Reducing Artifact Preprocessing in Heart Rate Variability-Based Personalized Psychosis Prediction Using Adaptive Long Short-Term Memory Models.
Tsakmaki PV; Tasoulis S; Georgakopoulos SV; Plagianakos VP · 2025 · International journal of neural systems
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
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…
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Metadata source: Europe PMC · DOI 10.1142/s0129065725500649