Learning to predict: Integration with Domain Knowledge for Intracranial Pressure Prediction Using Autoencoder Decoder Algorithm

Registration ID: IJNRD_197159

Published ID: IJNRD2305820

DOI: Click Here to Get

Dr.G.B.Santhi , Ms.G.Pavithra , Ms.J.Vennila , Ms.V.Vaarani Devi

Convolutional Neural Network (CNN), Deep Learning (DL), Neural Networks (NN), Random forest classifier

Precise management of patients with cerebral diseases often requires intracranial pressure (ICP) monitoring, which is highly invasive and requires a specialized ICU setting. The ability to noninvasively estimate ICP is highly compelling as an alternative to, or screening for, invasive ICP measurement. Most existing approaches for non-invasive ICP estimation aim to build a regression function that maps non-invasive measurements to an ICP estimate using statistical learning techniques. These data-based approaches have met limited success, likely because the amount of training data needed is onerous for this complex applications. Intracranial pressure (ICP) normally ranges from 5 to 15 mmHg. Elevation in ICP is an important clinical indicator of neurological injury, and ICP is therefore monitored routinely in several neurological conditions to guide diagnosis and treatment decisions. Current measurement modalities for ICP monitoring are highly invasive, largely limiting the measurement to critically ill patients. An accurate non-invasive method to estimate ICP would dramatically expand the pool of patients that could benefit from this cranial vital sign. Methods: This article presents a spectral approach to model based ICP estimation from arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) measurements. The model captures the relationship between the ABP, CBFV, and ICP waveforms and utilizes a second-order model of the cerebral vasculature to estimate. In this work, we discuss an alternative strategy that aims to better utilize non-invasive measurement data by leveraging mechanistic understanding of physiology. Specifically, we developed a CNN framework that combines a multiscale model of intracranial physiology with non-invasive measurements of cerebral blood flow using transcranial Doppler. Virtual experiments with synthetic data are conducted to verify and analyze the proposed framework. A preliminary clinical application study on two patients is also performed in which we demonstrate the ability of this method to improve ICP prediction.

Published Paper   E-Certificate

Dr.G.B.Santhi, Ms.G.Pavithra, Ms.J.Vennila, & Ms.V.Vaarani Devi (May-2023). Learning to predict: Integration with Domain Knowledge for Intracranial Pressure Prediction Using Autoencoder Decoder Algorithm. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), i151-i161. https://ijnrd.org/papers/IJNRD2305820.pdf

Volume 8 Issue 5, May-2023

Pages : i151-i161

Paper Reg. ID: IJNRD_197159

Published Paper Id: IJNRD2305820

Downloads: 000121979

Research Area: Computer Engineering 

Country: Chennai, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305820.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305820

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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