Bruser Christoph, Stadlthanner Kurt, Brauers Andreas, Leonhardt Steffen
Philips Chair for Medical Information Technology, RWTH Aachen University, Germany.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:1926-9. doi: 10.1109/IEMBS.2010.5628077.
Ballistocardiography is a technique in which the mechanical activity of the heart is recorded. We present a novel algorithm for the detection of individual heart beats in ballistocardiograms (BCGs). In a training step, unsupervised learning techniques are used to identify the shape of a single heart beat in the BCG. The learned parameters are combined with so-called "heart valve components" to detect the occurrence of individual heart beats in the signal. A refinement step improves the accuracy of the estimated beat-to-beat interval lengths. Compared to other algorithms this new approach offers heart rate estimates on a beat-to-beat basis and is designed to cope with arrhythmias. The proposed algorithm has been evaluated in laboratory and home settings for its agreement with an ECG reference. A beat-to-beat interval error of 14.16 ms with a coverage of 96.87% was achieved. Averaged over 10 s long epochs, the mean heart rate error was 0.39 bpm.
心冲击图描记法是一种记录心脏机械活动的技术。我们提出了一种用于检测心冲击图（BCG）中单个心跳的新算法。在训练步骤中，使用无监督学习技术来识别BCG中单个心跳的形状。将学习到的参数与所谓的“心脏瓣膜成分”相结合，以检测信号中单个心跳的出现。一个细化步骤提高了估计的逐搏间期长度的准确性。与其他算法相比，这种新方法提供逐搏心率估计，并设计用于应对心律失常。所提出的算法已在实验室和家庭环境中进行评估，以检验其与心电图参考值的一致性。实现了14.16毫秒的逐搏间期误差，覆盖率为96.87%。在10秒长的时段上进行平均，平均心率误差为0.39次/分钟。
