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Infant Cry Signal Diagnostic System Using Deep Learning and Fused Features
Yara Zayed1, Ahmad Hasasneh1, Chakib Tadj2
1Department of Natural, Engineering and Technology Sciences, Faculty of Graduate Studies, Arab American University, Ramallah P.O. Box 240, Palestine.
Insights
Diagnosing infant conditions like sepsis and respiratory distress syndrome (RDS) is challenging. This study developed a deep learning system analyzing infant cry audio signals (CAS) to accurately detect these critical conditions with 97.50% accuracy.
Area of Science:
- Medical Diagnostics
- Bioacoustics
- Machine Learning
Background:
- Infants cannot verbalize symptoms, making early diagnosis difficult.
- Infant crying is a primary communication method for distress and needs.
- Accurate diagnosis of conditions like neonatal respiratory distress syndrome (RDS) and sepsis is vital due to high mortality rates.
Purpose of the Study:
- To develop and evaluate a medical diagnostic system for interpreting infant cry audio signals (CAS).
- To improve the accuracy and classification rate of diagnosing infant pathologies using cry analysis.
- To leverage deep learning algorithms and fused audio features for enhanced diagnostic capabilities.
Main Methods:
- Utilized a dataset of labeled infant cry audio signals, including those with RDS, sepsis, and healthy cries.
- Extracted audio features: harmonic ratio (HR), Gammatone frequency cepstral coefficients (GFCCs), and spectrograms via a pre-trained CNN.
- Fused these features and applied machine learning models (RF, SVM, DNN), with a focus on deep learning for feature extraction and fusion.
Main Results:
- The system achieved a highest accuracy of 97.50% using fused spectrogram, HR, and GFCC features processed through a deep learning model.
- Feature fusion through the learning process, particularly with spectrograms, significantly improved classification compared to simple concatenation.
- The deep learning approach effectively extracted sparsely represented features, enhancing the separation between different infant pathologies.
Conclusions:
- Fusing diverse audio features, especially spectrograms, within a deep learning framework is crucial for accurate infant cry analysis.
- The proposed system demonstrates a promising, non-invasive method for the early diagnosis of critical infant medical conditions.
- This approach outperforms previous benchmarks by enabling multi-classification of pathologies using advanced feature engineering and deep learning.
Abstract:
Early diagnosis of medical conditions in infants is crucial for ensuring timely and effective treatment. However, infants are unable to verbalize their symptoms, making it difficult for healthcare professionals to accurately diagnose their conditions. Crying is often the only way for infants to communicate their needs and discomfort. In this paper, we propose a medical diagnostic system for interpreting infants' cry audio signals (CAS) using a combination of different audio domain features and deep learning (DL) algorithms. The proposed system utilizes a dataset of labeled audio signals from infants with specific pathologies. The dataset includes two infant pathologies with high mortality rates, neonatal respiratory distress syndrome (RDS), sepsis, and crying. The system employed the harmonic ratio (HR) as a prosodic feature, the Gammatone frequency cepstral coefficients (GFCCs) as a cepstral feature, and image-based features through the spectrogram which are extracted using a convolution neural network (CNN) pretrained model and fused with the other features to benefit multiple domains in improving the classification rate and the accuracy of the model. The different combination of the fused features is then fed into multiple machine learning algorithms including random forest (RF), support vector machine (SVM), and deep neural network (DNN) models. The evaluation of the system using the accuracy, precision, recall, F1-score, confusion matrix, and receiver operating characteristic (ROC) curve, showed promising results for the early diagnosis of medical conditions in infants based on the crying signals only, where the system achieved the highest accuracy of 97.50% using the combination of the spectrogram, HR, and GFCC through the deep learning process. The finding demonstrated the importance of fusing different audio features, especially the spectrogram, through the learning process rather than a simple concatenation and the use of deep learning algorithms in extracting sparsely represented features that can be used later on in the classification problem, which improves the separation between different infants' pathologies. The results outperformed the published benchmark paper by improving the classification problem to be multiclassification (RDS, sepsis, and healthy), investigating a new type of feature, which is the spectrogram, using a new feature fusion technique, which is fusion, through the learning process using the deep learning model.
