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An epilepsy classification system using electrocardiogram (ECG) data will ease the process of diagnosis. In epileptic patients, the seizures affect Heart Rate Variability (HRV). This emphasizes the importance of autonomic function changes in diagnosing epilepsy. The present work proposes an algorithm that classifies a person as epileptic or nonepileptic using ECG signal. Time Domain Features (TDF)...
Heart is the most vital organ which circulates blood along with nutrients and oxygen throughout the body. There are number of reasons which may affect its normal working. In this paper ten heart diseases, as well as normal, have been classified by extracting features from original ECG (electrocardiogram) signals and sixth level wavelet transformed ECG signals. The results have been compared and improved...
Standard Electrocardiogram (ECG) database is prepared for testing the performance of automatic detection and classification algorithms. At present, there are three mainstream standard databases used by computer-aided ECG diagnosis researchers: MIT-BIH arrhythmia database, CSE multi-lead database and AHA database. By the progress of ECG in both equipment and diagnosis theory, fatal deficiency was found...
In this paper, we investigate identification of human subjects from electrocardiogram (ECG) signals. We segment the ECG records into individual heartbeat based on the localization of R wave peaks. Two types of features, namely analytic and appearance features, are extracted to represent the characteristics of heartbeat signal of different subjects. Feature selection is performed to find out significant...
To satisfy the difficult requirements of ECG analysis such as large data volume, high accuracy and real-time, a classification algorithm for arrhythmia based on clustering analysis is developed. According to things-of-one-kind-come-together principle, this algorithm uses the similarity of heart cases of the same category and, at the same time, incorporates the factor of individual differences. It...
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