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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Vydané v:	PloS one Ročník 19; číslo 12; s. e0312763
Hlavný autor:	Fraiwan, Mohammad Amin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Public Library of Science 23.12.2024 Public Library of Science (PLoS)
Predmet:	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
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Abstract	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.
AbstractList	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. 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.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.
Audience	Academic
Author	Fraiwan, Mohammad Amin
AuthorAffiliation	Department of Computer Engineering, Jordan University of Science and Technology, Irbid, Jordan Bayer Crop Science United States: Bayer CropScience LP, UNITED STATES OF AMERICA
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/39715265$$D View this record in MEDLINE/PubMed
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Cites_doi	10.1016/j.neuroscience.2019.10.034 10.1109/SIU.2017.7960311 10.1016/j.jevs.2020.102973 10.1007/s12652-019-01312-3 10.1145/3065386 10.1109/CVPR.2016.90 10.3389/fninf.2019.00045 10.1016/j.cmpb.2005.02.006 10.3390/s21217230 10.1016/j.jneumeth.2019.03.017 10.4236/jbise.2017.105B002 10.17485/ijst/2016/v9i25/96628 10.1038/nrn2762 10.1016/j.jneumeth.2018.08.014 10.1111/jsr.12169 10.1109/ICBME.2014.7043905 10.1016/S0893-6080(98)00116-6 10.1109/CVPR.2015.7298594 10.1186/s40535-016-0027-9 10.3390/bioengineering10080901 10.1016/j.compbiomed.2022.106096 10.1016/j.jneumeth.2017.06.004 10.3389/fnhum.2015.00414 10.1109/CVPR.2016.308 10.1016/S0378-4371(03)00473-4 10.1016/j.neures.2021.03.012
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SubjectTerms	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
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Title	Detection and location of EEG events using deep learning visual inspection
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