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December 2, 2013SensorsOpen Access

Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier

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Population

4 subjects

Comparison

Wavelet transform method of HRV signals over… vs Conventional method using fast Fourier…

Design

Other

Authors

GLGang LiWCWan‐Young Chung

Discussion

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Overview

Wavelet-based short-term HRV may aid drowsiness detection; leaves open validation in real-world cohorts before adoption.

Structured PICO

P
Population
4 subjects
I
Intervention
Wavelet transform method of HRV signals over short time periods (1-min, 2-min, 3-min) with a support vector machine (SVM) classifier
C
Comparator
Conventional method using fast Fourier transform (FFT)-based features
O
Outcome
Classification performance (accuracy, sensitivity, specificity) of alert and drowsy driving eventssurrogate

Wavelet analysis of short-term HRV signals combined with an SVM classifier provides highly accurate detection of driver drowsiness compared to conventional FFT-based methods.

Cite This Study

Li et al. (2013) studied this question.

synapsesocial.com/papers/6a7055e92fdcb5703edb5ac5https://doi.org/10.3390/s131216494
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Also Consider

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  1. 1Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings2008 · 367 citations
  2. 2Changes in Physiological Parameters Induced by Indoor Simulated Driving: Effect of Lower Body Exercise at Mid-Term Break2009 · 70 citations
  3. 3Quantifying Errors in Spectral Estimates of HRV Due to Beat Replacement and Resampling2005 · 290 citations
  4. 4Driver Alertness Monitoring Using Fusion of Facial Features and Bio-Signals2012 · 193 citations
  5. 5Automated Scoring of Obstructive Sleep Apnea and Hypopnea Events Using Short-Term Electrocardiogram Recordings2009 · 146 citations