Cerebral Blood Flow Monitoring with Piezoeletric Film, Photoplethysmogram and an LSTM Neural Network

Abstract

To improve monitoring of cerebral blood flow and arterial response to clinical interventions following acute traumatic brain injury, we have created a small sensor suite that can be mounted around the diameter of a typical intracranial catheter. This instrument is driven by the need to prevent subsequent ischemic injury after a traumatic brain injury, due to elevated intracranial pressure and compromised cerebral autoregulation. To minimize effects on clinician workflow, our sensors can be integrated into catheters that are currently being used in accordance with accepted standards of care. This paper describes the use of a combination of thin piezoelectric material, a photoplethysmogram, and a long short-term memory regression network to track cerebral blood flow fluctuations. The results show a correlation (R2 = 0.76) between beat-to-beat waveform features collected using our sensor suite and reference blood flow measurements by ultrasound imaging. This method we introduce may help give medical professionals timely data on patients’ cerebral hemodynamic status and how well patients responded to clinical interventions.