Deep Learning Framework for Automated Health Monitoring Using Physiological Signals

Abstract

The accelerated rate of digitalization and Artificial Intelligence (AI) is transforming the economic structures and systems of governance and policies in all countries of the world especially in digital health technologies. The paper will discuss the application of AI-based deep learning models to identify cardiac arrhythmias in the early stages by using long-term electrocardiogram (ECG) signals, which is essential in controlling health in longduration space missions where conventional healthcare facilities are scarce. The study will evaluate how advanced AI systems can improve preventive care and minimize operational risks and shape autonomous healthcare policies based on evidence. Dividing structured ECG data into particular heartbeat categories and comparing different deep learning models, such as CNN, LSTM, BiLSTM, and Transformers architectures, the research results indicate that certain models, such as BiLSTM, outperform the rest in terms of detecting infrequent arrhythmic events. The study highlights the possibilities of AI to further the digital governance of healthcare, with ethical AI implementation and data governance as the suggested policy frameworks to achieve sustainable healthcare outcomes. Finally, the research states that health monitoring using AI is a potential source of more transparent governance and an improved allocation of resources, which supports the idea that resilient and future-oriented governance systems are needed both in space and on the ground.