ECG Classification System based on time Domain Features with Least Square Support Vector Machine (LS-SVM
The development of authentication and identification procedures has become a critical requirement to
ensure the integrity of device data. Although passwords provide enough control and authentication, they
have been shown to have significant speed and security flaws, making biometrics the preferred
authentication technique. Consequently, most authentication systems have given much consideration to
an electrocardiogram (ECG) signal. However, ECG signals are distinct, making them difficult to imitate
yet relatively frequent. We offer a novel ECG validation model that combines multi-domain
characteristics with an LSSVM in this work. ECG signals examine two characteristics to determine each
individual's ideal mix of characteristics. First, the best 3-band filter bank is used to extract time-domain
properties from ECG data. The retrieved characteristics are examined for the most relevant ones, and the
unimportant ones are removed. The Least Square Support Vector Machine(LS-SVM) classifier collects
specified characteristics data. Our ECG authentication technology outperformed the competition,
according to the findings. The suggested model was applied to categorize EKG signals, and it achieved an
average accuracy of 92 %and 91 %when time features were used. The suggested model is evaluated
based on its ability to interact with open data.
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