基于平稳和连续小波变换融合算法的心电信号P,T波检测

P, T波的检测在临床上是心血管疾病诊断的重要依据。由于其波形能量低、形态复杂，极易受到噪声干扰，导致现有检测算法精度仍有待提高。该文提出平稳和连续小波变换融合算法检测P, T波，利用连续小波变换的多尺度信息，获取心电图(ECG)信号中P, T波主要成分，融合其平稳小波对P, T波候选段进行平滑处理，消除波形中锯齿状毛刺对峰值点检测的影响，最后对P, T波过零点进行时移修正，保证过零点还原到原始信号过程中能够准确对应其峰值点，从而提高P, T波检测精度。该文算法在MIT-BIH arrhythmic数据库上进行验证，最终P波的误差率、敏感度、正确预测度达到：0.23%, 99.85%, 99.90%；T波的误差率、敏感度、正确预测度达到0.27%, 99.85%, 99.87%。

Detection of ECG Signal P and T Wave Based on Stationary and Continuous Wavelet Transform Fusion

The detection of P and T waves is an important basis for the diagnosis of cardiovascular disease in the clinic. Because of its low waveform energy and complex shape, it is extremely susceptible to noise interference, which leads to the need to improve the accuracy of existing detection algorithms. In this paper, a P and T wave algorithm based on stable and continuous wavelet transform is proposed .First, the smooth wavelet transform is used to smooth the ElectroCardioGram (ECG) signal to eliminate the effect of jagged burrs on the peak point detection. Then, the multiscale information of continuous wavelet transform is used to obtain the main components of P and T waves in the ECG signal. According to the translation correction rule, the time shift of the zero crossings of P and T waves is corrected to improve the detection accuracy of P and T waves. The algorithm of this paper is verified on the MIT-BIH arrhythmic database, and the final P-wave error rate, sensitivity, and correct prediction reach 0.23%, 99.85%, 99.90%; the T-wave error rate, sensitivity, and correct prediction reach 0.27%, 99.85%, 99.87%.