Department of Intensive Care Unit, West China Hospital of Sichuan University, Chengdu 610041, China.
J Healthc Eng. 2021 Jul 2;2021:6284035. doi: 10.1155/2021/6284035. eCollection 2021.
This article first studied the morphological characteristics of the EEG for intensive cardiac care; that is, based on the analysis of the mechanism of disease diagnosis and treatment, a signal processing and machine learning model was constructed. Then, the methods of signal preprocessing, signal feature extraction, new neural network model structure, training mechanism, optimization algorithm, and efficiency are studied, and experimental verification is carried out for public data sets and clinical big data. Then, the principle of intensive cardiac monitoring, the mechanism of disease diagnosis, the types of arrhythmia, and the characteristics of the typical signal are studied, and the rhythm performance, individual variability, and neurophysiological basis of electrical signals in intensive cardiac monitoring are researched. Finally, the automatic signal recognition technology is studied. In order to improve the training speed and generalization ability, a multiclassification model based on Least Squares Twin Support Vector Machine (LS-TWIN-SVM) is proposed. The computational complexity of the classification model algorithm is compared, and intelligence is adopted. The optimization algorithm selects the parameters of the classifier and uses the EEG signal to simulate the model. Support Vector Machines and their improved algorithms have achieved the ultimum in shallow neural networks and have achieved good results in the classification and recognition of bioelectric signals. The LS-TWIN-SVM algorithm proposed in this paper has achieved good results in the classification and recognition of bioelectric signals. It can perform bioinformatics processing on intensive cardiac care EEG signals, systematically biometric information, diagnose diseases, the real-time detection, auxiliary diagnosis, and rehabilitation of patients.
本文首先研究了重症监护的心电信号的形态特征；即基于疾病诊断和治疗机制的分析，构建了信号处理和机器学习模型。然后，研究了信号预处理、信号特征提取、新神经网络模型结构、训练机制、优化算法和效率的方法，并对公共数据集和临床大数据进行了实验验证。然后，研究了重症监护的原理、疾病诊断的机制、心律失常的类型以及典型信号的特征，研究了重症监护中心电信号的节律性能、个体变异性和神经生理基础。最后，研究了自动信号识别技术。为了提高训练速度和泛化能力，提出了一种基于最小二乘孪生支持向量机（LS-TWIN-SVM）的多分类模型。比较了分类模型算法的计算复杂度，并采用智能优化算法选择分类器的参数，并使用 EEG 信号对模型进行仿真。支持向量机及其改进算法在浅层神经网络中达到了最优，并在生物电信号的分类和识别中取得了良好的效果。本文提出的 LS-TWIN-SVM 算法在生物电信号的分类和识别中取得了良好的效果。它可以对重症监护的心电信号进行生物信息学处理，系统地进行生物计量信息、疾病诊断、患者的实时检测、辅助诊断和康复。
