Traditional spike detection algorithms can only achieve coarse temporal localization and struggle to identify specific electrodes with discharges. As shown in the figure above, traditional algorithms segment raw EEG data into two-second EEG segments to detect spike discharges. However, coarse localization cannot provide detailed information about spike discharges, especially for epilepsy types with dense discharges, such as childhood Rolandic epilepsy, where multiple discharges may occur within one second. The EEGX Intelligent EEG Analysis Platform, through advanced statistical and artificial intelligence analysis, can capture complex spatiotemporal relationships in brain electrical activity, achieving breakthroughs in both temporal and spatial resolution. The spike recognition and localization algorithm can achieve a temporal resolution of 1/500 second, capturing every subtle feature of the discharge. In the spatial dimension, the algorithm can locate specific electrodes where discharges occur, assisting clinical decision-making and providing more evidence for disease diagnosis.

High recognition accuracy is the relentless pursuit of medical AI. Due to the strong sample imbalance in medical data, balancing algorithm performance between false negatives (1-sensitivity) and false positives (1-specificity) is crucial for clinical applications of medical algorithms. The EEGX Intelligent EEG Analysis Platform, through comparison with numerous normal physiological waveforms and benign variants, has achieved significant improvement in specificity while maintaining high recognition sensitivity, becoming the first spike recognition algorithm comparable to human experts (Reference).