[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.

[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.

[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.

[An anesthesia depth computing method study based on wavelet transform and artificial neural network].

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.