Name
Title | ||
|---|---|---|
Supervised Classification Of Bradykinesia In Parkinson'S Disease From Smartphone Videos |
Abstract | ||
|---|---|---|
Background: Slowness of movement, known as bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best.Aim: We propose a low-cost, contactless system using smartphone videos to automatically determine the presence of bradykinesia.Methods: We collected 70 videos of finger-tap assessments in a clinical setting (40 Parkinson's hands, 30 control hands). Two clinical experts in Parkinson's, blinded to the diagnosis, evaluated the videos to give a grade of bradykinesia severity between 0 and 4 using the Unified Pakinson's Disease Rating Scale (UPDRS). We developed a computer vision approach that identifies regions related to hand motion and extracts clinically-relevant features. Dimensionality reduction was undertaken using principal component analysis before input to classification models (Naive Bayes, Logistic Regression, Support Vector Machine) to predict no/slight bradykinesia (UPDRS = 0-1) or mild/moderate/severe bradykinesia (UPDRS = 2-4), and presence or absence of Parkinson's diagnosis.Results: A Support Vector Machine with radial basis function kernels predicted presence of mild/moderate/severe bradykinesia with an estimated test accuracy of 0.8. A Naive Bayes model predicted the presence of Parkinson's disease with estimated test accuracy 0.67.Conclusion: The method described here presents an approach for predicting bradykinesia from videos of finger tapping tests. The method is robust to lighting conditions and camera positioning. On a set of pilot data, accuracy of bradykinesia prediction is comparable to that recorded by blinded human experts. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.artmed.2020.101966 | ARTIFICIAL INTELLIGENCE IN MEDICINE |
Keywords | DocType | Volume |
Classification, Parkinson's, Bradykinesia, Video, Computer vision, Diagnosis, Support vector machine | Journal | 110 |
ISSN | Citations | PageRank |
0933-3657 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefan B. Williams | 1 | 665 | 70.78 |
| Samuel D. Relton | 2 | 38 | 5.42 |
| Hui Fang | 3 | 114 | 14.47 |
| Jane E. Alty | 4 | 37 | 7.58 |
| Rami Qahwaji | 5 | 120 | 21.05 |
| Christopher D Graham | 6 | 0 | 0.34 |
| David C Wong | 7 | 0 | 0.34 |