TY - JOUR
T1 - The sound of Parkinson's disease
T2 - A model of audible bradykinesia
AU - de Graaf, Debbie
AU - Araújo, Rui
AU - Derksen, Madou
AU - Zwinderman, Koos
AU - de Vries, Nienke M.
AU - IntHout, Joanna
AU - Bloem, Bastiaan R.
N1 - Publisher Copyright: © 2024 The Authors
PY - 2024/3/1
Y1 - 2024/3/1
N2 - Introduction: Evaluation of bradykinesia is based on five motor tasks from the MDS-UPDRS. Visually scoring these motor tasks is subjective, resulting in significant interrater variability. Recent observations suggest that it may be easier to hear the characteristic features of bradykinesia, such as the decrement in sound intensity or force of repetitive movements. The objective is to evaluate whether audio signals derived during four MDS-UPDRS tasks can be used to detect and grade bradykinesia, using two machine learning models. Methods: 54 patients with Parkinson's disease and 28 healthy controls were filmed while executing the bradykinesia motor tasks. Several features were extracted from the audio signal, including number of taps, speed, sound intensity, decrement and freezes. For each motor task, two supervised machine learning models were trained, Logistic Regression (LR) and Support Vector Machine (SVM). Results: Both classifiers were able to separate patients from controls reasonably well for the leg agility task, area under the receiver operating characteristic curve (AUC): 0.92 (95%CI: 0.78–0.99) for LR and 0.93 (0.81–1.00) for SVM. Also, models were able to differentiate less severe bradykinesia from severe bradykinesia, particularly for the pronation-supination motor task, with AUC: 0.90 (0.62–1.00) for LR and 0.82 (0.45–0.97) for SVM. Conclusion: This audio-based approach discriminates PD from healthy controls with moderate-high accuracy and separated individuals with less severe bradykinesia from those with severe bradykinesia. Sound analysis may contribute to the identification and monitoring of bradykinesia.
AB - Introduction: Evaluation of bradykinesia is based on five motor tasks from the MDS-UPDRS. Visually scoring these motor tasks is subjective, resulting in significant interrater variability. Recent observations suggest that it may be easier to hear the characteristic features of bradykinesia, such as the decrement in sound intensity or force of repetitive movements. The objective is to evaluate whether audio signals derived during four MDS-UPDRS tasks can be used to detect and grade bradykinesia, using two machine learning models. Methods: 54 patients with Parkinson's disease and 28 healthy controls were filmed while executing the bradykinesia motor tasks. Several features were extracted from the audio signal, including number of taps, speed, sound intensity, decrement and freezes. For each motor task, two supervised machine learning models were trained, Logistic Regression (LR) and Support Vector Machine (SVM). Results: Both classifiers were able to separate patients from controls reasonably well for the leg agility task, area under the receiver operating characteristic curve (AUC): 0.92 (95%CI: 0.78–0.99) for LR and 0.93 (0.81–1.00) for SVM. Also, models were able to differentiate less severe bradykinesia from severe bradykinesia, particularly for the pronation-supination motor task, with AUC: 0.90 (0.62–1.00) for LR and 0.82 (0.45–0.97) for SVM. Conclusion: This audio-based approach discriminates PD from healthy controls with moderate-high accuracy and separated individuals with less severe bradykinesia from those with severe bradykinesia. Sound analysis may contribute to the identification and monitoring of bradykinesia.
KW - Bradykinesia
KW - Parkinson's disease
KW - Sound analysis
UR - http://www.scopus.com/inward/record.url?scp=85182561931&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.parkreldis.2024.106003
DO - https://doi.org/10.1016/j.parkreldis.2024.106003
M3 - Article
C2 - 38219529
SN - 1353-8020
VL - 120
JO - Parkinsonism and Related Disorders
JF - Parkinsonism and Related Disorders
M1 - 106003
ER -