ABSTRACT

In recorded Electrocardiogram (ECG) signal, clinical information is masked by several noises and distortion resulting in low signal-to-noise-ratio (SNR). In this situation, an efficient pre-processing technique is required to improve SNR for efficient analysis of ECG signals. In this research article, performance of five pre-processing techniques viz. digital bandpass filter (DBPF), wavelet transform (WT), independent principal component analysis (IPCA), savitzky golay digital FIR filter (SGDFF) and fractional wavelet transform (FrWT) have been evaluated and compared for their effects on the efficiency of R-peak detection. FrWT has been utilized for pre-processing of ECG signal for the first time in this paper. A FrWT-based technique is also proposed using Yule Walker autoregressive modeling (YWARM) and Principal Component Analysis (PCA) for feature extraction and R-peak detection, respectively. YWARM is selected due to its more stable output for long time recorded ECG signal than existing techniques, whereas PCA is selected to get optimal dimensional feature vectors out of higher dimensional feature vectors. The proposed technique has been evaluated and compared with others on the basis of various performance parameters; SNR, mean squared error (MSE), sensitivity (SE), accuracy (Acc), and positive predictive value (PPV). The proposed technique yielded interesting results among all the methods; 34.37 dB of output SNR, 0.026% of MSE, 99.98% of SE, 99.97% of Acc and 99.99% of PPV on real time ECG database (RT DB) and 24.81 dB of output SNR, 0.099% of MSE, 99.96% of SE, 99.93% of Acc, and 99.97% of PPV on MIT-BIH Arrhythmia database (MIT-BIH Arr DB).

摘要

在记录的心电图 (ECG) 信号中，临床信息被多种噪声和失真所掩盖，从而导致低信噪比 (SNR)。在这种情况下，需要一种有效的预处理技术来提高信噪比，从而有效地分析心电信号。在这篇研究文章中，五种预处理技术的性能，即。对数字带通滤波器 (DBPF)、小波变换 (WT)、独立主成分分析 (IPCA)、savitzky golay 数字 FIR 滤波器 (SGDFF) 和分数小波变换 (FrWT) 对 R- 效率的影响进行了评估和比较峰值检测。本文首次将 FrWT 用于心电信号的预处理。还提出了一种基于 FrWT 的技术，分别使用 Yule Walker 自回归建模 (YWARM) 和主成分分析 (PCA) 进行特征提取和 R 峰检测。选择 YWARM 是因为它对长时间记录的 ECG 信号的输出比现有技术更稳定，而选择 PCA 是为了从更高维度的特征向量中获得最优的维度特征向量。已根据各种性能参数对所提出的技术进行了评估并与其他技术进行了比较；SNR、均方误差 (MSE)、灵敏度 (SE)、准确度 (Acc) 和阳性预测值 (PPV)。所提出的技术在所有方法中产生了有趣的结果；34.37 dB 的输出 SNR，0.026% 的 MSE，99.98% 的 SE，99.97% 的 Acc 和 99.99% 的实时心电图数据库 (RT DB) 和 24.81 dB 的输出 SNR，0.099% 的 MSE，