| Autor: |
Moridani, Mohammad Karimi, Marjani, Shahrzad |
| Předmět: |
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| Zdroj: |
International Journal of Online & Biomedical Engineering; 2020, Vol. 16 Issue 9, p137-158, 22p |
| Abstrakt: |
Sudden cardiac death is an unexpected death of a person with or without knowing cardiac causes are often occurring in less than an hour after the incidence of symptoms. The purpose of this paper is to examine different methods for predicting sudden cardiac death using the ECG signals from 1998 to recent years. In this paper, studies using various methods to detection sudden cardiac death that has applied the data from the Physionet and MIT-BIH databases with a sampling frequency of 256 samples per second are reviewed. In the field of SCD prediction, various studies have addressed the processing of the ECG signal as well as the HRV signal in different domains, including time, time-frequency, and nonlinear domain. To evaluate the results of the proposed methods, each of the researchers analyzed the a-few-minute intervals before the SCD. Different classification methods are available to identify the efficiency of the proposed algorithm. The use of features introduced in different domains and different classifiers has led to the observation of different horizons of prediction in various studies. Accordingly, the most prominent of these evaluations is the mixture expert methodology in which the best feature extraction methods are used in a new method for selecting the optimal feature space locally. This method makes it possible to select different features every minute before the event by choosing the optimal features for each one-minute interval of the signal as an episode which increases the prediction time from 4 minutes before the death to 12 minutes and allows the interpretation of clinical symptoms in terms of multiplication of the presence of the features per minute. The analysis of various studies shows that by approaching the time of death, linear features (time and frequency) can be predictive of death according to the sensible behavior and variations in patients' signal. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
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