An epilepsy brain wave state detection method based on machine learning. The method comprises the following steps: input import: importing brain wave data of an epilepsy patient and marking the state thereof； normalized transformation processing: setting a suitable new maximum value and a suitable new minimum value, and mapping brain wave time-domain signal data to a smaller new value interval according to a normalized transformation technique； time domain to frequency domain conversion: carrying out fast Fourier transform on each piece of brain wave time-domain data, and carrying out comprehensive calculation on an amplitude frequency of each piece of data and taking same as a power spectrum thereof； frequency domain range selection: selecting a suitable low-frequency signal to replace an original frequency-domain signal, and removing high-frequency signal noise； linear adaptive dimension reduction of a frequency-domain signal: using a linear adaptive dimension reduction technique to carry out data dimension reduction, so as to effectively carry out classification processing； establishment of a support vector machine classification and prediction model: using a support vector machine classifier to establish a prediction model for a training data set； and epilepsy state classification and prediction: using the established prediction and classification model to carry out state classification and prediction on a brain wave in an unknown state.La présente invention concerne un procédé de détection d'état d'onde cérébrale d'épilepsie basé sur un apprentissage machine. Le procédé comprend les étapes suivantes : importation d'entrée : importer des données d'onde cérébrale d'un patient souffrant d'épilepsie et marquer son état ； traitement de transformation normée : régler une nouvelle valeur maximale appropriée et une nouvelle valeur minimale appropriée, et mapper des données de signal de domaine temporel d'onde cérébrale à un nouvel intervalle de valeurs plus petit selon
