[Research Progress on End-Tidal Carbon Dioxide Detection Technology Based on Non-Dispersive Infrared Method].

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.

[Wireless Pulse Wave Monitoring System Based on Reflective Flexible Probe and AFE4490].

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.

[Development of Wireless Wearable Sleep Monitoring System Based on EEG Signal].

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.

[Development of a Three-Wavelength Brain Tissue Oxygen Monitoring System Based on Near Infrared Spectrum].

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.

[A Ventricular Fibrillation Recognition Method Based on Random Forest and BP Neural Network].

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.

[Development of a Multi-parameter Pulmonary Function Test System].

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.

[Fetal ECG Detection System Based on WiFi Data Transmission].

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.

[An Atrial Fibrillation Classification Method Study Based on BP Neural Network and SVM].

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.

[Research on ICG Signal Preprocessing and Feature Recognition Method Based on CEEMDAN].

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.

[Design and Implementation of Urodynamic Monitoring System].

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.

[sEMG Wireless Acquisition System Based on CC3200 and ADS1299].

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.

[Design of Portable Noninvasive Positive Pressure Ventilator System Based on Threshold Trigger of Flow Rate Change].

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.

[Research and Development Trend of Medical Oxygen Production Equipment].

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.

[Infrared Sensor ZTP-135SR and Its Application in Infrared Body Temperature Measurement].

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.

[Development of a Wireless Wearable Body Temperature Measurement System Based on NTC].

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.

[Miniature Non-invasive Blood Pressure Measurement and Verification System].

Mercury sphygmomanometer based on traditional auscultation method is widely used in primary medical institutions in China, but a large amount of blood pressure data can not be directly recorded and applied in scientific research analysis, meanwhile auscultation data is the clinical standard to verify the accuracy of non-invasive electronic sphygmomanometer. Focusing on this, we designed a miniature non-invasive blood pressure measurement and verification system, which can assist doctors to record blood pressure data automatically during the process of auscultation. Through the data playback function,the software of this system can evaluate and verify the blood pressure algorithm of oscillographic method, and then continuously modify the algorithm to improve the measurement accuracy. This study introduces the hardware selection and software design process in detail. The test results show that the system meets the requirements of relevant standards and has a good application prospect.

[Current Status of Hyperbaric Oxygen Therapy and Its Application].

Hyperbaric oxygen therapy is a method of breathing pure oxygen or high-concentration oxygen in a highpressure environment to treat hypoxic diseases and related diseases. According to clinical verification, this therapy has an irreplaceable effect on certain diseases and has gradually become a comprehensive clinical treatment. One of the main methods of certain diseases is widely recognized by the medical field at home and abroad. The development history, treatment principles, key technologies, and future development trends of hyperbaric oxygen are discussed in detail, provide a research direction for the development of hyperbaric oxygen therapy in the future, and at the same time, it has also improved physicians' awareness of hyperbaric oxygen therapy, so as to improving Industry influence.

[Development of Respiratory Signal Monitoring System Based on Photoplethysmography].

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.

[Portable Multi Channel EEG Signal Acquisition System].

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.

[An Adaptive Method for Detecting and Removing EEG Noise].

To solve the problem of real-time detection and removal of EEG signal noise in anesthesia depth monitoring, we proposed an adaptive EEG signal noise detection and removal method. This method uses discrete wavelet transform to extract the low-frequency energy and high-frequency energy of a segment of EEG signals, and sets two sets of thresholds for the low-frequency band and high-frequency band of the EEG signal. These two sets of thresholds can be updated adaptively according to the energy situation of the most recent EEG signal. Finally, we judge the level of signal interference according to the range of low-frequency energy and high-frequency energy, and perform corresponding denoising processing. The results show that the method can more accurately detect and remove the noise interference in the EEG signal, and improve the stability of the calculated characteristic parameters.