Respiration Rate Fusion Estimation Based On ECG And Pulse Signal

With the continuous progress of society,the improvement of living standards,people’s health awareness of the body also continue to strengthen.Respiratory movement,as an important human life activity,which reflects the respiratory system at the same time,to a certain extent,also reflects the cardiovascular system of the disease.Therefore,the monitoring of human respiratory activity is an important part of modern medical monitoring technology.Through the monitoring of human respiratory function and status,it can early detect and prevent the lesions on respiratory tract,cardiovascular and other parts,and timely understand of the trend of the disease and determine the treatment program to ensure human health.At present,the main method of respiratory monitoring is by sensors,but its monitoring process will bring inconvenience and a certain degree of psychological pressure to the person being monitored.It is possible to make continuous monitoring of the two kinds of signals to obtain the respiration rate,because of the respiratory information contained in the ECG and pulse.The main purpose of this paper is to study respiration rate fusion estimation based on the ECG and pulse signals,using the fusion algorithm of respiration rate,and on the basis of the algorithm it is improved to simplify the algorithm process,reducing the complexity of the algorithm,for mobile and portable breath testing equipment providing an effective algorithm.The content of this paper is divided into the following parts:1.Acquisition of respiratory signals based on ECG and pulse.First,the wavelet transform is used for ECG and pulse pre treatment to remove the containing noise of the signals;then,according to the characteristics of ECG and pulse signals,choosing suitable wavelet function,scaling and so on,are used for the feature point detection of ECG and pulse signals,feature point sequence acquisition,and moving average processing sequences containing information about respiratory.Finally,the sequence of the resulting signal uses interpolation to obtain the respiratory signal.2.Signal quality evaluation.In order to reduce the complexity of the algorithm,this paper chooses the time domain analysis to evaluate the quality of the signal from the 3 factors of the feature points,the matching degree,the short-time energy and the short-term fluctuation of the signal.In this paper,the DT algorithm based on digital filtering and threshold is improved,which is suitable for the detection of the characteristic points of the pulse signal.Using improved DT algorithm and adaptive threshold algorithm completes matching degree detection of characteristic points of ECG and pulse signals,and combines with short-time energy and short-time fluctuation degree of these two parameters,finally calculate the quality index for the follow-up work of the ECG and pulse signals.3.Respiration rate fusion estimation.Based on the signal quality index,the Kalman filter is used to deal with the respiration rate of the ECG and pulse,and the final respiration rate estimation is obtained by using the weighted fusion method.The ECG,pulse and reference respiration data in Fantasia Database and MIMIC Database were analyzed in this paper.In Matlab software for signal waveform detection,the detected respiration rate is compared with the reference respiration rate.The experimental results show that the improved algorithm has a good effect,and it has a certain practical value.