title: ÐÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ ÑÐ»ÐµÐºÑÑÐ¾ÐºÐ°ÑÐ´Ð¸Ð¾Ð³ÑÐ°Ð¼Ð¼ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÐ¸ÑÑÐ¾Ð²Ð¾Ð¹ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ ÑÐ¸Ð³Ð½Ð°Ð»Ð° Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° Ð¼Ð°ÑÐ¸Ð½Ñ Ð²ÐµÐºÑÐ¾ÑÐ½Ð¾Ð¹ Ð¿Ð¾Ð´Ð´ÐµÑÐ¶ÐºÐ¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Classification of electrocardiograms using digital signal processing and the classifier of the vector support machine
Creators: name: ÐÐ¸ÑÑÐ¾Ð², ÐÐ²Ð³ÐµÐ½Ð¸Ð¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: Ð­Ð»ÐµÐºÑÑÐ¸ÑÐµÑÐºÐ¸Ðµ ÑÐ¸Ð³Ð½Ð°Ð»Ñ
ÐÑÐ¸Ð±Ð¾ÑÐ¾ÑÑÑÐ¾ÐµÐ½Ð¸Ðµ
ÑÐ»ÐµÐºÑÑÐ¾ÐºÐ°ÑÐ´Ð¸Ð¾Ð³ÑÐ°Ð¼Ð¼Ñ
ÑÐ¸ÑÑÐ¾Ð²Ð¾Ð¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð°ÑÐ¸ÑÐ¼Ð¸Ð¹
Ð¼Ð°ÑÐ¸Ð½Ð° Ð¾Ð¿Ð¾ÑÐ½ÑÑ Ð²ÐµÐºÑÐ¾ÑÐ¾Ð²
electrocardiograms
digital data analysis
arrhythmia classification
support vector machine

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description: Topic of the final qualification work: "Classification of electrocardiograms using digital signal processing and the classifier of the vector support machine". In this paper, we investigate the possibility of using the support vector machine method for classifying arrhythmias from electrocardiograms. The main goal of this work is to build an algorithm for classifying arrhythmia by electrocardiogram, which will use various machine learning techniques to create a support vector machine model. The objectives of this work are to study the characteristics of electrocardiograms, the study of various classification models, the study of the support vector machine, the study of various methods for improving the classification model, the construction of this model and the evaluation of its work. To form a classification model for arrhythmia according to the electrocardiogram, a pipelinerization method was chosen, in which several stages of data preprocessing are combined, and the support vector machine model is set as the last stage. Data preprocessing consists of two stages. The first step is to apply the Savitsky-Golay filter to smooth the incoming signal and remove noise from the signal. The second stage is scaling, which allows you to bring all inputs to the same view, thereby greatly simplifying the work of the model. Methods are also used to improve the model, such as grid search to select the optimal hyperparameters, as well as the One-Vs-Rest method for the transition from binary classification to multiclass. The result of this work is a trained model that combines the stages of preprocessing and support vector machines into a single entity. The performance of this model was evaluated using the cross-validation method, which showed an accuracy of 94 percent of the model. 7 This model can be used in medical practice to facilitate the diagnosis of arrhythmia, and not just to determine whether there is a rhythm disorder or not, but to determine the specific type of arrhythmia. Also, this model can be used as one of the main components of software that can be used to analyze large cardiograms, such as in 24-hour monitoring devices. Such software can greatly facilitate the determination of pathologies in the work of the heart and will provide serious assistance to the doctor for making a diagnosis. The main conclusion of this work is the assertion that the support vector machine method, coupled with the use of data preprocessing methods such as the Savitsky-Golay filter and scaling, as well as when using methods for determining optimal hyperparameters using grid search and the transition from binary classification to multiclass using the One-Vs-Rest method, shows good results for the task of classifying arrhythmias from electrocardiograms. The accuracy of this model is 94 percent.