School of Computer Science, South China Business College, Guangdong University of Foreign Studies, Guangzhou 510545, Guangdong, China.
Institute of Intelligent Information Processing, South China Business College, Guangdong University of Foreign Studies, Guangzhou 510545, Guangdong, China.
Comput Intell Neurosci. 2022 Jul 21;2022:6548811. doi: 10.1155/2022/6548811. eCollection 2022.
The efficient biological signal processing method can effectively improve the efficiency of researchers to explore the work of life mechanism, so as to better reveal the relationship between physiological structure and function, thus promoting the generation of major biological discoveries; high-precision medical signal analysis strategy can, to a certain extent, share the pressure of doctors' clinical diagnosis and assist them to formulate more favorable plans for disease prevention and treatment, so as to alleviate patients' physical and mental pain and improve the overall health level of the society. This article in biomedical signal is very representative of the two types of signals: mammary gland molybdenum target X-ray image (mammography) and the EEG signal as the research object, combined with the deep learning field of CNN; the most representative model is two kinds of biomedical signal classification, and reconstruction methods conducted a series of research: (1) a new classification method of breast masses based on multi-layer CNN is proposed. The method includes a CNN feature representation network for breast masses and a feature decision mechanism that simulates the physician's diagnosis process. By comparing with the objective classification accuracy of other methods for the identification of benign and malignant breast masses, the method achieved the highest classification accuracy of 97.0% under different values of and gamma, which further verified the effectiveness of the proposed method in the identification of breast masses based on molybdenum target X-ray images. (2) An EEG signal classification method based on spatiotemporal fusion CNN is proposed. This method includes a multi-channel input classification network focusing on spatial information of EEG signals, a single-channel input classification network focusing on temporal information of EEG signals, and a spatial-temporal fusion strategy. Through comparative experiments on EEG signal classification tasks, the effectiveness of the proposed method was verified from the aspects of objective classification accuracy, number of model parameters, and subjective evaluation of CNN feature representation validity. It can be seen that the method proposed in this paper not only has high accuracy, but also can be well applied to the classification and reconstruction of biomedical signals.
有效的生物信号处理方法可以有效地提高研究人员探索生命机制工作的效率，从而更好地揭示生理结构与功能之间的关系，进而推动重大生物学发现的产生；高精度的医学信号分析策略在一定程度上可以分担医生临床诊断的压力，协助他们制定更有利于疾病预防和治疗的方案，从而缓解患者身心的痛苦，提高社会整体健康水平。本文以生物医学信号中非常具有代表性的两种信号：乳腺钼靶 X 射线图像（乳腺摄影）和 EEG 信号为研究对象，结合深度学习领域的 CNN；对最具代表性的两种生物医学信号分类和重建方法进行了一系列研究：（1）提出了一种基于多层 CNN 的乳腺肿块分类新方法。该方法包括用于乳腺肿块的 CNN 特征表示网络和模拟医生诊断过程的特征决策机制。通过与其他用于识别良性和恶性乳腺肿块的方法的客观分类准确率进行比较，在不同值和γ下，该方法实现了 97.0%的最高分类准确率，进一步验证了该方法在钼靶 X 射线图像基础上识别乳腺肿块的有效性。（2）提出了一种基于时空融合 CNN 的 EEG 信号分类方法。该方法包括一个专注于 EEG 信号空间信息的多通道输入分类网络、一个专注于 EEG 信号时间信息的单通道输入分类网络和一个时空融合策略。通过 EEG 信号分类任务的对比实验，从客观分类准确率、模型参数数量和 CNN 特征表示有效性的主观评价等方面验证了所提出方法的有效性。可以看出，本文提出的方法不仅具有较高的准确率，而且可以很好地应用于生物医学信号的分类和重建。
