Consistent Ovulation Window Prediction based on Physiological Temporal Variability Patterns from Wearable Devices

Accurate ovulation prediction is crucial for fertility management. Although calendar-based methods are widely used, they are effective only for regular cycles. Furthermore, machine learning-based ovulation prediction studies obtained declined accuracy for irregular cycles and became less reliable. To address this limitation, we propose an ovulation prediction framework that (i) generates novel features by integrating temporal heart rate variability (HRV) patterns from ECG with temporal temperature values and 10-min resolution temperature features, and (ii) employs a light gradient boosting machine (LGBM). Participants wore an ECG device and a temperature sensor during sleep. The prediction focused on a 8-day period, covering 5 days before to 2 days after ovulation, to capture key physiological changes in the fertile window and improve prediction performance. The proposed framework obtained an area under the receiver operating characteristic curve (AUROC) of 0.73 and their performance was superior performance when compared with various machine and deep learning models. Notably, the model excelled in predicting ovulation for irregular cycles, achieving AUROC of 0.84 in the highly irregular group and 0.88 in the undefined group. These findings highlight the importance of temporal segmentation and multimodal feature integration for enhanced ovulation prediction. The proposed framework accurately predicts the ovulation date up to 5 days in advance for premenopausal women, significantly enhancing fertility management.