A Multi-Modal Stacked Ensemble Model for Bipolar Disorder Classification

We propose an automatic ternary classification model for Bipolar Disorder (BD) states. As input information, the model uses speech signals from patients' audio-visual recordings of structured interviews. The model classifies the patient's clinical state as Mania, Hypo-Mania, or Remission. We capture Mel-Frequency Cepstral Coefficients (MFCCs) and Geneva Minimalistic Acoustic Parameter Set (GeMAPS) as audio features. We compute linguistic and sentiment features for each subject's transcript. We present a stacked ensemble classifier to classify all fused features after feature selection. A set of three homogeneous Convolutional Neural Networks (CNNs) and a Multi Layer Perceptron (MLP) construct the first-level and second-level of the stacked ensemble classifier respectively. Moreover, we use reinforcement learning to optimize the networks and their hyperparameters. We show that our stacked ensemble framework outperforms existing models on the BD Turkish corpus with a %59.3 Unweighted Average Unit (UAR) on the test set. To the best of our knowledge, this is the highest UAR achieved on this dataset.