EEG Signal Complexity Analysis: MATLAB-Based Comparison of Comatose Patients and Normal Sleep Patterns

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Resource Overview

MATLAB-based platform for analyzing EEG complexity in comatose patients with comparative analysis against normal sleep states. Implementation includes entropy algorithms and signal processing techniques to quantify cognitive activity levels, where higher complexity typically indicates greater alertness and cognitive processing.

Detailed Documentation

This study focuses on analyzing EEG signal complexity in comatose patients using MATLAB platform, with comparative evaluation against normal subjects during sleep states. The implementation involves processing raw EEG data through signal filtering techniques and calculating complexity metrics using algorithms like Approximate Entropy (ApEn) or Sample Entropy (SampEn). Typically, higher cognitive activity and alertness correlate with increased EEG complexity measured through these computational methods. By investigating EEG complexity patterns in comatose patients, we can better understand their conscious states and provide more accurate clinical guidance for treatment protocols. The MATLAB implementation includes functions for signal preprocessing, feature extraction, and statistical comparison between patient groups.

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