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Published on: October 11, 2018
Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry
M Hariharan1, R Sindhu2, Vikneswaran Vijean3
1Department of Biomedical Engineering, SRM Institute of Science and Technology, (Deemed to be University under section 3 of UGC Act 1956), Kattankulathur, 603203, Tamil Nadu, India.
Insights
This study introduces a novel method for classifying infant cry signals using wavelet packet features and an improved dragonfly optimization algorithm. The approach achieved high accuracy in identifying various infant conditions, aiding early diagnosis.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Machine Learning
Background:
- Infant cry signals provide crucial information for early diagnosis of conditions like hunger, pain, asphyxia, deafness, jaundice, and prematurity.
- Automated analysis of infant cries can significantly aid in timely medical intervention.
Purpose of the Study:
- To propose a hybrid method combining wavelet packet transform features with an Improved Binary Dragonfly Optimization (IBDFO) algorithm for accurate infant cry signal classification.
- To evaluate the effectiveness of the proposed feature selection and classification approach for diagnosing various infant states.
Main Methods:
- Extracted 568 features from cry signals using wavelet packet transform, Linear Predictive Coding (LPC), and Mel-frequency Cepstral Coefficients (MFCCs).
- Employed the IBDFO algorithm to select the most informative features, addressing the curse of dimensionality.
- Utilized an Extreme Learning Machine (ELM) kernel classifier for binary and multi-class cry signal classification.
Main Results:
- Achieved high accuracies in binary classification, including 100% for asphyxia vs. normal and deaf vs. normal.
- Demonstrated excellent performance in multi-class classification, reaching 100% accuracy for three classes (normal, asphyxia, deaf) and 97.62% for seven classes.
- The IBDFO-selected features (204 out of 568) were sufficient for high-accuracy classification.
Conclusions:
- The proposed method effectively classifies infant cry signals, offering a promising tool for early diagnosis.
- The combination of advanced feature extraction and optimized feature selection enhances classification accuracy for detecting subtle cry signal changes.
Background And Objective:
Infant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. In this work, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals.
Methods:
Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) and 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) and 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 features), Linear Predictive Coding (LPC) based cepstral features (56 features), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 features). The combined feature set consists of 568 features. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Finally, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well.
Results:
Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%.
Conclusion:
The experimental results indicated that the proposed combination of feature extraction and selection method offers suitable classification accuracy and may be employed to detect the subtle changes in the cry signals.
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