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QRS detection using morphological and rhythm information

机译：使用形态和节奏信息检测QRS检测

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An approach has been developed using artificial neural networks to detect QRS complexes within an ambulatory ECG signal. The method employs the use of an artificial neural network classifier to recognise the morphology of a QRS complex based on amplitude and derivative features. The feature vectors are derived from a representative annotated ECG trace and are used in the formulation of the ANN's training set. The outputs, or p.d.f. estimates generated by the neural network are then used to determined if a "QRS-like spike" has occurred. These spike detections then undergo further post-processing which, biases these detections such that the spike detection "nearest" the anticipated location of the next QRS is confirmed as a QRS complex. This anticipation of the QRS complex location is based on the estimation of the next RR interval using past RR intervals of previously confirmed QRS complexes. Such post-processing has the effect of greatly reducing the number of false positive detections, particularly in noisy ECG traces.
机译：使用人工神经网络开发了一种方法，以检测动态ECG信号内的QRS复合物。该方法采用人工神经网络分类器来识别基于幅度和衍生特征的QRS复合物的形态。特征向量源自代表注释的ECG轨迹，并用于ANN培训集的配方。输出或p.d.f.然后使用神经网络产生的估计来确定是否已经发生了“QRS样秒筒”。然后，这些尖峰检测进一步处理后处理，这些检测偏置这些检测，使得峰值检测“最近”的下一个QRS的预期位置被确认为QRS复合物。该预期QRS复杂位置基于使用先前证实的QRS复合物的过去的RR间隔估计下一个RR间隔。这种后处理具有大大减少假阳性检测的数量，特别是在嘈杂的心电图迹线中。

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