UCSF Department of Neurology, University of California, San Francisco, 505 Parnassus Avenue, Room M-830, San Francisco, CA, 94143-0114, USA.
Neurocrit Care. 2020 Aug;33(1):58-63. doi: 10.1007/s12028-019-00845-x.
Cranial accelerometry is used to detect cerebral vasospasm and concussion. We explored this technique in a cohort of code stroke patients to see whether a signature could be identified to aid in the diagnosis of large vessel occlusion (LVO) stroke.
A military-grade three-axis accelerometer was affixed to a headset. Accelerometer and electrocardiogram (ECG) outputs were digitized at 1.6 kHz. We call the resulting digitized signals the "headpulse." Three-minute recordings were performed immediately after computed tomography (CT) angiography (CTA) and/or immediately before and after attempted mechanical thrombectomy in patents with suspected stroke. The resulting waveforms were inspected by eye and then subjected to supervised machine learning (MATLAB Classification Learner R2018a) to train a model using fivefold cross-validation.
Of 42 code stroke subjects with recordings, 19 (45%) had LVO and 23 (55%) had normal CTAs. In patients without LVO, ECG-triggered waveforms followed a self-similar time course revealing that the headpulse is highly coupled to the cardiac contraction. However, in most patients with LVO, headpulses showed little cardiac contraction correlation. We term this abnormality "chaos" and parameterized it with 156 measures of trace-by-trace variation from the ECG-signal-averaged mean for machine learning model training. Selecting the best model, using biometric data only, we properly classified 15/19 LVOs and 20/23 non-LVO patients, with receiver operating characteristic curve area = 0.79, sensitivity of 73%, and specificity of 87%, P < 0.0001. Headpulse waveforms following thrombectomy showed return of cardiac contraction correlation.
Headpulse recordings performed on patients with suspected acute stroke significantly identify those with LVO. The lack of temporal correlation of the headpulse with cardiac contraction and resolution to normal may reflect changes in cerebral blood flow and may provide a useful technique to triage stroke patients for thrombectomy using a noninvasive device.
颅加速度计用于检测脑血管痉挛和脑震荡。我们在一组中风患者中探索了这种技术,以确定是否可以识别出一种特征,以帮助诊断大血管闭塞(LVO)中风。
将军用三轴加速度计固定在耳机上。加速度计和心电图(ECG)输出以 1.6 kHz 的频率数字化。我们将由此产生的数字化信号称为“头脉冲”。在怀疑中风的患者进行计算机断层血管造影(CTA)后立即进行 3 分钟记录,或在尝试机械血栓切除术前后立即进行记录。通过肉眼检查所得到的波形,然后使用监督机器学习(MATLAB Classification Learner R2018a)对模型进行训练,使用五重交叉验证进行训练。
在 42 名有记录的中风患者中,19 名(45%)有 LVO,23 名(55%)有正常 CTA。在没有 LVO 的患者中,ECG 触发的波形呈现出相似的时间过程,表明头脉冲与心脏收缩高度耦合。然而,在大多数 LVO 患者中,头脉冲显示出与心脏收缩的相关性很小。我们将这种异常称为“混沌”,并使用 156 个参数来描述从 ECG 信号平均到机器学习模型训练的跟踪间变化。仅使用生物特征数据选择最佳模型,我们正确地将 15/19 例 LVO 和 20/23 例非 LVO 患者分类,接收器操作特征曲线面积=0.79,敏感性为 73%,特异性为 87%,P<0.0001。血栓切除术后的头脉冲波形显示出心脏收缩相关性的恢复。
对怀疑患有急性中风的患者进行头脉冲记录可以显著识别出那些患有 LVO 的患者。头脉冲与心脏收缩之间缺乏时间相关性以及恢复正常可能反映了脑血流的变化,并且可能为使用非侵入性设备对中风患者进行血栓切除术提供一种有用的技术。