This cardiac output detector uses AT89C52 as the core MCU, carries the pulse signal sampling from pulse sensor into the SCM after A/D conversion, and then figures out the cardiac output value and displays it on the LED. Software analysis works out the cardiac output value through five-point difference threshold for feature location of the pulse graph method theory. Experiment results show that the normal measured cardiac output is 5.411 L/min, the standard deviation of 0.873, while the catheter method as the gold standard of the mean 5.51 L/min, the standard deviation of 1.09. This system can meet the testing requirements of normal cardiac output. It is a non-invasive, convenient and new cardiac output measurement instrument with continuous testing, easy operation and low cost.
Cardiac Output ; Monitoring, Physiologic ; instrumentation ; methods ; Pulse

Anesthesia as a necessary procedure in the process of surgical operation could restrain the response of patients to the damage stimulation; However, improper anesthesia could also result in severe misfortune for patients. At the present time, one kind of monitor technology assuring highly effectual anesthesia is exigently required in clinical practice and many researchers have actively undertaken investigations to seek the parameters predicting the depth of anesthesia (DOA). Electroencephalogram (EEG) assumes a dominant position in the current researches on detecting the depth of anesthesia. In this paper, the achievements of detecting the depth of anesthesia by means of EEG are systematically reviewed and the potentials are anticipated.
Anesthesia ; Anesthesiology ; methods ; Electroencephalography ; Humans ; Monitoring, Physiologic

Ballistocardiogram (BCG) signal is a physiological signal, reflecting heart mechanical status. It can be measured without any electrodes touching subject's body surface and can realize physiological monitoring ubiquitously. However, BCG signal is so weak that it would often be interferred by superimposed noises. For measuring BCG signal effectively, we proposed an approach using joint time-frequency distribution and empirical mode decomposition (EMD) for BCG signal denoising. We set up an adaptive optimal kernel for BCG signal and extracted BCG signals components using it. Then we de-noised the BCG signal by combing empirical mode decomposition with it. Simulation results showed that the proposed method overcome the shortcomings of empirical mode decomposition for the signals with identical frequency content at different times, realized the filtering for BCG signal and also reconstructed the characteristics of BCG.
Algorithms ; Ballistocardiography ; methods ; Humans ; Monitoring, Physiologic

At present, the monitoring methods fwor intracranial pressure adopted in clinical practice are almost all invasive. The invasive monitoring methods for intracranial pressure were accurate, but they were harmful to the patient's body. Therefore, non-invasive methods for intracranial pressure monitoring must be developed. Since 1980, many non-invasive methods have been sprung out in succession, but they can not be used clinically. In this paper, research contents and progress of present non-invasive intracranial pressure monitoring are summarized. Advantages and disadvantages of various ways are analyzed. And finally, perspectives of development for intracranial pressure monitoring are presented.
Humans ; Intracranial Pressure ; Monitoring, Physiologic ; methods

A NTC thermistor-based wearable body temperature sensor was designed. This paper described the design principles and realization method of the NTC-based body temperature sensor. In this paper the temperature measurement error sources of the body temperature sensor were analyzed in detail. The automatic measurement and calibration method of ADC error was given. The results showed that the measurement accuracy of calibrated body temperature sensor is better than ± 0.04 degrees C. The temperature sensor has high accuracy, small size and low power consumption advantages.
Body Temperature ; Calibration ; Humans ; Monitoring, Physiologic ; methods

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.

The continuous glucose monitoring system (CGMS) has been clinically applied to monitor the dynamic change of the subcutaneous interstitial glucose concentration which is a function of the blood glucose level by glucose sensors. It can track blood glucose levels all day along, and thus provide comprehensive and reliable information about blood glucose dynamics. The clinical application of CGMS enables monitoring of blood glucose fluctuations and the discovery of hidden hyperglycemia and hypoglycemia that are difficult to be detected by traditional methods. As a CGMS needs to work subcutaneously for a long time, a series of factors such as biocompatibility, enzyme inactivation, oxygen deficiency, foreign body reaction, implant size, electrode flexibility, error correction, comfort, device toxicity, electrical safety, et al. should be considered beforehand. The study focused on the difficulties in the technology, and compared the products of Abbott, Medtronic and DexCom, then summarized their cutting-edge. Finally, this study expounded some key technologies in dynamic blood glucose monitoring and therefore can be utilized as a reference for the development of CGMS.
Blood Glucose ; Blood Glucose Self-Monitoring/methods* ; Humans ; Hyperglycemia ; Hypoglycemia ; Monitoring, Ambulatory/methods* ; Monitoring, Physiologic

Hemodynamic monitoring is important for critically ill patients in emergency medicine. While the conventional static hemodynamic monitoring may not accurately reflect the hemodynamic status, functional hemodynamic monitoring can dynamicly and individually monitor the hemodynamic status, and thus becomes a valuable supplementation for conventional static hemodynamic monitoring. This article reviews the limitations of conventional hemodynamic monitoring and introduces the methodology of functional hemodynamic monitoring.
Emergency Medicine ; methods ; Hemodynamics ; physiology ; Humans ; Monitoring, Physiologic ; 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*

The aging population and the increasing prevalence of chronic diseases in the elderly have brought a significant economic burden to families and society. The non-invasive wearable sensing system can continuously and real-time monitor important physiological signs of the human body and evaluate health status. In addition, it can provide efficient and convenient information feedback, thereby reducing the health risks caused by chronic diseases in the elderly. A wearable system for detecting physiological and behavioral signals was developed in this study. We explored the design of flexible wearable sensing technology and its application in sensing systems. The wearable system included smart hats, smart clothes, smart gloves, and smart insoles, achieving long-term continuous monitoring of physiological and motion signals. The performance of the system was verified, and the new sensing system was compared with commercial equipment. The evaluation results demonstrated that the proposed system presented a comparable performance with the existing system. In summary, the proposed flexible sensor system provides an accurate, detachable, expandable, user-friendly and comfortable solution for physiological and motion signal monitoring. It is expected to be used in remote healthcare monitoring and provide personalized information monitoring, disease prediction, and diagnosis for doctors/patients.
Humans ; Aged ; Monitoring, Physiologic/methods* ; Wearable Electronic Devices ; Chronic Disease