Heart rate is a crucial indicator of human health with significant physiological importance. Traditional contact methods for measuring heart rate, such as electrocardiograph or wristbands, may not always meet the need for convenient health monitoring. Remote photoplethysmography (rPPG) provides a non-contact method for measuring heart rate and other physiological indicators by analyzing blood volume pulse signals. This approach is non-invasive, does not require direct contact, and allows for long-term healthcare monitoring. Deep learning has emerged as a powerful tool for processing complex image and video data, and has been increasingly employed to extract heart rate signals remotely. This article reviewed the latest research advancements in rPPG-based heart rate measurement using deep learning, summarized available public datasets, and explored future research directions and potential advancements in non-contact heart rate measurement.
Humans ; Deep Learning ; Heart Rate/physiology* ; Photoplethysmography/methods* ; Video Recording ; Face ; Monitoring, Physiologic/methods* ; Signal Processing, Computer-Assisted

In order to accurately capture the respiratory muscle movement and extract the synchronization signals corresponding to the breathing phases, a comprehensive signal sensing system for sensing the movement of the respiratory muscle was developed with applying the thin-film varistor FSR402 IMS-C07A in this paper. The system integrated a sensor, a signal processing circuit, and an application program to collect, amplify and denoise electronic signals. Based on the respiratory muscle movement sensor and a STM32F107 development board, an experimental platform was designed to conduct experiments. The respiratory muscle movement data and respiratory airflow data were collected from 3 healthy adults for comparative analysis. In this paper, the results demonstrated that the method for determining respiratory phase based on the sensing the respiratory muscle movement exhibited strong real-time performance. Compared to traditional airflow-based respiratory phase detection, the proposed method showed a lead times ranging from 33 to 210 ms [(88.3 ± 47.9) ms] for expiration switched into inspiration and 17 to 222 ms [(92.9 ± 63.8) ms] for inspiration switched into expiration, respectively. When this system is applied to trigger the output of the ventilator, it will effectively improve the patient-ventilator synchrony and facilitate the ventilation treatment for patients with respiratory diseases.
Humans ; Respiratory Muscles/physiology* ; Signal Processing, Computer-Assisted ; Movement/physiology* ; Respiration ; Monitoring, Physiologic/methods* ; Adult

Heart failure (HF) is the end-stage of all cardiac diseases, characterized by high prevalence, high mortality, and heavy social and economic burden. Early warning of HF exacerbation is of great value for outpatient management and reducing readmission rates. Currently, remote dynamic monitoring technology, which captures changes in hemodynamic and physiological parameters of HF patients, has become the primary method for early warning and is a hot research topic in clinical studies. This paper systematically reviews the progress in this field, which was categorized into invasive monitoring based on implanted devices, non-invasive monitoring based on wearable devices, and other monitoring technologies based on audio and video. Invasive monitoring primarily involves direct hemodynamic parameters such as left atrial pressure and pulmonary artery pressure, while non-invasive monitoring covers parameters such as thoracic impedance, electrocardiogram, respiration, and activity levels. These parameters exhibit characteristic changes in the early stages of HF exacerbation. Given the clinical heterogeneity of HF patients, multi-source information fusion analysis can significantly improve the prediction accuracy of early warning models. The results of this study suggest that, compared with invasive monitoring, non-invasive monitoring technology, with its advantages of good patient compliance, ease of operation, and cost-effectiveness, combined with AI-driven multimodal data analysis methods, shows significant clinical application potential in establishing an outpatient management system for HF.
Humans ; Heart Failure/physiopathology* ; Monitoring, Physiologic/methods* ; Wearable Electronic Devices ; Remote Sensing Technology ; Early Diagnosis ; Electrocardiography ; Hemodynamics

