Abstract: Disclosed is a method of training a sleep-related event classification model. The method may comprise a step of providing a pre-trained classification model. The classification model may be configured to classify one or more sleep-related events of a subject based on one or more physiological measurements of the subject. The classification model may be pre-trained using first training data associated with a source population of subjects. The method may comprise a step of finetuning the classification model using second training data associated with a target population of subjects. In addition, a corresponding method of classifying one or more sleep-related events of a subject as well as a corresponding computer program, data processing apparatus or system and data structure is provided.

Abstract: Proposed are concepts for generating and using a prediction model that enables the prediction of the quality (e.g. accuracy and/or reliability) of an electrode configuration (i.e. arrangement) for electrophysiological measurements. It is proposed to use training data to determine parameter values of a prediction model so that the prediction model is configured to accurately predict a quality value (e.g. measure of accuracy) of an arrangement or pattern of electrodes for electrophysiological measurements of a subject. Embodiments may thus facilitate the assessment of an electrode configuration and/or there commendation of changes to an electrode configuration so as to enable more reliable and/or accurate electrophysiological measurements to be obtained.