A Raspberry Pi-based Traumatic Brain Injury Detection System for Single-Channel Electroencephalogram

• URL: http://arxiv.org/abs/2101.10869v2
• Date: Fri, 29 Jan 2021 06:26:14 GMT
• Title: A Raspberry Pi-based Traumatic Brain Injury Detection System for Single-Channel Electroencephalogram
• Authors: Navjodh Singh Dhillon, Agustinus Sutandi, Manoj Vishwanath, Miranda M. Lim, Hung Cao, Dong Si
• Abstract summary: Existing tools for Traumatic Brain Injury diagnosis are either subjective or require extensive clinical setup and expertise. This work describes a Raspberry Pi based portable, real-time data acquisition, and automated processing system that uses machine learning to efficiently identify TBI. We achieve a peak classification accuracy of more than 90% with a classification time of less than 1 s across 16 s - 64 s epochs for TBI vs control conditions.
• Score: 0.6282171844772422
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traumatic Brain Injury (TBI) is a common cause of death and disability. However, existing tools for TBI diagnosis are either subjective or require extensive clinical setup and expertise. The increasing affordability and reduction in size of relatively high-performance computing systems combined with promising results from TBI related machine learning research make it possible to create compact and portable systems for early detection of TBI. This work describes a Raspberry Pi based portable, real-time data acquisition, and automated processing system that uses machine learning to efficiently identify TBI and automatically score sleep stages from a single-channel Electroen-cephalogram (EEG) signal. We discuss the design, implementation, and verification of the system that can digitize EEG signal using an Analog to Digital Converter (ADC) and perform real-time signal classification to detect the presence of mild TBI (mTBI). We utilize Convolutional Neural Networks (CNN) and XGBoost based predictive models to evaluate the performance and demonstrate the versatility of the system to operate with multiple types of predictive models. We achieve a peak classification accuracy of more than 90% with a classification time of less than 1 s across 16 s - 64 s epochs for TBI vs control conditions. This work can enable development of systems suitable for field use without requiring specialized medical equipment for early TBI detection applications and TBI research. Further, this work opens avenues to implement connected, real-time TBI related health and wellness monitoring systems.

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