Quantification of Hypsarrhythmia in Infantile Spasmatic EEG: A Large Cohort Study

Ruolin Hou, Qiongru Guo, Qinman Wu, Zihao Zhao, Xindan Hu, Yumei Yan, Wenyuan He, Peize Lyu, Ruisheng Su, Tao Tan, Xiaoqiang Wang, Yuanning Li, Dake He, Lin Xu

Research output: Contribution to journalArticlepeer-review


Infantile spasms (IS) is a neurological disorder causing mental and/or developmental retardation in many infants. Hypsarrhythmia is a typical symptom in the electroencephalography (EEG) signals with IS. Long-Term EEG/video monitoring is most frequently employed in clinical practice for IS diagnosis, from which manual screening of hypsarrhythmia is time consuming and lack of sufficient reliability. This study aims to identify potential biomarkers for automatic IS diagnosis by quantitative analysis of the EEG signals. A large cohort of 101 IS patients and 155 healthy controls (HC) were involved. Typical hypsarrhythmia and non-hypsarrhythmia EEG signals were annotated, and normal EEG were randomly picked from the HC. Root mean square (RMS), teager energy (TE), mean frequency, sample entropy (SamEn), multi-channel SamEn, multi-scale SamEn, and nonlinear correlation coefficient were computed in each sub-band of the three EEG signals, and then compared using either a one-way ANOVA or a Kruskal-Wallis test (based on their distribution) and the receiver operating characteristic (ROC) curves. The effects of infant age on these features were also investigated. For most of the employed features, significant ({p} < {0}.{05} ) differences were observed between hypsarrhythmia EEG and non-hypsarrhythmia EEG or HC, which seem to increase with increased infant age. RMS and TE produce the best classification in the delta and theta bands, while entropy features yields the best performance in the gamma band. Our study suggests RMS and TE (delta and theta bands) and entropy features (gamma band) to be promising biomarkers for automatic detection of hypsarrhythmia in long-Term EEG monitoring. The findings of our study indicate the feasibility of automated IS diagnosis using artificial intelligence.

Original languageEnglish
Pages (from-to)350-357
Number of pages8
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Publication statusPublished - 2024


  • Biomarkers
  • electroencephalography
  • hypsarrhythmia
  • infantile spasms
  • west syndrome


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