A Narrative Review on Different Novel Machine Learning Techniques for Detecting Pathologies in Infants From Born Baby
1Department of Electronics and Communication Engineering, Birla Institute of Technology Mesra, Ranchi, Jharkhand, India.
Insights
This review examines 50 years of research on analyzing infant cries for early clinical diagnosis. It highlights signal processing, machine learning, and neural network models for pathological cry classification.
Area of Science:
- Medical signal processing
- Infant health diagnostics
- Machine learning applications
Background:
- Early clinical diagnosis of infant pathologies is crucial.
- Infant cries contain vital diagnostic information.
- Analyzing pathological infant cries presents significant challenges.
Purpose of the Study:
- To review 50 years of research on pathological infant cry analysis and classification.
- To cover cry signal acquisition, processing, and classification techniques.
- To identify future research directions for robust infant cry analysis.
Main Methods:
- Comprehensive literature review of pathological infant cry research.
- Analysis of signal processing techniques (preprocessing, feature extraction, selection).
- Review of traditional and neural network-based machine learning classifiers.
Main Results:
- Detailed comparison of experimental results for pathological cry identification and classification.
- Overview of various feature extraction domains (time, spectral, wavelet, etc.).
- Summary of classifier performance, including Bayesian networks, SVM, and CNNs.
Conclusions:
- Future research should focus on database preparation and advanced feature extraction.
- Development of non-invasive, robust automatic infant cry analysis models is recommended.
- Neural network classifiers show promise for improved pathological cry detection.
Abstract:
This paper reviews the research work on the analysis and classification of pathological infant cries in the last 50 years. The literature review mainly covers the need and role of early clinical diagnosis, pathologies detected from cry samples, challenges in pathological cry signal data acquisition, signal processing techniques, and signal classifiers. The signal processing techniques include preprocessing, feature extraction from domains, such as time, spectral, time-frequency, prosodic, wavelet, etc, and feature selection for selecting dominant features. Literature covers traditional machine learning classifiers, such as Bayesian networks, decision trees, K-nearest neighbor, support vector machine, Gaussian mixture model, etc, and recently added neural network models, such as convolutional neural networks, regression neural networks, probabilistic neural networks, graph neural networks, etc. Significant experimental results of pathological cry identification and classification are listed for comparison. Finally, it suggests future research in the direction of database preparation, feature analysis and extraction, neural network classifiers to provide a non-invasive and robust automatic infant cry analysis model.
