Accurate QRS detection is an important first step for almost every electrocardiogram (ECG) signal analysis. However, detecting QRS is difficult, not only because of the large variety, but also as a result of interference caused by various types of noise. This paper employs a hybrid feature extraction technique of ECG signal for the detection of cardiac abnormalities. Noise removal and accurate QRS detection play a major role in the analysis of ECG signals. In this paper various types of noises such as additive white Gaussian noise, baseline wander and power line interference is eliminated to enhance the signal quality. This study proposes an improved QRS complex detection algorithm based on the combination of empirical mode decomposition-discrete wavelet transform (EMD-DWT) with threshold and compared with ordinary discrete wavelet transform. The system efficacy and performance have been evaluated using accuracy, sensitivity (Se), positive predictive value (PPV) and detection error rate (DER). The results show the high accuracy of the proposed EMD-DWT algorithm, which attains a detection error rate of 1.1233%, a sensitivity of 99.28%, and a positive predictive value of 99.99%, evaluated using the MIT-BIH arrhythmia database. The proposed algorithm improves the accuracy of QRS detection compared to state-of-art methods.

几乎所有心电图（ECG）信号分析中，准确的QRS检测都是重要的第一步。但是，不仅由于种类繁多，而且由于各种类型的噪声引起的干扰，都很难检测QRS。本文采用ECG信号的混合特征提取技术来检测心脏异常。噪声消除和精确的QRS检测在ECG信号分析中起着重要作用。在本文中，消除了各种类型的噪声，例如加性高斯白噪声，基线漂移和电源线干扰，以提高信号质量。该研究提出了一种改进的QRS复杂检测算法，该算法将经验模态分解-离散小波变换（EMD-DWT）与阈值相结合，并与普通离散小波变换进行了比较。使用准确性，灵敏度（Se），阳性预测值（PPV）和检测错误率（DER）评估了系统功效和性能。结果表明，所提出的EMD-DWT算法具有很高的准确性，使用MIT-BIH心律失常数据库评估该算法的检测错误率为1.1233％，灵敏度为99.28％，阳性预测值为99.99％。与最新技术相比，该算法提高了QRS检测的准确性。使用MIT-BIH心律失常数据库进行评估。与最新技术相比，该算法提高了QRS检测的准确性。使用MIT-BIH心律失常数据库进行评估。与最新技术相比，该算法提高了QRS检测的准确性。