PURPOSE: Non-operative management (NOM) has been validated for blunt liver and splenic injuries. Literature on continuous intra-abdominal pressure (IAP) monitoring as a part of NOM remains to be equivocal. The study aimed to find any correlation between clinical parameters and IAP, and their effect on the NOM of patients with blunt liver and splenic injury. METHOD: A prospective cross-sectional study conducted at a level I trauma center from October 2018 to January 2020 including 174 patients who underwent NOM following blunt liver and splenic injuries. Hemodynamically unstable patients or those on ventilators were excluded, as well as patients who suffered significant head, spinal cord, and/or bladder injuries. The study predominantly included males (83.9%) with a mean age of 32.5 years. IAP was monitored continuously and the relation of IAP with various parameters, interventions, and outcomes were measured. Data were summarized as frequency (percentage) or mean ± SD or median (Q₁, Q₃) as indicated. χ² or Fisher's exact test was used for categorical variables, while for continuous variables parametric (independent t-test) or nonparametric tests (Wilcoxon rank sum test) were used as appropriate. Clinical and laboratory correlates of IAP < 12 with p < 0.200 in the univariable logistic regression analysis were included in the multivariable analysis. A p < 0.05 was used to indicate statistical significance. RESULTS: Intra-abdominal hypertension (IAH) was seen in 19.0% of the study population. IAH was strongly associated with a high injury severity score (p < 0.001), and other physiological parameters like respiratory rate (p < 0.001), change in abdominal girth (AG) (p < 0.001), and serum creatinine (p < 0.001). IAH along with the number of solid organs involved, respiratory rate, change in AG, and serum creatinine was associated with the intervention, either operative or non-operative (p = 0.001, p = 0.002, p < 0.001, p < 0.001, p = 0.013, respectively). On multivariable analysis, IAP (p = 0.006) and the mean change of AG (p = 0.004) were significantly associated with the need for intervention. CONCLUSION As a part of NOM, IAP should be monitored as a continuous vital. However, the decision for any intervention, either operative or non-operative cannot be guided by IAP values alone.
Humans ; Male ; Adult ; Female ; Wounds, Nonpenetrating/physiopathology* ; Spleen/injuries* ; Prospective Studies ; Cross-Sectional Studies ; Liver/injuries* ; Middle Aged ; Monitoring, Physiologic/methods* ; Pressure ; Abdominal Injuries/physiopathology* ; Intra-Abdominal Hypertension ; Young Adult

To ensure the quality of care for inpatients in ophthalmic hospitals, address the complex and variable conditions of postoperative patients, and conduct more comprehensive, accurate and real-time monitoring of patients, an intelligent monitoring system based on computer vision and artificial intelligence has been designed. This system is employed for real-time monitoring of patient health conditions and intelligent care, with primary applications in medical monitoring, rehabilitation therapy, and inpatient care. It comprises intelligent data acquisition devices, smart cameras, continuous physiological data analysis algorithms, AI algorithms, and software. Given the complex and variable conditions of postoperative patients in ophthalmic hospitals, a comprehensive, accurate, and real-time monitoring of patients is required. Therefore, it is necessary to explore a monitoring technology that imposes low physiological and psychological burdens. The intelligent monitoring system can continuously collect patients' physiological parameter indicators and transmit the monitoring data to doctors' workstations or nurse stations after analysis using intelligent algorithms, providing new tools for patient monitoring, disease assessment, risk warning, and more. Furthermore, through the application of computer vision and artificial intelligence technologies, the system can analyze facial expressions, body postures, and other data to identify patients' emotional states and bedridden postures, enabling the timely detection of abnormal situations and implementation corresponding measures. This helps improving the daily work of medical staff, enhance the nursing safety in single-patient rooms in wards, and potentially find applications in the care of critically ill patients and elderly patients, thereby improving nursing efficiency and quality.
Artificial Intelligence ; Humans ; Monitoring, Physiologic/methods* ; Algorithms

Hemodynamic monitoring can reflect cardiac function and blood perfusion and is an indispensable monitoring method in clinical practice. Invasive hemodynamic monitoring methods represented by the thermodilution method are limited in their clinical application scope because they require vascular cannulation. Non-invasive hemodynamic monitoring has attracted extensive attention from medical companies and clinicians at home and abroad in recent years due to its advantages such as safety, non-invasiveness, continuous monitoring, simple operation, and low cost. This paper designs a non-invasive hemodynamic monitoring system based on the impedance cardiography, including hardware, algorithm, software design, and performance parameter evaluation. Among them, the hardware part mainly includes a differential high-frequency constant current source stimulation circuit, impedance cardiogram signal acquisition, and ECG signal acquisition circuit. Signal processing includes wave filtering, impedance cardiogram signal calibration, and ECG signal and impedance cardiogram signal feature point recognition. According to the collected impedance cardiogram and ECG signals, hemodynamic parameters such as heart rate (HR), stroke volume (SV), cardiac output (CO), stroke index (SI), cardiac index (CI), and cardiac contractility index (ICON) are calculated based on the Nyboer thoracic cylinder model. After testing, the key technical indicators of the system hardware are better than that of the relevant medical device standards. The system was used to collect impedance cardiogram and ECG signal data from 40 volunteers. The calculated HR, SV, and CO, three important hemodynamic indicators, were compared with the ICONCore non-invasive cardiac output monitor of OSYPKA Medical in Germany. Their Pearson correlation coefficients were 0.992 ( P<0.001), 0.948 ( P<0.001), and 0.933 ( P<0.001), respectively, verifying that the designed system has high accuracy and reliability.
Cardiography, Impedance/methods* ; Humans ; Hemodynamic Monitoring/methods* ; Equipment Design ; Signal Processing, Computer-Assisted ; Hemodynamics ; Algorithms ; Monitoring, Physiologic/methods* ; Electrocardiography

