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Background: Blood oxygen saturation of vein (SvO2) is an important clinical parameter for patient monitoring. However, the existing clinical methods are invasive, expensive, which are also painful for patients.Methods: Based on light-absorption, this study describes a new noninvasive SvO2 measurement method by using external stimulation signal to generate cyclical fluctuation signal in the vein, which overcomes the low signal-to-noise ratio problem in the measurement process. In this way, the value of SvO2 can be obtained continuously in real time.Results: The experimental results demonstrate that the method can successfully measure venous oxygen saturation by artificial addition of stimulation. Under hypoxic conditions, the system can reflect the overall decline of venous oxygen saturation better. When the results measured by the new method are compared with those measured by the invasive method, the root mean square error of the difference is 5.31 and the correlation coefficient of the difference is 0.72. The new method can be used to measure SvO2 and evaluate body oxygen consumption, and its accuracy needs improvement.Conclusions: Real-time and continuous monitoring can be achieved by replacing invasive method with noninvasive method, which provides more comprehensive clinical information in a timely manner and better meet the needs of clinical treatment. However, the accuracy of the new noninvasive SvO2 measurement based on light-absorption has to be further improved. [ABSTRACT FROM AUTHOR]

Cardiopulmonary exercise testing (CPET) refers to a method of measuring various indicators of the human body under gradually increasing exercise loads to objectively evaluate cardiopulmonary reserve function and exercise endurance. Currently, CPET detection systems primarily measure subjects' ECG, respiratory flow, oxygen (O 2 ), and carbon dioxide (CO 2 ) parameters. This paper introduces a non-invasive multi-parameter exercise cardiopulmonary function evaluation system that incorporates impedance cardiography monitoring. The system integrates impedance cardiography monitoring with conventional CPET detection parameters and detects changes in the hemodynamic parameters of the body during exercise, aiding in the evaluation of exercise capacity. Additionally, the system features a portable design with Wi-Fi wireless transmission, which enhances its applicability.

To address the issue of the difficulty in implementing non-invasive continuous blood pressure measurement technology in China, this study developed a high-performance synchronous electrocardiogram (ECG) and photoplethysmography (PPG) signal acquisition system. A PC-based human-computer interaction software platform was constructed, and continuous blood pressure measurement-related algorithms were integrated. Multiple feature parameters such as pulse wave transit time based on synchronous ECG and PPG signals were extracted, enabling non-invasive continuous blood pressure measurement. To verify the measurement accuracy of the system, tests and comparative verifications were carried out. The results demonstrated that the system could meet the requirements of relevant standards and have good application value.

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.

Sleep disordered breathing (SDB) is a common sleep disorder with an increasing prevalence. The current gold standard for diagnosing SDB is polysomnography (PSG), but existing PSG techniques have some limitations, such as long manual interpretation times, a lack of data quality control, and insufficient monitoring of gas metabolism and hemodynamics. Therefore, there is an urgent need in China's sleep clinical applications to develop a new intelligent PSG system with data quality control, gas metabolism assessment, and hemodynamic monitoring capabilities. The new system, in terms of hardware, detects traditional parameters like nasal airflow, blood oxygen levels, electrocardiography (ECG), electroencephalography (EEG), electromyography (EMG), electrooculogram (EOG), and includes additional modules for gas metabolism assessment via end-tidal CO 2 and O 2 concentration, and hemodynamic function assessment through impedance cardiography. On the software side, deep learning methods are being employed to develop intelligent data quality control and diagnostic techniques. The goal is to provide detailed sleep quality assessments that effectively assist doctors in evaluating the sleep quality of SDB patients.

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.

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.

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.

In the past 20 years, near infrared spectrum technology has been widely used in human body monitoring due to its non-invasive and real-time characteristics. Oxygen, as the main metabolic substance of the human body, is consumed the most in brain tissue. In order to prevent complications caused by a decrease in brain tissue oxygen during treatment, the patient's brain tissue blood oxygen saturation needs to be monitored in real time. Currently, most of the clinically used non-invasive cerebral blood oxygen detection equipments use dual wavelengths. Other substances on the detection path will cause errors in the measurement results. Therefore, this article proposes a three-wavelength method based on the basic principle of non-invasive monitoring of cerebral blood oxygen using near-infrared spectrum. The brain tissue oxygen saturation monitoring method of detecting light sources was initially verified through the built system, laying the foundation for subsequent system engineering.

