Six-Center Assessment of CNN-Transformer with Belief Matching Loss for Patient-Independent Seizure Detection in EEG

Abstract

Neurologists typically identify epileptic seizures from electroencephalograms (EEGs) by visual inspection. This process is often time-consuming, especially for EEG recordings that last hours or days. To expedite the process, a reliable, automated, and patient-independent seizure detector is essential. However, developing a patient-independent seizure detector is challenging as seizures exhibit diverse characteristics across patients and recording devices. In this study, we propose a patient-independent seizure detector to automatically detect seizures in both scalp EEG and intracranial EEG (iEEG). First, we deploy a convolutional neural network with transformers and belief matching loss to detect seizures in single-channel EEG segments. Next, we extract regional features from the channel-level outputs to detect seizures in multi-channel EEG segments. At last, we apply post-processing filters to the segment-level outputs to determine seizures’ start and end points in multi-channel EEGs. Finally, we introduce the minimum overlap evaluation scoring as an evaluation metric that accounts for minimum overlap between the detection and seizure, improving upon existing assessment metrics. We trained the seizure detector on the Temple University Hospital Seizure (TUH-SZ) dataset and evaluated it on five independent EEG datasets. We evaluate the systems with the following metrics: sensitivity (SEN), precision (PRE), and average and median false positive rate per hour (aFPR/h and mFPR/h). Across four adult scalp EEG and iEEG datasets, we obtained SEN of 0.617–1.00, PRE of 0.534–1.00, aFPR/h of 0.425–2.002, and mFPR/h of 0–1.003. The proposed seizure detector can detect seizures in adult EEGs and takes less than 15[Formula: see text]s for a 30[Formula: see text]min EEG. Hence, this system could aid clinicians in reliably identifying seizures expeditiously, allocating more time for devising proper treatment.

A lightweight Transformer‐based neural network for large‐scale masonry arch bridge point cloud segmentation

“…The Transformer architecture (Vaswani et al., 2017) has achieved success in various NLP (Devlin et al., 2018; Yang, Dai, et al., 2019; Liu, Han, et al., 2023) and image processing (Carion et al., 2020; Fan et al., 2021; Peh et al., 2022) tasks. It is particularly suitable for point cloud processing since the attention mechanism of the Transformer is invariant to the input point cloud permutation (Zhao et al., 2021).…”

TSD: Transformers for Seizure Detection

“…CNN-RNNs and CNN-AE architectures were similar because they both coupled CNN with RNN or AE modules to diagnose seizures [9, 33, 34, 35, 36, 37, 17, 38]. Transformer-based networks usually add a Transformer module after the CNN convolution module to improve the model’s accuracy [39, 40, 41].…”

“…We designed a TSD system to identify epilepsy seizures using pre-recorded EEG signals by short-time Fourier transform (STFT) on the most extensive publicly available EEG dataset, TUH. This paper is part of a newly formed set of papers analysing the use of Transformers on EEG signals and seizure detection to locate and detect epilepsy seizures [43, 41].…”

Estimating Patient-Level Uncertainty in Seizure Detection Using Group-Specific Out-of-Distribution Detection Technique

“…Multiple studies have shown its ability to detect seizures with an accuracy of over 90% [9,11,13,[16][17][18][19][20][21]. While these algorithms show promise, they face multiple challenges in being able to consistently detect seizures across all patients suffering from different types of seizures.…”