Research On Compression Sensing Of Pacemaker Ecg Signal In Embedded

With the rapid development of medical Internet of things, it has become very convenient, wide that real-time monitor multiple physiological parameters such as ECG(Electrocardiograph, ECG) signal, blood pressure and blood oxygen by wearable wireless body area network and mobile terminal. At the same time, it has put forward higher requirements to wireless sensing and acquisition of complex physiological signals, such as the high frequency ECG signal and pacemaker ECG signal. However, according to the traditional Nyquist law, it cause massive data collection by the higher inherent frequency of the above two kinds of ECG signals. Therefore, to explore efficient physiological signal compression method is very necessary. In this paper, pacemaker ECG signal compression sensing is studied. Paper main research work lies in:(1) The embedded environment pacemaker ECG signal perception and transmissionUsing the wearable wireless body area network and mobile terminals realize the pacemaker ECG signal perception and transmission. And through Bluetooth 4.0 module implements short-range wireless transmission of pacemaker ECG signal, remove the bondage of the traditional cable transmission, and reduces the error and loss rate through signal transmission.(2) Pacemaker ECG signal sparse decomposition basis function designAt first, analyze the characteristic of wavelet basis function; Then, choose typical wavelet function to the signal sparse decomposition, respectively. At last, through the experimental results to prove when the choice of wavelet base function and pacemaker ECG signal with similar characteristics, the signal sparse decomposition effect is better; And the db wavelet, coiflet wavelet and symlet wavelet basis function, can do good ECG signal sparse expression.(3) The optimization design of measurement matrix in pacemaker ECG signal compression perceptionThrough the search on the construction methods of common measurement matrices and their own applicable scopes, it is concluded that random Gaussian measurement matrix is more suitable for the observation of dimension reduction of ECG signal, and provides theory basis and the safeguard for the follow-up accurate reconstruction of signal.(4) The pacemaker ECG signal embedded compression perception of reconstruction and its performance evaluationLooking for the appropriate reconstructing algorithm to reconstruct the signal accurately. And choosing the common performance evaluation standard to evaluate the performance of refactoring. It shows that the orthogonal matching pursuit algorithm can achieve good effect of reconstructing signal, and compressed sensing signal reconstruction precision is closely related to compression ratio, the M value of measurement matrix, in order to obtain high reconstruction accuracy, need higher M value and small compression ratio, and computational complexity will rise accordingly by the experimental conclusion.