Early life stress detection using physiological signals and machine learning pipelines

HIGHLIGHTS

SUMMARY

Exposure to adversity is associated with a higher risk of negative health outcomes, such as early all-cause death, metabolic, mental, and neurodegenerative disorders. Evaluating the early life stress based on the physiological signals using and verifying a multimodal CNN classifier based on Cont-RPs for stress classification. Utilizing the FGSR, HGSR, and HR signals, which are short-term (30 s or less) and have not been completely employed in other studies on stress classification. The sample with serum cytokine data has n=53, while the subsample of these 53 women having gene_expression data . . .

 



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