Feature Selection Optimization for Heart Rate Variability

Feature selection optimization has gained attention since it helps extract good insights about a given subject. It is commonly used to reduce the dimensionality of the problem and to identify the best combination of features with aim of improving the prediction or classification model performance. T...

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Bibliographic Details
Published in:2023 11th International Symposium on Digital Forensics and Security (ISDFS) pp. 1 - 6
Main Authors: Rita Antunes, Ana, Silva, Joaquim P., Cristina Braga, Ana, Goncalves, Joaquim
Format: Conference Proceeding
Language:English
Published: IEEE 11.05.2023
Subjects:
Accuracy
Artificial intelligence
Drowsiness at the wheel
Feature extraction
Heart rate variability
Machine learning algorithms
Measurement
Optimization
Predictive models
Recall
Time series analysis
Time-frequency analysis
Visualization
ISBN:9798350336993
Online Access:Get full text
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Summary:Feature selection optimization has gained attention since it helps extract good insights about a given subject. It is commonly used to reduce the dimensionality of the problem and to identify the best combination of features with aim of improving the prediction or classification model performance. Thereby, the present study intended to determine the best subset of features to classify the drowsiness state, into awake and drowsy, considering the heart rate variability. This approach is a gap in the literature since the studies available only classify the drowsy state without understanding which features have more impact on the transition from awake to drowsy. Single-objective optimization was performed using the genetic algorithm, with the purpose of maximizing accuracy, F1 score, and recall. Different machine learning algorithms were computed, and comparisons were made. Therefore, logistic regression and the extra tree algorithms had the best results for all the evaluation metrics. The best results were achieved with fewer heart rate variability features than initially introduced. With the proposed methodology it was possible to identify 13 features that occurred most to classify the drowsiness state. Where from the time, frequency, and nonlinear domains there were 6, 5, and 2 features, respectively. As future work, time series must be implemented to verify if it is possible to predict the drowsiness state and visualize trends and seasonality.
ISBN:9798350336993
DOI:10.1109/ISDFS58141.2023.10131786