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

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Abstract

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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Data availability

The dataset of EEG signals is publicly available from www.doi.org/10.5281/ZENODO.2650142. The images generated from the EEG signals during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work would not be possible without the financial support of Jordan University of Science and Technology, deanship of research.

Funding

This work was funded by Jordan University of Science and Technology (JUST), deanship of research, award number 20220146.

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Authors and Affiliations

  1. Department of Software Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110, Jordan

    Natheer Khasawneh

  2. Department of Computer Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110, Jordan

    Mohammad Fraiwan

  3. Department of Biomedical Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110, Jordan

    Luay Fraiwan

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  1. Natheer Khasawneh
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Contributions

Conceptualization: NK, and LF; Methodology: NK, and MF; Formal analysis and investigation: NK, and MF; Writing-original draft preparation: MF; Writing-review and editing: NK, MF, and LF; Funding acquisition: MF and LF; Resources: MF; Supervision: NK, and LF. Visualization: NK

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Correspondence to Natheer Khasawneh.

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Khasawneh, N., Fraiwan, M. & Fraiwan, L. Detection of K-complexes in EEG signals using deep transfer learning and YOLOv3. Cluster Comput 26, 3985–3995 (2023). https://doi.org/10.1007/s10586-022-03802-0

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