基于k -最近邻(KNN)算法的心电波分量描述:基于KNN的心电波描述

I. Saini, Dilbag Singh, A. Khosla
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引用次数: 26

摘要

以合理的精度检测心电图特征波的边界一直是一项困难的任务。KNN是一种经典的统计模式识别算法,具有精度高、稳定性好等特点,被用来定位心电信号中的波形边界(P波、QRS波和T波的起始点和偏移点)。首先,从心电信号中检测各拍的qrs复合体。然后,确定每个QRS复合物的起始点和偏移量。然后使用该算法识别相对于每个QRS复合体的P波和T波以及它们的起始点和偏移点。利用心电波基点计算QRS持续时间、心率、qt间期、p波持续时间和pr间期。该算法在我们自己的实验室使用ATRIA®6100心电机获得的心电数据集上进行了测试。将所提出的算法用于性能评估的结果,与基于ATRIA机器的内置软件检测器的输出结果进行了比较。
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Delineation of ECG Wave Components Using K-Nearest Neighbor (KNN) Algorithm: ECG Wave Delineation Using KNN
Detection of the boundaries of electrocardiogram (ECG) characteristic waves with a reasonable accuracy has been a difficult task. As a classical statistical pattern recognition algorithm characterized with high accuracy and stability, KNN has been proposed for locating the waveform boundaries (the onsets and offsets of P, QRS, and T waves) in ECG signals. First, the QRS-complex of each beat is detected from the ECG signal. Next, the onset and offset of each QRS complex are located. The P wave and T wave, relative to each QRS complex along with their onset and offset points, are then identified using this algorithm. Further, QRS duration, heart rate, QT-interval, P-wave duration and PR-interval have also been computed using ECG wave fiducial points. This algorithm is tested on the ECG dataset acquired using ATRIA®6100 ECG machine in our own laboratory. The results obtained using the proposed algorithm presented for the assessment of performance, has been compared with the output of inbuilt software based detector of ATRIA machine.
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