Real-Time Multi-Class Classification of Respiratory Diseases Through Dimensional Data Combinations

In recent times, there has been active research on multi-disease classification that aim to diagnose lung diseases and respiratory conditions using respiratory data. Recorded respiratory data can be used to diagnose various chronic diseases, such as asthma and pneumonia by applying different feature extraction methods. Previous studies have primarily focused on respiratory disease classification using 2D image conversion techniques, such as spectrograms and mel frequency cepstral coefficients (MFCC) for respiratory data. However, as the number of respiratory disease classes increased, the classification accuracy tended to decrease. To address this challenge, this study proposes a novel approach that combines 1D and 2D data to enhance the multi-classification performance regarding respiratory disease. We incorporated widely used 2D representations such as spectrograms, gammatone-based spectrograms, and MFCC images, along with raw data. The proposed respiratory disease classification method comprises 2D data conversion, combined data generation, classification model development, and multi-disease classification steps. Our method achieved high classification accuracies of 92.93%, 91.30%, and 88.58% using the TCN, Wavenet, and BiLSTM models, respectively. Compared to using solely 1D data, our approach demonstrated a 4.89% improvement in accuracy and more than 3 times better training speed when using only 2D data. These results confirmed the superiority of the proposed method. This allows us to leverage the advantages of fast learning provided by time-series models, as well as the high classification accuracy demonstrated by 2D image approaches.