SSS: Signature-Sequence-Statistical Model for Exploring the Impact of Prenatal Depression on Newborns' Brain Using FNIRS

Research has shown that prenatal depression impacts newborn brain development, with previous studies primarily using statistical analysis. This paper introduces deep learning models to investigate this effect, using rsfNIRS data to predict maternal prenatal depression. Traditional fNIRS classification methods struggle with high-dimensional dependencies and global features, leading to unsatisfactory performance. To overcome this, we propose the SSS model, which leverages path Signature, temporal Sequence, and global Statistical features, achieving an accuracy of 73% and an F1-score of 84%. Our experiments reveal that using data from the entire brain yields the best classification results, suggesting that prenatal depression may affect the newborn's entire brain.