## 2 Related Work
### UDA for ECG Signal Classification  
Recently, there are many recent works that adopt the UDA method in interclass ECG classification tasks. [PERSON] et al. [1] proposed a multipath network as a baseline classification model assigned with cluster-aligning separating loss to reduce the discrepancy between train and test distributions for ECG-enabled arrhythmia classification. [PERSON] et al. [2] proposed a multisource UDA method that utilizes multiple data sources to incorporate generalization. This method comprises a two-stage adaptation and imbalance-aware mixing strategy to learn features across all domains from multiple sources. [PERSON] et al. [3] introduced a UDA method that learns discriminative features from clustering characteristics of source and target data. They also utilized cluster-aligning and maintaining losses to regulate and structure feature information of the source and target data in invariant space. [PERSON] et al. [4] introduced a hypergraph-based UDA method that exploits cross-attention dual-channel networks in adaptation tasks. They also used a domain alignment method based on Wasserstein distance for edge features, which also applies a pseudo-label generation technique for retaining category-level fine-grained information of the distribution. [PERSON] et al. [5] proposed a multilevel UDA method, which comprises an ASPP-R module to extract SP features; then, they used Graph-CN to extract structural features of the data. The whole framework exhibits three feature alignment stages, namely, domain, semantic, and structure alignment. [PERSON] et al. [10] utilized domain transfer SVM and weighted KLR methods to compare within the MIT-BIH dataset to see the statistical shift between the train and test ECG samples. [PERSON] et al. [11] proposed an adversarial DA method that comprises three different modules, namely, feature extractor, discriminator, and classification tasks. These modules reduced the performance gap under discrepant ECG data distribution. [PERSON] et al. [12] introduced a self-adjustable domain adaptation method to dispute the overfitting issue and unlabeled data exploitation. They adapted data augmentation methods to extend the ECG database in training conditions and a self-obtained map of the unlabeled data features with the transfer learning strategy. [PERSON] et al. [13] introduced an online cross-domain DA approach, which is based on a shared subspace classifier. It reduces the interclass discrepancy. It also includes an online adaptation method for emotion recognition via ECG data, which apparently refers from the ECG-based disease classification. [PERSON] et al. [14] addressed the generalization issue of DA methods in lab-to-field data scenarios in ECG-based cocaine use detection. This works limits the prior probability, covariate, and label granularity shifts in lab-field data usage. [PERSON] et al. [15] proposed a UDA method that uses a CNN to extract deep features from raw data; then, they used MMD to increase distribution gaps between source and target features. [PERSON] [16] proposed an asymmetric DANN method that uses a denoising autoencoder to learn features and FCNN layers to alienate data-shift. [PERSON] et al. [17] introduced a [PERSON]'s linear discriminant method for DA that determines the expected risk of the combined hypothesis of the target distribution and also estimates a convex coefficient of BV-trade between source and target samples. This method explores both ECG and EEG data in the DA setting. [PERSON] [18] et al. proposed a DA method that learns the user-independent linear transformation of the ECG data. They also modeled heartbeat-based sparse representations. This transformation method maps the patient-specific dictionaries in modeling ECG heartbeats, which is usable for online ECG monitoring. [PERSON] et al. [19] proposed a UDA method based on an adaptive region network; they utilized adversarial training to reduce the discrepancy of the domain.