Detection and location of EEG events using deep learning visual inspection

The electroencephalogram (EEG) is a major diagnostic tool that provides detailed insight into the electrical activity of the brain. This signal contains a number of distinctive waveform patterns that reflect the subject’s health state in relation to sleep, neurological disorders, memory functions, a...

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Bibliographic Details
Published in:PloS one Vol. 19; no. 12; p. e0312763
Main Author: Fraiwan, Mohammad Amin
Format: Journal Article
Language:English
Published: United States Public Library of Science 23.12.2024
Public Library of Science (PLoS)
Subjects:
Accuracy
Activity patterns
Algorithms
Artificial neural networks
Biology and Life Sciences
Brain - physiology
Classification
Computer and Information Sciences
Datasets
Deep Learning
EEG
Electroencephalography
Electroencephalography - methods
Engineering and Technology
Evaluation
Fourier transforms
Health aspects
Humans
Inspection
Machine learning
Medicine and Health Sciences
Neural networks
Neurological diseases
Object recognition
Performance evaluation
Physical Sciences
Research and Analysis Methods
Sensors
Signal processing
Signal Processing, Computer-Assisted
Sleep
Sleep - physiology
Sleep Stages - physiology
Visual discrimination learning
Waveforms
Wavelet transforms
Jordan
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:The electroencephalogram (EEG) is a major diagnostic tool that provides detailed insight into the electrical activity of the brain. This signal contains a number of distinctive waveform patterns that reflect the subject’s health state in relation to sleep, neurological disorders, memory functions, and more. In this regard, sleep spindles and K-complexes are two major waveform patterns of interest to specialists, who visually inspect the recordings to identify these events. The literature typically follows a traditional approach that examines the time-varying signal to identify features representing the events of interest. Even though most of these methods target individual event types, their reported performance results leave significant room for improvement. The research presented here adopts a novel approach to visually inspect the waveform, similar to how specialists work, to develop a single model that can detect and determine the location of both sleep spindles and K-complexes. The model then produces bounding boxes that accurately delineate the location of these events within the image. Several object detection algorithms (i.e., Faster R-CNN, YOLOv4, and YOLOX) and multiple backbone CNN architectures were evaluated under a wide range of conditions, revealing their true representative performance. The results show exceptional precision (>95% mAP@50) in detecting sleep spindles and K-complexes, albeit with less consistency across backbones and thresholds for the latter.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0312763