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An ECG feature extraction with wavelet algorithm for personal healthcare

机译:小波算法的个人医疗心电图特征提取

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This paper presents an electrocardiogram (ECG) feature extraction with wavelet algorithm for personal healthcare monitoring. Developing a wireless ECG examination system employed to monitor the cardiovascular disease (CVD) is signification, especially uses a low-power device anywhere and anytime detecting the real-time ECG signal for self-examination applications. The continuous time Mexican-Hat wavelet transform (CTMHWT) algorithm is quickly and easily applied to analyze the approximation of P-QRS-T complex fiducial points. The database, MIT-BIH discrete points, is adopted to efficiently extract the ECG signal according to CTMHWT algorithm with Matlab simulator. Moreover, the simulation result reveals that the feature extraction of ECG signal is satisfied to the required error range.
机译:本文提出了一种基于小波算法的心电图(ECG)特征提取,用于个人医疗保健监测。开发用于监视心血管疾病(CVD)的无线ECG检查系统意义重大,尤其是在任何地方和任何时间使用低功率设备检测实时ECG信号以进行自我检查的应用。连续时间墨西哥-哈特小波变换(CTMHWT)算法被快速轻松地用于分析P-QRS-T复基准点的逼近。采用数据库MIT-BIH离散点,通过Matlab仿真器根据CTMHWT算法有效提取心电信号。仿真结果表明,心电信号的特征提取可以满足要求的误差范围。

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