Refined matrix completion for spectrum estimation of heart rate variability.

Heart rate variability (HRV) is an important metric in cardiovascular health monitoring. Spectral analysis of HRV provides essential insights into the functioning of the cardiac autonomic nervous system. However, data artefacts could degrade signal quality, potentially leading to unreliable assessments of cardiac activities.

Open-world electrocardiogram classification via domain knowledge-driven contrastive learning.

Automatic electrocardiogram (ECG) classification provides valuable auxiliary information for assisting disease diagnosis and has received much attention in research. The success of existing classification models relies on fitting the labeled samples for every ECG type. However, in practice, well-annotated ECG datasets usually cover only limited ECG types.

Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification.

Aims: Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of artificial intelligence (AI) to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. We hypothesize that AI models with a specific design can provide fine-grained interpretation of ECGs to advance cardiovascular diagnosis, stratify mortality risks, and identify new clinically useful information.

HGCTNet: Handcrafted Feature-Guided CNN and Transformer Network for Wearable Cuffless Blood Pressure Measurement.

Biosignals collected by wearable devices, such as electrocardiogram and photoplethysmogram, exhibit redundancy and global temporal dependencies, posing a challenge in extracting discriminative features for blood pressure (BP) estimation. To address this challenge, we propose HGCTNet, a handcrafted feature-guided CNN and transformer network for cuffless BP measurement based on wearable devices. By leveraging convolutional operations and self-attention mechanisms, we design a CNN-Transformer hybrid architecture to learn features from biosignals that capture both local information and global temporal dependencies.

Multi-View Cross-Fusion Transformer Based on Kinetic Features for Non-Invasive Blood Glucose Measurement Using PPG Signal.

Noninvasive blood glucose (BG) measurement could significantly improve the prevention and management of diabetes. In this paper, we present a robust novel paradigm based on analyzing photoplethysmogram (PPG) signals. The method includes signal pre-processing optimization and a multi-view cross-fusion transformer (MvCFT) network for non-invasive BG assessment.

Intelligent Electrocardiogram Acquisition Via Ubiquitous Photoplethysmography Monitoring.

Recent advances in machine learning, particularly deep neural network architectures, have shown substantial promise in classifying and predicting cardiac abnormalities from electrocardiogram (ECG) data. Such data are rich in information content, typically in morphology and timing, due to the close correlation between cardiac function and the ECG. However, the ECG is usually not measured ubiquitously in a passive manner from consumer devices, and generally requires 'active' sampling whereby the user prompts a device to take an ECG measurement.

Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction: A Multi-Dataset Study.

Electrocardiography (ECG) is a non-invasive tool for predicting cardiovascular diseases (CVDs). Current ECG-based diagnosis systems show promising performance owing to the rapid development of deep learning techniques. However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models.

The 2023 wearable photoplethysmography roadmap.

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making.

Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring.

Cardiovascular diseases (CVDs) are a leading cause of death worldwide. For early diagnosis, intervention and management of CVDs, it is highly desirable to frequently monitor blood pressure (BP), a vital sign closely related to CVDs, during people's daily life, including sleep time. Towards this end, wearable and cuffless BP extraction methods have been extensively researched in recent years as part of the mobile healthcare initiative.

Cuffless Blood Pressure Measurement Using Smartwatches: A Large-Scale Validation Study.

This study aimed to evaluate the performance of cuffless blood pressure (BP) measurement techniques in a large and diverse cohort of participants. We enrolled 3077 participants (aged 18-75, 65.16% women, 35.

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review.

Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis.

Causal inference based cuffless blood pressure estimation: A pilot study.

Enabled by wearable sensing, e.g., photoplethysmography (PPG) and electrocardiography (ECG), and machine learning techniques, study on cuffless blood pressure (BP) measurement with data-driven methods has become popular in recent years.

Scenario Adaptive Cuffless Blood Pressure Estimation by Integrating Cardiovascular Coupling Effects.

Adding cuffless blood pressure (BP) measurement function to wearable devices is of great value in the fight against hypertension. The widely used arterial pulse transit time (PTT)-based method for BP monitoring relies primarily on vascular status-determined BP models and typically exhibits degraded performance over time and is sensitive to measurement procedures. Developing alternative methods with improved accuracy and adaptability to various application scenarios is highly desired for cuffless BP measurement.

Video Based Cocktail Causal Container for Blood Pressure Classification and Blood Glucose Prediction.

With the development of modern cameras, more physiological signals can be obtained from portable devices like smartphone. Some hemodynamically based non-invasive video processing applications have been applied for blood pressure classification and blood glucose prediction objectives for unobtrusive physiological monitoring at home. However, this approach is still under development with very few publications.

A Life Well Lived: In Memory of Máximo E. Valentinuzzi.

Throughout his career, Dr. Máximo (Max) E. Valentinuzzi worked long and hard for the development of the BME profession locally, regionally, and internationally.

