Отрывок: The first group of features includes various entropic features, which indicate a measure of unpredictability in the signal. The following types of entropies are used: approximate entropy, sample entropy, and Shannon entropy. The next group of features is based on a recurrence plot. This plot shows the frequency and duration of recurrences in the signal. Based on this plot, the following features are calculated: density of points on the plot, percentage of points t...
Название : Ensemble of algorithms for coronary heart disease detection based on electrocardiogram
Авторы/Редакторы : Guryanova, V.N.
Ключевые слова : ECG Signal Processing
Ensemble Learning
Automatic CAD Detection
ECG classification
Дата публикации : 2018
Издательство : Новая техника
Библиографическое описание : Guryanova V.N. Ensemble of algorithms for coronary heart disease detection based on electrocardiogram // Сборник трудов IV международной конференции и молодежной школы «Информационные технологии и нанотехнологии» (ИТНТ-2018) - Самара: Новая техника, 2018. - С. 2865-2870
Аннотация : Coronary heart disease (CHD) is the leading cause of death in the world. This disease can be asymptomatic for a long time and over time can progress and result in death. Today electrocardiogram (ECG) can be done at home with the help of special equipment from CardioQvark. In this paper, the possibility of CHD detection based on such ECGs was explored. Different approaches to the classification of such electrocardiograms were surveyed. New algorithms and modifications to existing algorithms were proposed. A new method − ensemble of different algorithms – has shown the best quality.
URI (Унифицированный идентификатор ресурса) : http://repo.ssau.ru/handle/Informacionnye-tehnologii-i-nanotehnologii/Ensemble-of-algorithms-for-coronary-heart-disease-detection-based-on-electrocardiogram-69476
Другие идентификаторы : Dspace\SGAU\20180517\69476
Располагается в коллекциях: Информационные технологии и нанотехнологии

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