Non-stationary neural signal to image conversion framework for image-based deep learning algorithms

Patel, Sahaj Anilbhai; Yildirim, Abidin

doi:10.3389/fninf.2023.1081160

Cited by 5 publications

(2 citation statements)

References 38 publications

(36 reference statements)

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“…The size of each epoch is set to 56 samples. The preprocessed segmented datasets were used ( Patel and Yildirim, 2023 ).…”

Section: Datasetsmentioning

confidence: 99%

Gershgorin circle theorem-based feature extraction for biomedical signal analysis

Patel,

Smith,

Yildirim

2024

Front. Neuroinform.

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Recently, graph theory has become a promising tool for biomedical signal analysis, wherein the signals are transformed into a graph network and represented as either adjacency or Laplacian matrices. However, as the size of the time series increases, the dimensions of transformed matrices also expand, leading to a significant rise in computational demand for analysis. Therefore, there is a critical need for efficient feature extraction methods demanding low computational time. This paper introduces a new feature extraction technique based on the Gershgorin Circle theorem applied to biomedical signals, termed Gershgorin Circle Feature Extraction (GCFE). The study makes use of two publicly available datasets: one including synthetic neural recordings, and the other consisting of EEG seizure data. In addition, the efficacy of GCFE is compared with two distinct visibility graphs and tested against seven other feature extraction methods. In the GCFE method, the features are extracted from a special modified weighted Laplacian matrix from the visibility graphs. This method was applied to classify three different types of neural spikes from one dataset, and to distinguish between seizure and non-seizure events in another. The application of GCFE resulted in superior performance when compared to seven other algorithms, achieving a positive average accuracy difference of 2.67% across all experimental datasets. This indicates that GCFE consistently outperformed the other methods in terms of accuracy. Furthermore, the GCFE method was more computationally-efficient than the other feature extraction techniques. The GCFE method can also be employed in real-time biomedical signal classification where the visibility graphs are utilized such as EKG signal classification.

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“…The size of each epoch is set to 56 samples. The preprocessed segmented datasets were used ( Patel and Yildirim, 2023 ).…”

Section: Datasetsmentioning

confidence: 99%

Gershgorin circle theorem-based feature extraction for biomedical signal analysis

Patel,

Smith,

Yildirim

2024

Front. Neuroinform.

Self Cite

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“…The GCFE algorithm performance was evaluated using the 1D and 2D-CNN models for feature extraction and classification. Figure 3 shows the complete architecture for both deep-learning models [27]. The model architecture was mostly similar for all the experiments, besides a few internal layer settings, such as kernel or padding size, which were modified.…”

Section: Classificationmentioning

confidence: 99%

Overcoming Dimensionality Constraints: A Gershgorin Circle Theorem-Based Feature Extraction for Weighted Laplacian Matrices in Computer Vision Applications

Patel,

Yildirim

2024

J. Imaging

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In graph theory, the weighted Laplacian matrix is the most utilized technique to interpret the local and global properties of a complex graph structure within computer vision applications. However, with increasing graph nodes, the Laplacian matrix’s dimensionality also increases accordingly. Therefore, there is always the “curse of dimensionality”; In response to this challenge, this paper introduces a new approach to reducing the dimensionality of the weighted Laplacian matrix by utilizing the Gershgorin circle theorem by transforming the weighted Laplacian matrix into a strictly diagonal domain and then estimating rough eigenvalue inclusion of a matrix. The estimated inclusions are represented as reduced features, termed GC features; The proposed Gershgorin circle feature extraction (GCFE) method was evaluated using three publicly accessible computer vision datasets, varying image patch sizes, and three different graph types. The GCFE method was compared with eight distinct studies. The GCFE demonstrated a notable positive Z-score compared to other feature extraction methods such as I-PCA, kernel PCA, and spectral embedding. Specifically, it achieved an average Z-score of 6.953 with the 2D grid graph type and 4.473 with the pairwise graph type, particularly on the E_Balanced dataset. Furthermore, it was observed that while the accuracy of most major feature extraction methods declined with smaller image patch sizes, the GCFE maintained consistent accuracy across all tested image patch sizes. When the GCFE method was applied to the E_MNSIT dataset using the K-NN graph type, the GCFE method confirmed its consistent accuracy performance, evidenced by a low standard deviation (SD) of 0.305. This performance was notably lower compared to other methods like Isomap, which had an SD of 1.665, and LLE, which had an SD of 1.325; The GCFE outperformed most feature extraction methods in terms of classification accuracy and computational efficiency. The GCFE method also requires fewer training parameters for deep-learning models than the traditional weighted Laplacian method, establishing its potential for more effective and efficient feature extraction in computer vision tasks.

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Fault classification and location of a PMU-equipped active distribution network using deep convolution neural network (CNN)

Siddique,

Shafiullah,

Mekhilef

et al. 2024

Electric Power Systems Research

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Non-stationary neural signal to image conversion framework for image-based deep learning algorithms

Cited by 5 publications

References 38 publications

Gershgorin circle theorem-based feature extraction for biomedical signal analysis

Gershgorin circle theorem-based feature extraction for biomedical signal analysis

Overcoming Dimensionality Constraints: A Gershgorin Circle Theorem-Based Feature Extraction for Weighted Laplacian Matrices in Computer Vision Applications

Fault classification and location of a PMU-equipped active distribution network using deep convolution neural network (CNN)

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