Guided deep embedded clustering regularization for multifeature medical signal classification

Medical signal classification often focuses on one representation (raw signal or time frequency). In that context, recent works have shown the value of exploiting different representations simultaneously. We propose a regularized end-to-end trained model for classification in a medical context exploiting both the raw signal and a time-frequency representation (TFR). First, a 2D convolutional neural network (CNN) encoder and a 1D CNN-transformer encoder start by extracting embedded representations from the TFR and the raw signal, respectively. Then, the obtained embeddings are fused to form a common latent space that is used for classification. We propose to guide the training of each encoder by applying two iterated losses. Moreover, we propose to regularize the fused common latent space using deep embedded clustering. Extensive experiments on three medical datasets and ablation studies show the adaptability and good performance of our method for medical signal classification. Our method makes it possible to improve the classification performance from 4% to 12% MCC on a transcranial Doppler dataset, when compared with single-feature counterparts, while giving more stable models. The code is available at: https://github.com/gdec-submission/gdec/.