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Kernel based support vector machine for the early detection of syncope during head-up tilt test.

N Khodor1, D Matelot, G Carrault

  • 1Azm Platform for Research in Biotechnology and its Applications, LASTRE Laboratory, Lebanese University, Tripoli, Lebanon. INSERM, U1099, Rennes, F-35000, France and Université de Rennes 1, LTSI, Rennes, F-35000, France.

Physiological Measurement
|September 23, 2014
PubMed
Summary

This study analyzes autonomic nervous system responses during the head-up tilt test (HUTT) to predict syncope. Specific heart rate variability patterns in the initial 15 minutes can accurately identify patients experiencing syncope.

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Area of Science:

  • Cardiology
  • Autonomic Neuroscience
  • Medical Diagnostics

Background:

  • Syncope is a common clinical issue often diagnosed using the head-up tilt test (HUTT).
  • Understanding autonomic nervous system (ANS) responses during HUTT is crucial for accurate syncope diagnosis.
  • Dynamic properties of heart rate variability (HRV) offer insights into ANS function.

Purpose of the Study:

  • To analyze ANS response during HUTT by examining HRV dynamic properties.
  • To identify predictive markers for HUTT outcomes in patients with and without syncope.
  • To evaluate the efficacy of machine learning models in classifying syncope during HUTT.

Main Methods:

  • Collected ECG data from 66 subjects (35 with syncope, 31 without) during HUTT.
  • Extracted baroreflex response, linear, and non-linear HRV parameters from RR-interval time series.
  • Utilized kernel support vector machines (SVM) for patient classification.

Main Results:

  • In the first 15 minutes of HUTT, increased total power spectrum, standard deviation, fractal scale of RR-interval and ΔRR-interval, and decreased sample entropy were observed in the syncope group.
  • These HRV indices show potential as predictors for positive HUTT responses in reflex syncope patients.
  • Kernel SVM achieved 85% classification accuracy (80.6% specificity, 88.5% sensitivity) within the first 15 minutes.

Conclusions:

  • Specific HRV dynamic properties during the initial phase of HUTT can predict syncope.
  • Machine learning models, particularly SVM, can aid in early and accurate syncope detection during HUTT.
  • This approach may reduce examination time and avoid false-negative diagnoses, improving clinical syncope management.