Training framework for multi-group electrocardiography (mg-ecg) analysis
AssigneeTencent America LLC
InventorsKun WANG, Tao YANG, Min TU, Yaliang LI, Hui TANG, Wei FAN
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Abstract
A method of performing electrocardiography (ECG) analysis by at least one processor, the method including receiving ECG data that is from multiple leads; grouping the ECG data into groups of data; generating, from each group of the groups of data, a feature vector using a respective machine learning model; and performing ECG analysis using the feature vectors generated from each of the groups of data.
Claims20
A method of performing electrocardiography (ECG) analysis by at least one processor, the method comprising:
- receiving ECG data that is from multiple leads;
- grouping the ECG data into groups of data;
- generating, from each group of the groups of data, a feature vector using a respective machine learning model;
- and
- performing ECG analysis using the feature vectors generated from each of the groups of data.
A device for performing electrocardiography (ECG) analysis, the device comprising:
- at least one memory configured to store computer program code;
- at least one processor configured to access said computer program code and operate as instructed by said computer program code, said computer program code including:
- grouping code configured to cause the at least one processor to group ECG data, that is from multiple leads and received by the at least one processor, into groups of data;
- generating code configured to cause the at least one processor to generate, from each group of the groups of data, a feature vector using a respective machine learning model stored in the at least one memory;
- and
- performing code configured to cause the at least one processor to perform ECG analysis using the feature vectors generated from each of the groups of data.
A non-transitory computer-readable medium storing computer instructions that, when executed by at least one processor of a device, cause the at least one processor to:
- receive electrocardiography (ECG) data that is from multiple leads;
- group the ECG data into groups of data;
- generate, from each group of the groups of data, a feature vector using a respective machine learning model stored in the memory;
- and
- perform ECG analysis using the feature vectors generated from each of the groups of data.
Dependencies are inferred from the claim text.