Pedro A S Rocha-Filho, Patricia V Santos
Headaches, quality of life, and academic performance in schoolchildren and adolescents
Headache
sleep monitoring extensively utilized electroencephalogram eeg data collected scalp yielding large data repositories welltrained analysis models yet wealth data lacking emerging less intrusive modalities eareeg current study seeks harness abundance opensource scalp eeg datasets applying models pretrained data either directly minimal finetuning achieved context effective sleep analysis eareeg data recorded using single inear electrode referenced ipsilateral mastoid developed inhouse described previous work unlike previous studies research uniquely focuses older cohort 17 subjects aged 6583 mean age 718 years health conditions employs lightgbm transfer learning diverging previous deep learning approaches results show initial accuracy pretrained model eareeg 701 finetuning model eareeg data improved classification accuracy 737 finetuned model exhibited statistically significant improvement p 005 dependent ttest 10 13 participants reflected enhanced average cohens kappa score statistical measure interrater agreement categorical items 0639 indicating stronger agreement automated expert classifications sleep stages comparative shap value analysis revealed shift feature importance n3 sleep stage underscoring effectiveness finetuning process findings underscore potential finetuning pretrained scalp eeg models eareeg data enhance classification accuracy particularly within older population using featurebased methods transfer learning approach presents promising avenue eareeg analysis sleep studies offering new insights applicability transfer learning across different populations computational techniques enhanced eareeg method could pivotal remote monitoring settings allowing continuous noninvasive sleep quality assessment elderly patients conditions like dementia sleep apnea