The paper proposes sigmoidal activation functions based on atomic functions. The properties of atomic functions are described, which allow them to satisfy the conditions for transfer functions in artificial neural networks. The applied problems of using the presented functions are considered on the example of problems of analysis of biophysical signals of cardiac activity. The results obtained using the constructed classifiers using various architectures of neural networks, including MLP, RNN, LSTM, GRU, CNN networks, are presented. The efficiency of using atomic functions in the constructed neural networks on the examples of the problem of automated diagnostics of pathologies based on seismocardiography data and biometric authentication by heart rate was determined using metrics of accuracy, recall, precision, sensitivity, specificity, and F-measure.
Recent studies demonstrated the clinical utility of seismocardiography (hereinafter SCG) signals for the detection and monitoring of cardiovascular conditions. Renewed interest in investigating the utility of SCG has been accelerated recently and benefited from new advances in low-cost lightweight sensors and machine learning methods. This article compares various machine learning algorithms (the method of nearest neighbors, the method of support vectors, decision trees, the ensemble of models) and neural networks: based on the architecture of long short-term memory and convolutional ones. An original numerical experiment was carried out using the developed mathematical software, where all of the mentioned methods and algorithms were implemented. During this study, much attention was paid to the preparation and preliminary processing of data. In particular, signal filtering is carried out using the Butterworth filter, and the issues of extracting features from the signal, which will become an input vector for machine learning algorithms, are also discussed. To compare the effectiveness of the considered models for solving the problem of diagnosing diseases, Accuracy, Recall, Sensitivity, Specificity, Precision, F1-measure, etc. are given. For each algorithm and data set, confusion matrices and ROC curves were constructed. Results of this research show that convolutional neural networks are very effective at diagnosing the states of the human cardiovascular system and supporting decision-making in cardiology and cardiac surgery.
The paper considers the possibilities of using neural network methods of machine learning to diagnose the states of the human cardiovascular system and support decision-making in cardiology and cardiac surgery. The issues of processing and preparation of electrocardiography signals, selection of architecture and tuning of neural network parameters for automation of diagnosis are discussed. Here, the results obtained with the help of multilayer perceptrons and convolutional neural networks to assign the submitted input cardiovascular data to one of the classes of states in the selected space are examined. Based on a specialized developed software, the proprietary numerical experiments with real clinical data were carried out. Given the above results, demonstrating the applicability of the used deep learning methods and algorithms to diagnostic automation, a model of a hierarchical decision support system is proposed.
The development of medical decision support systems is an important social and economically significant task, and one of the most important and acute directions in this field of research is cardiology supporting decision-making systems. The report considers the main requirements for the recognition system based on artificial intelligence methods and used to assess the functional state of the cardiovascular system (CVS). The description of the general scheme of the developed decision support system based on the identification and classification of CVS states is given. As various types of neural networks and other classifiers based on machine learning are often used in problems of the cardiovascular states identification, here, the main attention is paid to the use of convolutional and other deep neural networks for the recognition of images in cardiology for diagnostic purposes.
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