DeepHeart Approximation (Apple Heart Study framework)

DeepHeart (approximation) — Explanation and Clinical Context DeepHeart in the original publications is a semi-supervised deep learning pipeline trained on large amounts of wearable heart-rate time series and limited labeled clinical data to predict multiple cardiometabolic conditions and to flag abnormal rhythms. The model learns features from continuous heart rate and activity signals rather than relying solely on hand-engineered single biomarkers. This page implements a transparent heuristic that maps commonly available summary inputs (age, sex, BMI, resting heart rate, estimated HRV, sleep, and known cardiovascular diagnoses) into a 0–100% screening-style risk score meant to reflect the kinds of patterns DeepHeart models capture.

It is not identical to the published DeepHeart neural network; reproducing the exact model would require the original network weights and time-series inputs. Use this tool only for educational/screening simulation; do not use it as a diagnostic substitute for ECG or clinician evaluation. Key clinical context: The Apple Heart Study established that wearable pulse-based algorithms can notify users of pulse irregularities that often correspond to atrial fibrillation on subsequent ECG patch recordings (positive predictive value ~71–84% in study cohorts).

DeepHeart-style models use continuous wearable data to improve identification of cardiovascular risk states from longitudinal signals. Clinical application requires caution: device signal noise, motion artifact, and population differences affect real-world performance; positive alerts should prompt clinical assessment and ECG confirmation.