Machine Unlearning for Seizure Prediction

In recent years, companies and organizations have been required to provide individuals with the right to be forgotten to alleviate privacy concerns. In machine learning, this requires researchers not only to delete data from databases but also to remove data information from trained models. Thus, machine unlearning is becoming an emerging research problem. In seizure prediction field, prediction applications are established most on private electroencephalogram (EEG) signals. To provide the right to be forgotten, we propose a machine unlearning method for seizure prediction. Our proposed unlearning method is based on knowledge distillation using two teacher models to guide the student model toward achieving model-level unlearning objective. One teacher model is used to induce the student model to forget data information of patients with unlearning request (forgetting patients), while the other teacher model is used to enable the student model to retain data information of other patients (remaining patients). Experiments were conducted on CHBMIT and Kaggle databases. Results show that our proposed unlearning method can effectively make trained ML models forget the information of forgetting patients and maintain satisfactory performance on remaining patients. To the best of our knowledge, it is the first work of machine unlearning in seizure prediction field.