[论文解读] Analysis of Cardiovascular Changes Caused by Epileptic Seizures in Human Photoplethysmogram Signal

Seyede Mahya Safavi, Ninaz Valisharifabad+7 more|arXiv (Cornell University)|Dec 11, 2019

Non-Invasive Vital Sign Monitoring参考文献 29被引用 6

一句话总结

本研究提出了一种基于血流动力学的、无需个体特异性的癫痫发作检测系统，利用可穿戴设备采集的容积脉搏波（PPG）信号。通过提取12个PPG特征——7个与心率变异性（HRV）相关，5个与血流动力学相关——并训练长短期记忆（LSTM）神经网络，该方法实现了92%的敏感度和43%的阳性预测值，每小时仅0.52次误报，优于仅基于HRV的模型，能够可靠检测日间和夜间发作。

ABSTRACT

Objectives: This study examines human Photoplethysmogram (PPG) along with Electrocardiogram (ECG) signals to study cardiac autonomic imbalance in epileptic seizures. The significance and the prevalence of changes in PPG morphological parameters have been investigated to find common patterns among subjects. Alterations in cardiovascular parameters measured by PPG/ECG signals are used to train a neural network based on LSTM for automatic seizure detection. Methods: Electroencephalogram (EEG), ECG, and PPG signals from 12 different subjects ( 8 males;4 females;age 34.3$\pm$ 13.8) were recorded including 57 seizures and 101 hours of inter-ictal data. 12 PPG features significantly changing due to epileptic seizures were extracted and normalized based on a proposed z-score metric. 7 feature are heart rate variability related and 5 features hemodynamic related. Results: A consistent pattern of ictal change was observed for all the features across the subjects/seziures. The proposed seizure detector is subject independent and works for both nocturnal and diurnal seizures. With an average of 0.52 false alarms per hour, positive predictive value of $43\%$ and sensitivity of $92\%$, the new proposed hemodynamic based seizure detector shows improvement over the the heart rate variability based detector. Conclusion: The cardiac autonomic imbalance due to seizure manifests itself in variations of peripheral hemodynamics measured by PPG signal, suggesting vasoconstriction in limbs. These variations can be used on a consumer seizure detecting devices with optical sensors for seizure detection. Significance: The stereotyped pattern is common among all the subjects which can help understand the mechanism of cardiac autonomic imbalance induced by epileptic seizures.

研究动机与目标

• 探究PPG形态特征是否反映癫痫发作期间的心脏自主神经失衡。
• 识别在多个受试者中一致且具有特征性的心血管变化。
• 开发一种基于PPG信号的无需个体特异性的癫痫发作检测系统，以实现真实场景部署。
• 通过整合血流动力学特征，改进现有基于HRV的检测器，以提高敏感度并降低误报率。

提出的方法

• 在12名癫痫受试者（8名男性，4名女性；平均年龄34.3 ± 13.8岁）中采集了101小时的发作间期和57次发作的PPG、ECG和EEG信号。
• 提取了12个PPG特征：7个与心率变异性（HRV）相关，5个与血流动力学参数（如脉搏波幅、波峰时间、脉搏传导时间）相关。
• 使用z分数度量对特征进行标准化，以减少个体间差异并消除心率影响。
• 使用12个特征输入向量训练基于LSTM的深度学习模型以检测癫痫发作，分别测试了仅基于HRV（LSTM7）和融合血流动力学特征（LSTM12）的模型。
• 评估模型在日间和夜间发作中的表现，性能指标包括敏感度、阳性预测值（PPV）和误报率（FAR）。

实验结果

研究问题

• RQ1 PPG形态特征是否在不同受试者中于癫痫发作期间表现出一致且具有特征性的变化？
• RQ2 PPG信号中的血流动力学变化（如脉搏波幅降低和波峰时间延长）是否可作为可靠的癫痫发作生物标志物？
• RQ3 在LSTM模型中整合血流动力学特征是否能提升检测性能，优于仅基于HRV的模型？
• RQ4 无需个体特异性校准的、基于PPG的无需个体特异性的癫痫发作检测器是否能在日间和夜间发作检测中实现高敏感度和低误报率？

主要发现

• 在全部12名受试者中观察到一致的发作期PPG特征变化模式，表明癫痫发作引发了一种特征性的心血管反应。
• 基于血流动力学的LSTM检测器（LSTM12）实现了92%的敏感度和43%的阳性预测值，显著优于仅基于HRV的模型。
• 误报率降低至每小时0.52次，较基于HRV的模型（每小时0.91次）改善了42%，显示出更高的可靠性。
• 所提出的系统在无需个体特异性校准的情况下，成功检测了日间和夜间发作。
• 脉搏波幅降低和归一化波峰时间增加，提示发作期间由于儿茶酚胺释放导致外周血管收缩和血管阻力增加。
• 本研究证明，PPG形态包含可被利用的临床相关血流动力学信息，可用于非侵入性、可穿戴的癫痫发作检测。