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<p>The electroencephalography (EEG) signals are the cheapest approach to study the brain information, commonly used for epilepsy and seizure detection. This study presents a QFF-ML Net (Quantum Feature Fused Machine Learning Network). This novel approach integrates Quantum Wavelet Transform (QWT) and Quantum Fourier Transform (QFT) with a dimensionality reduction method. The raw EEG signals are not directly transmitted for remote monitoring; signal preprocessing and feature extraction are done using PCA applied dataset with QWT and QFT, then the quantum encrypted file is transferred via Ultra-reliable low-latency communication (uRLLC), which facilitates real-time remote diagnosis. This approach effectively reduces computational complexity while preserving critical seizure-related features, making it well-suited for bandwidth-constrained environments. Experimental findings show that QFF-ML Net achieved high performance across datasets, with accuracies of 92.3% on Bonn, 91.0% on CHB-MIT, and 88.3% on real-time 5G remote data, maintaining strong precision, recall, and F1-scores in all cases in EEG-based epilepsy and seizure detection on 5G-enabled remote healthcare networks.</p>

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Quantum inspired wavelet and Fourier feature fusion for EEG based epilepsy and seizure detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月14日

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  • Vajiram Jayanthi,
  • S. Sivakumar

摘要

The electroencephalography (EEG) signals are the cheapest approach to study the brain information, commonly used for epilepsy and seizure detection. This study presents a QFF-ML Net (Quantum Feature Fused Machine Learning Network). This novel approach integrates Quantum Wavelet Transform (QWT) and Quantum Fourier Transform (QFT) with a dimensionality reduction method. The raw EEG signals are not directly transmitted for remote monitoring; signal preprocessing and feature extraction are done using PCA applied dataset with QWT and QFT, then the quantum encrypted file is transferred via Ultra-reliable low-latency communication (uRLLC), which facilitates real-time remote diagnosis. This approach effectively reduces computational complexity while preserving critical seizure-related features, making it well-suited for bandwidth-constrained environments. Experimental findings show that QFF-ML Net achieved high performance across datasets, with accuracies of 92.3% on Bonn, 91.0% on CHB-MIT, and 88.3% on real-time 5G remote data, maintaining strong precision, recall, and F1-scores in all cases in EEG-based epilepsy and seizure detection on 5G-enabled remote healthcare networks.