{"id":4064,"date":"2022-07-15T16:04:34","date_gmt":"2022-07-15T08:04:34","guid":{"rendered":"https:\/\/inventec2.mjitec.tw\/?page_id=4064"},"modified":"2023-07-18T14:07:26","modified_gmt":"2023-07-18T06:07:26","slug":"mixed-domain-self-attention-network","status":"publish","type":"page","link":"https:\/\/inventec2.mjitec.tw\/zh-hans\/ai\/mixed-domain-self-attention-network\/","title":{"rendered":"Mixed-Domain Self-Attention Network"},"content":{"rendered":"

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A Mixed-Domain Self-Attention Network for Multilabel Cardiac Irregularity Classification Using Reduced-Lead Electrocardiogram <\/h2>\n\t <\/div><\/div>[vc_column_text css=”.vc_custom_1660543742004{margin-bottom: 20px !important;}”]<\/p>\n
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IEEE Computers in Cardiology 2021<\/p>\n<\/div>\n

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Hao-Chun Yang,\u00a0Wan-Ting Hsieh,\u00a0Trista Pei-Chun Chen<\/p>\n<\/div>\n

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Jan-22<\/p>\n<\/div>\n

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\u6982\u8981 <\/h2>\n\t <\/div><\/div>[\/vc_column_inner][vc_column_inner el_class=”m_p” width=”2\/3″][vc_column_text]Electrocardiogram(ECG) is commonly used to detect cardiac irregularities such as atrial fibrillation, bradycardia, and other irregular complexes. While previous studies have achieved great accomplishment classifying these irregularities with standard 12-lead ECGs, there existed limited evidence demonstrating the utility of reduced-lead ECGs in capturing a wide-range of diagnostic information. In addition, classification model’s generalizability across multiple recording sources also remained uncovered.<\/p>\n

As part of the PhysioNet Computing in Cardiology Challenge 2021, our team HaoWan AIeC, proposed Mixed-Domain Self-Attention Resnet (MDARsn) to identify cardiac abnormalities from reduced-lead ECG. Our classifiers received scores of 0.602, 0.593, 0.597, 0.591, and 0.589 (ranked 54th, 37th, 38th, 38th, and 39th) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions of the hidden validation set with the evaluation metric defined by the challenge.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row][vc_column]

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