Multilevel Features for Sensor-Based Assessment of Motor Fluctuation in Parkinson's Disease Subjects

• Published in: IEEE journal of biomedical and health informatics Vol. 24; no. 5; pp. 1284 - 1295
• Main Authors: Ghoraani, Behnaz, Hssayeni, Murtadha D., Bruack, Michelle M., Jimenez-Shahed, Joohi
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activities of Daily Living; activities of daily living dynamics; Aged; Antiparkinson Agents - therapeutic use; Data analysis; Data mining; Decision making; Decomposition; Diseases; Drugs; Feature extraction; Female; Fluctuations; Humans; Inertial platforms; Inertial sensing devices; Male; Middle Aged; Movement - physiology; Movement disorders; Multilevel; Neurodegenerative diseases; Parkinson Disease - drug therapy; Parkinson Disease - physiopathology; Parkinson's disease; pattern classification; Pattern Recognition, Automated; Representations; sensor fusion; Sensors; Signal Processing, Computer-Assisted; Signs and symptoms; Spatio-Temporal Analysis; Support Vector Machine; Support vector machines; tensor decomposition; Tensors; time-frequency analysis; Wearable sensors
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2019.2943866

Motor fluctuations are a frequent complication in patients with Parkinson's disease (PD) where the response to medication fluctuates between ON states (medication working) and OFF states (medication has worn off). This paper describes a new data analysis approach that can be used along with two wearable IMU (inertial measurement units) sensors to continuously assess motor fluctuations in PD patients while moving in their natural environment. We hypothesized that joint analysis of the sensor data in its spectral, temporal and sensor domain could generate multilevel features that can be used to detect PD-related patterns successfully as the subject's motor state fluctuates between medication ON and OFF states. For this purpose, we utilized time-frequency (TF) representation and multiway data analysis tools (i.e., tensor decomposition) to decompose the TF representation of the two sensors' data into its multilevel structures, which were next used to extract multilevel features representing the PD symptoms in different medication states. The extracted multilevel features were used in a classification model based on support vector machine to detect medication ON and OFF states. For comparison purposes, we implemented a traditional feature extraction method. We also developed a hierarchical feature extraction method based on the combination of those two methods. The performances of the three methods were evaluated using a dataset of 19 PD subjects with a total duration of 17.54 hours. The multilevel features achieved 8.25% improvement in the accuracy over the traditional features, and the hierarchical features resulted in 10.73% improvement indicating that our approach holds great promise to continuously detect medication states from continuous monitoring of the subjects' movement. Such information can be used by the treating physician to tailor the adjustments to each subject's unique impairment(s), thereby improving therapeutic decision-making and patient outcomes.