Abstract
Breathing phase detection is fundamental for various clinical and digital health applications, yet standard Mel Frequency Cepstral Coefficients (MFCCs) settings often limit classification performance. This study systematically optimized MFCC parameters, specifically the number of coefficients, frame length, and hop length, using a proprietary dataset of respiratory sounds (n = 1500 segments). Classification performance was evaluated using Support Vector Machines (SVMs) and benchmarked against deep learning models (VGGish, YAMNet, MobileNetV2). Optimal parameters (30 MFCC coefficients, 800 ms frame length, 10 ms hop length) substantially enhanced accuracy (87.16%) compared to default settings (80.96%) and performed equivalently or better than deep learning methods. A trade-off analysis indicated that a clinically practical frame length of 200–300 ms balanced accuracy (85.08%) and latency effectively. The study concludes that optimized MFCC parameters significantly improve respiratory phase classification, providing efficient and interpretable solutions suitable for real-time clinical monitoring. Future research should focus on validating these parameters in broader clinical contexts and exploring multimodal and federated learning strategies.