Cardio Disease Analysis (IoT – Deep Learning)

Abstract: Cardiovascular diseases (CVDs) are among the leading causes of mortality worldwide, emphasizing the need for early diagnosis and continuous monitoring. In this study, we propose an IoT-based electrocardiogram (ECG) monitoring system using ESP32 and the AD8232 sensor (1-lead) to acquire real-time heart signals. The collected ECG data is analyzed using a deep learning model that classifies the heartbeat as normal or abnormal based on an existing ECG dataset. The system integrates IoT technology for remote patient monitoring and deep learning algorithms, including 1D Convolutional Neural Networks (1D-CNNs) and Long Short-Term Memory (LSTM) networks, to enhance the accuracy of arrhythmia detection. The results demonstrate the feasibility of using low-cost, portable devices for early cardiac abnormality detection, providing an effective solution for telemedicine and healthcare in resource-constrained areas. Keywords: ECG Monitoring, IoT, AD8232, ESP32, Deep Learning, Cardio Vascular Diseases, Arrhythmia Detection, Machine Learning, Convolutional Neural Networks, Long Short-Term Memory, Telemedicine.