In order to effectively treat respiratory diseases, a non-invasive positive pressure ventilator system is designed, the overall structure design of the system is proposed, and the hardware construction is completed. The breathing state of the patient is identified by the threshold triggering method of the flow rate of change, and the calculation of the flow rate of change is realized by the least squares method. At the same time, the breathing parameters are calculated in real time according to the flow-time and pressure-time characteristic curves. In addition, CMV, CPAP, BiPAP and PSV ventilation modes are also implemented. Finally, the parameter measurement accuracy and ventilation mode setting tests are carried out. The results show that the calculation of key breathing parameters provided by the system meets the relevant standards, and supports the stable output of 4 ventilation modes at the same time, provides breathing treatment for patients, and meets the basic functional requirements of the ventilator.

Fetal ECG monitoring is a routine clinical detection method that can reflect the changes of fetal heart in utero in real time. At present, most of the clinical fetal heart rate detection adopts the ultrasonic Doppler method, which is technically difficult and highly specialized in operation and expensive. This study introduces a fetal ECG detection system based on the maternal abdominal electrode method. The weak fetal ECG changes are sensed through the maternal abdominal electrode, and the mixed ECG signal is obtained through the corresponding amplification and filtering circuit. Finally, the obtained signal is passed through WiFi, transmitted to the host computer. The host computer uses the adaptive filtering algorithm to estimate the fetal ECG signal. The system has strong feasibility, low operation expertise, low cost, and is more convenient.

Ventricular fibrillation is the most common pathophysiological mechanism leading to cardiac arrest. If cardiac arrest can be rescued in time, the survival rate of patients can be greatly improved. Therefore, rapid and accurate identification of ventricular fibrillation is extremely important. This paper proposes an automatic detection algorithm for ventricular fibrillation based on random forest and BP (back propagation) neural network. Pass the ECG signal through a 6 s moving window, calculate 6 kinds of characteristic parameters according to the time-frequency domain information of the signal, use these 6 kinds of characteristic parameters as the input of the classifier, carry out classification and test, and give the authoritative experts in the database. A total of 44 cases of related data were used to evaluate the method. The results show that using the ten-fold cross-validation method, the accuracy of classification of ventricular fibrillation in the CU database (Creighton University Ventricular Tachyarrhythmia Database) and the AHA database (the American Heart Association Database) has reached 96.38% and 99.45%, which has certain applicability.

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.

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.

Oxygen therapy is an effective clinical method for the treatment of respiratory disorders, oxygen concentrator as a necessary medical auxiliary equipment in hospitals, its research and development has been a hot spot. The study reviewed the development history of the ventilator, introduced the two preparation technique of the oxygen generator pressure swing absorption (PSA) and vacuum pressure swing adsorption (VPSA), and analyzed the core technology development of the oxygen generator. In addition, the study compared some major brands of oxygen concentrators on the market and prospected the development trend of oxygen concentrators.

A method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) combined with differential, threshold iterative processing and signal segmentation is proposed to preprocess the impedance cardiogram (ICG) signal and identify multiple feature points. First, CEEMDAN decomposes the ICG signal to obtain multiple modal function components (IMF). According to the highfrequency and low-frequency noise frequencies existing in the ICG, the correlation coefficient method is used to remove the interference noise, and then the ICG signal after noise removal is differentiated and segmented. Looking for feature points B, C, X, and perform the above processing on the signals of 20 volunteers collected clinically to evaluate the accuracy of the algorithm. The final results show that the method can locate the feature points with an accuracy rate of 95.8%, the feature positioning effect is good.

A multi-channel surface electromyography wireless acquisition system is designed, which is mainly composed of ADS1299 integrated analog front-end chip and CC3200 wireless MCU of TI company. The key indicators of hardware are measured according to the industry standard, and the results are better than the industry standard, which can meet the continuous use of multi-scene tasks. This system has the advantages of high performance, low power consumption and small size. It has been applied to the detection of surface EMG signal in motion gesture recognition and has a good application value.

In order to assess and diagnose lower urinary tract dysfunction in patients and help patients with lower urinary tract rehabilitation training, an accurate and reliable urodynamic monitoring and automatic voiding system was designed. The system realizes the signal acquisition circuit of bladder pressure, abdominal pressure and urine volume based on the pressure sensor of urinary catheter and the load sensor. Meanwhile, the dynamic waveforms of urinary flow rate, bladder pressure and abdominal pressure are drawn in real time on the software of urodynamic monitoring. Signal processing and analysis of each signal are carried out, and the system performance is verified by building a simulation experiment. The experimental results show that the system is stable, reliable, accurate and meets the expected design goals, which can provide support for subsequent engineering design and clinical applications.

