Multi-source unsupervised domain adaptation for ECG classification

Deng, Fucheng; Tu, Shikui; Xu, Lei

doi:10.1109/bibm52615.2021.9669755

Cited by 8 publications

(8 citation statements)

References 12 publications

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“…Net) in Deng et al 30 : DAC-Net design a feature channel attention module to identify transferable features and combines pseudo-labeling with a class compactness loss to minimize the distance between the target features and the classifier's weight vectors.…”

Section: Domain Attention Consistency Network (Dac-mentioning

confidence: 99%

“…MFSAN uses K domain-specific feature extractors and K domain discriminators to build two-stage alignment framework. 29 In DAC-Net, 30 the attention weights parameter is fixed to 0.999 and the prediction threshold is set as 0.95. Similar to the proposed method, all the trade-offs in the objective function of the comparison methods are set to 1.…”

Section: Domain Attention Consistency Network (Dac-mentioning

confidence: 99%

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Multi-representation symbolic convolutional neural network: a novel multisource cross-domain fault diagnosis method for rotating system

Jia

Mao

et al. 2023

Structural Health Monitoring

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Multisource domain adaptation (MDA) methods have been preliminarily applied in cross-domain fault diagnosis of rotating system due to its correlation ability between different but related fields. However, it still remains challenging to learn domain-invariant representations under multisource scenarios. This article proposes a multi-representation symbolic convolutional neural network (MR-SCNN) for multisource cross-domain fault diagnosis of rotating system. The novelty of our work lies in three aspects. First, the proposed method combines symbolic dynamics with CNN to obtain a coarse-grained description of vibration signals, which could eliminate the negative transfer caused by subtle changes in dynamic characteristics among different source domains. Second, considering that most MDA methods ignore the significant limitations brought by statistical properties of the specific working condition, a multi-representation Softmax (MR-Softmax) is developed to learn domain-invariant discriminative representations by allowing the diversity of the predictions of samples with the same label. In addition, an undifferentiated adversarial training strategies is proposed to narrow the domain discrepancies and reasonably assess the residual negative transfer risk of different source domains. Based on the assessment, confidence coeffients are defined and embedded into MR-Softmax to extract and utilize the useful diagnostic knowledge on each source domain. Compared with several state-of-the-art diagnostic models, the experimental results confirm the validation of the proposed MR-SCNN method.

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Section: Domain Attention Consistency Network (Dac-mentioning

confidence: 99%

Section: Domain Attention Consistency Network (Dac-mentioning

confidence: 99%

Multi-representation symbolic convolutional neural network: a novel multisource cross-domain fault diagnosis method for rotating system

Jia

Mao

et al. 2023

Structural Health Monitoring

View full text Add to dashboard Cite

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“…After effectively capturing the clustered target features, it employed both the generated source samples and a classifier trained on the source data to generalize the performance of the model for unseen data classification. Deng [30] introduced a multisource unsupervised domain-adaptation neural network to efficiently utilize diverse source data in ECG classification and enhance model generalization. The model is distinguished by a two-branch domain adaptation and a sample imbalance-aware mixing strategy, facilitating the integration of information across domains.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Domain Adaptation for Individual Identification from Electrocardiogram Signals

Byeon,

Kwak

2023

Applied Sciences

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When acquiring electrocardiogram (ECG) signals, the placement of electrode patches is crucial for acquiring electrocardiographic signals. Constant displacement positions are essential for ensuring the consistency of the ECG signal when used for individual identification. However, achieving constant placement of ECG electrode patches in every trial for data acquisition is challenging. This is because different individuals may attach patches, and even when the same person attaches them, it may be difficult to specify the exact position. Therefore, gathering ECG data from various locations is necessary. However, this process requires a substantial amount of labor and time, owing to the requirement for multiple attempts. Nonetheless, persisting with these efforts enables the endurance of some ECG differences. To reduce labor and time, we propose a semi-supervised domain adaptation for individual identification using ECG signals. The method operates with a full set of original ECG signals and a small set of ECG signals from different placements to account for the differences between the signals in the generative adversarial network (CycleGAN). Specifically, to train the CycleGAN, the ECG signals were transformed into time–frequency representations, and the trained generator was used to generate ECG signals to expand the small set of ECG signals from different placements. Subsequently, both the original and generated signals were used to train the classifier for individual identification. This scenario can also be applied to the classification of ECG signals from different sensors. The PTB-ECG dataset was used for this experiment. We found that the proposed method showed higher accuracy than when only the original ECG signals were used for the training classifier.

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“…Similar to SDA, a straightforward approach for multi-source domain adaptation (MDA) to deal with multi-source data is also to merge all sources into one domain [39], which leads to an insufficient variance elimination in MSST [36]. In order to fully exploit multiple subjects' data distribution, some MDA began exploring feature representation approaches and combination of pre-learned classifiers [39][40][41][42][43]. The former approaches try to align the latent space of different domains based on optimizing the discrepancy loss, such as Rényi-divergence [48], L2 distance [49] or align the features through adversarial objectives, such as GAN loss [57], Wasserstein distance [58], [69].…”

Section: Introductionmentioning

confidence: 99%

“…The former approaches try to align the latent space of different domains based on optimizing the discrepancy loss, such as Rényi-divergence [48], L2 distance [49] or align the features through adversarial objectives, such as GAN loss [57], Wasserstein distance [58], [69]. The later approaches attempt to train per source separately and pairwise align the target with each source [37], [41], [48], [69]. Another solution is to assign a weight for each pre-learned classifier according to the relationship between source and target domain [39], [58].…”

Section: Introductionmentioning

confidence: 99%

SFDA: Domain Adaptation With Source Subject Fusion Based on Multi-Source and Single-Target Fall Risk Assessment

Wu,

Shu,

Song

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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In cross-subject fall risk classification based on plantar pressure, a challenge is that data from different subjects have significant individual information. Thus, the models with insufficient generalization ability can't perform well on new subjects, which limits their application in daily life. To solve this problem, domain adaptation methods are applied to reduce the gap between source and target domain. However, these methods focus on the distribution of the source and the target domain, but ignore the potential correlation among multiple source subjects, which deteriorates domain adaptation performance. In this paper, we proposed a novel method named domain adaptation with subject fusion (SFDA) for fall risk assessment, greatly improving the cross-subject assessment ability. Specifically, SFDA synchronously carries out source target adaptation and multiple source subject fusion by domain adversarial module to reduce source-target gap and distribution distance within source subjects of same class. Consequently, target samples can learn more task-specific features from source subjects to improve the generalization ability. Experiment results show that SFDA achieved mean accuracy of 79.17 % and 73.66 % based on two backbones in a cross-subject classification manner, outperforming the state-of-the-art methods on continuous plantar pressure dataset. This study proves the effectiveness of SFDA and provides a novel tool for implementing cross-subject and few-gait fall risk assessment.

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Multi-source unsupervised domain adaptation for ECG classification

Cited by 8 publications

References 12 publications

Multi-representation symbolic convolutional neural network: a novel multisource cross-domain fault diagnosis method for rotating system

Multi-representation symbolic convolutional neural network: a novel multisource cross-domain fault diagnosis method for rotating system

Semi-Supervised Domain Adaptation for Individual Identification from Electrocardiogram Signals

SFDA: Domain Adaptation With Source Subject Fusion Based on Multi-Source and Single-Target Fall Risk Assessment

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