Reducing Artifact Preprocessing in Heart Rate Variability-Based Personalized Psychosis Prediction Using Adaptive Long Short-Term Memory Models

This research looks at the use of long-short-term memory (LSTM) networks to predict psychosis, in patients within the schizophrenia spectrum, based on Heart Rate Variability (HRV) data acquired from wearable devices. Our primary objective is to test whether the personalized relapse prediction remains accurate when eliminating the artifact-removal preprocessing. We first analyzed 7 patients sleep HRV recordings (7-113 days each), and then validated the methodology on a separate 30-patient psychosis cohort from another clinical setting. In this framework, HRV characteristics are computed directly from the unprocessed time series for each patient, without artifact correction, at any stage prior to feature extraction. HRV features are then, organized into sequential inputs, where the model uses the first n-1 steps to predict the <i>n</i>th step. This structure allows the model to learn from temporal relationships and individual physiological trends in HRV. The sequence length used by the LSTM is optimized for each patient, allowing the model to account for individual physiological patterns. Through this, on the 7 patient cohort, the LSTM model reaches a mean F1 score of 0.9817, marking its strength across diverse patient profiles. The method provides predictions for each individual by learning from their own HRV history. Results using both traditional and state-of-the-art noise-removal techniques, like wavelet and GAN-based denoising, showed that omitting these data cleaning steps did not reduce, and in some cases even improved, prediction accuracy. These findings indicate that, for psychosis prediction based on wearable HRV data, additional data cleaning may not be necessary.