This study introduces a portable multi-channel EEG signal acquisition system. The system is mainly composed of EEG electrode connector, signal conditioning circuit, EEG acquisition part, main control MCU and power supply part. The low-power EEG acquisition front-end ADS1299 and STM32 are used to form the signal acquisition and data communication part. The collected EEG signal can be transmitted to the PC for real-time display. After relevant tests, the system has small volume, low power consumption, high signal-to-noise ratio, and meets the requirements of portable wearable medical devices.

Body temperature is an important physiological parameter of the human body and is used in medicine to reflect the physiological state and health status of the human body. At present, the commonly used clinical thermometers on the market are mainly divided into contact and non-contact types. Most of them are used for rapid body temperature measurement, and it is not easy to monitor body temperature changes in real time. This article introduces a new wearable wireless body temperature monitoring system based on NTC, which senses through NTC. The temperature changes are amplified and filtered, zeroed, and calibrated, and then the temperature data is uploaded to the mobile phone APP via Bluetooth in real time to achieve real-time accurate measurement of body temperature.

Breathing is of great significance in the monitoring of patients with obstructive sleep apnea hypopnea syndrome, perioperative monitoring and intensive care. In this study, a respiratory monitoring and verification system based on optical capacitance product pulse wave (PPG) is designed, which can synchronously collect human PPG signals. Through algorithm processing, the characteristic parameters of PPG signal are calculated, and the respiratory signal and respiratory frequency can be extracted in real time. In order to verify the accuracy of extracting respiratory signal and respiratory rate by the algorithm, the system adds the nasal airflow respiratory signal acquisition module to synchronously collect the nasal airflow respiratory signal as the standard signal for comparison and verification. Finally, the root mean square error between the respiratory rate extracted by the algorithm from the pulse wave and the standard respiratory rate is only 1.05 times/min.

Body temperature is an essential physiological parameter. Conducting non-contact, fast and accurate measurement of temperature is increasing important under the background of COVID-19. The study introduces an infrared temperature measurement system based on the thermopile infrared temperature sensor ZTP-135SR. Extracting original temperature date of sensor, post-amplification and filter processing have been performed to ensure accuracy of the system. In addition, the temperature data of environmental compensation which obtained by polynomial fitting is added to the system to further improve measurement accuracy.

General anesthesia is an essential part of surgery to ensure the safety of patients. Electroencephalogram (EEG) has been widely used in anesthesia depth monitoring for abundant information and the ability of reflecting the brain activity. The paper proposes a method which combines wavelet transform and artificial neural network (ANN) to assess the depth of anesthesia. Discrete wavelet transform was used to decompose the EEG signal, and the approximation coefficients and detail coefficients were used to calculate the 9 characteristic parameters. Kruskal-Wallis statistical test was made to these characteristic parameters, and the test showed that the parameters were statistically significant for the differences of the four levels of anesthesia: awake, light anesthesia, moderate anesthesia and deep anesthesia ( P < 0.001). The 9 characteristic parameters were used as the input of ANN, the bispectral index (BIS) was used as the reference output, and the method was evaluated by the data of 8 patients during general anesthesia. The accuracy of the method in the classification of the four anesthesia levels of the test set in the 7:3 set-out method was 85.98%, and the correlation coefficient with the BIS was 0.977 0. The results show that this method can better distinguish four different anesthesia levels and has broad application prospects for monitoring the depth of anesthesia.

Ventilator is an indispensable emergency medical equipment in hospitals. The global outbreak of the coronavirus disease 2019 (COVID-19) has highlighted the importance of the ventilator, which has attracted the attention and research on ventilators of all countries in the world. This article reviews the development history of the ventilator, briefly introduces the main air circuit structure and working principle of the ventilator, and then deeply analyzes the key technologies of this device. In addition, it compares some major brands of ventilators from several aspects in the market. Finally, the development trend and perspective of ventilators are presented.

The bioelectrical impedance measurement is a detection technique that uses the electrical characteristics and changes in human tissues and organs to detect biomedical information related to human physiological and pathological conditions. This article makes a comprehensive introduction from the aspects of impedance cardiography, electrical impedance respiratory monitoring, electrical impedance tomography, electrical impedance gastric dynamics detection technique, contact impedance and so on, as well as comprehensively introduces the progress and application status of bioelectrical impedance measurement methods.

