Cardiovascular disorders are often diagnosed using an electrocardiogram (ECG). It is
a painless method that mimics the cyclical contraction and relaxation of the heart’s muscles. By
monitoring the heart’s electrical activity, an ECG can be used to identify irregular heartbeats, heart
attacks, cardiac illnesses, or enlarged hearts. Numerous studies and analyses of ECG signals to
identify cardiac problems have been conducted during the past few years. Although ECG heartbeat
classification methods have been presented in the literature, especially for unbalanced datasets, they
have not proven to be successful in recognizing some heartbeat categories with high performance.
This study uses a convolutional neural network (CNN) model to combine the benefits of dense
and residual blocks. The objective is to leverage the benefits of residual and dense connections
to enhance information flow, gradient propagation, and feature reuse, ultimately improving the
model’s performance. This proposed model consists of a series of residual-dense blocks interleaved
with optional pooling layers for downsampling. A linear support vector machine (LSVM) classified
heartbeats into five classes. This makes it easier to learn and represent features from ECG signals. We
first denoised the gathered ECG data to correct issues such as baseline drift, power line interference,
and motion noise. The impacts of the class imbalance are then offset by resampling techniques that
denoise ECG signals. An RD-CNN algorithm is then used to categorize the ECG data for the various
cardiac illnesses using the retrieved characteristics. On two benchmarked datasets, we conducted
extensive simulations and assessed several performance measures. On average, we have achieved
an accuracy of 98.5%, a sensitivity of 97.6%, a specificity of 96.8%, and an area under the receiver
operating curve (AUC) of 0.99. The effectiveness of our suggested method for detecting heart disease
from ECG data was compared with several recently presented algorithms. The results demonstrate
that our method is lightweight and practical, qualifying it for continuous monitoring applications in
clinical settings for automated ECG interpretation to support cardiologists.