Abstract：
OBJECTIVE: Seizures are characterized by periictal autonomic changes. Wearable devices could help improve our understanding of these phenomena through long-term monitoring. In this study, we used wearable electrocardiogram (ECG) data to evaluate differences between temporal and extratemporal focal impaired awareness (FIA) seizures monitored in the hospital and at home. We assessed periictal heart rate, respiratory rate, heart rate variability (HRV), and respiratory sinus arrhythmia (RSA).
METHODS: We extracted ECG signals across three time points - five minutes baseline and preictal, ten minutes postictal - and the seizure duration. After automatic Rpeak selection, we calculated the heart rate and estimated the respiratory rate using the ECG-derived respiration methodology. HRV was calculated in both time and frequency domains. To evaluate the influence of other modulators on the HRV after removing the respiratory influences, we recalculated the residual power in the high-frequency (HF) and low-frequency (LF) bands using orthogonal subspace projections. Finally, 5-minute and 30-second (ultra-short) ECG segments were used to calculate RSA using three different methods. Seizures from temporal and extratemporal origins were compared using mixed-effects models and estimated marginal means.
RESULTS: The mean preictal heart rate was 69.95 bpm (95 % CI 65.6 - 74.3), and it increased to 82 bpm, 95 % CI (77.51 - 86.47) and 84.11 bpm, 95 % CI (76.9 - 89.5) during the ictal and postictal periods. Preictal, ictal and postictal respiratory rates were 16.1 (95 % CI 15.2 - 17.1), 14.8 (95 % CI 13.4 - 16.2) and 15.1 (95 % CI 14 - 16.2), showing not statistically significant bradypnea. HRV analysis found a higher baseline power in the LF band, which was still significantly higher after removing the respiratory influences. Postictally, we found decreased power in the HF band and the respiratory influences in both frequency bands. The RSA analysis with the new methods confirmed the lower cardiorespiratory interaction during the postictal period. Additionally, using ultra-short ECG segments, we found that RSA decreases before the electroclinical seizure onset. No differences were observed in the studied parameters between temporal and extratemporal seizures.
CONCLUSIONS: We found significant increases in the ictal and postictal heart rates and lower respiratory rates. Isolating the respiratory influences on the HRV showed a postictal reduction of respiratory modulations on both LF and HF bands, suggesting a central role of respiratory influences in the periictal HRV, unlike the baseline measurements. We found a reduced cardiorespiratory interaction during the periictal period using other RSA methods, suggesting a blockade in vagal efferences before the electroclinical onset. These findings highlight the importance of respiratory influences in cardiac dynamics during seizures and emphasize the need to longitudinally assess HRV and RSA to gain insights into long-term autonomic dysregulation.
METHODS: We extracted ECG signals across three time points - five minutes baseline and preictal, ten minutes postictal - and the seizure duration. After automatic Rpeak selection, we calculated the heart rate and estimated the respiratory rate using the ECG-derived respiration methodology. HRV was calculated in both time and frequency domains. To evaluate the influence of other modulators on the HRV after removing the respiratory influences, we recalculated the residual power in the high-frequency (HF) and low-frequency (LF) bands using orthogonal subspace projections. Finally, 5-minute and 30-second (ultra-short) ECG segments were used to calculate RSA using three different methods. Seizures from temporal and extratemporal origins were compared using mixed-effects models and estimated marginal means.
RESULTS: The mean preictal heart rate was 69.95 bpm (95 % CI 65.6 - 74.3), and it increased to 82 bpm, 95 % CI (77.51 - 86.47) and 84.11 bpm, 95 % CI (76.9 - 89.5) during the ictal and postictal periods. Preictal, ictal and postictal respiratory rates were 16.1 (95 % CI 15.2 - 17.1), 14.8 (95 % CI 13.4 - 16.2) and 15.1 (95 % CI 14 - 16.2), showing not statistically significant bradypnea. HRV analysis found a higher baseline power in the LF band, which was still significantly higher after removing the respiratory influences. Postictally, we found decreased power in the HF band and the respiratory influences in both frequency bands. The RSA analysis with the new methods confirmed the lower cardiorespiratory interaction during the postictal period. Additionally, using ultra-short ECG segments, we found that RSA decreases before the electroclinical seizure onset. No differences were observed in the studied parameters between temporal and extratemporal seizures.
CONCLUSIONS: We found significant increases in the ictal and postictal heart rates and lower respiratory rates. Isolating the respiratory influences on the HRV showed a postictal reduction of respiratory modulations on both LF and HF bands, suggesting a central role of respiratory influences in the periictal HRV, unlike the baseline measurements. We found a reduced cardiorespiratory interaction during the periictal period using other RSA methods, suggesting a blockade in vagal efferences before the electroclinical onset. These findings highlight the importance of respiratory influences in cardiac dynamics during seizures and emphasize the need to longitudinally assess HRV and RSA to gain insights into long-term autonomic dysregulation.
