Cloud-Connected Wireless Holter Monitor Machine with Neural Networks Based ECG Analysis for Remote Health Monitoring (2310.13965v1)

Abstract: 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.