Signature analysis of wearable data detects brain cancer progression
Detecting Brain Cancer Progression Through Signatures of Wearable Accelerometry Data
Machine Learning
Summary
Brain cancer patients often experience worsening symptoms over time, but traditional scans to monitor this are expensive and infrequent. This paper shows that data from wrist-worn devices tracking movement can help identify when the disease is getting worse. The authors convert walking data into patterns and use a mathematical technique called rough path signatures to spot changes specific to each patient. This method could lead to cheaper, continuous monitoring of brain cancer progression outside the hospital.
What this means in practice
- •For hospital data teams: Use wearable accelerometer data processed via rough path signatures to monitor brain cancer patient's functional changes more frequently than MRI scans allow.
- •For digital health device developers: Incorporate signature-based feature extraction into wearable activity monitors to create tools that flag brain cancer progression in real time.$Commercial implications: Enables development of wearable-based monitoring products targeting neuro-oncology patients for continuous disease tracking.
Tested on one dataset.
Authors
Rishit Das, Barbora Barancikova, Matt Williams
Abstract
Glioblastoma and other high-grade gliomas (HGG) have a poor prognosis and are associated with progressive functional decline. The standard of care to assess progressive disease is contrast-enhanced MRI, which is expensive, claustrophobic, and performed only once every 2-3 months. Wearable accelerometers offer a non-invasive alternative for continuously monitoring patient function between clinic visits. However, accelerometry data streams are typically noisy and highly patient-specific, making conventional time series methods and summary statistics poorly suited to detecting clinically meaningful change. This work aims to classify stable versus progressive disease states by applying rough path signatures for feature extraction from wrist-worn accelerometer data. We convert accelerometer recordings from HGG patients enrolled in the BrainWear study into walking-path segments and use signature kernel maximum mean discrepancy (SigMMD) to quantify change in patients' walking behaviour. We overcome the challenge of inter-patient heterogeneity by adjusting for the natural variation in a patient's walking during stable tumour periods. This patient-specific normalisation substantially improves separation, yielding a candidate biomarker of tumour progression. Our results provide proof-of-concept evidence that signature-based analysis of wearable accelerometry can distinguish stable from progressive disease states, motivating larger longitudinal studies of continuous wearable monitoring as a digital biomarker in neuro-oncology.