About this Research Topic
Our bodies can tell us much about our state of health if we can listen and understand. That is why physiological signals play a crucial role in understanding and managing human health events. These signals, which include various biological measurements, provide valuable information about a person's health status and can be used to monitor, diagnose, and predict health events. Physiological signals such as heart rate, blood pressure, respiratory rate, and body temperature have been linked with many underlying medical conditions, including mortality and co-morbidities. Furthermore, physiological signals are strong measures found in human beings that demonstrate sensitivity to emotional changes linked to different mental health conditions. We are still in the early stages of understanding all the messages our bodies can tell us about our health through physiological signal monitoring.
When measuring these physiological signals wearable technologies have emerged as powerful tools in patient care, offering various benefits to enhance the overall patient experience and encouraging them to take an active role in managing their health. Devices such as smartwatches and fitness trackers enable continuous monitoring of various physiological parameters, including heart rate, blood pressure, sleep patterns, and activity levels. This real-time data provides healthcare professionals with a comprehensive and up-to-date view of a patient's health, allowing for early detection of potential health issues and personalized treatment plans. Several research-grade wearable devices have also become popular in detecting conditions such as epilepsy and sleep apnea, as well as major health events which can occur due to these conditions. Major health events refer to significant occurrences or incidents that have a profound impact on public health, healthcare systems, or individuals' well-being. Some examples include mortality, sepsis, stroke, SIDS, and mental health crises.
At the same time, rapid advancements in artificial intelligence (AI) and machine learning (ML) methods have opened new avenues for revolutionizing healthcare. With the advent of big data hardware and software tools, collecting and accessing large-scale physiological signals from hospitals and medical centers have become easier. Resulting approaches such as Dynamic Treatment Regimes (DTRs) promise augmented critical care, medical diagnosis, and may even be able to predict the outcomes of treatments applied. There is a huge potential for using advanced AI techniques to detect major health incidents through the analysis of physiological signals.
The goal of this Research Topic is to highlight studies that leverage the power of artificial intelligence techniques to develop intelligent systems capable of detecting major health incidents using physiological signals. The potential benefits of early detection and timely interventions through such systems could lead to improved patient outcomes and enhanced healthcare delivery. By exploring and harnessing the capabilities of AI algorithms, this collection seeks to contribute to the advancement of medical diagnostics and patient care.
The scope of this Research Topic includes but is not limited to,
1. Development of machine learning techniques to detect specific major health events using physiological signals
2. Novel signal analysis, feature extraction, and selection methods relating to significant health events
3. AI methods to detect changes in physical/mental health relating to health events
4. Novel physiological datasets linked to significant health events
5. Novel techniques for detecting uncertainty in physiological signals relating to major health events
6. Detection of physiological signals from video, thermal or hyperspectral video
7. Privacy preservation of recorded signals as an integral part of detecting major health events
8. Applications for real-time monitoring and detection of major health events using physiological signals.
When measuring these physiological signals wearable technologies have emerged as powerful tools in patient care, offering various benefits to enhance the overall patient experience and encouraging them to take an active role in managing their health. Devices such as smartwatches and fitness trackers enable continuous monitoring of various physiological parameters, including heart rate, blood pressure, sleep patterns, and activity levels. This real-time data provides healthcare professionals with a comprehensive and up-to-date view of a patient's health, allowing for early detection of potential health issues and personalized treatment plans. Several research-grade wearable devices have also become popular in detecting conditions such as epilepsy and sleep apnea, as well as major health events which can occur due to these conditions. Major health events refer to significant occurrences or incidents that have a profound impact on public health, healthcare systems, or individuals' well-being. Some examples include mortality, sepsis, stroke, SIDS, and mental health crises.
At the same time, rapid advancements in artificial intelligence (AI) and machine learning (ML) methods have opened new avenues for revolutionizing healthcare. With the advent of big data hardware and software tools, collecting and accessing large-scale physiological signals from hospitals and medical centers have become easier. Resulting approaches such as Dynamic Treatment Regimes (DTRs) promise augmented critical care, medical diagnosis, and may even be able to predict the outcomes of treatments applied. There is a huge potential for using advanced AI techniques to detect major health incidents through the analysis of physiological signals.
The goal of this Research Topic is to highlight studies that leverage the power of artificial intelligence techniques to develop intelligent systems capable of detecting major health incidents using physiological signals. The potential benefits of early detection and timely interventions through such systems could lead to improved patient outcomes and enhanced healthcare delivery. By exploring and harnessing the capabilities of AI algorithms, this collection seeks to contribute to the advancement of medical diagnostics and patient care.
The scope of this Research Topic includes but is not limited to,
1. Development of machine learning techniques to detect specific major health events using physiological signals
2. Novel signal analysis, feature extraction, and selection methods relating to significant health events
3. AI methods to detect changes in physical/mental health relating to health events
4. Novel physiological datasets linked to significant health events
5. Novel techniques for detecting uncertainty in physiological signals relating to major health events
6. Detection of physiological signals from video, thermal or hyperspectral video
7. Privacy preservation of recorded signals as an integral part of detecting major health events
8. Applications for real-time monitoring and detection of major health events using physiological signals.
Keywords: Physiological signals, Artificial intelligence, Machine learning, Major health events, Signal analysis
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
