LV-CadeNet: Long View Feature Convolution-Attention Fusion Encoder-Decoder Network for Clinical MEG Spike Detection
Journal:
arXiv
Published Date:
Dec 12, 2024
Abstract
It is widely acknowledged that the epileptic foci can be pinpointed by source
localizing interictal epileptic discharges (IEDs) via Magnetoencephalography
(MEG). However, manual detection of IEDs, which appear as spikes in MEG data,
is extremely labor intensive and requires considerable professional expertise,
limiting the broader adoption of MEG technology. Numerous studies have focused
on automatic detection of MEG spikes to overcome this challenge, but these
efforts often validate their models on synthetic datasets with balanced
positive and negative samples. In contrast, clinical MEG data is highly
imbalanced, raising doubts on the real-world efficacy of these models. To
address this issue, we introduce LV-CadeNet, a Long View feature
Convolution-Attention fusion Encoder-Decoder Network, designed for automatic
MEG spike detection in real-world clinical scenarios. Beyond addressing the
disparity between training data distribution and clinical test data through
semi-supervised learning, our approach also mimics human specialists by
constructing long view morphological input data. Moreover, we propose an
advanced convolution-attention module to extract temporal and spatial features
from the input data. LV-CadeNet significantly improves the accuracy of MEG
spike detection, boosting it from 42.31\% to 54.88\% on a novel clinical
dataset sourced from Sanbo Brain Hospital Capital Medical University. This
dataset, characterized by a highly imbalanced distribution of positive and
negative samples, accurately represents real-world clinical scenarios.
