Shemla Ori, Tsutsui Kenta, Behar Joachim A, Yaniv Yael
Biomedical Engineering Faculty, Technion-IIT, Haifa, Israel.
Department of Cardiovascular Medicine, Saitama Medical University International Medical Center, Saitama, Japan.
Front Neurosci. 2021 Feb 17;14:614141. doi: 10.3389/fnins.2020.614141. eCollection 2020.
Because of the complexity of the interaction between the internal pacemaker mechanisms, cell interconnected signals, and interaction with other body systems, study of the role of individual systems must be performed under and conditions. The approach is valuable when exploring the mechanisms that govern the beating rate and rhythm of the sinoatrial node (SAN), the heart's primary pacemaker. SAN beating rate changes on a beat-to-beat basis. However, to date, there are no standard methods and tools for beating rate variability (BRV) analysis from electrograms (EGMs) collected from different mammals, and there is no centralized public database with such recordings.
We used EGM recordings obtained from control SAN tissues of rabbits ( = 9) and mice ( = 30) and from mouse SAN tissues ( = 6) that were exposed to drug intervention. The data were harnessed to develop a beat detector to derive the beat-to-beat interval time series from EGM recordings. We adapted BRV measures from heart rate variability and reported their range for rabbit and mouse.
The beat detector algorithm performed with 99% accuracy, sensitivity, and positive predictive value on the test (mouse) and validation (rabbit and mouse) sets. Differences in the frequency band cutoff were found between BRV of SAN tissue vs. heart rate variability (HRV) of recordings. A significant reduction in power spectrum density existed in the high frequency band, and a relative increase was seen in the low and very low frequency bands. In isolated SAN, the larger animal had a slower beating rate but with lower BRV, which contrasted the phenomena reported for analysis. Thus, the non-linear inverse relationship between the average HR and HRV is not maintained under conditions. The beat detector, BRV measures, and databases were contributed to the open-source PhysioZoo software (available at: https://physiozoo.com/).
Our approach will enable standardization and reproducibility of BRV analysis in mammals. Different trends were found between beating rate and BRV or HRV in isolated SAN tissue vs. recordings collected under conditions, respectively, implying a complex interaction between the SAN and the autonomic nervous system in determining HRV .
由于内部起搏器机制、细胞间相互连接信号以及与身体其他系统相互作用之间的复杂性，对各个系统作用的研究必须在特定条件下进行。当探索控制心脏主要起搏器窦房结（SAN）的搏动频率和节律的机制时，这种方法很有价值。SAN的搏动频率逐搏变化。然而，迄今为止，尚无用于分析从不同哺乳动物采集的心电图（EGM）中搏动频率变异性（BRV）的标准方法和工具，也没有包含此类记录的集中式公共数据库。
我们使用了从兔（n = 9）和小鼠（n = 30）的对照SAN组织以及接受药物干预的小鼠SAN组织（n = 6）获得的EGM记录。利用这些数据开发了一种搏动检测器，以从EGM记录中得出逐搏间隔时间序列。我们采用了心率变异性中的BRV测量方法，并报告了兔和小鼠的BRV范围。
搏动检测器算法在测试（小鼠）和验证（兔和小鼠）数据集上的准确率、灵敏度和阳性预测值均达到99%。发现SAN组织的BRV与体表心电图记录的心率变异性（HRV）之间在频段截止方面存在差异。高频段的功率谱密度显著降低，低频段和极低频段相对增加。在分离的SAN中，较大的动物搏动频率较慢，但BRV较低，这与体表心电图分析所报道的现象形成对比。因此，在离体条件下，平均心率与HRV之间的非线性反比关系不再成立。搏动检测器、BRV测量方法和数据库已贡献给开源的PhysioZoo软件（可在https://physiozoo.com/获取）。
我们的方法将使哺乳动物BRV分析实现标准化和可重复性。在离体SAN组织中，搏动频率与BRV或HRV之间分别呈现出不同的趋势，这意味着在决定HRV方面，SAN与自主神经系统之间存在复杂的相互作用。
