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Performance analysis for the Feature Extraction algorithm of an ECG signal

机译:ECG信号特征提取算法的性能分析

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An Electrocardiogram is a significant recording for measuring the electrical activity of the heart. The Feature Extraction of an ECG signal plays a significant role in diagnosing most of the cardiac diseases. This paper concentrates on the algorithm for extraction of the fiducial points of an ECG signal and its performance analysis. To analyze these signals, time-frequency domain is the best suited method. In order to analyze these physiological and non-stationary signals, a Wavelet Transform in which Discrete Wavelet Transform is preferred to Fast Fourier and Short time - Fourier transform. A Discrete Wavelet Transform (DWT) is employed for the better understanding of the ECG signal in Time -Frequency plane with (Haar) as a Mother Wavelet function. The Samples are taken from the available ECG databases PTBDB & QTDB and are checked against the CSE tolerance limits. Since, real time signals like ECG can be easily processed using a DSP processor, the algorithm is targeted onto a ADSP - 2181 to reduce the complexity of the code and obtain optimized power levels.
机译:心电图是测量心脏电活动的重要记录。 ECG信号的特征提取在诊断大多数心脏疾病中起重要作用。本文着重于心电信号基准点的提取算法及其性能分析。为了分析这些信号,时频域是最合适的方法。为了分析这些生理和非平稳信号,小波变换中,离散小波变换比快速傅里叶变换和短时间傅里叶变换更可取。离散小波变换(DWT)用于更好地理解时频平面中的ECG信号,并以(Haar)作为母小波函数。样本取自可用的ECG数据库PTBDB和QTDB,并对照CSE容限进行检查。由于可以使用DSP处理器轻松处理ECG之类的实时信号,因此将该算法定位到ADSP-2181上,以降低代码的复杂度并获得优化的功率水平。

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