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Volume 20 No 7 (2022)
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ECG Signal Classification for Abnormality Detection with Adaptive LMS Algorithm and Deep Recurrent Neural Learning Framework
Priyadharshini Jayaraman , Madheswaran Muthusamy
Abstract
Automated Electrocardiogram (ECG) processing facilitates the cardiologist to make a diagnosis and is beneficial as
listening to these recordings becomes a time-consuming analysis. The non-linearity of ECG data and the large variation
of ECG morphologies among different persons becomes the main problem with automated ECG analysis. Therefore,
new abnormality detection employing ECG signal is introduced in this research study. The acquired raw ECG data is
initially pre-processed using the novel Adaptive Least Mean Square (LMS) algorithm
Keywords
ECG signal; Improved DWT; Improved FFT; Deep Recurrent learning; SA-AHBA technique; Abnormality identification
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