Neural Networks to Recognize Patterns in Topographic Images of Cortical Electrical Activity of Patients with Neurological Diseases

Meneses, Francisco Gerson A. de; Teles, Ariel Soares; Nunes, Monara; Farias, Daniel da Silva; Teixeira, Silmar

doi:10.1007/s10548-022-00901-4

Cited by 3 publications

(1 citation statement)

References 90 publications

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“…More recently, (de Meneses et al. 2022 ) has proved the effectiveness of the use of topographic maps for supervised group classification using Convolutional Networks. CORRMAP, which is the most popular clustering method for IC scalp maps, works as an open-source plugin for the popular EEGLAB software (Delorme and Makeig 2004 ), which further provides the possibility of clustering IC scalp maps using other clustering algorithms.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Genetic Algorithm for Clustering IC Topographies of EEGs

et al. 2023

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Clustering of independent component (IC) topographies of Electroencephalograms (EEG) is an effective way to find brain-generated IC processes associated with a population of interest, particularly for those cases where event-related potential features are not available. This paper proposes a novel algorithm for the clustering of these IC topographies and compares its results with the most currently used clustering algorithms. In this study, 32-electrode EEG signals were recorded at a sampling rate of 500 Hz for 48 participants. EEG signals were pre-processed and IC topographies computed using the AMICA algorithm. The algorithm implements a hybrid approach where genetic algorithms are used to compute more accurate versions of the centroids and the final clusters after a pre-clustering phase based on spectral clustering. The algorithm automatically selects the optimum number of clusters by using a fitness function that involves local-density along with compactness and separation criteria. Specific internal validation metrics adapted to the use of the absolute correlation coefficient as the similarity measure are defined for the benchmarking process. Assessed results across different ICA decompositions and groups of subjects show that the proposed clustering algorithm significantly outperforms the (baseline) clustering algorithms provided by the software EEGLAB, including CORRMAP.

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Section: Introductionmentioning

confidence: 99%

Hybrid Genetic Algorithm for Clustering IC Topographies of EEGs

et al. 2023

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Refining PD classification through ensemble bionic machine learning architecture with adaptive threshold based image denoising

Kumar

2023

Biomedical Signal Processing and Control

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Preliminary Study Based on Myocardial Infarction Classification of 12-Lead Electrocardiography Images with Deep Learning Methods

Latifoğlu,

Aigul Zhusupova,

İnce

et al. 2024

EJRnD

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In contemporary medicine, the development of computer-aided diagnostic systems using Electrocardiography (ECG) signals has gained significance for the diagnosis of heart diseases. Myocardial infarction (MI) is recognized as the condition where blood flow to the heart muscle is obstructed due to blockages in coronary vessels. In this study, four deep learning approaches were employed to automatically identify different MI conditions (STEMI, NSTEMI, USAP) using images generated from 12-lead ECG signals. The utilized architectures include deep neural networks such as Visual Geometry Group-16 (VGG-16), AlexNet, Residual Neural Network (ResNet), SqueezeNet and an ensemble model composed of these networks. With the proposed method, classification was performed based on 10-second grayscale images of 12-lead ECG signals for HC-STEMI, HC-NSTEMI, HC-USAP, and NSTEMI-STEMI conditions. According to the obtained results, the HC-STEMI group achieved the highest performance with a cross-validated 0.8237 F1 score using the AlexNet architecture. Among the novel contributions of this study is the image-based ECG classification method that can be more easily adapted to clinical applications and the analysis of the potential use of detecting different MI conditions in clinical practices. In conclusion, this study sheds light on future research by demonstrating the significant potential of using multi-channel ECG signals in image format for MI diagnosis, paving the way for advancements in this field.

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Neural Networks to Recognize Patterns in Topographic Images of Cortical Electrical Activity of Patients with Neurological Diseases

Cited by 3 publications

References 90 publications

Hybrid Genetic Algorithm for Clustering IC Topographies of EEGs

Hybrid Genetic Algorithm for Clustering IC Topographies of EEGs

Refining PD classification through ensemble bionic machine learning architecture with adaptive threshold based image denoising

Preliminary Study Based on Myocardial Infarction Classification of 12-Lead Electrocardiography Images with Deep Learning Methods

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