An Effective Respiratory Sound Endpoint Detection for Electronic Stethoscope Based on Machine Learning

Compared to human speech and multimedia audio, the amplitude of lung respiratory sounds is extremely weak, with minimal differences between the respiratory sounds of various lung diseases. Traditional methods, such as Mel‐frequency cepstral coefficients (MFCCs) and the Fourier transform, struggle to accurately extract respiratory sound characteristics of different lung diseases. Therefore, this article innovatively employs an improved C 0 complexity method to analyze the subtle differences in respiratory sounds, identifying the optimal tuning factor value that captures more local information and better characterizes the nonlinear stochastic properties of respiratory sounds. Subsequently, the second component of the MFCC was extracted and fused with the improved C 0 complexity and short‐term energy to propose a novel Mel‐energy complexity (MEC) feature. Finally, the fuzzy C‐means (FCM) clustering method was employed to process the MEC features, detecting the periodic endpoints of respiratory sounds, specifically, the endpoints of respiratory sounds during inhalation and exhalation, thereby laying a technical foundation for further research on respiratory sound recognition and visual management. To evaluate performance, we established a clinically collected respiratory sound database containing respiratory sounds from 154 individuals across four categories, including asthma and chronic obstructive pulmonary disease (COPD), with sounds recorded from eight chest locations per participant. The proposed method achieved average accuracy and F1 scores of 71% and 72%, respectively, outperforming multiple respiratory sound endpoint detection (RSED) approaches while demonstrating high robustness, particularly in processing pulmonary crackles.