اولین همایش بین المللی تحول خدمات سلامت: تلفیق مراقبت، درمان، بازتوانی و امیدبخشی

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کد مقاله 1351

عنوان Artificial Intelligence in Physiological Signal Analysis for Autonomic Nervous System Monitoring in Digital Health

نویسندگان Hajaralsadat Hosseini-Dastgerdi - Azadehalsadat Hosseini Dastgerdi

چکیده Background Digital health and artificial intelligence have been coupled in the past decade to allow for potentially continuous physiological monitoring while supporting advances in personalized care. Of these categories of data, signals such as electrocardiogram (ECG), heart rate variability (HRV), photoplethysmography (PPG), electroencephalogram (EEG) and electromyography (EMG) are given a priority position because they serve as the main source reflecting functions of multiple body systems, specifically those related to the autonomic nervous system. Method This review article is aimed at the application of AI in physiological signal analysis, highlights the evaluation of autonomic function, wearable-based digital health technologies in healthcare systems, algorithms used in clinical diagnosis as well as challenges and prospects for development. Finding Recent advancements in machine and deep learning have allowed us to reap the wealth of complex patterns hidden within these signals, yielding an increase in diagnostic and prognostic accuracy for cardiovascular, neurological and metabolic diseases. Conclusion From the physiological standpoint, the changes in sympathetic and parasympathetic activity risk can be recognized within HRV and other biosignals well before any clinical manifestation presents itself, hence intelligent analysis can help provide early detection.

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