Recognition of the Life-Threatening Cardiac Arrhythmias in the Frequency Domain
| Autor: | Liudmila A. Manilo, Boris E. Alekseev, Anastasia Sokolova, Anatoliy P. Nemirko |
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| Rok vydání: | 2020 |
| Předmět: |
Arrhythmia detection
Convex hull 0303 health sciences business.industry Computer science Human life Human heart Pattern recognition 02 engineering and technology Support vector machine 03 medical and health sciences Statistical classification symbols.namesake Fourier transform Frequency domain 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Artificial intelligence business 030304 developmental biology |
| Zdroj: | 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). |
| Popis: | During the clinical monitoring of the human heart activity the main goal is to detect heart arrhythmias and capture their precursors as early as possible. We decided to divide ECG fragments into six classes depending on their danger to the human life. As a first step in solving the problem we have grouped these classes into two parts: threatening humans’ life and others. For maintaining low response time of arrhythmia detection during long-term monitoring, we are using a 2 seconds long ECG fragments. As a classification features Fourier transform with spectrum up to 15 Hz were picked. In this paper we describe the formed dataset of ECG fragments and compare efficiency of different simple classification algorithms for this two-class problem. The following algorithms were tested: k-nearest neighbors, nearest convex hull algorithm, nearest mean and SVMs with different kernels. The results appeared to be sufficiently appropriate. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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