分数阶傅市叶变换域的心电信号滤波算法

心电信号是一种非平稳的低频微弱信号，与干扰噪声具有较强的时频耦合．经典的滤波方法难以实现有效的信噪分离．提出了一种基于分数阶傅立叶域的LMS自适应滤波算法，既结合了适合处理非平稳信号和减少时频耦合的特点，又能够有效地提高信噪比．首先将信号进行分数阶傅立叶变换，寻找最优变换域，再利用LMS自适应滤波算法在最优变换域滤波，然后对滤波后的信号进行分数阶傅立叶反变换．通过对MIT—BIH中的心电数据进行Mat—lab仿真，表明信噪比从-6dB提高到14dB，清晰地还原出心电信号的波形及特征点．

ECG Signal Filtering Algorithm in Fractional Fourier Transform Domain

ECG is a non--stationary, low-frequency and weak signal,it contains the characteristic of time-frequency coupling. So the classical filtering methods are difficult to separate noises from signal effectively. An adaptive filtering algorithm in fractional Fourier transform domain is proposed. It has the features of reducing the time-frequency coupling and processing non-stationary signals, and it improves SNR without increasing computation. The optimal transform domain of the ECG signal is searched after the signal is transformed. And then, in the optimal transform domain, the signal is filtered by the adaptive filtering algorithm. The simulation of the algorithm is based on the MIT-BIH ECG signal database. The results show： The SNR increases from --6 dB to 14dB; The waveform and feature points are restored clearly.