earticle

영어

Respiratory diseases are one of the major causes of death worldwide. Therefore, research on respiratory disease classification using respiratory data is considered an important task. Previous studies mainly focused on respiratory disease classification using 2D feature extraction methods such as spectrograms and MFCCs. However, these methods have drawbacks such as long classification time and decreased accuracy as the number of respiratory disease types increases. To address this issue, we propose a solution that combines data with different dimensions to improve the performance of respiratory disease classification. We utilize the gammatone based spectrogram feature extraction method along with raw 1D respiratory data. By combining these two approaches, we can achieve both fast classification speed from 1D time-series models and high classification accuracy from 2D feature extraction methods. Our proposed respiratory disease classification study consists of four stages: data preprocessing, combined data generation, construction of a respiratory disease classification model, and decision-making for respiratory disease diagnosis. We validate our approach using a TCN (Temporal Convolutional Network) model and achieve a high respiratory disease classification accuracy of 98.93%. Moreover, our proposed method significantly reduces the training time for classification by more than four times compared to previous methods, thus demonstrating its superiority.