Interfacial Laser-Induced Graphene Enabling High-Performance Liquid-Solid Triboelectric Nanogenerator.

Laser-induced graphene (LIG) has emerged as a promising and versatile method for high-throughput graphene patterning; however, its full potential in creating complex structures and devices for practical applications is yet to be explored. In this study, an in-situ growing LIG process that enables to pattern superhydrophobic fluorine-doped graphene on fluorinated ethylene propylene (FEP)-coated polyimide (PI) is demonstrated. This method leverages on distinct spectral responses of FEP and PI during laser excitation to generate the environment preferentially for LIG formation, eliminating the need for multistep processes and specific atmospheres.

Interactive Effects of HRV and P-QRS-T on the Power Density Spectra of ECG Signals.

Different from the traditional methods of assessing the cardiac activities through heart rhythm statistics or P-QRS-T complexes separately, this study demonstrates their interactive effects on the power density spectrum (PDS) of ECG signal with applications for the diagnosis of ST-segment elevation myocardial infarction (STEMI) diseases. Firstly, a mathematical model of the PDS of ECG signal with a random pacing pulse train (PPT) mimicking S-A node firings was derived. Secondly, an experimental PDS analysis was performed on clinical ECG signals from 49 STEMI patients and 42 healthy subjects in PTB Diagnostic Database.

Recommendation to Use Wearable-Based mHealth in Closed-Loop Management of Acute Cardiovascular Disease Patients During the COVID-19 Pandemic.

Because of the rapid and serious nature of acute cardiovascular disease (CVD) especially ST segment elevation myocardial infarction (STEMI), a leading cause of death worldwide, prompt diagnosis and treatment is of crucial importance to reduce both mortality and morbidity. During a pandemic such as coronavirus disease-2019 (COVID-19), it is critical to balance cardiovascular emergencies with infectious risk. In this work, we recommend using wearable device based mobile health (mHealth) as an early screening and real-time monitoring tool to address this balance and facilitate remote monitoring to tackle this unprecedented challenge.

Non-Invasive Capillary Blood Pressure Measurement Enabling Early Detection and Classification of Venous Congestion.

Capillary blood pressure (CBP) is the primary driving force for fluid exchange across microvessels. Subclinical systemic venous congestion prior to overt peripheral edema can directly result in elevated peripheral CBP. Therefore, CBP measurements can enable timely edema control in a variety of clinical cases including venous insufficiency, heart failure and so on.

Scutellarin regulates osteoarthritis by inhibiting the PI3K/AKT/mTOR signaling pathway.

Osteoarthritis (OA) is a highly prevalent disease worldwide that causes disability and diminishes the quality of life of affected individuals. The disease is characterized by cartilage destruction, increased inflammatory responses and cholesterol metabolic disorder. Scutellarin is the major active ingredient extracted from , and it has been demonstrated to possess various pharmacological functions in the treatment of the disease.

DBAN: Adversarial Network With Multi-Scale Features for Cardiac MRI Segmentation.

With the development of medical artificial intelligence, automatic magnetic resonance image (MRI) segmentation method is quite desirable. Inspired by the power of deep neural networks, a novel deep adversarial network, dilated block adversarial network (DBAN), is proposed to perform left ventricle, right ventricle, and myocardium segmentation in short-axis cardiac MRI. DBAN contains a segmentor along with a discriminator.

PCA-Based Multi-Wavelength Photoplethysmography Algorithm for Cuffless Blood Pressure Measurement on Elderly Subjects.

The prevalence of hypertension has made blood pressure (BP) measurement one of the most wanted functions in wearable devices for convenient and frequent self-assessment of health conditions. The widely adopted principle for cuffless BP monitoring is based on arterial pulse transit time (PTT), which is measured with electrocardiography and photoplethysmography (PPG). To achieve cuffless BP monitoring with more compact wearable electronics, we have previously conceived a multi-wavelength PPG (MWPPG) strategy to perform BP estimation from arteriolar PTT, requiring only a single sensing node.

Quantifying Spatial Activation Patterns of Motor Units in Finger Extensor Muscles.

The ability to expertly control different fingers contributes to hand dexterity during object manipulation in daily life activities. The macroscopic spatial patterns of muscle activations during finger movements using global surface electromyography (sEMG) have been widely researched. However, the spatial activation patterns of microscopic motor units (MUs) under different finger movements have not been well investigated.

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic.

Coronavirus disease 2019 (COVID-19) has emerged as a pandemic with serious clinical manifestations including death. A pandemic at the large-scale like COVID-19 places extraordinary demands on the world's health systems, dramatically devastates vulnerable populations, and critically threatens the global communities in an unprecedented way. While tremendous efforts at the frontline are placed on detecting the virus, providing treatments and developing vaccines, it is also critically important to examine the technologies and systems for tackling disease emergence, arresting its spread and especially the strategy for diseases prevention.