Machine Learning and Wearable Sensors for the Early Detection of Balance Disorders in Parkinson's Disease

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 24; p. 9903
• Main Authors: Castelli Gattinara Di Zubiena, Francesco, Menna, Greta, Mileti, Ilaria, Zampogna, Alessandro, Asci, Francesco, Paoloni, Marco, Suppa, Antonio, Del Prete, Zaccaria, Palermo, Eduardo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 16-12-2022; MDPI
• Subjects: Abnormalities; Accelerometers; Accuracy; Algorithms; Asymptomatic; Balance; Classification; Comparative analysis; Criteria; Data mining; Decision trees; Discriminant analysis; Gait; Health aspects; Humans; Kinematics; Machine Learning; Neural networks; Neural Networks, Computer; Parkinson Disease - diagnosis; Parkinson's disease; Patients; Postural Balance - physiology; postural instability; Posture; posturography; Sensors; Support Vector Machine; Support vector machines; Wearable Electronic Devices; wearable sensors; Wearable technology; Yaw; posturography; wearable sensors; postural instability; machine learning; Parkinson’s disease
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22249903

Dynamic posturography combined with wearable sensors has high sensitivity in recognizing subclinical balance abnormalities in patients with Parkinson's disease (PD). However, this approach is burdened by a high analytical load for motion analysis, potentially limiting a routine application in clinical practice. In this study, we used machine learning to distinguish PD patients from controls, as well as patients under and not under dopaminergic therapy (i.e., ON and OFF states), based on kinematic measures recorded during dynamic posturography through portable sensors. We compared 52 different classifiers derived from Decision Tree, K-Nearest Neighbor, Support Vector Machine and Artificial Neural Network with different kernel functions to automatically analyze reactive postural responses to yaw perturbations recorded through IMUs in 20 PD patients and 15 healthy subjects. To identify the most efficient machine learning algorithm, we applied three threshold-based selection criteria (i.e., accuracy, recall and precision) and one evaluation criterion (i.e., goodness index). Twenty-one out of 52 classifiers passed the three selection criteria based on a threshold of 80%. Among these, only nine classifiers were considered "optimum" in distinguishing PD patients from healthy subjects according to a goodness index ≤ 0.25. The Fine K-Nearest Neighbor was the best-performing algorithm in the automatic classification of PD patients and healthy subjects, irrespective of therapeutic condition. By contrast, none of the classifiers passed the three threshold-based selection criteria in the comparison of patients in ON and OFF states. Overall, machine learning is a suitable solution for the early identification of balance disorders in PD through the automatic analysis of kinematic data from dynamic posturography.