Deep learning systems for automatic diagnosis of infant cry signals. (January 2022)

• Record Type: Journal Article
• Authors: Lahmiri, Salim; Tadj, Chakib; Gargour, Christian; Bekiros, Stelios
• Is Part Of: Chaos, solitons and fractals. Volume 154(2022)
• Publication Date: 2022-01
• Subjects: Deep learning -- Deep feedforward neural networks -- Long short-term memory neural networks -- Convolutional neural networks -- Infant cry record -- Classification; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2021.111700
• Languages: English
• ISSNs: 0960-0779

Abstract

Highlights: We design and validate various deep learning systems to improve diagnosis of infant cry records. Considered deep learning systems are deep feedforward neural networks (DFFNN), long short-term memory (LSTM) neural networks, and convolutional neural networks (CNN). All deep learning systems are trained with cepstrum analysis-based coefficients. Compared to existing models, all deep learning systems were found to be more effective in distinguishing between healthy and unhealthy infant cry records. Abstract: Nowadays, deep learning architectures are promising artificial intelligence systems in various applications of biomedical engineering. For instance, they can be combined with signal processing techniques to build computer-aided diagnosis systems used to help physician making appropriate decision related to the diagnosis task. The goal of the current study is to design and validate various deep learning systems to improve diagnosis of infant cry records. Specifically, deep feedforward neural networks (DFFNN), long short-term memory (LSTM) neural networks, and convolutional neural networks (CNN) are designed, implemented and trained with cepstrum analysis-based coefficients as inputs to distinguish between healthy and unhealthy infant cry records. All deep learning systems are validated on expiration and inspiration sets separately. The number of convolutional layers and number of neurons in hidden layers are respectively varied in CNN and DFFNN. It is found that CNN achieved the highest accuracy and sensitivity, followed by DFFNN. The latter, obtained the highest specificity. Compared to similar work in the literature, it is concluded that deep learning systems trained with cepstrum analysis-based coefficients are powerful machines that can be employed for accurate diagnosis of infant cry records so as to distinguish between healthy and pathological signals.