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

In order to improve the effect of transcranial electrical stimulation treatment and realize personalized treatment for patients with varying severity levels, this paper designed an integrated four-channel EEG recording multimodal transcranial electrical stimulation system. This system can conduct real-time monitoring on EEG and related characteristic analysis before stimulation, in stimulation, and after stimulation. This enables physicians and researchers to resolve real-time brain states, evaluate transcranial electrical stimulation effect, and then artificially adjust the stimulation parameters. After relevant testing and verification, the system can select four stimulation modes: TACS, TDCS, TPCS and TRNS, which can output the constant stimulation current of 0.03 mA accuracy in the range of ±2 mA and the stimulation frequency of low frequency of 0~4 kHz (precision of 0.01 Hz) and high frequency 50~100 kHz, which can obtain more accurate EEG signals under stimulation interference, demonstrating a good market application prospect.
Electroencephalography/methods* ; Transcranial Direct Current Stimulation/instrumentation* ; Humans ; Equipment Design

This paper introduces a 16-channel multimodal high-precision transcranial electrical stimulation system specifically for non-invasive brain stimulation. This system added TMCS mixed four traditional stimulation modes with TACS, TDCS, TPCS and TRNS. By designing a compensated high-precision constant current source, the constant stimulation current with an accuracy of 0.03 mA in the range of ±2 mA and the stimulation frequency of 50~200 kHz with low frequency of 0~4 kHz (high frequency of 0.1 Hz) are realized. In TACS stimulation mode, there are five adjustable wave forms: triangular wave, sine wave, sawtooth wave, square wave and mixed wave. The system has dual closed-loop control overcurrent detection and simultaneous real-time electrode contact impedance detection. After relevant tests and verification, the system has good stimulation accuracy, high safety and reliability. Compared with the existing products at home and abroad, it features lower cost, richer stimulation mode and waveforms, demonstrating a certain market application value.
Transcranial Direct Current Stimulation/instrumentation* ; Equipment Design ; Humans

A wireless wearable sleep monitoring system based on EEG signals is developed.The collected EEG signals are wirelessly sent to the PC or mobile phone Bluetooth APP for real-time display.The system is small in size,low in power consumption,and light in weight.It can be worn on the patient's forehead and is comfortable.It can be applied to home sleep monitoring scenarios and has good application value.The key performance indicators of the system are compared with the industry-related medical device measurement standards,and the measurement results are better than the special standards.

The concentration of end-tidal carbon dioxide is one of the important indicators for evaluating whether the human respiratory system is normal.Accurately detecting of end-tidal carbon dioxide is of great significance in clinical practice.With the continuous promotion of the localization of end-tidal carbon dioxide monitoring technology,its application in clinical practice in China has become increasingly widespread in recent years.The study is based on the non-dispersive infrared method and comprehensively elaborates on the detection principle,gas sampling methods,key technologies,and technological progress of end-tidal carbon dioxide detection technology.It comprehensively introduces the current development status of this technology and provides reference for application promotion and further improvement.

Pulse rate and blood oxygen levels are crucial physiological parameters that reflect physiological and pathological information within the human body.The system designs a wireless pulse wave monitoring system utilizing a flexible reflective probe and the AFE4490,which is capable of monitoring pulse wave and blood oxygen levels on the human forehead.The system is predominantly based on a reflective flexible probe,the AFE4490,a power supply module,a control microcontroller unit(MCU),and a Wi-Fi module.Post-processing by a slave computer,the collected pulse wave data is wirelessly transmitted to a smartphone.The real-time pulse waveform,pulse rate,and blood oxygen levels are displayed on an application.Following relevant tests and verifications,the system can accurately detect pulse wave signals,meet the requirements for wearable technology,and possesses significant market application potential.

ECG signals and sleep monitoring parameters complement each other and can be used for qualitative diagnosis of sleep apnea syndrome and cardio-related diseases.However,due to the limitations of the instrument volume and the detection environment,it is often challenging to integrate these two functions in practical applications.In this paper,a 12-lead dynamic electrocardiograph integrated with sleep monitoring is designed.The system's volume is reduced by combining the integrated ECG simulation front end with a miniature sensor.The system achieves the extraction,conditioning,and calculation of 12-lead ECG signals and sleep-related parameters and writes the data to a memory card in real time,which offers convenience for users and doctors in the diagnostic process.

Atrial fibrillation is a common arrhythmia, and its diagnosis is interfered by many factors. In order to achieve applicability in diagnosis and improve the level of automatic analysis of atrial fibrillation to the level of experts, the automatic detection of atrial fibrillation is very important. This study proposes an automatic detection algorithm for atrial fibrillation based on BP neural network (back propagation network) and support vector machine (SVM). The electrocardiogram (ECG) segments in the MIT-BIH atrial fibrillation database are divided into 10, 32, 64, and 128 heartbeats, respectively, and the Lorentz value, Shannon entropy, K-S test value and exponential moving average value are calculated. These four characteristic parameters are used as the input of SVM and BP neural network for classification and testing, and the label given by experts in the MIT-BIH atrial fibrillation database is used as the reference output. Among them, the use of atrial fibrillation in the MIT-BIH database, the first 18 cases of data are used as the training set, and the last 7 cases of data are used as the test set. The results show that the accuracy rate of 92% is obtained in the classification of 10 heartbeats, and the accuracy rate of 98% is obtained in the latter three categories. The sensitivity and specificity are both above 97.7%, which has certain applicability. Further validation and improvement in clinical ECG data will be done in next study.
Humans ; Atrial Fibrillation/diagnosis* ; Support Vector Machine ; Heart Rate ; Algorithms ; Neural Networks, Computer ; Electrocardiography

To comprehensively evaluate the human body's respiratory, circular metabolism and other functions, and to diagnose lung disease, an accurate and reliable pulmonary function test (PFT) is developed. The system is divided into two parts:hardware and software. It realizes the collection of respiratory, pulse oxygen, carbon dioxide, oxygen and other signals, and draws flow-volume curve (FV curve), volume-time curve (VT curve), respiratory waveform, pulse wave, carbon dioxide and oxygen waveform in real time on the upper computer of the PFT system, and conducts signal processing and parameter calculation for each signal. The experimental results prove that the system is safe and reliable, it can accurately measure the basic functions of human body, and provide reliable parameters, and has good application prospects.
Humans ; Carbon Dioxide ; Respiratory Function Tests ; Oxygen ; Heart Rate