Fraiwan Mohammad Amin
Department of Computer Engineering, Jordan University of Science and Technology, Irbid, Jordan.
PLoS One. 2024 Dec 23;19(12):e0312763. doi: 10.1371/journal.pone.0312763. eCollection 2024.
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.
脑电图（EEG）是一种主要的诊断工具，可提供对大脑电活动的详细洞察。该信号包含许多独特的波形模式，这些模式反映了受试者在睡眠、神经紊乱、记忆功能等方面的健康状况。在这方面，睡眠纺锤波和K复合波是专家们感兴趣的两种主要波形模式，他们通过目视检查记录来识别这些事件。文献通常采用传统方法，即检查随时间变化的信号以识别代表感兴趣事件的特征。尽管这些方法大多针对单个事件类型，但其报告的性能结果仍有很大的改进空间。本文提出的研究采用了一种新颖的方法，类似于专家的工作方式，对波形进行目视检查，以开发一个能够检测并确定睡眠纺锤波和K复合波位置的单一模型。该模型随后生成边界框，准确描绘这些事件在图像中的位置。在广泛的条件下评估了几种目标检测算法（即Faster R-CNN、YOLOv4和YOLOX）以及多种骨干卷积神经网络（CNN）架构，揭示了它们的真实代表性性能。结果显示，在检测睡眠纺锤波和K复合波方面具有出色的精度（>95% mAP@50），尽管后者在不同骨干和阈值之间的一致性较低。
