{'fraction_non_alphanumeric': 0.02554890219560878, 'fraction_numerical': 0.004391217564870259, 'mean_word_length': 5.3443037974683545, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
AUTOMATIC ARRHYTHMIA DETECTION BASED ON THE ANALYSIS OF ELECTROCARDIOGRAMS WITH DEEP LEARNING

According to the World Health Organization, cardiovascular diseases (CVD) are one of the most common causes of death in the world. The most effective clinical method for visualizing the cardiac electrical activity is electrocardiography (ECG). Automated ECG analysis has been of great interest in the medical researches. The problem of automated detection of cardiac arrhythmias may be reduced to the ECG signals classification. To solve this task such methods were used as Hidden Markov Models (HMM), discrete wavelet transforms (DWT), support vector machine (SVM) etc. Now days, the deep learning models began to play the major role in solving this problem. In this paper, for the classification of ECG signals, a number of models of deep neural networks, including deep convolutional, recurrent based on short-term long memory have been developed and implemented. To improve the classification accuracy of individual classes of the studied data, the CNN-LSTM deep model was built, which combines convolutional and recurrent networks. In addition the following machine learning algorithms were used for ECG signals classification: support vector machine (SVM), decision trees (DT), random forest (RF) and extreme gradient boosting (XGB). To test the performance of the proposed models, MIT-BIH database was used, a freely available dataset that is widely used to evaluate the effectiveness of ECG signal classification algorithms. The results of a comparative analysis of various algorithms for the quality of classification for individual classes showed that machine learning algorithms classify classes with a large volume of samples well. For example, SVM and DT classify samples from class N and Q with an accuracy of 92 and 97%, respectively, while samples from classes S and F are classified with much worse accuracy of 63%. At the same time, analyzing and comparing the performance of various neural network models based on the obtained estimates of the classification accuracy, it can be argued that CNN LSTM model allows not only a high classification accuracy of 99.37%, but also high values of other indicators of classification quality, such as F1- metric, precision, and recall.The proposed algorithms for the automated detection of cardiac arrhythmias can be applied in biomedical applications that analyze the electrocardiogram and help physicians diagnose cardiac arrhythmias more accurately.