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Cluster Computing

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09.11.2022

Detection of K-complexes in EEG signals using deep transfer learning and YOLOv3

verfasst von: Natheer Khasawneh, Mohammad Fraiwan, Luay Fraiwan

Erschienen in: Cluster Computing | Ausgabe 6/2023

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The K-complex is one of the most important and noticeable features in the electroencephalography (EEG) signal, therefore its detection is critical for EEG signal analysis. It is used 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. In 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, you only look once v3 (YOLOv3) detector was designed, trained, and tested. Extensive performance evaluation was performed using five deep transfer learning feature extraction models; Darknet-53, MobileNets, ResNet-18, SqueezeNet, and Darknet-53-coco. The dataset was comprised of 10948 images of EEG waveforms, with the K-complex location automatically annotated with bounding boxes. The Darknet-53 model performed consistently high (i.e., 89.84–99.44% precision and 10.41–0.55% miss rate). Thus, it is possible to perform automatic K-complex detection in real-time with high accuracy that aid practitioners in speedy EEG inspection.

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Titel: Detection of K-complexes in EEG signals using deep transfer learning and YOLOv3
verfasst von: Natheer Khasawneh
Mohammad Fraiwan
Luay Fraiwan
Publikationsdatum: 09.11.2022
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 6/2023
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-022-03802-0

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