MEA信号锋电位的小波变换分类

小波变换是信号处理领域最近若干年发展起来的先进方法，对信号的时域信息和频域信息都有很好的表现。我们利用小波变换对MEA信号的锋电位数据矩阵进行小波分解，将各锋电位变换后的最后一个尺度的逼近信号和各尺度上的细节信号的方差对波形非相似性测度的贡献率的大小作为依据，鉴别波形的不同。找出贡献率和大于90％的8个数据进行系统聚类，大大地降低了数据的维数和计算机的机时，并得到了很好的分类结果。

Wavelet Transformation Sorting for Spike Signal in MEA

Wavelet transformation which has been developed resent years in signal processing,has good representation in time domain and frequency domain of signal.The wavelet transformation is used here to make wavelet analysis to spike matrix in MEA.We identify the different wave shapes according to the contributing rate of the nonsimilarity of the shapes,which is calculated by the square error of approached signal in the last scale and the square error of detail signal in each scale of each analyzed spike.The eight datum whose contributing rate summed up is more than 90% are found out and have system cluster.By this way,the sorting result is good,and the datum dimension and the calculating time are reduced.