Motor intent recognition of multi-feature fusion EEG signals by UMAP algorithm

Du, Yu; Sui, Jing; Wang, Shiwei; Fu, Rongrong; Jia, Chengcheng

doi:10.1007/s11517-023-02878-z

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…The Implementation of UMAP Is based on the following key steps [10,37]: Finding nearest neighbors: In the high-dimensional space (X), the Euclidean distance between pairs of data points is calculated to determine the nearest neighbor set Nr(i) for each data point xi. Local density estimation:…”

Section: Umap Dimensionality Reduction Visualizationmentioning

confidence: 99%

“…In this study, we delved into the intricacies of our CNN-based ECG signal classification model by employing UMAP, a powerful nonlinear dimensionality reduction technique [10,37]. We subjected the output from the final fully connected layer of our CNN model to UMAP, reducing the data to two dimensions and presenting it in Figure 7 The UMAP visualization revealed that our model achieved high accuracy in distinguishing among nearly all the categories, underscoring its robust predictive capabilities and the interpretability of its decision-making process.…”

Section: Umap Analysismentioning

confidence: 99%

“…To enhance the model's transparency and interpretability, this study introduces Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technology for visualizing the model's classification boundaries and employs Shapley Additive exPlanations (SHAP) value analysis to quantitatively assess the specific contribution of each ECG signal feature to the model's prediction outcome [8][9][10][11][12]. UMAP technology provides us with a macro perspective, helping us to observe the structure of the data as a whole [13], while SHAP value analysis reveals the feature contributions behind each prediction from a microscopic perspective [14].…”

Section: Introductionmentioning

confidence: 99%

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Convolutional Neural Network-Based ECG Signal Classification Model: A Study on Addressing Class Imbalance and Enhancing Model Interpretability

Wang,

Zheng,

Yang

et al. 2024

Preprint

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Convolutional Neural Networks (CNNs) are often criticized for their lack of transparency, acting as 'black boxes' in decision-making, a challenge compounded by class imbalance in ECG datasets, which limits their clinical application. This study introduces a CNN-based ECG signal classification model that enhances interpretability and addresses class imbalance through the Synthetic Minority Over-sampling Technique (SMOTE). The model also integrates Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and SHAP value analysis, facilitating the visualization of decision boundaries and the assessment of feature contributions. Our evaluation using the MIT-BIH Arrhythmia Database highlights the model's high performance, with accuracy and precision nearing 1.00 for Normal ECG (NOR), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), and Ventricular Premature Beat (PVC) in the six-class task. In the ten-class task, the model demonstrated robustness, particularly with an accuracy of 0.9846, precision of 0.9783, recall of 0.9736, and F1 score of 0.9760 in Pacemaker Fusion Beat (PFHB), supported by an AUC of 0.9999 and AP of 0.9885. These results underscore the model's efficacy in cardiac rhythm recognition and resilience to class imbalance. Future research will explore sophisticated model architectures and feature extraction methods to enhance the model's generalization and clinical applicability for early heart disease diagnosis and personalized treatment.

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Section: Umap Dimensionality Reduction Visualizationmentioning

confidence: 99%

Section: Umap Analysismentioning

confidence: 99%