.png){kind=icon}
返回
Multimodal multi-instance learning for long-term ECG classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2023.110555.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recently, deep learning-based models have been widely used for electrocardiogram (ECG) classification tasks. Most ECG signals are long-term time series that contain a large number of sample points. However, existing deep learning-based models resize or crop the original long-term ECG signal due to the limitation of input size and hardware, which results in information loss. To address this issue, a multimodal multi-instance learning neural network (MAMIL) is proposed for long-term ECG classification. The proposed MAMIL has three major components. First, the original ECG signal and Gramian Angular Field (GAF) image converted from the ECG signal are utilized as multimodal inputs, which enables the model to learn complementary information between different modalities. Second, multi-instance learning (MIL) is introduced to avoid information loss. Each long-term ECG signal and GAF image are treated as bags, and each heartbeat from a long-term ECG signal and each patch from a GAF image are treated as instances. Convolutional neural networks (CNNs) are utilized to extract instance features from different modalities. Third, a novel attention mechanism-based feature fusion method is proposed to aggregate the instance features from multiple modalities to obtain the bag feature for final classification. Our feature fusion method adopts pooling to obtain positive instances, which can effectively eliminate redundant information and achieve low computational complexity. The proposed MAMIL is evaluated on both intrapatient and interpatient patterns of two commonly used ECG datasets. Experimental results show that our model not only outperforms common deep learning-based models, but also outperforms previous MIL-based models. (c) 2023 Elsevier B.V. All rights reserved.
Keyword:
Long-term ECG
Multimodal learning
Multi-instance learning
Attention mechanism
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
A novel and efficient xanthenic dye–organometallic ion‐pair complex for photoinitiating polymerization一种用于光引发聚合的新型高效的黄原胶染料-有机金属离子对配合物
PREAI
分享收藏Automatic diagnosis of arrhythmia with electrocardiogram using multiple instance learning: From rhythm annotation to heartbeat prediction使用多实例学习的心电图自动诊断心律失常: 从节奏注释到心跳预测
OAAI
OAAI分享收藏分享收藏N-acetyl-beta-D-glucosaminidase activity within BAL from macaques exposed to generic coal dusts.png)
Lung
LungIF0
PREAI
分享收藏
查看更多内容