Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 16, 2026

Tilt Testing with Combined Lower Body Negative Pressure: a "Gold Standard" for Measuring Orthostatic Tolerance
14:09

Tilt Testing with Combined Lower Body Negative Pressure: a "Gold Standard" for Measuring Orthostatic Tolerance

Published on: March 21, 2013

Deep Learning-Based Early Prediction of Syncope Onset During Tilt Table Testing via Temporal Convolutional

Alex Wee Wong1, Wee Jian Chin1, Maw Pin Tan2

  • 1Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, 43000, Petaling Jaya, Selangor, Malaysia.

Annals of Biomedical Engineering
|May 19, 2026
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Mediating Role of Plasma Biomarkers Associated with Metabolic Syndrome in Cognitive Decline Among Older Adults.

Diabetes, metabolic syndrome and obesity : targets and therapy·2026
Same author

Prevalence and Characteristics of Depression, Anxiety and Stress Among Older Persons in Malaysia during the COVID-19 Pandemic.

Journal of cross-cultural gerontology·2026
Same author

ITO-PET electrochemical biosensor functionalized with carboxylated graphene oxide for prostate cancer biomarker detection.

Scientific reports·2026
Same author

Intercontinental difference in the association between obesity and falls among older persons.

Postgraduate medicine·2026
Same author

Willingness, motivators and barriers to time bank participation: A scoping review.

PloS one·2026
Same author

Development and Preliminary Evaluation of an EfficientNet-Based Deep Learning System for Ultrasound Assessment of Neck Disorders: A Single-Center Study.

Diagnostics (Basel, Switzerland)·2026
This summary is machine-generated.

This study developed an autoencoder-based anomaly detection method for early syncope prediction during head-up tilt table tests (HUTT). The model achieved high accuracy, enabling preemptive test termination and reducing patient discomfort.

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Head-up tilt table tests (HUTT) are lengthy and uncomfortable, often inducing syncope and associated symptoms like nausea and pallor.
  • Current HUTT procedures lack methods for early syncope onset prediction, leading to prolonged patient discomfort.

Purpose of the Study:

  • To develop and evaluate an autoencoding-based anomaly detection system for early syncope prediction during HUTT.
  • To enable preemptive termination of HUTT, thereby minimizing patient discomfort and adverse events.

Main Methods:

  • Utilized heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and high frequency normalized RRI (Hfnu_RRI) signals.
  • Processed signals into feature images for autoencoder (AE) input, coupled with an anomaly severity algorithm.
Keywords:
Anomaly detectionDeep learningEarly predictionReal-timeSyncopeTilt table test

Related Experiment Videos

Last Updated: Jun 16, 2026

Tilt Testing with Combined Lower Body Negative Pressure: a "Gold Standard" for Measuring Orthostatic Tolerance
14:09

Tilt Testing with Combined Lower Body Negative Pressure: a "Gold Standard" for Measuring Orthostatic Tolerance

Published on: March 21, 2013

  • Evaluated six AE architectures, fine-tuned the best model, and validated the syncope detector over 100 iterations.

    • Main Results:

    • The temporal convolutional autoencoder anomaly detector (TCAAD) achieved high performance metrics: accuracy (0.9424), recall (0.9838), precision (0.9141), F1 score (0.9461).
    • The TCAAD demonstrated a significant early prediction time of 523.69 seconds.
    • Model performance was comparable to existing real-time prediction methods.

    Conclusions:

    • Anomaly detection methods combined with signal correlation monitoring are effective for syncope prediction.
    • The developed TCAAD offers one of the longest early prediction times, improving HUTT safety and patient experience.
    • This approach facilitates early intervention, potentially avoiding syncope and its associated symptoms.