ArticleJournal of healthcare engineering2021
An Efficient Classification of Neonates Cry Using Extreme Gradient Boosting-Assisted Grouped-Support-Vector Network.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The application of machine learning for infant cries classification and pathological cries detection: A systematic review.Science progressPooled it
- A Nomogram for Predicting Cardiovascular Diseases in Chronic Obstructive Pulmonary Disease Patients.Journal of healthcare engineering · 2022Trial
- A novel class-attention transformer-driven feature fusion technique-based speech disorder classification.Frontiers in medicine · 2026Article
- Noise-Resilient Bioacoustics Feature Extraction Methods and Their Implications on Audio Classification Performance: Systematic Review.JMIR biomedical engineering · 2025Review
- Advances in Infant Cry Paralinguistic Classification-Methods, Implementation, and Applications: Systematic Review.JMIR rehabilitation and assistive technologies · 2025Review
- Classification of Infant Crying Sounds Using SE-ResNet-Transformer.Sensors (Basel, Switzerland) · 2024Article
- Infant Cry Signal Diagnostic System Using Deep Learning and Fused Features.Diagnostics (Basel, Switzerland) · 2023Article
- Identifying Glucose Metabolism Status in Nondiabetic Japanese Adults Using Machine Learning Model with Simple Questionnaire.Computational and mathematical methods in medicine · 2022Article
- An Unsupervised Deep Learning-Based Model Using Multiomics Data to Predict Prognosis of Patients with Stomach Adenocarcinoma.Computational and mathematical methods in medicine · 2022Article
- Automated Detection and Characterization of Colon Cancer with Deep Convolutional Neural Networks.Journal of healthcare engineering · 2022Article
- Multithreshold Segmentation and Machine Learning Based Approach to Differentiate COVID-19 from Viral Pneumonia.Computational intelligence and neuroscience · 2022Article
- A Multistage Heterogeneous Stacking Ensemble Model for Augmented Infant Cry Classification.Frontiers in public health · 2022Article
- Stress Estimation Model for the Sustainable Health of Cancer Patients.Computational and mathematical methods in medicine · 2022Article
- Lightweight Multireceptive Field CNN for 12-Lead ECG Signal Classification.Computational intelligence and neuroscience · 2022Article
- Rib Fracture Detection with Dual-Attention Enhanced U-Net.Computational and mathematical methods in medicine · 2022Article
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Authors and funding
5 authors.
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Abstract
The cry is a loud, high pitched verbal communication of infants. The very high fundamental frequency and resonance frequency characterize a neonatal infant cry having certain sudden variations. Furthermore, in a tiny duration solitary utterance, the cry signal also possesses both voiced and unvoiced features. Mostly, infants communicate with their caretakers through cries, and sometimes, it becomes difficult for the caretakers to comprehend the reason behind the newborn infant cry. As a result, this research proposes a novel work for classifying the newborn infant cries under three groups such as hunger, sleep, and discomfort. For each crying frame, twelve features get extracted through acoustic feature engineering, and the variable selection using random forests was used for selecting the highly discriminative features among the twelve time and frequency domain features. Subsequently, the extreme gradient boosting-powered grouped-support-vector network is deployed for neonate cry classification. The empirical results show that the proposed method could effectively classify the neonate cries under three different groups. The finest experimental results showed a mean accuracy of around 91% for most scenarios, and this exhibits the potential of the proposed extreme gradient boosting-powered grouped-support-vector network in neonate cry classification. Also, the proposed method has a fast recognition rate of 27 seconds in the identification of these emotional cries.
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