Audio-based Recognition of Bipolar Disorder Utilising Capsule Networks

Bipolar disorder (BD) is an acute mood condition, in which states can drastically shift from one extreme to another, considerably impacting an individual's wellbeing. Automatic recognition of a BD diagnosis can help patients to obtain medical treatment at an earlier stage and therefore have a better overall prognosis. With this in mind, in this study, we utilise a Capsule Neural Network (CapsNet) for audio-based classification of patients who were suffering from BD after a mania episode into three classes of Remission, Hypomania, and Mania. The CapsNet attempts to address the limitations of Convolutional Neural Networks (CNNs) by considering vital spatial hierarchies between the extracted images from audio files. We develop a framework around the CapsNet in order to analyse and classify audio signals. First, we create a spectrogram from short segments of speech recordings from individuals with a bipolar diagnosis. We then train the CapsNet on the spectrograms with 32 low- level and three high-level capsules, each for one of the BD classes. These capsules attempt both to form a meaningful representation of the input data and to learn the correct BD class. The output of each capsule represents an activity vector. The length of this vector encodes the presence of the corresponding type of BD in the input, and its orientation represents the properties of this specific instance of BD. We show that using our CapsNet framework, it is possible to achieve competitive results for the aforementioned task by reaching a UAR of 46.2 % and 45.5 % on the development and test partitions, respectively. Furthermore, the efficacy of our approach is compared with a sequence to sequence autoencoder and a CNN-based neural network.