In order to solve the problem of wide input range of photocurrent, aiming at the shortage of existing pulse signal acquisition system, an adaptive pulse signal acquisition system based on ADS125H02 is designed, which has high collection accuracy, low noise, anti-interference, wide input range, high integration, etc. The measurement range of the system can reach ±10 V, and the accuracy is 0.009 μV. Experimental tests show that the design of the system can extract weak pulse signals, and has good practicality.

Oxygen saturation and respiratory signals are important physiological signals of human body, respiratory monitoring plays an important role in clinical and daily life. A system was established to extract respiratory signals from photoplethysmography in this study. Including the collection of pulse wave signal, the extraction of respiratory signal, and the calculation of respiratory rate and pulse rate transmitted from the slave computer to the host computer in real time.

The ECG signal is susceptible to interference from the external environment during the acquisition process, affecting the analysis and processing of the ECG signal. After the traditional soft-hard threshold function is processed, there is a defect that the signal quality is not high and the continuity at the threshold is poor. An improved threshold function wavelet denoising is proposed, which has better regulation and continuity, and effectively solves the shortcomings of traditional soft and hard threshold functions. The Matlab simulation is carried out through a large amount of data, and various processing methods are compared. The results show that the improved threshold function can improve the denoising effect and is superior to the traditional soft and hard threshold denoising.

This paper designs a bluetooth-based low-power multi-parameter monitoring system. The system is mainly composed of ECG signal acquisition, respiratory signal acquisition, body temperature acquisition, bluetooth 4.0 transmission module and Android mobile phone APP display. The system collects the corresponding physiological signals through various collection parts, and can realize the monitoring of three physiological signals of electrocardiogram, respiration and body temperature. The Android mobile APP can display ECG, respiratory waveform and temperature data in real time. The system is small in size and low in power consumption, and has a good application prospect in portable and wearable medical applications.

This paper describes how to develop a practical new type of atherosclerosis detection device, which can realize real-time measurement and analysis of human atherosclerosis. According to the mechanism of human atherosclerosis, the design objectives of the system are formulated to determine the construction of the platform. The system calculates the pulse wave velocity by measuring the pulse wave of human fingers and toes, adds four blood pressure measurements to the system design, calculates the ankle-brachial index, and comprehensively measures and analyses the degree of human arteriosclerosis.

Muscle relaxant monitor is a research hotspot in the field of clinical anesthesiology. According to the research status at home and abroad, combing the developing history of muscle relaxant monitor, detecting principle, a variety of electrical stimulation mode, and a variety of detection ways, this study reviews and analyzes the advantages and disadvantages of various testing methods and application status, provides technical research foundation for the degree of nerve block for quantitative assessment of muscle relaxant monitor instrument design. Meanwhile, we advocate that clinicians should use quantitative muscle relaxant monitor as much as possible in the perioperative period to reasonably guide the use of muscle relaxants so as to reduce the risk of complications caused by residual effects of muscle relaxants and provide more scientific and accurate digital guidance for assessing the degree of muscle relaxants of patients.

In order to study the effects of sleep-disordered breathing on human physiology and function, and to accurately and objectively evaluate the level of human sleep quality at night, it can help patients with respiratory disorders improve sleep quality. This paper elaborates the development process of a new multi-parameter sleep quality evaluation system from the hardware circuit design, software and algorithm analysis design of the system. The system hardware platform collects the physiological signals of the 11-channel nighttime sleep period, and displays and stores them in real time on the monitoring platform. After collecting the sleep data of the whole night, it can effectively assist the sleep doctor to sleep by combining the judgment of the sleep-time respiratory disorder, the determination of the sleep cardiovascular event, the determination of the sleep-aware response event, and the sleep structure staging. The quality of sleep in patients with disorders was deeply evaluated.

EEG is a weak physiological electrical signal, which has important value in clinical and laboratory research. This paper mainly introduces several common methods of EEG signal processing, including power spectrum analysis, time-frequency analysis, bispectral analysis, etc, it mainly introduces their principles and applications in EEG signal processing, and provides methods and approaches for studying EEG.

Objective: To establish the system software of multi-parameter monitoring by embedded Linux kernel and Qt library., Methods: To determine the hardware system needed for the development of the system, carry out system Bootloader (Bootloader), Linux kernel, file system and Qt/Embedded (QtE) tailoring and transplantation and application development on the basis of the hardware system, and achieve the characteristic UI design., Results: The changes of physiological parameters were observed in real time to improve the stability and real-time performance of the whole system and increase users' experience with QtE., Conclusions: The embedded Linux+Qt multi-reference monitoring system can improve the stability, operability and functionality of real-time monitoring and multi-physiological information, and has good extensibility and maintainability.

