Key result
A fine tree classifier using time and frequency domain features from ECG signals achieved a maximum accuracy of 95.2% in distinguishing between male and female genders.
Why the study?
Can an ECG signal-based machine learning system accurately classify gender?
Can an ECG signal-based machine learning system accurately classify gender?
An ECG-based machine learning system using EMD, Kernel PCA, and a Fine tree classifier can accurately distinguish between male and female genders with 95.2% accuracy.
ECG ML gender classification is feasible at high accuracy; leaves open clinical utility pending prospective validation.
ECG signal having distinguishable features is used for the classification of gender. This paper presents time and frequency domain features for the classification of gender. A system is designed which can accurately distinguish between genders using an ECG signal. This study gives us a complete framework of gender classification based on ECG signals. The raw ECG signals taken from Physio Bank are pre-processed using empirical mode decomposition (EMD) are used. Kernel PCA is used for reducing the features from 42 to 7. Fine tree classifier has been used to distinguish between different classes of genders. In this particular study, two classes i.e. males and females have been used and obtained a maximum accuracy of 95.2%. This result shows that it is a competent system that can differentiate between different genders.
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Khan et al. (2020) studied this question. Fine tree classifier with Kernel PCA and EMD was evaluated on Gender classification accuracy. A fine tree classifier using time and frequency domain features from ECG signals achieved a maximum accuracy of 95.2% in distinguishing between male and female genders.
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