Telecommunications
Afshin Koliji; Sara Mihandoost; Nematollah Ezzati; Ehsan Mostafapour
Abstract
Sudden Cardiac Death (SCD) leads to the killing of millions of people worldwide every year. In this article, sudden cardiac death is predicted by utilizing electrocardiogram signal processing. For this purpose, after extracting the signal of heart rate variations from the electrocardiogram signal, temporal ...
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Sudden Cardiac Death (SCD) leads to the killing of millions of people worldwide every year. In this article, sudden cardiac death is predicted by utilizing electrocardiogram signal processing. For this purpose, after extracting the signal of heart rate variations from the electrocardiogram signal, temporal and non-linear features have been extracted. In the next step, by applying LDA to the combined feature vector, the feature dimensions are reduced and finally, healthy people and high-risk people are classified through Hybrid-RBF classifiers. The obtained results show that there are features in the signal of heart rate variations related to risk-taking individuals near the occurrence of sudden cardiac death, that completely distinguish them from healthy persons. It has also been shown that from 6 minutes before the occurrence of cardiac death, this increase in the probability of risk is quite evident, so that as we get closer to the occurrence of the accident, the probability of its occurrence also increases, and this is enough time to adopt strategies to prevent it. The simulation results achieved by the data available in the MIT-BIH database prove the ability of the presented methods to achieve accurate diagnosis.
Telecommunications
Amir Hatamian; Farzad Farshidi; Changiz Ghobadi; Javad Nourinia; Ehsan Mostafapour
Abstract
The increasing risk of cardiovascular diseases, stress, high blood pressure, obesity, sleep disorders, and depression causes electrocardiogram (ECG) monitors to be used for diagnosing health. The main objective of this research is to enhance the quality of the ECG signal using wavelet transform and adaptive ...
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The increasing risk of cardiovascular diseases, stress, high blood pressure, obesity, sleep disorders, and depression causes electrocardiogram (ECG) monitors to be used for diagnosing health. The main objective of this research is to enhance the quality of the ECG signal using wavelet transform and adaptive filters. This research has been made as descriptive-analytic and the method is used in the signal processing stages to calculate the ECG modulation spectrum, the spectral-modulation filtering scheme, and the ECG database from the standard algorithm and performance criteria. The results of the simulation indicate that the conversion of Sym4 and the adaptive filter with the size of 0.0005 and the length of the filter of 25 signals to the noise will be greatly improved to reveal the main features of the ECG signal.