Use of machine learning for classification of magneto cardiograms
First Claim
1. A method for automating the identification of meaningful features and the formulation of expert rules for classifying magnetocardiography data, comprising:
- applying a direct kernel transform to sensed data acquired from sensors sensing magnetic fields generated by a patient'"'"'s heart activity, resulting in transformed data; and
identifying said meaningful features and formulating said expert rules from said transformed data, using machine learning.
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Abstract
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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Citations
20 Claims
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1. A method for automating the identification of meaningful features and the formulation of expert rules for classifying magnetocardiography data, comprising:
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applying a direct kernel transform to sensed data acquired from sensors sensing magnetic fields generated by a patient'"'"'s heart activity, resulting in transformed data; and identifying said meaningful features and formulating said expert rules from said transformed data, using machine learning. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. An apparatus for automating the identification of meaningful features and the formulation of expert rules for classifying magnetocardiography data, comprising computerized storage, processing and programming for:
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applying a direct kernel transform to sensed data acquired from sensors sensing magnetic fields generated by a patient'"'"'s heart activity, resulting in transformed data; and identifying said meaningful features and formulating said expert rules from said transformed data, using machine learning. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification