Classification of Electroencephalogram in a Mouse Model of Traumatic Brain Injury Using Machine Learning Approaches

Vishwanath, Manoj; Jafarlou, Salar; Shin, Ikhwan; Dutt, Nikil; Rahmani, Amir M.; Lim, Miranda M.; Cao, Hung

doi:10.1109/embc44109.2020.9175915

Cited by 10 publications

(6 citation statements)

References 19 publications

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“…In a mouse model of traumatic brain injury (TBI), Vishwanath et al [70] classified EEG data using machine learning methods, namely CNNs, and achieved accuracies of up to 92.03% when evaluating sleep and wake data. Their results point to the possibility of using these methods to diagnose neurological disorders such traumatic brain injury.…”

Section: Brain Injury Assessmentmentioning

confidence: 99%

EEG Innovations in Neurological Disorder Diagnostics: A Five-Year Review

Basak,

Maiti,

Das

2024

Asian J. Res. Com. Sci.

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The study provides a description of electroencephalography (EEG) advancements and their application in diagnosing and assessing various neurological diseases over the previous five years. The paper covers how EEG is used to examine epilepsy, sleep disorders, movement disorders, cognitive function, and brain damage. In epilepsy, EEG remains critical for seizure diagnosis, categorization, and localization of epileptogenic zones. Recent enhancements include the integration of machine learning techniques with high-density EEG equipment. In terms of sleep disorders, aberrant patterns suggestive of illnesses such as sleep apnea or narcolepsy may be diagnosed by a sleep architecture study utilizing EEGs, which can also be used to track therapy response. Cortical involvement occurs in Parkinson’s disease and Huntington’s disease, as well as other areas of the brain stem or basal ganglia. It helps researchers learn more about the cortical damage produced by these disorders, which contributes greatly to understanding their pathophysiology. Aside from that, cognitive evaluation based on EEG has evolved via the creation of quantifiable biomarkers for early identification and monitoring of deterioration in Alzheimer’s disease, among others. Traumatic injuries can damage brain functioning, hence knowledge regarding severity predicted outcomes can be acquired by Traumatic Brain Injury evaluation utilizing EEG.

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Section: Brain Injury Assessmentmentioning

confidence: 99%

EEG Innovations in Neurological Disorder Diagnostics: A Five-Year Review

Basak,

Maiti,

Das

2024

Asian J. Res. Com. Sci.

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“…The first implementation used a CNN model [10] to automatically extract features suitable for classification from an EEG signal with an epoch duration of 16 s to 64 s. The second implementation involved XGBoost that also used a 16 s to 64 s epoch duration. In this case, decibel normalized sub-band powers and ratio of theta to alpha sub-band power were extracted from the EEG signal as described in [23]. The extracted features were fed to an XGBoost classifier to obtain the predicted classes.…”

Section: Classification Systemmentioning

confidence: 99%

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

Dhillon

Sutandi

Vishwanath

et al. 2021

Sensors

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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 the 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 Electroencephalogram (EEG) signal. We discuss the design, implementation, and verification of the system that can digitize the 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 16–64 s epochs for TBI vs. control conditions. This work can enable the 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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“…Moreover, greater decreases in parasympathetic nervous system (PNS) activation (heart rate variability [HRV]) have also been meaningfully associated with anxiety and depression [42,43]. Similarly, objective bio-signal analysis to detect stress has been shown to be expandable to detect other psychological disorders, such as bipolar disorder that can significantly affect aforementioned physiological measurements [44,45,46].…”

Section: Objective Mental Health Assessmentmentioning

confidence: 99%

Personal Mental Health Navigator: Harnessing the Power of Data, Personal Models, and Health Cybernetics to Promote Psychological Well-being

Rahmani¹,

Lai²,

Jafarlou³

et al. 2020

Preprint

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Traditionally, the regime of mental healthcare has followed an episodic psychotherapy model wherein patients seek care from a provider through a prescribed treatment plan developed over multiple provider visits. Recent advances in wearable and mobile technology have generated increased interest in digital mental healthcare that enables individuals to address episodic mental health symptoms such as depression and anxiety. However, despite providers' best intentions, these efforts are typically reactive and symptom-focused, and do not provide comprehensive, wrap-around, customized treatments that capture an individual's holistic mental health model as it unfolds over time. Recognizing that each individual is unique and requires personally tailored mental health treatment, we present the notion of Personalized Mental Health Navigation (MHN): a therapist-in-the-loop, cybernetic goal-based system that deploys a continuous cyclic loop of measurement, estimation, guidance, to steer the individual's mental health state towards a healthy zone. We outline the major components of MHN that is premised on the development of an individual's personal mental health state, holistically represented by a high-dimensional cover of multiple knowledge layers such as emotion, biological patterns, sociology, behavior, and cognition. We demonstrate the feasibility of the personalized MHN approach via a 12-month pilot case study for holistic stress reduction and management in college students, and highlight an instance of a therapist-in-the-loop intervention using MHN for monitoring, estimating, and proactively addressing moderately severe depression over a sustained period of time. We believe MHN paves the way to transform mental healthcare from the current passive, episodic, reactive process (where individuals seek help to address symptoms that have already manifested) to a continuous and navigational paradigm that leverages a personalized model of the individual, promising to deliver timely interventions to individuals in a holistic manner.

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Classification of Electroencephalogram in a Mouse Model of Traumatic Brain Injury Using Machine Learning Approaches

Cited by 10 publications

References 19 publications

EEG Innovations in Neurological Disorder Diagnostics: A Five-Year Review

EEG Innovations in Neurological Disorder Diagnostics: A Five-Year Review

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

Personal Mental Health Navigator: Harnessing the Power of Data, Personal Models, and Health Cybernetics to Promote Psychological Well-being

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