Application of recurrent analysis to classify realizations of encephalograms
DOI:
https://doi.org/10.34185/1562-9945-6-143-2022-08Keywords:
classification using machine learning, time series classification, recurrent diagrams, Electroencephalogram implementation, deep residual networks.Abstract
The current state of science and technology is characterized by a variety of methods and approaches to solving various tasks, including in the fields of time series analysis and computer vision. This abstract explores a novel approach to the classification of time series based on the analysis of brain activity using recurrent diagrams and deep neural networks. The work begins with an overview of recent achievements in the field of time series analysis and the application of machine learning methods. The importance of time series classification in various domains, including medicine, finance, technology, and others, is em-phasized. Next, the methodology is described, in which time series are transformed into gray-scale images using recurrent diagrams. The key idea is to use recurrent diagrams to visualize the structure of time series and identify their nonlinear properties. This transformed informa-tion serves as input data for deep neural networks. An important aspect of the work is the selection of deep neural networks as classifiers for the obtained images. Specifically, residual neural networks are applied, known for their ability to effectively learn and classify large volumes of data. The structure of such networks and their advantages over other architectures are discussed. The experimental part of the work describes the use of a dataset of brain activity, which includes realizations from different states of a person, including epileptic seizures. The ob-tained visualization and classification methods are applied for binary classification of EEG realizations, where the class of epileptic seizure is compared with the rest. The main evalua-tion metrics for classification are accuracy, precision, recall, and F1-score. The experimental results demonstrate high classification accuracy even for short EEG realizations. The quality metrics of classification indicate the potential effectiveness of this method for automated di-agnosis of epileptic seizures based on the analysis of brain signals. The conclusions highlight the importance of the proposed approach and its potential usefulness in various domains where time series classification based on the analysis of brain activity and recurrent diagrams is required.
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