Electrocardiogram (ECG) is easily submerged in noise of the complex environment during remote medical treatment, and this affects the intelligent diagnosis of cardiovascular diseases. Considering this situation, this paper proposes an echo state network (ESN) denoising algorithm based on recursive least square (RLS) for ECG signals. The algorithm trains the ESN through the RLS method, and can automatically learn the deep nonlinear and differentiated characteristics in the noisy ECG data, and then the network can use these characteristic to separate out clear ECG signals automatically. In the experiment, the proposed method is compared with the wavelet transform with subband dependent threshold and the S-transform method by evaluating the signal-to-noise ratio and root mean square error. Experimental results show that the denoising accuracy is better and the low frequency component of the signal is well preserved. This method can effectively filter out complex noise and effectively preserve the effective information of ECG signals, which lays a foundation for the recognition of ECG signal feature waveform and the intelligent diagnosis of cardiovascular disease.
远程医疗的复杂环境中，心电信号极易被噪声淹没，从而影响心血管疾病的智能诊断。基于此，本文提出了一种基于递归最小二乘法的回声状态网络心电信号降噪算法。该算法通过递归最小二乘法对该网络进行训练，可自动学习得到含噪心电数据中非线性的且具有区分度的深层次特征，并利用这些特征自动分离心电信号与噪声。实验中，采用信噪比和均方根误差为指标，将本文方法与基于子带自适应阈值的小波变换法和 S 变换法进行比较。实验结果表明，本方法降噪精度更优，同时信号的低频成分也得到了很好的保持。本文方法可做到消除心电信号中的复杂噪声并完整保留心电信号的形态，为心电图的特征检测和心血管疾病的智能诊断奠定了基础。.
Keywords: denoising; echo state network; electrocardiogram signal; recursive least square.