Histograms of Frequency-Intensity Distribution Deep Learning to Predict the Seizure Liability of Drugs in Electroencephalography

Abstract

Abstract Detection of seizures as well as that of seizure auras is effective in improving the predictive accuracy of seizure liability of drugs. Whereas electroencephalography has been known to be effective for the detection of seizure liability, no established methods are available for the detection of seizure auras. We developed a method for detecting seizure auras through machine learning using frequency-characteristic images of electroencephalograms. Histograms of frequency-intensity distribution prepared from electroencephalograms of rats analyzed during seizures induced with 4-aminopyridine (6 mg/kg), strychnine (3 mg/kg), and pilocarpine (400 mg/kg), were used to create an artificial intelligence (AI) system that learned the features of frequency-characteristic images during seizures. The AI system detected seizure states learned in advance with 100% accuracy induced even by convulsants acting through different mechanisms, and the risk of seizure before a seizure was detected in general observation. The developed AI system determined that the unlearned convulsant Tramadol (150 mg/kg) was the risk of seizure and the negative compounds aspirin and vehicle were negative. Moreover, the AI system detected seizure liability even in electroencephalography data associated with the use of 4-aminopyridine (3 mg/kg), strychnine (1 mg/kg), and pilocarpine (150 mg/kg), which did not induce seizures detectable in general observation. These results suggest that the AI system developed herein is an effective means for electroencephalographic detection of seizure auras, raising expectations for its practical use as a new analytical method that allows for the sensitive detection of seizure liability of drugs that has been overlooked previously in preclinical studies.