Abstract:
The primary objective of this work is to use the patient’s electrocardiogram (ECG) to develop
a method for early diagnosis of Arrhythmia disease. Traditional methods of diagnosing Ar-
rhythmia often require extensive tests and a laborious workup in a hospital setting. Improved
diagnostic technologies are needed to enable early and precise detection of cardiovascular
diseases, particularly Arrhythmias. In this work, deep learning is integrated with signal
processing techniques to detect Arrhythmia disease, using a dataset from the well-known
MIT-BIH Arrhythmia Database. The first step is to convert the raw ECG information into
visual representations using Wavelet transforms. These transforms capture precise temporal
and frequency properties, unveiling detailed information about the underlying complexities
of the heart rhythm. A Convolutional Neural Network (CNN) model specifically designed
for Arrhythmia detection is then trained using this transformed data. Deep learning uti-
lizes pre-existing knowledge from established architectures, enhancing the model’s ability to
detect patterns indicative of Arrhythmia diseases. The proposed methodology is carefully
evaluated on a testing set using performance metrics such as accuracy, sensitivity, and speci-
ficity. These performance metrics are crucial indicators of the model’s ability to accurately
distinguish between normal, web, and even cardiac rhythms. Overall, this research aims
to develop a novel approach combining deep learning and signal processing techniques to
enhance the detection of Arrhythmia diseases using ECG data.
