Table 3.

Comparative summary of predictive models for neurological prognostication

Research	Number of subjects	Feature set	Classifier	The most discriminative features	Best AUC (%)
[45]Zheng et al., IEEE Transactions (2021)	1038 cardiac arrest patients	Longitudinal EEG features, including power spectral density (PSD), coherence, and dynamic changes in EEG patterns over time	Deep learning (RNN)	Longitudinal EEG patterns during recovery phase	88.0
[46]MJ Kim et al., Scientific Reports (2022)	54 cardiac arrest survivors	Quantitative EEG biomarkers: power spectral density (PSD), event-related spectral perturbation (ERSP), and spectral entropy (SE) across multiple frequency bands	Linear mixed model	Alpha-power in EEG	90.3
[47]MC Tjepkema-Cloostermans et al., Critical Care Medicine (2017)	283 comatose patients	EEG-based Cerebral Recovery Index (CRI): phase-amplitude coupling, coherence, alpha band power, and other integrative metrics	Random forest classifier	Integrative quantitative EEG measures	92.0
[48]FH Krones et al., arXiv Preprint (2024)	300 comatose patients	Multimodal features combining EEG (functional connectivity, phase synchrony) and autonomic nervous system (HRV, baroreflex sensitivity) metrics	Multimodal deep learning	Combined EEG and autonomic nervous system features	93.0
[16]B Hermann et al., Wiley Online Library (2024)	181 ICU patients post-cardiac arrest	Brain–heart coupling metrics: heart-rate variability (time-domain and frequency-domain analysis) and EEG complexity measures	Logistic regression	Heart-rate variability combined with EEG complexity	90
Present Study	277 Cardiac arrest patients	BHC and HEP	SVM and LR	Brain–heart interplay and Heartbeat-evoked potentials	98.0

Reported performance metrics are provided for contextual reference only and are not directly comparable due to differences in patient cohorts, feature extractionmethods, and validation strategies