IEEE Reviews in Biomedical Engineering最新文献

Cardiovascular disease (CVD), the leading global cause of death, highlights the critical need for effective blood pressure management. Non-invasive blood pressure (NIBP) monitoring, compared with invasive methods, enables home-based and long-term use, supporting early detection and continuous care. Despite significant progress, challenges remain, including accuracy issues, insufficient validation in real-world settings, limited application-specific sensor designs, and inadequate calibration standards and validation platforms. These gaps call for a systematic review to clarify the unmet needs and future research directions. This article reviews current advances in four key areas: (1) novel NIBP estimation principles designed to minimize user intervention; (2) flexible and wearable electronics that improve accuracy and comfort; (3) integration with theranostic applications and broader healthcare scenarios enabled by NIBP technologies; (4) calibration and validation strategies that enhance reliability and accuracy. With the rapid growth of home healthcare and AI-enabled wearable systems, addressing these challenges is essential to advance personalized, precise and stable cardiovascular medicine.

Microcirculation is essential for maintaining tissue health and overall physiological function. Over the past few decades, various optical techniques have been developed to measure, visualize, and assess microvasculature. The skin has easily an accessible vascular bed allowing for noninvasive evaluation of microvascular function. Alterations in cutaneous microcirculation have been linked to dysfunctions in other target organs and vascular regions reinforcing the idea that cutaneous microcirculation can provide insights into systemic vascular conditions. Currently, there is no unified review focusing specifically on microcirculation-related optical techniques nor comprehensive analyses connecting these technological innovations to clinical evidence. This review aims to bridge that gap by systematically examining the wide spectrum of optical technologies used in assessing cutaneous microvascular function. We review techniques based on non-coherent light including oximetry, photoplethysmography, and microscopic methods and coherent light-based techniques, including speckle contrast imaging, diffuse correlation spectroscopy, photoacousting imaging, laser Doppler flowmetry and self-mixing interferometry. We emphasize cardiovascular research and evaluate the clinical relevance and technical maturity of the techniques. Additionally, brief explanation of skin structure and skin microvasculature while explaining light skin interaction is discussed. Lastly, we discuss these findings on wider context by including discussions and advancements in multimodal monitoring and machine learning.

Ultrasound imaging is widely used owing to its affordability, radiation-free, and non-invasive advantages. However, limitations stemming from operator dependence and artifacts have been noted. To address these issues, deep learning (DL) is increasingly being introduced. In oncology and cardiology, DL-equipped devices are transitioning to clinical use following approval. Nevertheless, DL faces challenges such as generalization, safety, and operational burden, making strategic implementation essential to maximize patient benefit. Existing reviews often list individual technologies but lack evaluation frameworks tailored to clinical implementation. Therefore, this review (i) organizes and formalizes limitations specific to ultrasound diagnosis, (ii) explains the latest DL methods addressing these limitations in terms of principles, implementation, and evaluation metrics, and (iii) examines recent clinical applications, including approved devices, supported by evidence, demonstrating that DL possesses substantial utility beyond the research stage for improving clinical workflows. It also critically evaluates remaining challenges, presents evaluation criteria to aid implementation, and identifies future research challenges.

Brain-heart interaction (BHI) is fundamental to autonomic regulation and also shapes perceptual salience, attentional control, decision-making under load, and affective reactivity. Beyond these functions, BHI has been consistently implicated in clinical studies in cardiovascular, neurological, and psychiatric conditions. This reality makes the investigation of bidirectional BHI mechanisms-and the derivation of interpretable biomarkers- - indispensable for cardiovascular, physiological, and neuroscientific research that treats the body as an interoceptive network of interacting organs rather than isolated systems. The growing interest in this perspective has generated a broad spectrum of frameworks, from signal-processing pipelines and computational models to dynamical systems. Building on previous surveys that have thoroughly mapped the field and deepened our understanding, this review offers a complementary perspective centered on mechanistic, physiology-inspired models of dynamical systems. For each model, we identify the physiological subsystem described, clarify core assumptions, and assess strengths and limitations. We then outline the technical perspectives necessary to realize the full potential of these approaches - especially for inferring latent interoceptive quantities that govern directional BHI but are not directly observable, and for integrating explicit brain modeling into these frameworks to better capture the neural mechanisms driving autonomic and cardiovascular dynamics. Mechanistic dynamical modeling has, over decades, deepened our understanding of physiology and pathology and informed the mapping and treatment of diverse conditions. Our objective is to provide a comprehensive account of state-of-the-art dynamical models, delineate methodological directions, and highlight application areas where such models can yield explanatory insight, reliable prediction, and actionable clinical targets.

Resistive MEMS sensors have become increasingly significant in biomedical and bioenvironmental monitoring due to their compact dimensions, low energy demand, and high sensitivity. Despite structural simplicity and integration benefits, these sensors face performance constraints arising from intrinsic nonidealities such as nonlinearity, thermal drift, parasitic interactions, and process mismatches. These limitations intensify at micro and nanoscale dimensions and generate substantial DC offset in the output. This review presents a systematic analysis of resistive sensor architectures, including single resistor, half bridge, and full bridge configurations, and evaluates their susceptibility to distortion and noise through analytical modeling. Comparative assessment reveals tradeoffs in sensitivity, linearity, noise resilience, and thermal stability. The paper also examines advanced readout methodologies designed for precision measurement, low power operation, and compact integration, including voltage to voltage, voltage to frequency, resistance to digital, and RC delay based interfaces. Particular emphasis is placed on DC offset compensation strategies that address sensor nonidealities, such as resistive, current driven, and capacitive DAC techniques, implemented across different stages of the signal chain. These approaches are critically appraised for their effectiveness in extending dynamic range, reducing energy consumption, and preserving signal fidelity in implantable and wearable platforms. The survey synthesizes recent designs and proposes a classification framework to guide the selection of interface and compensation strategies designed to sensor topology and application constraints. By integrating theoretical insights with practical design considerations, this work provides a comprehensive reference for developing robust, precise, and energy efficient resistive sensor interfaces.