Patent ID: 11944444
Assignee: TECHNION RESEARCH & DEVELOPMENT FOUNDATION LIMITED
Field: Medical technology (Instruments)
Classification: CPC A  G | IPC A  G

Claim 11:
12. A method of predicting an onset time of a VF event, the method comprising:
receiving, as input, target Heart Rate Variability (HRV) parameters representing temporal beat activity in a subject; and
applying a trained machine learning model to said target HRV parameters, to predict the onset time of a VF event in said subject,, wherein the machine learning model is a convolutional neural network (CNN) trained to predict an onset of a Ventricular Fibrillation (VF) event, on a training set comprising:
(i) Heart Rate Variability (HRV) parameters extracted from temporal beat activity signal samples, wherein at least some of said samples include a representation of a VF event, and
(ii) labels associated with one of: a first period of time immediately preceding a VF event in a temporal beat activity sample, a second period of time immediately preceding the first period of time in a temporal beat activity sample, and all other periods of time in a temporal beat activity sample,, wherein said training set is generated by:
(i) segmenting each of said samples into a plurality of sample segments, based, at least in part, on a specified minimum number of said beats in each of said sample segments;
(ii) randomly selecting one or more subsets of said sample segments from said plurality of sample segments; and, (iii) combining one or more of said subsets into said training set.