Key result
LS-TWIN-SVM model accurately classifies and recognizes bioelectric signals for intensive cardiac care.
Why the study?
The study aimed to develop and evaluate an improved machine learning model for classification and recognition of EEG signals in intensive cardiac care to enhance disease diagnosis and monitoring.
A novel LS-TWIN-SVM machine learning model demonstrates effectiveness in classifying and recognizing bioelectric signals for intensive cardiac care.
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.
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Chen et al. (2021) studied intensive cardiac care. Least Squares Twin Support Vector Machine (LS-TWIN-SVM) was evaluated on classification and recognition of bioelectric signals. A multiclassification model based on Least Squares Twin Support Vector Machine achieved good results in classifying and recognizing bioelectric signals for intensive cardiac care.
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