Using an unbiased symbolic movement representation to characterize Parkinson’s disease states

Abrami, Avner; Heisig, Stephen; Ramos, Vesper; Thomas, Kevin; Ho, Bryan; Caggiano, Vittorio

doi:10.1038/s41598-020-64181-3

Cited by 16 publications

(14 citation statements)

References 46 publications

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“…In addition, studies used NNs to successfully discern PD from ET using surface electromyography data [ 32 ] and assess tremor severity in PD [ 33 ]. Deep learning NN have also been used as an advanced classification method to characterize PD severity [ 34 ] and movement quality in PD [ 35 ]. Other machine learning algorithms such as support vector machine (SVM) and k-nearest neighbor (kNN) have been used to differentiate between PD and ET based on IMU sensors, but they mainly investigated upper body tremors [ 36 – 39 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Parkinson’s disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach

Moon

Song

Sharma

et al. 2020

J NeuroEngineering Rehabil

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Background Parkinson’s disease (PD) and essential tremor (ET) are movement disorders that can have similar clinical characteristics including tremor and gait difficulty. These disorders can be misdiagnosed leading to delay in appropriate treatment. The aim of the study was to determine whether balance and gait variables obtained with wearable inertial motion sensors can be utilized to differentiate between PD and ET using machine learning. Additionally, we compared classification performances of several machine learning models. Methods This retrospective study included balance and gait variables collected during the instrumented stand and walk test from people with PD (n = 524) and with ET (n = 43). Performance of several machine learning techniques including neural networks, support vector machine, k-nearest neighbor, decision tree, random forest, and gradient boosting, were compared with a dummy model or logistic regression using F1-scores. Results Machine learning models classified PD and ET based on balance and gait characteristics better than the dummy model (F1-score = 0.48) or logistic regression (F1-score = 0.53). The highest F1-score was 0.61 of neural network, followed by 0.59 of gradient boosting, 0.56 of random forest, 0.55 of support vector machine, 0.53 of decision tree, and 0.49 of k-nearest neighbor. Conclusions This study demonstrated the utility of machine learning models to classify different movement disorders based on balance and gait characteristics collected from wearable sensors. Future studies using a well-balanced data set are needed to confirm the potential clinical utility of machine learning models to discern between PD and ET.

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Section: Introductionmentioning

confidence: 99%

Classification of Parkinson’s disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach

Moon

Song

Sharma

et al. 2020

J NeuroEngineering Rehabil

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“…Comparing our algorithm to unobtrusive methods [8,13,28] shows that our model outperforms Ref. [8] with 0.64 even though they only estimated bradykinesia.…”

Section: Comparison To Related Workmentioning

confidence: 90%

“…However, dyskinesia is a side effect of taking levodopa and not a PD symptom and is not included in the UPDRS-III assessments. Abrami et al [28] developed an unsupervised algorithm based on clustering and Markov-Chain. They applied a multidimensional scaling algorithm to estimate each subject's daily UPDRS-III score as the sum of tremor, bradykinesia, and gait items for each day.…”

Section: Comparison To Related Workmentioning

confidence: 99%

Ensemble deep model for continuous estimation of Unified Parkinson’s Disease Rating Scale III

et al. 2021

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Background Unified Parkinson Disease Rating Scale-part III (UPDRS III) is part of the standard clinical examination performed to track the severity of Parkinson’s disease (PD) motor complications. Wearable technologies could be used to reduce the need for on-site clinical examinations of people with Parkinson’s disease (PwP) and provide a reliable and continuous estimation of the severity of PD at home. The reported estimation can be used to successfully adjust the dose and interval of PD medications. Methods We developed a novel algorithm for unobtrusive and continuous UPDRS-III estimation at home using two wearable inertial sensors mounted on the wrist and ankle. We used the ensemble of three deep-learning models to detect UPDRS-III-related patterns from a combination of hand-crafted features, raw temporal signals, and their time–frequency representation. Specifically, we used a dual-channel, Long Short-Term Memory (LSTM) for hand-crafted features, 1D Convolutional Neural Network (CNN)-LSTM for raw signals, and 2D CNN-LSTM for time–frequency data. We utilized transfer learning from activity recognition data and proposed a two-stage training for the CNN-LSTM networks to cope with the limited amount of data. Results The algorithm was evaluated on gyroscope data from 24 PwP as they performed different daily living activities. The estimated UPDRS-III scores had a correlation of $$0.79\, (\textit{p}<0.0001)$$ 0.79 ( p < 0.0001 ) and a mean absolute error of 5.95 with the clinical examination scores without requiring the patients to perform any specific tasks. Conclusion Our analysis demonstrates the potential of our algorithm for estimating PD severity scores unobtrusively at home. Such an algorithm could provide the required motor-complication measurements without unnecessary clinical visits and help the treating physician provide effective management of the disease.

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“…The Tufts Clinical data set consists of 35 participants (mean age 68 years, SD 8 years; 23 males and 12 females; mean total UPDRS-III score 25, SD 13) with idiopathic PD. The protocol was run at Tufts Medical Center in Boston, Massachusetts and was approved by the Tufts Health Sciences Campus Institutional Review Board (IRB #12371) (the complete study design [34] and related analyses conducted on the data set [35][36][37] have been reported previously). Patients were video recorded by means of a Microsoft Kinect camera (Microsoft Corp) at 30 frames per second.…”

Section: Hypomimia and Drug Statementioning

confidence: 99%

Automated Computer Vision Assessment of Hypomimia in Parkinson Disease: Proof-of-Principle Pilot Study

Abrami¹,

Gunzler²,

Kilbane³

et al. 2021

J Med Internet Res

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Background Facial expressions require the complex coordination of 43 different facial muscles. Parkinson disease (PD) affects facial musculature leading to “hypomimia” or “masked facies.” Objective We aimed to determine whether modern computer vision techniques can be applied to detect masked facies and quantify drug states in PD. Methods We trained a convolutional neural network on images extracted from videos of 107 self-identified people with PD, along with 1595 videos of controls, in order to detect PD hypomimia cues. This trained model was applied to clinical interviews of 35 PD patients in their on and off drug motor states, and seven journalist interviews of the actor Alan Alda obtained before and after he was diagnosed with PD. Results The algorithm achieved a test set area under the receiver operating characteristic curve of 0.71 on 54 subjects to detect PD hypomimia, compared to a value of 0.75 for trained neurologists using the United Parkinson Disease Rating Scale-III Facial Expression score. Additionally, the model accuracy to classify the on and off drug states in the clinical samples was 63% (22/35), in contrast to an accuracy of 46% (16/35) when using clinical rater scores. Finally, each of Alan Alda’s seven interviews were successfully classified as occurring before (versus after) his diagnosis, with 100% accuracy (7/7). Conclusions This proof-of-principle pilot study demonstrated that computer vision holds promise as a valuable tool for PD hypomimia and for monitoring a patient’s motor state in an objective and noninvasive way, particularly given the increasing importance of telemedicine.

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Using an unbiased symbolic movement representation to characterize Parkinson’s disease states

Cited by 16 publications

References 46 publications

Classification of Parkinson’s disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach

Classification of Parkinson’s disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach

Ensemble deep model for continuous estimation of Unified Parkinson’s Disease Rating Scale III

Automated Computer Vision Assessment of Hypomimia in Parkinson Disease: Proof-of-Principle Pilot Study

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