Enhanced rocket for the automated detection of epileptic tonic-clonic seizures using accelerometer data

The detection of epileptic tonic-clonic seizures during everyday life based on accelerometric data from wearable devices would enhance the diagnostic and the follow-up of the epileptic patients. We develop an algorithm which may contribute to recognition of tonic-clonic epileptic seizure based on accelerometer data that can be collected from mobile and wearable devices. We consider this task to be a multivariate time-series classification problem. State-of-the art solutions to this problem are based on machine learning techniques, such as Random Convolutional Kernel Transform (ROCKET). We enhance ROCKET by replacing standard convolution with dynamic convolution. Dynamic convolution was originally defined for univariate time series, therefore, we extend it to multivariate time series. We perform experiments on two publicly available real-world datasets related to tonicclonic seizures. The experimental results show that the proposed enhancements of the ROCKET algorithm significantly reduce the average classification error. Moreover, our approach outperforms other time series classifiers, including several types of deep neural networks that are commonly used in the domain of time-series classification. An enhanced version of the ROCKET algorithm is proposed for the automated detection of epileptic tonic-clonic seizures using accelerometer data. To assist reproducibility and follow-up works, we made our implementation publicly available at https://github.com/kr7/seizure .