Heart2Mind: Human-Centered Contestable Psychiatric Disorder Prediction System Using Wearable ECG Monitors

Abstract

Psychiatric disorders affect millions, yet diagnosis depends on subjective assessments and uneven access to care. To address these challenges, there is a growing need for Contestable AI (CAI), a framework that extends beyond Explainable AI (XAI) by allowing clinicians to inspect, question, and revise algorithmic outputs, thereby reducing automation bias and strengthening accountability. We present Heart2Mind 1 , a human-centered CAI system for psychiatric disorder prediction that provides objective evidence while preserving clinical oversight. Heart2Mind collects R-R interval (RRI) time series from Polar H9/H10 wearable ECG sensors via a Cardiac Monitoring Interface and analyzes them using a Multi-Scale Temporal-Frequency Transformer (MSTFT) that combines time-domain and frequency-domain features. For contestability, the Contestable Diagnosis Interface integrates model explanations with dialogue. Self-Adversarial Explanations compare attention-based and gradient-based explanation maps to flag inconsistent predictions, and a collaboration chatbot helps users verify and challenge outputs. On the HRV-ACC dataset, MSTFT achieved 91.7% accuracy under leave-one-out cross-validation, outperforming benchmark methods. Human-centered evaluation with the Human-CAI Consensus Rate showed experts and CAI could confirm correct decisions and correct errors through readable, efficient dialogues ( \(FKGL\approx 15\) , median 8.3 minutes, 4 turns). These results support low-cost wearable CAI screening with objective biomarkers, safeguards, and an interactive path for clinicians to refine recommendations.

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