Research On Multi-physiological Parameter Data Processing System Of Mobile Medical Based On Deep Learning

The health of the human body can be reflected by the physiological signals of the human body,and real-time monitoring of the parameters of the physiological signals of the human body can analyze and diagnose the patient's condition in time.With the rapid development of various portable smart mobile devices and artificial intelligence,in the process of disease screening and prediction,in addition to discovering the clues of the disease through the results of biochemical and imaging examinations,it is also possible to use mobile smart devices to check people's language and analyze the rules of text formation,and the data from this analysis can help doctors more effectively predict and track early developmental disorders,mental illnesses,and degenerative neurological diseases.The human body multi-physiological parameter data processing system designed in this article monitors and analyzes human ECG,heart rate,blood pressure,and blood oxygen saturation.It plays a role in the early detection,early diagnosis,early intervention,and early rehabilitation of medical heart diseases.critical use.The main work of this paper includes:First,design the corresponding hardware equipment to collect the human body's ECG signal and pulse wave signal,and preprocess these two signals respectively.For the processing of the ECG signal,an infinite impulse response filter is used to preprocess the original waveform,the Hilbert Transform algorithm is used to detect the QRS complex,and then the Wavelet Transform algorithm is used to extract the R peak value and the RR interval,and calculate the heart rate.For the pulse wave signal processing,the median filter is used to preprocess the original waveform,and then the improved differential algorithm is used to extract the main peak of the signal.Second,based on the characteristic value of the ECG signal and the characteristic value of the pulse wave signal,the Pulse Wave Transit Time,heart rate,blood pressure,and blood oxygen saturation are calculated.When predicting blood pressure,a recurrent neural network overview model based on Deep Learning is used.This article combines theoretical principles and practical problems for analysis and processing.Compared with the current research,the method realized in this article is more stable and universal.Third,a simple multi-physiological parameter data processing system is built based on the Qt Creator platform,which integrates the visualization of the ECG waveform and the result display of heart rate,blood pressure,and blood oxygen saturation.Experimental results show that this system can more stably complete the monitoring and processing of multiple physiological signals of the human body,which has promoted the development of modern medical equipment.