ArticleBMC medical informatics and decision making2022
Detection of K-complexes in EEG waveform images using faster R-CNN and deep transfer learning.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
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- An Infusion Containers Detection Method Based on YOLOv4 with Enhanced Image Feature Fusion.Entropy (Basel, Switzerland) · 2023Article
- On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal Features.Life (Basel, Switzerland) · 2023Article
- Localization and Classification of Venusian Volcanoes Using Image Detection Algorithms.Sensors (Basel, Switzerland) · 2023Article
- A Novel Baseline Removal Paradigm for Subject-Independent Features in Emotion Classification Using EEG.Bioengineering (Basel, Switzerland) · 2023Article
- A study on feature selection using multi-domain feature extraction for automated k-complex detection.Frontiers in neuroscience · 2023Article
- A Deep-Learning-Based Artificial Intelligence System for the Pathology Diagnosis of Uterine Smooth Muscle Tumor.Life (Basel, Switzerland) · 2022Article
- Robust Iris-Localization Algorithm in Non-Cooperative Environments Based on the Improved YOLO v4 Model.Sensors (Basel, Switzerland) · 2022Article
- Research on Pedestrian Detection Model and Compression Technology for UAV Images.Sensors (Basel, Switzerland) · 2022Article
- Evaluation of the Second Premolar's Bud Position Using Computer Image Analysis and Neural Modelling Methods.International journal of environmental research and public health · 2022Article
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3 authors.
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Abstract
backgroundThe electroencephalography (EEG) signal carries important information about the electrical activity of the brain, which may reveal many pathologies. This information is carried in certain waveforms and events, one of which is the K-complex. It is used by neurologists to diagnose neurophysiologic and cognitive disorders as well as sleep studies. Existing detection methods largely depend on tedious, time-consuming, and error-prone manual inspection of the EEG waveform.
methodsIn this paper, a highly accurate K-complex detection system is developed. Based on multiple convolutional neural network (CNN) feature extraction backbones and EEG waveform images, a regions with faster regions with convolutional neural networks (Faster R-CNN) detector was designed, trained, and tested. Extensive performance evaluation was performed using four deep transfer learning feature extraction models (AlexNet, ResNet-101, VGG19 and Inceptionv3). The dataset was comprised of 10948 images of EEG waveforms, with the location of the K-complexes included as separate text files containing the bounding boxes information.
resultsThe Inceptionv3 and VGG19-based detectors performed consistently high (i.e., up to 99.8% precision and 0.2% miss rate) over different testing scenarios, in which the number of training images was varied from 60% to 80% and the positive overlap threshold was increased from 60% to 90%.
conclusionsOur automated method appears to be a highly accurate automatic K-complex detection in real-time that can aid practitioners in speedy EEG inspection.
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