Automated Machine Learning for EEG-Based Classification of Parkinson's Disease Patients

• Published in: 2019 IEEE International Conference on Big Data (Big Data) pp. 4845 - 4852
• Main Authors: Koch, Milan, Geraedts, Victor, Wang, Hao, Tannemaat, Martijn, Back, Thomas
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2019
• Subjects: Automated Machine Learning; Biomarkers; Brain modeling; Computational modeling; EEG; Electroencephalography; Feature extraction; Machine learning; Parkin-son's Disease; Time series analysis; Time Series Classification
• DOI: 10.1109/BigData47090.2019.9006599

The treatment of Parkinson's Disease (PD) with Deep Brain Stimulation (DBS) can provide a constant level of motor functioning. Several patients, however, may suffer from postoperative cognitive deterioration. The DBS screening therefore includes an assessment of cognitive functioning prior to DBS surgery. However, these assessments may be influenced by factors such as fatigue or motivation and there is a need for novel biomarkers of cognitive dysfunction to complement the DBS screening. Electroencephalography (EEG) has been previously suggested to identify potential cognitive impairment in PD patients and may have utility during the DBS screening. A limited set of biomarkers (features) from the EEG has been identified for this purpose. Finding new biomarkers is time-consuming and there is no driving hypothesis on which new biomarkers may be important. Based on EEG time series of 40 DBS candidates, this research focuses on automated machine learning techniques to develop EEG-based algorithms for the evaluation of the cognitive function of PD patients. The automated pipeline consists of feature extraction, feature selection, modelling algorithm and optimization. With this approach we extract 794 features from each of the 21 EEG channels which results in a massive feature space. From this feature space the most significant features are selected and used for modelling. The hyperparameters of the model are optimized by a Bayesian technique as part of the automated approach. Aside from the automatically computed features, we also explore the use of features commonly used during clinical evaluation of the EEG, with the result that the model based on automatically computed features achieves a significant higher accuracy (84.0%). The newly identified features are potentially new biomarkers. We used the knowledge gathered from our automated approach to build a hand-crafted model resulting in an accuracy of 91.0%.