Explainable ResNet-long short-term memory model for the classification of bowel sounds frequency based on multifeature fusion.

ABSTRACT: ObjectiveTo develop an explainable ResNet-long short-term memory model based on multifeature fusion for classifying bowel sound frequency-a key indicator of gastrointestinal motility. Accurate and objective classification of bowel sound activity levels holds significant clinical value in clinical settings.MethodsAs a prospective multicenter study conducted across three medical institutions, the primary outcome involved three-way classification of bowel sounds as normoactive, hyperactive, or hypoactive. Bowel sounds were collected and segmented into 10-s clips. Audio features-Chroma, Filter Bank Energies, and Mel-Frequency Cepstral Coefficients-were extracted to train deep learning models using transfer learning with ResNet50 V2, followed by feature fusion and classification via long short-

SUBMITTER: Zhang W 

PROVIDER: S-EPMC12484921 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature