. The recommended system comprises recommended tasks, classifier, classifier hyperparameters, and resampling technique. The proposed approach is based on the above-average rule of five advanced metrics results of four subdatasets, six resampling techniques, six classifiers besides signal processing, and features extraction techniques. The results of this study indicate that tasks that do not involve direct wrist movements are better than tasks that involve direct wrist movements for tremor severity measurements. Furthermore, resampling techniques improve classification performance significantly. The findings of this study suggest that a recommended system consists of support vector machine (SVM) classifier combined with BorderlineSMOTE oversampling technique and data collection while performing set of recommended tasks, which are sitting, stairs up and down, walking straight, walking while counting, and standing.

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A machine learning approach to distinguish Parkinson’s disease (PD) patient’s with shuffling gait from older adults based on gait signals using 3D motion analysis

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.29.18547 ◽

2018 ◽

Vol 7 (3.29) ◽

pp. 153 ◽

Cited By ~ 3

Author(s):

Satyabrata Aich ◽

Pyari Mohan Pradhan ◽

Jinse Park ◽

Hee Cheol Kim

Keyword(s):

Machine Learning ◽

Older Adults ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Rating Scale ◽

Support Vector ◽

Svm Classifier ◽

3D Motion ◽

Intelligent Technique ◽

Gait Parameters

In recent times the adverse impact of Parkinson’s disease (PD) getting worse and worse with the increasing rate of old age population through out the world. This disease is the second common neurological disorder and has a tremendous economical and social impact because the cost associated with the healthcare as well as service is extremely high. The diagnosis process of this disease mostly done by closely observing the patient in the clinic as well as using the rating scale. However, this kind of diagnosis is subjective in nature and usually takes long time and assessment of this disease is complicated and cannot replicated in other patients. This kind of diagnosis method is also not suitable for the early detection of the PD. So, with this shortcoming it is necessary to find a suitable method that can automate the process as well as useful in the initial phase of diagnosis of PD. Recently with the invention of motion capture equipment’s and artificial intelligent technique, the feasibility of the objective nature-based diagnosis is getting lot of attention, especially the objective quantification of gait parameters. Shuffling of gait is one of the important characteristics of PD patients and it is usually defined y shorter stride length and low foot clearance. In this study a novel method is proposed to quantify the gait parameters using 3D motion captures and then various feature selection algorithm have used to select the effective features and finally machine learning based techniques were implemented to automate the classification process of two groups composed of PD patients as well as older adults. We have found maximum accuracy of 98.54 %by using support vector machine (SVM) classifier with radial basis function coupled with minimum redundancy and maximum relevance (MRMR) algorithm-based feature set. Our result showed that the proposed method can help the clinicians to distinguish PD patients from the older adults. This method helps to detect the PD at early stage.

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MicroRNA Candidate Biomarkers for Parkinson’s Disease and Idiopathic REM Sleep Behavior Disorder

10.21203/rs.3.rs-1163742/v1 ◽

2021 ◽

Author(s):

Ligang Wu ◽

Jun Liu ◽

Yuanyuan Li ◽

Ying Cao ◽

Wei Liu ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Rem Sleep ◽

Rem Sleep Behavior Disorder ◽

Behavior Disorder ◽

Support Vector ◽

Svm Classifier ◽

Healthy Individuals ◽

Mirna Signature ◽

Sleep Behavior

Abstract Background: Parkinson’s disease (PD), a severe neurodegenerative disorder, and idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD), a parasomnia recognized as the prodromal stage of synucleinopathies (including PD), both lack reliable, non-invasive biomarker tests for early intervention and management. Objectives: To investigate whether plasma extracellular vesicle (EV)-associated sncRNAs could discriminate PD and/or iRBD patients from healthy individuals.Methods: We optimized a cDNA library construction method, EVsmall-seq, for high throughput sequencing of sncRNAs associated with plasma EVs. We profiled EV-sncRNAs from the plasma of 60 normal controls, 56 iRBD patients, and 53 PD patients, and constructed a support vector machine (SVM) classifier to identify the informative miRNA features to distinguish PD and/or iRBD patients from healthy individuals. Results: First, a sixteen-miRNA signature was found to distinguish PD patients from healthy individuals with 88% sensitivity, 90.43% specificity, and 89.13% accuracy. Second, a three-miRNA signature was found to distinguish iRBD patients from healthy individuals with 96% sensitivity, 86.36% specificity, and 91.49% accuracy. Third, tweenty 20 miRNAs were found consistently increased or decreased in expression from healthy subjects to iRBD to PD patients, which might be linked to PD development through iRBD.Conclusions: Current study provides a valuable and highly informative dataset of EV-associated sncRNAs from plasma of iRBD and PD patients. We identified miRNA signature features that could serve as minimally-invasive, blood-based surveillance biomarkers for distinguishing iRBD or PD from healthy individuals with high sensitivity, specificity, and accuracy.

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Vocal Feature Extraction-Based Artificial Intelligent Model for Parkinson’s Disease Detection

Diagnostics ◽

10.3390/diagnostics11061076 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1076

Author(s):

Muntasir Hoq ◽

Mohammed Nazim Uddin ◽

Seung-Bo Park

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Nearest Neighbor ◽

Neurodegenerative Disorder ◽

Imbalanced Data ◽

Principal Component ◽

Gradient Boosting ◽

Support Vector ◽

K Nearest Neighbor ◽

Extreme Gradient Boosting

As a neurodegenerative disorder, Parkinson’s disease (PD) affects the nerve cells of the human brain. Early detection and treatment can help to relieve the symptoms of PD. Recent PD studies have extracted the features from vocal disorders as a harbinger for PD detection, as patients face vocal changes and impairments at the early stages of PD. In this study, two hybrid models based on a Support Vector Machine (SVM) integrating with a Principal Component Analysis (PCA) and a Sparse Autoencoder (SAE) are proposed to detect PD patients based on their vocal features. The first model extracted and reduced the principal components of vocal features based on the explained variance of each feature using PCA. For the first time, the second model used a novel Deep Neural Network (DNN) of an SAE, consisting of multiple hidden layers with L1 regularization to compress the vocal features into lower-dimensional latent space. In both models, reduced features were fed into the SVM as inputs, which performed classification by learning hyperplanes, along with projecting the data into a higher dimension. An F1-score, a Mathews Correlation Coefficient (MCC), and a Precision-Recall curve were used, along with accuracy to evaluate the proposed models due to highly imbalanced data. With its highest accuracy of 0.935, F1-score of 0.951, and MCC value of 0.788, the probing results show that the proposed model of the SAE-SVM surpassed not only the former model of the PCA-SVM and other standard models including Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), and Random Forest (RF), but also surpassed two recent studies using the same dataset. Oversampling and balancing the dataset with SMOTE boosted the performance of the models.

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