Early warning signatures of sudden cardiac arrest (SCA) remain poorly characterised in long-duration ECG. We quantified pre-event changes in ECG morphology using a motif-based trajectory framework. Holter ECGs from 23 patients with annotated SCA were analysed over non-overlapping 10 s windows. Window-level motifs were extracted to quantify trajectories of instability, consistency, dispersion, heterogeneity, and personalised-baseline distance. Each trajectory was normalised to an early baseline using z-scores and aligned to ventricular fibrillation (VF) onset. Abnormal burden was defined as the proportion of windows with z>=3 within a rolling 10-minute window. Median sustained abnormal burden onset occurred 5.8-8.4 h before VF across morphological metrics. Motif dispersion showed the most consistent long-horizon detection, with sustained abnormal burden >=1 h before VF in 100% of patients and >=2 h in 89%. Motif consistency showed the strongest late-stage change, with 70% median abnormal burden in the final 10 min. Peak morphological deviation occurred ~2h prior to VF. Our label-free framework transforms longitudinal ECG analysis from an event detection approach towards a continuous characterisation of evolving cardiac change, enabling a personalised early warning of SCA, well suited to long-duration wearable ECG monitoring.