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.
Auscultation ; Blood Pressure/physiology* ; Blood Pressure Determination ; Oscillometry ; Sphygmomanometers

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.
Algorithms ; Electroencephalography ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Wavelet Analysis