His findings show that DFA-based metrics outperform conventional heart rate variability measures in distinguishing healthy and pathological cases. Notably, a short-term beat-to-beat dynamics recorded at rest emerged as a strong predictor of sudden cardiac death, independent of conventional risk factors. These results suggest that DFA-based metrics offers a simple, interpretable, and non-invasive tool for early cardiac screening, suitable for integration into wearable devices and remote health monitoring.
The doctoral dissertation of MSc (Tech) Teemu Pukkila in the field of Physics titled Early Prediction of Cardiac Diseases from Dynamic RR Interval Correlations will be publicly examined at the Faculty of Engineering and Natural Sciences at Tampere University on 30 January 2026.
The Opponent will be Professor Claus Graff from Aalborg University, Denmark. The Custos will be Professor Esa Räsänen from the Faculty of Engineering and Natural Sciences at Tampere University.