AmtNet: Attentional multi-scale temporal network for phonocardiogram signal classification

Phonocardiogram (PCG) signals reflect the mechanical activity of the heart. With the automatic diagnosis of PCG signals, cardiovascular diseases can be effectively detected. This paper presents an effective PCG signal classification model, called the attentional multi-scale temporal network (AmtNet), which is constructed by one-dimensional convolution and pooling as the basic components. AmtNet can directly classify raw PCG signals without complicated feature engineering processes. Specifically, we design a multi-scale feature extraction architecture using dense connections and three different dilated convolutional paths. We adopt a one-dimensional convolutional block attention module (CBAM) to adaptively refine the intermediate feature map and design a temporal pyramid pooling layer to incorporate the multi-scale temporal information. Moreover, we further study the effectiveness of several time series data augmentation and transfer learning techniques for improving the performance of AmtNet. Extensive experiments on four public PCG datasets indicate that the proposed AmtNet can achieve competitive results for PCG signal classification.