X-iPPGNet: A novel one stage deep learning architecture based on depthwise separable convolutions for video-based pulse rate estimation

Intelligent Detection Method of Atrial Fibrillation by CEPNCC-BiLSTM Based on Long-Term Photoplethysmography Data

Atrial fibrillation (AF) is the most prevalent arrhythmia characterized by intermittent and asymptomatic episodes. However, traditional detection methods often fail to capture the sporadic and intricate nature of AF, resulting in an increased risk of false-positive diagnoses. To address these challenges, this study proposes an intelligent AF detection and diagnosis method that integrates Complementary Ensemble Empirical Mode Decomposition, Power-Normalized Cepstral Coefficients, Bi-directional Long Short-term Memory (CEPNCC-BiLSTM), and photoelectric volumetric pulse wave technology to enhance accuracy in detecting AF. Compared to other approaches, the proposed method demonstrates faster preprocessing efficiency and higher sensitivity in detecting AF while effectively filtering out false alarms from photoplethysmography (PPG) recordings of non-AF patients. Considering the limitations of conventional AF detection evaluation systems that lack a comprehensive assessment of efficiency and accuracy, this study proposes the ET-score evaluation system based on F-measurement, which incorporates both computational speed and accuracy to provide a holistic assessment of overall performance. Evaluated with the ET-score, the CEPNCC-BiLSTM method outperforms EEMD-based improved Power-Normalized Cepstral Coefficients and Bi-directional Long Short-term Memory (EPNCC-BiLSTM), Support Vector Machine (SVM), EPNCC-SVM, and CEPNCC-SVM methods. Notably, this approach achieves an outstanding accuracy rate of up to 99.2% while processing PPG recordings within 5 s, highlighting its potential for long-term AF monitoring.

Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement

In recent decades, there has been ongoing development in the application of computer vision (CV) in the medical field. As conventional contact-based physiological measurement techniques often restrict a patient's mobility in the clinical environment, the ability to achieve continuous, comfortable and convenient monitoring is thus a topic of interest to researchers. One type of CV application is remote imaging photoplethysmography (rPPG), which can predict vital signs using a video or image. While contactless physiological measurement techniques have an excellent application prospect, the lack of uniformity or standardization of contactless vital monitoring methods limits their application in remote healthcare/telehealth settings. Several methods have been developed to improve this limitation and solve the heterogeneity of video signals caused by movement, lighting, and equipment. The fundamental algorithms include traditional algorithms with optimization and developing deep learning (DL) algorithms. This article aims to provide an in-depth review of current Artificial Intelligence (AI) methods using CV and DL in contactless physiological measurement and a comprehensive summary of the latest development of contactless measurement techniques for skin perfusion, respiratory rate, blood oxygen saturation, heart rate, heart rate variability, and blood pressure.

Non-Contact Vision-Based Techniques of Vital Sign Monitoring: Systematic Review

The development of non-contact techniques for monitoring human vital signs has significant potential to improve patient care in diverse settings. By facilitating easier and more convenient monitoring, these techniques can prevent serious health issues and improve patient outcomes, especially for those unable or unwilling to travel to traditional healthcare environments. This systematic review examines recent advancements in non-contact vital sign monitoring techniques, evaluating publicly available datasets and signal preprocessing methods. Additionally, we identified potential future research directions in this rapidly evolving field.

An effective cross-scenario remote heart rate estimation network based on global-local information and video transformer

Remote photoplethysmography (rPPG) technology is a non-contact physiological signal measurement method, characterized by non-invasiveness and ease of use. It has broad application potential in medical health, human factors engineering, and other fields. However, current rPPG technology is highly susceptible to variations in lighting conditions, head pose changes, and partial occlusions, posing significant challenges for its widespread application. In order to improve the accuracy of remote heart rate estimation and enhance model generalization, we propose PulseFormer, a dual-path network based on transformer. By integrating local and global information and utilizing fast and slow paths, PulseFormer effectively captures the temporal variations of key regions and spatial variations of the global area, facilitating the extraction of rPPG feature information while mitigating the impact of background noise variations. Heart rate estimation results on the popular rPPG dataset show that PulseFormer achieves state-of-the-art performance on public datasets. Additionally, we establish a dataset containing facial expressions and synchronized physiological signals in driving scenarios and test the pre-trained model from the public dataset on this collected dataset. The results indicate that PulseFormer exhibits strong generalization capabilities across different data distributions in cross-scenario settings. Therefore, this model is applicable for heart rate estimation of individuals in various scenarios.

Contactless Blood Oxygen Saturation Estimation from Facial Videos Using Deep Learning

Blood oxygen saturation (SpO2) is an essential physiological parameter for evaluating a person's health. While conventional SpO2 measurement devices like pulse oximeters require skin contact, advanced computer vision technology can enable remote SpO2 monitoring through a regular camera without skin contact. In this paper, we propose novel deep learning models to measure SpO2 remotely from facial videos and evaluate them using a public benchmark database, VIPL-HR. We utilize a spatial-temporal representation to encode SpO2 information recorded by conventional RGB cameras and directly pass it into selected convolutional neural networks to predict SpO2. The best deep learning model achieves 1.274% in mean absolute error and 1.71% in root mean squared error, which exceed the international standard of 4% for an approved pulse oximeter. Our results significantly outperform the conventional analytical Ratio of Ratios model for contactless SpO2 measurement. Results of sensitivity analyses of the influence of spatial-temporal representation color spaces, subject scenarios, acquisition devices, and SpO2 ranges on the model performance are reported with explainability analyses to provide more insights for this emerging research field.

Challenges and prospects of visual contactless physiological monitoring in clinical study

The monitoring of physiological parameters is a crucial topic in promoting human health and an indispensable approach for assessing physiological status and diagnosing diseases. Particularly, it holds significant value for patients who require long-term monitoring or with underlying cardiovascular disease. To this end, Visual Contactless Physiological Monitoring (VCPM) is capable of using videos recorded by a consumer camera to monitor blood volume pulse (BVP) signal, heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2) and blood pressure (BP). Recently, deep learning-based pipelines have attracted numerous scholars and achieved unprecedented development. Although VCPM is still an emerging digital medical technology and presents many challenges and opportunities, it has the potential to revolutionize clinical medicine, digital health, telemedicine as well as other areas. The VCPM technology presents a viable solution that can be integrated into these systems for measuring vital parameters during video consultation, owing to its merits of contactless measurement, cost-effectiveness, user-friendly passive monitoring and the sole requirement of an off-the-shelf camera. In fact, the studies of VCPM technologies have been rocketing recently, particularly AI-based approaches, but few are employed in clinical settings. Here we provide a comprehensive overview of the applications, challenges, and prospects of VCPM from the perspective of clinical settings and AI technologies for the first time. The thorough exploration and analysis of clinical scenarios will provide profound guidance for the research and development of VCPM technologies in clinical settings.