End-tidal carbon dioxide monitoring is an important means of evaluating human lung function and is widely used in fields such as clinical emergency treatment and cardiopulmonary resuscitation. This paper develops a microstream end-tidal carbon dioxide monitoring system. It adopts an integrated gas circuit design to further reduce the size of the equipment. The system uses the method of calculating the root mean square (RMS) of differential pressure signals to regulate the gas circuit flow, enabling the system to stably operate at a flow state of 30 mL/min. In addition, by simultaneously detecting multiple environmental parameters such as temperature and pressure, the system realizes system state monitoring and gas parameter compensation. The test results show that various indicators of the system meet the requirements of relevant standards, laying a good foundation for subsequent engineering applications.
Carbon Dioxide/analysis* ; Equipment Design ; Monitoring, Physiologic/methods* ; Humans

BACKGROUND

Dengue is a global health concern, particularly in tropical regions such as the Philippines. In 2019,Cebu City reported the highest number of dengue cases in Central Visayas with 3,290 cases and 20 deaths, an 11.8% increase compared to 20181 . To help predict disease outcomes and provide timely management, a scoring system, the Daily Dengue Severity Score (DDSS)² was utilized.

To determine the clinicodemographic profile of dengue patients, determine the accuracy of the DDSS in assessing disease severity, and determine a cut off score that suggests severe dengue.

Patients 1 month to 18 years admitted for dengue at Perpetual Succour Hospital from January 2018 to December 2020 were included. Cases were classified as Dengue without Warning Signs, Dengue with Warning Signs, and Severe Dengue, and scored using the DDSS. Statistical analysis used were Geometric mean and Area Under the Receiver Operating Characteristic (AUROC) curves to analyze the discriminative performance of the DDSS among the different disease severity states.

Out of 327 cases, 34 were classified as Dengue without Warning Signs, 271 Dengue with Warning Signs, and 22 Severe Dengue. The highest mean DDSS was 17.7 ±14.0 at Day -4 among those with Severe Dengue, and the lowest mean DDSS was 1.1 ± 2.0 at Day +3 among those with Dengue without Warning Signs. A cut off point of 10 on Day -1 predicted subsequent Severe Dengue among patients with Dengue with Warning Signs. In 91.39% of cases, there was a significant relationship between the DDSS and dengue classification, and the higher the DDSS, the more severe the disease.

Majority of dengue patients were males, aged 8.1 to 9.2 years. DDSS showed 66.67% sensitivity, 92.86% specificity, a positive likelihood ratio of 9.3, and a cutoff of 10 is predictive of severe dengue among patients with dengue with warning signs.

Prehospital emergency care is the primary stage in the treatment of critically ill patients, where efficient and accurate monitoring methods are crucial for patient survival and prognosis. End-tidal carbon dioxide (EtCO₂) monitoring is a real-time, non-invasive method that can sensitively capture the status of respiratory, circulatory, and metabolic functions, particularly in the urgent and complex pre-hospital environment, a immediate detection and non-invasive method, can sensitively capture the respiratory, circulatory, and metabolic status of patients. It provides valuable guidance for rapid decision-making and precise interventions. This is particularly valuable in the complex and urgent prehospital environment, providing critical data for rapid decision-making and precise intervention. This paper systematically reviews the advancements in the application of EtCO₂ monitoring across various fields, including sepsis identification, trauma assessment, cardiac arrest, respiratory critical care, endotracheal intubation confirmation, and management of metabolic diseases, aiming to explore its application value and prospects in pre-hospital emergency care.
Humans ; Emergency Medical Services/methods* ; Carbon Dioxide/analysis* ; Monitoring, Physiologic/methods* ; Critical Illness ; Capnography/methods*

This review article aims to explore the major challenges that the healthcare system is currently facing and propose a new paradigm shift that harnesses the potential of wearable devices and novel theoretical frameworks on health and disease. Lifestyle-induced diseases currently account for a significant portion of all healthcare spending, with this proportion projected to increase with population aging. Wearable devices have emerged as a key technology for implementing large-scale healthcare systems focused on disease prevention and management. Advancements in miniaturized sensors, system integration, the Internet of Things, artificial intelligence, 5G, and other technologies have enabled wearable devices to perform high-quality measurements comparable to medical devices. Through various physical, chemical, and biological sensors, wearable devices can continuously monitor physiological status information in a non-invasive or minimally invasive way, including electrocardiography, electroencephalography, respiration, blood oxygen, blood pressure, blood glucose, activity, and more. Furthermore, by combining concepts and methods from complex systems and nonlinear dynamics, we developed a novel theory of continuous dynamic physiological signal analysis-dynamical complexity. The results of dynamic signal analyses can provide crucial information for disease prevention, diagnosis, treatment, and management. Wearable devices can also serve as an important bridge connecting doctors and patients by tracking, storing, and sharing patient data with medical institutions, enabling remote or real-time health assessments of patients, and providing a basis for precision medicine and personalized treatment. Wearable devices have a promising future in the healthcare field and will be an important driving force for the transformation of the healthcare system, while also improving the health experience for individuals.
Humans ; Artificial Intelligence ; Wearable Electronic Devices ; Monitoring, Physiologic/methods*