The use of machine learning for pattern recognition in magnetocardiography
(MCG) that measures magnetic fields emitted by the electrophysiological
activity of the heart is disclosed herein. Direct kernel methods are used
to separate abnormal MCG heart patterns from normal ones. For
unsupervised learning, Direct Kernel based Self-Organizing Maps are
introduced. For supervised learning Direct Kernel Partial Least Squares
and (Direct) Kernel Ridge Regression are used. These results are then
compared with classical Support Vector Machines and Kernel Partial Least
Squares. The hyper-parameters for these methods are tuned on a validation
subset of the training data before testing. Also investigated is the most
effective pre-processing, using local, vertical, horizontal and
two-dimensional (global) Mahanalobis scaling, wavelet transforms, and
variable selection by filtering. The results, similar for all three
methods, were encouraging, exceeding the quality of classification
achieved by the trained experts. Thus, a device and associated method for
classifying cardiography data is disclosed, comprising applying a kernel
transform to sensed data acquired from sensors sensing electromagnetic
heart activity, resulting in transformed data, prior to classifying the
transformed data using machine learning.