About this Research Topic
Sleep disorders significantly impact public health and well-being. With the advent of new technologies and the use of Artificial Intelligence (AI), there is an opportunity to move forward in how we assess and understand sleep quality.
Traditionally, methods like the apnea-hypopnea index (AHI) have been used in sleep medicine to assess the severity of sleep apnea, yet AHI has some limitations, it gives us an idea of the frequency of respiratory events during sleep time and does not directly measure the impact of these events on sleep quality or the severity of oxygen desaturation events.
Integrating Artificial Intelligence (AI) and machine learning into sleep disorder analysis represents a pivotal shift toward more efficient, fast, and accurate diagnosis and management. In this future scenario, healthcare professionals can offer more personalized and effective treatment plans for individuals suffering from sleep disorders, improving overall public health outcomes.
This Research Topic explores applications of Artificial Intelligence (AI) in sleep, the application of AI in analyzing key PSG variables—EEG, ECG, SpO2—to diagnose and understand sleep disorders and instability, particularly sleep apnea.
By automating the analysis of these vital signs, AI could streamline the diagnostic process, reduce the reliance on manual labor, and enhance the precision of treatments.
The goal of this Research Topic is to bridge the technological innovation in AI with practical applications in sleep medicine looking into sleep instability measures, sleep phases and disturbances, especially in apnea patients, and potentially offering novel solutions for the diagnostic and treatment challenges in sleep disorders.
We aim to explore current and emerging AI methodologies and contribute to enhancing patient care and improving public health outcomes related to sleep instability.
Key areas of interest include:
- Application of AI in Sleep Disorder Analysis: Utilizing AI to interpret EEG, ECG, and SpO2 data.
- AI Algorithms for Sleep Disorder Detection and AI models for early detection of sleep instability and apnea.
- Comparative analysis of different machine learning and deep learning approaches.
- Techniques for extracting relevant features from EEG data.
- Technological Advances in Sleep Assessment: Tools and technologies for sleep monitoring, including wearable devices and continuous monitoring sensors.
- AI-Driven Diagnosis and Treatment of Sleep Disorders: AI and machine learning impact on the detection of sleep disorders, data interpretation automation, and the development of personalized treatment plans.
- Evaluating AI's Clinical Efficacy: Assessing the reliability and effectiveness of AI tools in clinical settings, and their impact on patient outcomes.
Traditionally, methods like the apnea-hypopnea index (AHI) have been used in sleep medicine to assess the severity of sleep apnea, yet AHI has some limitations, it gives us an idea of the frequency of respiratory events during sleep time and does not directly measure the impact of these events on sleep quality or the severity of oxygen desaturation events.
Integrating Artificial Intelligence (AI) and machine learning into sleep disorder analysis represents a pivotal shift toward more efficient, fast, and accurate diagnosis and management. In this future scenario, healthcare professionals can offer more personalized and effective treatment plans for individuals suffering from sleep disorders, improving overall public health outcomes.
This Research Topic explores applications of Artificial Intelligence (AI) in sleep, the application of AI in analyzing key PSG variables—EEG, ECG, SpO2—to diagnose and understand sleep disorders and instability, particularly sleep apnea.
By automating the analysis of these vital signs, AI could streamline the diagnostic process, reduce the reliance on manual labor, and enhance the precision of treatments.
The goal of this Research Topic is to bridge the technological innovation in AI with practical applications in sleep medicine looking into sleep instability measures, sleep phases and disturbances, especially in apnea patients, and potentially offering novel solutions for the diagnostic and treatment challenges in sleep disorders.
We aim to explore current and emerging AI methodologies and contribute to enhancing patient care and improving public health outcomes related to sleep instability.
Key areas of interest include:
- Application of AI in Sleep Disorder Analysis: Utilizing AI to interpret EEG, ECG, and SpO2 data.
- AI Algorithms for Sleep Disorder Detection and AI models for early detection of sleep instability and apnea.
- Comparative analysis of different machine learning and deep learning approaches.
- Techniques for extracting relevant features from EEG data.
- Technological Advances in Sleep Assessment: Tools and technologies for sleep monitoring, including wearable devices and continuous monitoring sensors.
- AI-Driven Diagnosis and Treatment of Sleep Disorders: AI and machine learning impact on the detection of sleep disorders, data interpretation automation, and the development of personalized treatment plans.
- Evaluating AI's Clinical Efficacy: Assessing the reliability and effectiveness of AI tools in clinical settings, and their impact on patient outcomes.
Keywords: Sleep, Machine learning, Sleep disorders, sleep quality, Artificial Intelligence, EEG, ECG, SpO2, monitoring sensors, data interpretation
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