Classification of arteriovenous fistula sounds using a convolutional block attention module and long short-term memory neural network.

The assessment of vascular accessibility in patients undergoing hemodialysis is predominantly reliant on manual inspection, a method that is associated with several limitations. In this study, we propose an alternative approach by recording the acoustic signals produced by the arteriovenous fistula (AVF) and employing deep learning techniques to analyze these sounds as an objective complement to traditional AVF evaluation methods. Auscultation sounds were collected from 800 patients, with each recording lasting between 24 and 30 s. Features were extracted by combining Mel-Frequency Cepstral Coefficients with Mel-Spectrogram data, generating a novel set of feature parameters. These parameters were subsequently used as input to a model that integrates the Convolutional Block Attention Module and a Long Short-Term Memory neural network, designed to classify the severity of AVF stenosis based on two sound categories (normal and abnormal). The experimental results demonstrate that the CBAM-LSTM model achieves an Area Under the Receiver Operating Characteristic curve of 99%, Precision of 99%, Recall of 97%, and F1 Score of 98%. Comparative analysis with other models, including VGG, Bi-LSTM, DenseNet121, and ResNet50, indicates that the proposed CBAM-LSTM model outperforms these alternatives in classifying AVF stenosis severity. These findings suggest the potential of the CBAM-LSTM model as a reliable tool for monitoring AVF maturation.