In order to comprehensively evaluate the human body's functions of breathing, heart function and circulation metabolism under controlled conditions to diagnose respiratory and cardiac functions, a cardiopulmonary exercise test (CPET) system was developed. The system is divided into two parts:hardware and software. The hardware part includes measurement of six life information monitoring signals such as 12-lead ECG, respiratory mechanics, blood pressure, blood oxygen, carbon dioxide and oxygen. The software part realizes waveform display, storage and playback, et al. The experimental results show that the system is safe and reliable which provides reliable measurement parameters for evaluating human respiratory, cardiac function, circulation, metabolism and other functions. It has a good application prospect.

This study designs an intermittent pneumatic pressurization device with STM32 series single chip as the core. The working state of the air pump and the plurality of air chambers is controlled by the IO port of the single chip microcomputer, and the circulating inflation of the plurality of air bags is realized. The pressure monitoring system consists of a silicon piezoresistive pressure sensor, a high-precision operational amplifier and a 12-bit AD converter which monitors the gas pressure of each gas path in real time to ensure the safety of the equipment. The system is easy to operate, simple in function, and has strong practicability.

This study describes the development of a wireless and wearable ECG monitoring system with ultra-low power consumption. The system is mainly composed of a connection part of an ECG electrode sticker, an electrocardiogram collecting part, a data storage part, a Bluetooth main control unit, a charging module, a voltage regulator and a lithium battery. The low-power ECG acquisition chip ADS1292R and the ultra-low-power Bluetooth microcontroller nRF51822 together constitute the ECG signal acquisition and wireless data communication part. The collected ECG signals can be sent to the mobile APP through the Bluetooth function provided by the MCU, and can completly display and analysis to achieve low power system. After testing, the system power consumption is only (3.7 V×2.87 mA)10.619 mW, and if it is optimized, it can further reduce power consumption, therefore, the system design can have good applicability.

The study comprehensively puts forward several detection methods to arteriosclerosis and mainly discusses the method for pulse wave velocity (PWV for short) and ankle brachial index (ABI for short). On the basis of methods mentioned above, a portable system device for arteriosclerosis detection based on non-invasive PWV and limb blood pressure is introduced, and expectations for the subsequent engineering design and development direction are given for reference.

This paper proposes a method of generating a square wave excitation signal through an analog circuit, using an analog switch to select the detection channel. In addition, high precision and low temperature drift resistors have been added for calibration. The calibration calculation and impedance detection are performed every 5 minutes. This system greatly reduces the errors caused by analog circuit and external factors and improves the accuracy of measurement. It can be used in the multi-lead EEG acquisition system and has good application value.

This article describes the design of a portable blood oxygen simulation system that can be used to simulate various blood gas saturation states of the human body. The system can be used to simulate various states of blood gas saturation, and can also simulate large blood oxygen saturation dynamic range, pulse rate range and perfusion index range. It can be used for testing, but not for clinical examination instruments. Moreover, the system has the characteristics of small size and low cost compared with the commercial blood oxygen simulator. Although the simulation system is not directly used for the detection of blood gas saturation of patients, it is also an essential equipment in the production and testing process, so it has certain practical value.

In order to improve the accuracy of the pacemaker's parameter adjustment and to avoid the surgical replacement of the pacemaker when the battery is exhausted, this paper designs a novel single-chamber pacemaker circuit based on low-power single-chip microcomputer. The circuit uses digital control to accurately control the amplitude, pulse width and frequency of the pacing pulse. The circuit is also designed with wireless charging function, and wireless communication with the programmer can wirelessly charge the pacemaker and know the charging information in real time. Wireless charging function can reduce the number of times the patient replaces the pacemaker or even completely avoid it.

The removal of baseline drift is a difficult and important point in ECG preprocessing. This article discusses several commonly used methods to suppress ECG baseline drift:median filtering, high-pass filtering, curve fitting, wavelet transform, ECG-based feature points and morphological filtering methods. Under the circumstances of the baseline drift serious or weak, several methods for data are analyzed and compared. This article compares several commonly used methods as well as evaluation of application effects to provide a reference for the selection of baseline drift removal methods for ECG signals.

Cardiovascular disease has become a serious disease that threatens the health of human beings, cardiac output is an important indicator of cardiovascular function, monitoring cardiac output and related hemodynamic parameters have significant clinical value. This article summarizes the development history, principle, method, advantages and disadvantages of various monitoring technologies from three aspects:invasive, minimally invasive and noninvasive, the development and application of cardiac output monitoring technology are prospected.