  This study describes the creation of a wireless, transportable Holter monitor
to improve the accuracy of cardiac disease diagnosis. The main goal of this
study is to develop a low-cost cardiac screening system suited explicitly for
underprivileged areas, addressing the rising rates of cardiovascular death. The
suggested system includes a wireless Electrocardiogram (ECG) module for
real-time cardiac signal gathering using attached electrodes, with data
transfer made possible by WiFi to a cloud server for archival and analysis. The
system uses a neural network model for automated ECG classification,
concentrating on the identification of cardiac anomalies. The diagnostic
performance of cardiologist-level ECG analysis is surpassed by our upgraded
deep neural network architecture, which underwent thorough evaluation and
showed a stunning accuracy rate of more than 88\%. A quick, accurate, and
reasonably priced option for cardiac screening is provided by this
ground-breaking technology, which smoothly merges wireless data transfer with
AI-assisted diagnostics. In addition to providing a thorough overview of the
development process, this paper also highlights methods used to improve model
accuracy, such as data preparation, class imbalance correction using
oversampling, and model fine-tuning. The work shows the viability of a
comprehensive remote cardiac screening system powered by AI and maximising the
use of wearable and cloud computing resources. Such cutting-edge remote health
monitoring technologies have great promise for improved health outcomes and
early identification, especially in resource-constrained countries.
