Multi-feature fusion and improved BO and IGWO metaheuristics based models for automatically diagnosing the sleep disorders from sleep sounds

• Published in: Computers in biology and medicine Vol. 157; p. 106768
• Main Authors: Akyol, Sinem, Yildirim, Muhammed, Alatas, Bilal
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.05.2023; Elsevier Limited
• Subjects: Accuracy; Algorithms; Animals; Artificial intelligence; Bonobo optimizer; Chroma; Classification; Cough; Datasets; Feature extraction; Feature maps; Gray wolf optimization; Heuristic methods; Humans; Internal Medicine; Machine learning; Medical diagnosis; Mel-spectrogram; MFCC; Night; Optimization; Optimization algorithms; Other; Pan paniscus; Researchers; Sleep; Sleep apnea; Sleep disorders; Sleep Wake Disorders; Snoring; Sound; Support vector machines; Bonobo optimizer; Chroma; MFCC; Gray wolf optimization; Mel-spectrogram
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2023.106768

A night of regular and quality sleep is vital in human life. Sleep quality has a great impact on the daily life of people and those around them. Sounds such as snoring reduce not only the sleep quality of the person but also reduce the sleep quality of the partner. Sleep disorders can be eliminated by examining the sounds that people make at night. It is a very difficult process to follow and treat this process by experts. Therefore, this study, it is aimed to diagnose sleep disorders using computer-aided systems. In the study, the used data set contains seven hundred sound data which has seven different sound class such as cough, farting, laugh, scream, sneeze, sniffle, and snore. In the model proposed in the study, firstly, the feature maps of the sound signals in the data set were extracted. Three different methods were used in the feature extraction process. These methods are MFCC, Mel-spectrogram, and Chroma. The features extracted in these three methods are combined. Thanks to this method, the features of the same sound signal extracted in three different methods are used. This increases the performance of the proposed model. Later, the combined feature maps were analyzed using the proposed New Improved Gray Wolf Optimization (NI-GWO), which is the improved version of the Improved Gray Wolf Optimization (I-GWO) algorithm, and the proposed Improved Bonobo Optimizer (IBO) algorithm, which is the improved version of the Bonobo Optimizer (BO). In this way, it is aimed to run the models faster, reduce the number of features, and obtain the most optimum result. Finally, Support Vector Machine (SVM) and k-nearest neighbors (KNN) supervised shallow machine learning methods were used to calculate the metaheuristic algorithms' fitness values. Different types of metrics such as accuracy, sensitivity, F1 etc., were used for the performance comparison. Using the feature maps optimized by the proposed NI-GWO and IBO algorithms, the highest accuracy value was obtained from the SVM classifier with 99.28% for both metaheuristic algorithms. •Feature fusion based on MFCC, Mel-spectrogram, and Chroma are performed in order to increase the performance.•New I-GWO (NI-GWO) and improved BO (IBO) algorithms are proposed for general purpose solution search problems.•NI-GWO and IBO algorithms proposed in this study were used to optimize the combined feature maps.•Optimized features obtained by the proposed optimization methods were classified by popular machine learning methods.•Proposed two metaheuristic-based methods achieved better results than those of similar studies in the literature.