Method and system for classifying an in-stent re-stenosis

Computer-implemented method for providing a trained machine learning algorithm configured to classify an in-stent re-stenosis comprising the steps of:

• receiving a pre-acquired first training data set comprising ECG-data of a plurality of patients acquired by a respective implantable medical device;
• receiving a second training data set comprising a classification result representing a probability of an in-stent re-stenosis;
• training the machine learning algorithm by an optimization algorithm which calculates an extreme value of a loss function for classification of an in-stent re-stenosis.

System for providing a trained machine learning algorithm configured to classify an in-stent re-stenosis comprising:

• a training computing unit configured to receive a first training data set comprising ECG-data of a plurality of patients acquired by a respective implantable medical device, said training computing unit being further configured to receive a second training data set comprising a classification result representing a probability of an in-stent re-stenosis, and wherein the training computing unit is configured to train the machine learning algorithm by an optimization algorithm which calculates an extreme value of a loss function for classification of an in-stent re-stenosis.