Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN. (22nd November 2018)

• Record Type: Journal Article
• Authors: Ke, Hengjin; Chen, Dan; Shah, Tejal; Liu, Xianzeng; Zhang, Xinhua; Zhang, Lei; Li, Xiaoli
• Other Names: Ranjan Rajiv guestEditor.; Villari Massimo guestEditor.; Shen Haiying guestEditor.; Rana Omer guestEditor.; Buyya Rajkumar guestEditor.
• Is Part Of: Software, practice & experience. Volume 50:Number 5(2020)
• Page Start: 596
• Page End: 610
• Publication Date: 2018-11-22
• Subjects: brain healthcare -- Cloud computing -- CNN -- depression -- EEG classification; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2668
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Brain healthcare, when supported by Internet of Things, can perform online and accurate analysis of brain big data for the classification of multivariate Electroencephalogram (EEG), which is a prerequisite for the recent boom in neurofeedback applications and clinical practices. However, it remains a grand research challenge due to (1) the embedded intensive noises and the intrinsic nonstationarity determined by the evolution of brain states; and (2) the lack of a user‐friendly computing platform to sustain the complicated analytics. This study presents the design of an online EEG classification system aided by Cloud centering on a lightweight Convolutional Neural Network (CNN). The system incrementally trains the CNN on Cloud and enables hot deployment of the trained classifier without the need to restart the gateway to adapt to the users' needs. The classifier maintains a High Convolutional Layer to gain the ability of processing high‐dimensional EEG segments. The number of hidden layers is minimized to ensure the efficiency of training. The lightweight CNN adopts an "hourglass" block of fully connected layers to reduce the number of neurons quickly toward the output end. A case study of depression evaluation has been performed against raw EEG datasets to distinguish between (1) Healthy and Major Depression Disorder with an accuracy, sensitivity, and specificity of [98.59 % ± 0.28 % ], [97.77 % ± 0.63 % ], and [99.51 % ± 0.19 % ], respectively; and (2) Effective and Noneffective treatment outcome with an accuracy, sensitivity, and specificity of [99.53 % ± 0.002 % ], [99.50 % ± 0.01 % ], and [99.58 % ± 0.02 % ], respectively. The results show that the classification can be completed several magnitudes faster when EEG is collected on the gateway (several milliseconds vs. 4 seconds).