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An efficient wavelet-based automated R -peaks detection method using Hilbert transform

机译：使用Hilbert变换的基于基于小波的自动R -Peaks检测方法

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Abstract Machine-aided detection of R -peaks is becoming a vital task to automate the diagnosis of critical cardiovascular ailments. R -peaks in Electrocardiogram (ECG) is one of the key segments for diagnosis of the cardiac disorder. By recognizing R -peaks, heart rate of the patient can be computed and from that point onwards heart rate variability (HRV), tachycardia, and bradycardia can also be determined. Most of the R -peaks detectors suffer due to non-stationary behaviors of the ECG signal. In this work, a wavelet transform based automated R -peaks detection method has been proposed. A wavelet-based multiresolution approach along with Shannon energy envelope estimator is utilized to eliminate the noises in ECG signal and enhance the QRS complexes. Then a Hilbert transform based peak finding logic is used to detect the R -peaks without employing any amplitude threshold. The efficiency of the proposed work is validated using all the ECG signals of MIT-BIH arrhythmia database, and it attains an average accuracy of 99.83%, sensitivity of 99.93%, positive predictivity of 99.91%, error rate of 0.17% and an average F -score of 0.9992. A close observation of the simulation and validation indicates that the suggested technique achieves superior performance indices compared to the existing methods for real ECG signal.
机译：摘要r-ppeaks的机器辅助检测成为自动化临界心血管疾病的诊断至关重要的任务。心电图（ECG）中的R -PEAKS是诊断心脏病的关键段之一。通过识别R -PEAKS，还可以计算患者的心率，并且也可以确定点心心率变异性（HRV），心动过速和心动过缓。由于ECG信号的非静止行为，大多数R -Peaks探测器受到影响。在这项工作中，已经提出了一种基于小波变换的自动R -Peaks检测方法。利用小波的多分辨率方法以及Shannon能量包络估计器来消除ECG信号中的噪声并增强QRS复合物。然后，基于希尔伯特变换的峰值查找逻辑用于检测R -Peaks而不采用任何幅度阈值。使用MIT-BIH心律失常数据库的所有ECG信号验证所提出的工作的效率，其平均精度为99.83％，灵敏度为99.93％，阳性预测性为99.91％，误差率为0.17％，平均为0.17％ -20992的跳跃。密切观察模拟和验证表明，与现有的真实ECG信号的方法相比，建议的技术实现了卓越的性能指标。

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