Photoplethysmography (PPG) is a cost-effective and accessible optical sensing technique that can be easily incorporated into wearable devices for non-invasive and continuous monitoring of physiological and hemodynamic changes such as heart rate, respiratory rate, oxygen saturation, vascular stiffness, and cardiac output. The measurement of these changes over a period of time provides insights into cardiovascular and respiratory health, offering opportunities for just-in-time health information that can help in the diagnostication of elusive conditions such as paroxysmal arrhythmia and in the assessment of risk for cardiovascular and neurological diseases.

Continuous monitoring enables early screening of diseases by providing real-time detection of important physiological changes. This can significantly enhance risk stratification, improve the accuracy of clinical decision-making, and lead to better patient outcomes by enabling a timely and appropriate intervention. To achieve these objectives, advanced techniques are needed for identifying complex and peculiar changes in the PPG signal and extracting relevant hemodynamic parameters. Overcoming challenges such as artifacts that significantly alters the PPG signal morphology is essential for the veracity of findings and the trustworthiness of PPG as a diagnostic tool. Emerging biomedical signal processing, pattern recognition, and artificial intelligence methods contributed to the marked advancement of PPG technology and its applications. There remains however potential for tapping further into the complex nature of PPG signals and extract new and nuanced physiological signatures of health. As new techniques emerge that can overcome technical barriers such as refractory motion artifacts, PPG can lead to novel applications in personalized health monitoring ultimately enabling better chronic disease management and preventative care.

This Research Topic addresses the key requirements for expanding the use of PPG in both daily life and clinical settings, focusing on developing novel hemodynamic parameters, new methods to extract clinical information, and strategies to deal with noise. Contributions to this Research Topic highlight these advancements, which are summarized below.

This Research Topic provided multidisciplinary research based on physiological studies of signal processing, focusing on relevant applications of the PPG signal and emphasizing the large spectrum of applications and the importance of this technology in continuous health monitoring. Technical challenges surrounding the use of PPG, particularly in mobile health applications, remain. Efforts to address these challenges can be grouped into three main lines of research and development areas:

The PPG signal contains a wealth of valuable physiological information. There remains a compelling need to innovate and develop novel algorithms and techniques to extract additional clinically relevant hemodynamic and physiological parameters from PPG data. Such advancements could encompass more detailed metrics of cardiovascular health, respiratory function, and even markers of stress or fatigue. By refining these methodologies, the utility of PPG for comprehensive health monitoring can be significantly enhanced.

The formulation of robust methodologies to mitigate or correct noise-induced artifacts is instrumental in guaranteeing the fidelity of PPG-based evaluations in practical and real-word scenarios.

AI-driven healthcare innovations enable the identification of nuanced health trajectories and deliver tailored insights derived from individual physiological datasets. Furthermore, sophisticated AI algorithms facilitate the automated extraction of salient parameters, recognize intricate patterns that elude conventional detection methods, and significantly advance predictive health analytics.

Addressing these three foundational pillars of development is essential for realizing the full potential of PPG technology. By surmounting current limitations and enhancing the precision in extracting and interpreting PPG data, this research lays the groundwork for broader, more effective, and reliable applications of PPG in routine health monitoring and clinical practice.

TP: Writing–original draft, Writing–review and editing. KG: Writing–original draft, Writing–review and editing. RX: Writing–original draft, Writing–review and editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

The editors of the Research Topic thank all the authors and reviewers who have participated in this Research Topic.

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declare that no Generative AI was used in the creation of this manuscript.

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