Advances Brain Research and Neuroscience

CSF-EEG FusionNet: A Novel EEG-Based Algorithm for Detecting Brainstem Distress in Chiari Malformation Patients

Abstract

Rajendra Nath Dasari

Chiari Malformation Type I (CM-I) is a neurological disorder in which the cerebellar tonsils extend into the spinal canal, often disrupting cerebrospinal fluid (CSF) flow. While current diagnostic tools like MRI identify structural issues, they frequently fail to explain the debilitating symptoms reported by patients, such as cognitive fog and headaches, which are likely signs of brainstem distress. This study introduces CSF-EEG FusionNet, a novel algorithm that uses electroencephalography (EEG) to detect these subtle signs of neural distress non-invasively. The algorithm extracts three key neurophysiological signatures—intermittent rhythmic delta activity (IRDA), nonlinear entropy, and phase-amplitude coupling (PAC)—from simulated EEG data to generate a composite distress index. Our results demonstrate that FusionNet successfully identifies these specific markers, suggesting that an EEG-based approach can provide a functional complement to traditional structural imaging. This method holds promise as a new, objective tool for diagnosing and monitoring functional deficits in CM-I patients, offering a way to better correlate their symptoms with physiological evidence.

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