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Updated: Jan 28, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

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使用混合深度学习框架与时间和时间频率的Hjorth特征对发作的分类

Neerja Dharmale1, Rupesh Mahamune1, Kamlesh Kahar1

  • 1Department of Electronics & Telecommunication Engineering, Shri Sant Gajanan Maharaj College of Engineering, Shegaon, India.

Computer methods in biomechanics and biomedical engineering
|January 27, 2026
PubMed
概括

这项研究引入了一个新的框架,使用Hjorth参数和注意力增强的时间建模来分类发作阶段. 多域方法在二进制,三类和五类发作检测中实现了高精度.

关键词:
电脑电图 (EEG) 是一种电脑电图.霍尔斯的参数参数注意力机制注意力机制双向长期短期记忆 (Bi-LSTM) 是一种双向的长期短期记忆.卷积神经网络 (CNN) 是一个神经网络.离散波形变换 (DWT) 是指一个离散波形变换.

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