Khosrow-Khavar Farzad, Tavakolian Kouhyar, Blaber Andrew, Menon Carlo
IEEE Trans Biomed Eng. 2017 Aug;64(8):1701-1710. doi: 10.1109/TBME.2016.2616382. Epub 2016 Oct 12.
The purpose of this research was to design a delineation algorithm that could detect specific fiducial points of the seismocardiogram (SCG) signal with or without using the electrocardiogram (ECG) R-wave as the reference point. The detected fiducial points were used to estimate cardiac time intervals. Due to complexity and sensitivity of the SCG signal, the algorithm was designed to robustly discard the low-quality cardiac cycles, which are the ones that contain unrecognizable fiducial points.
The algorithm was trained on a dataset containing 48,318 manually annotated cardiac cycles. It was then applied to three test datasets: 65 young healthy individuals (dataset 1), 15 individuals above 44 years old (dataset 2), and 25 patients with previous heart conditions (dataset 3).
The algorithm accomplished high prediction accuracy with the rootmean- square-error of less than 5 ms for all the test datasets. The algorithm overall mean detection rate per individual recordings (DRI) were 74, 68, and 42 percent for the three test datasets when concurrent ECG and SCG were used. For the standalone SCG case, the mean DRI was 32, 14 and 21 percent.
When the proposed algorithm applied to concurrent ECG and SCG signals, the desired fiducial points of the SCG signal were successfully estimated with a high detection rate. For the standalone case, however, the algorithm achieved high prediction accuracy and detection rate for only the young individual dataset.
The presented algorithm could be used for accurate and non-invasive estimation of cardiac time intervals.
本研究的目的是设计一种描绘算法，该算法能够在使用或不使用心电图（ECG）R波作为参考点的情况下，检测心震图（SCG）信号的特定基准点。检测到的基准点用于估计心脏时间间隔。由于SCG信号的复杂性和敏感性，该算法被设计为能够稳健地舍弃低质量的心搏周期，即那些包含无法识别的基准点的心搏周期。
该算法在一个包含48318个手动标注心搏周期的数据集上进行训练。然后将其应用于三个测试数据集：65名年轻健康个体（数据集1）、15名44岁以上个体（数据集2）和25名有心脏病史的患者（数据集3）。
对于所有测试数据集，该算法实现了高预测精度，均方根误差小于5毫秒。当同时使用ECG和SCG时，三个测试数据集的算法总体平均个体记录检测率（DRI）分别为74%、68%和42%。对于单独使用SCG的情况，平均DRI分别为32%、14%和21%。
当将所提出的算法应用于同时采集的ECG和SCG信号时，能够以高检测率成功估计SCG信号所需的基准点。然而，对于单独使用SCG的情况，该算法仅在年轻个体数据集中实现了高预测精度和检测率。
所提出的算法可用于准确、无创地估计心脏时间间隔。
