Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Neural Regulation of Blood Pressure

Neural Regulation of Blood Pressure

The neural regulation of blood pressure involves intricate interactions between the autonomic nervous system (ANS) and cardiovascular system, ensuring adequate perfusion of tissues. This regulation primarily occurs through baroreceptor and chemoreceptor reflexes, involving both short-term and long-term mechanisms.
Baroreceptor Reflex
Baroreceptors, located in the carotid sinuses and aortic arch, detect changes in blood pressure. When blood pressure rises, these stretch-sensitive receptors...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Targeting of Gpx8-CSF1 axis resets the immune milieu of lung tumor and overcomes resistance to anti-PD-1 therapy.

Cell death and differentiation·2026

Same author

3D lithography of diamond thermal emitters for microscale emissivity control.

Nature communications·2026

Same author

A CsNF-YB4-CsWAT1-CsARF9 regulatory cascade mediates auxin-dependent suppression of lignin biosynthesis and resistance to Colletotrichum camelliae in tea plants.

Plant physiology·2026

Same author

Glucose-to-potassium ratio has a non-linear J-shaped association with acute kidney injury risk in traumatic brain injury.

Scientific reports·2026

Same author

Current-induced creation and dynamics of embedded magnetic skyrmion bags.

Nature communications·2026

Same author

Advances in silicon carbide pressure sensors for high-temperature extreme environment sensing.

Microsystems & nanoengineering·2026

Search research articles

Related Experiment Video

Updated: Jul 15, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Multitask Attention-Based Neural Network for Intraoperative Hypotension Prediction.

Meng Shi¹, Yu Zheng¹, Youzhen Wu²

¹School of Electronics, Peking University, Beijing 100871, China.

Bioengineering (Basel, Switzerland)

|September 28, 2023

Summary

Predicting intraoperative hypotension (IOH) is vital for patient safety. A novel ResNet-BiLSTM model with multitask training and attention significantly improved IOH prediction accuracy compared to other machine learning methods.

Keywords:

bio-signal prediction deep learning intraoperative hypotension multitask training

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

A Detailed Protocol for Physiological Parameters Acquisition and Analysis in Neurosurgical Critical Patients

A Detailed Protocol for Physiological Parameters Acquisition and Analysis in Neurosurgical Critical Patients

Published on: October 17, 2017

Related Experiment Videos

Last Updated: Jul 15, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

A Detailed Protocol for Physiological Parameters Acquisition and Analysis in Neurosurgical Critical Patients

A Detailed Protocol for Physiological Parameters Acquisition and Analysis in Neurosurgical Critical Patients

Published on: October 17, 2017

Area of Science:

Medical Informatics
Machine Learning in Healthcare
Anesthesiology

Background:

Intraoperative Hypotension (IOH) poses significant risks for postoperative complications.
Existing machine learning models for IOH prediction require performance enhancement.

Purpose of the Study:

To develop and evaluate a superior machine learning model for predicting IOH.
To leverage deep learning architectures, multitask training, and attention mechanisms for improved prediction.

Main Methods:

A ResNet-BiLSTM model was developed for IOH prediction using bio-signal waveforms from non-cardiac surgery.
The proposed model was trained and tested, and compared against WaveNet, CNN, and TCN models.
Ablation studies were performed to assess the impact of multitask and attention mechanisms.

Main Results:

The ResNet-BiLSTM model achieved optimal Mean Squared Error (MSE) of 43.83 and accuracy of 0.9224.
Performance was superior to WaveNet (MSE: 51.52, Acc: 0.9087), CNN (MSE: 318.52, Acc: 0.5861), and TCN (MSE: 62.31, Acc: 0.9045).
Multitask and attention mechanisms demonstrably improved both MSE and accuracy.

Conclusions:

The proposed ResNet-BiLSTM model demonstrates superior performance in predicting Intraoperative Hypotension.
The integration of multitask training and attention mechanisms is effective in enhancing prediction accuracy.
This model offers a promising tool for timely IOH detection and improved surgical patient outcomes.