发明名称 |
Use of machine learning for classification of magneto cardiograms |
摘要 |
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.
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申请公布号 |
US8391963(B2) |
申请公布日期 |
2013.03.05 |
申请号 |
US20100819095 |
申请日期 |
2010.06.18 |
申请人 |
STERNICKEL KARSTEN;SZYMANSKI BOLESLAW;EMBRECHTS MARK;CARDIOMAG IMAGING, INC. |
发明人 |
STERNICKEL KARSTEN;SZYMANSKI BOLESLAW;EMBRECHTS MARK |
分类号 |
A61B5/04;A61B5/08;G06F17/00;G06F19/00;G06K9/00 |
主分类号 |
A61B5/04 |
代理机构 |
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代理人 |
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主权项 |
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地址 |
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