摘要：
目的：癫痫发作的特点是围手术期自主神经的改变。可穿戴设备可以通过长期监测来帮助我们提高对这些现象的理解。在这项研究中,我们使用可穿戴心电图(ECG)数据评估在医院和家中监测的颞部和颞部局灶性意识障碍(FIA)癫痫发作之间的差异.我们评估了周心率，呼吸频率,心率变异性（HRV），和呼吸性窦性心律失常（RSA）。
方法：我们提取了三个时间点的ECG信号-五分钟基线和发作前，十分钟后-和癫痫发作持续时间。在自动Rpeak选择之后,我们使用ECG衍生的呼吸方法计算心率并估计呼吸频率.在时域和频域计算HRV。为了在去除呼吸影响后评估其他调节剂对HRV的影响，我们使用正交子空间投影重新计算了高频（HF）和低频（LF）频带中的剩余功率。最后,5分钟和30秒（超短）ECG段用于使用三种不同方法计算RSA。使用混合效应模型和估计的边际均值比较了来自时间和颞外起源的癫痫发作。
结果：平均发作前心率为69.95bpm（95％CI65.6-74.3），它增加到82bpm，95%CI（77.51-86.47）和84.11bpm，发作期和发作期的95%CI（76.9-89.5）。预防前,发作和发作后呼吸频率为16.1(95%CI15.2-17.1)，14.8（95%CI13.4-16.2）和15.1（95%CI14-16.2），显示无统计学意义的呼吸暂停。HRV分析发现LF波段的基线功率较高，在去除呼吸影响后，这一比例仍然显著更高。可能的话，我们发现HF频段的功率下降，并且两个频段的呼吸都受到影响。使用新方法的RSA分析证实了在后期期间较低的心肺相互作用。此外,使用超短的心电图片段，我们发现RSA在电临床癫痫发作前降低.在颞叶和颞外癫痫发作之间的研究参数未观察到差异。
结论：我们发现发作和发作后心率显著增加，呼吸频率降低。隔离呼吸对HRV的影响表明，在LF和HF波段上呼吸调制的近期减少，提示呼吸影响在围病期HRV中的核心作用，与基线测量不同。我们使用其他RSA方法发现围手术期心肺相互作用减少，提示在电临床发作之前迷走神经流出存在阻滞。这些发现强调了癫痫发作期间呼吸影响心脏动力学的重要性，并强调需要纵向评估HRV和RSA以了解长期自主神经失调。
方法：我们提取了三个时间点的ECG信号-五分钟基线和发作前，十分钟后-和癫痫发作持续时间。在自动Rpeak选择之后,我们使用ECG衍生的呼吸方法计算心率并估计呼吸频率.在时域和频域计算HRV。为了在去除呼吸影响后评估其他调节剂对HRV的影响，我们使用正交子空间投影重新计算了高频（HF）和低频（LF）频带中的剩余功率。最后,5分钟和30秒（超短）ECG段用于使用三种不同方法计算RSA。使用混合效应模型和估计的边际均值比较了来自时间和颞外起源的癫痫发作。
结果：平均发作前心率为69.95bpm（95％CI65.6-74.3），它增加到82bpm，95%CI（77.51-86.47）和84.11bpm，发作期和发作期的95%CI（76.9-89.5）。预防前,发作和发作后呼吸频率为16.1(95%CI15.2-17.1)，14.8（95%CI13.4-16.2）和15.1（95%CI14-16.2），显示无统计学意义的呼吸暂停。HRV分析发现LF波段的基线功率较高，在去除呼吸影响后，这一比例仍然显著更高。可能的话，我们发现HF频段的功率下降，并且两个频段的呼吸都受到影响。使用新方法的RSA分析证实了在后期期间较低的心肺相互作用。此外,使用超短的心电图片段，我们发现RSA在电临床癫痫发作前降低.在颞叶和颞外癫痫发作之间的研究参数未观察到差异。
结论：我们发现发作和发作后心率显著增加，呼吸频率降低。隔离呼吸对HRV的影响表明，在LF和HF波段上呼吸调制的近期减少，提示呼吸影响在围病期HRV中的核心作用，与基线测量不同。我们使用其他RSA方法发现围手术期心肺相互作用减少，提示在电临床发作之前迷走神经流出存在阻滞。这些发现强调了癫痫发作期间呼吸影响心脏动力学的重要性，并强调需要纵向评估HRV和RSA以了解长期自主神经失调。
