Detecting early signs of depressive and manic episodes in patients with bipolar disorder using the signature-based model
This addresses the clinical need for timely mood stabilization in bipolar disorder patients, but appears incremental as it applies an existing method to a new domain.
The study tackled the problem of early detection of depressive and manic episodes in bipolar disorder patients using self-reported mood data, applying a signature-based model to identify episode onsets, though no concrete results or numbers were provided.
Recurrent major mood episodes and subsyndromal mood instability cause substantial disability in patients with bipolar disorder. Early identification of mood episodes enabling timely mood stabilisation is an important clinical goal. Recent technological advances allow the prospective reporting of mood in real time enabling more accurate, efficient data capture. The complex nature of these data streams in combination with challenge of deriving meaning from missing data mean pose a significant analytic challenge. The signature method is derived from stochastic analysis and has the ability to capture important properties of complex ordered time series data. To explore whether the onset of episodes of mania and depression can be identified using self-reported mood data.