Automatic seizure detection based on Gray Level Co-occurrence Matrix of STFT imaged-EEG

Shayeste, Haniye; Asl, Babak Mohammadzadeh

doi:10.1016/j.bspc.2022.104109

Cited by 17 publications

(7 citation statements)

References 38 publications

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“… Reference Techniques Sen (%) Acc (%) FP rate (%) Delay (s) Zarei, A. et al (2021) [ 5 ] DWT + SVM 96.81 97.09 2.74 – Abdelhameed, A. et al (2021) [ 25 ] LSTM 98.72 98.79 1.14 – Alharthi, M.K. et al (2022) [ 4 ] DWT + LSTM 96.85 96.87 3.02 – Amiri, M. et al (2022) [ 12 ] STFT + SVM 98.44 98.81 0.81 – Shayeste, H. et al (2022) [ 11 ] STFT + Decision Tree 99.52 99.56 0.38 – L. Jiang. et al (2022) [ 26 ] Functional brain network + SVM 97.72 96.67 4.38 – Liu, S. et al (2023) [ 27 ] Power spectrum density + SVM 99.90 99.95 0.01 – Our previous work (2022) [ 13 ] DWT + RUS Boosted 96.15 97.00 3.24 10.42 Our previous work (20...…”

Section: Discussionmentioning

confidence: 99%

“…They supported the result of a sensitivity of 93.6 % and a false positive rate of 0.16 per hour in their study. Shayeste, H. et al developed a short-time Fourier transform (STFT) algorithm based on the bagging technique and a decision tree model for automatic seizure detection [ 11 ]. Their approach received high accuracy, sensitivity, and specificity, with reported values of 99.56 %, 99.52 %, and 99.62 %, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A real-time epilepsy seizure detection approach based on EEG using short-time Fourier transform and Google-Net convolutional neural network

Shen,

Yang,

Wen

et al. 2024

Heliyon

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A real-time epilepsy seizure detection approach based on EEG using short-time Fourier transform and Google-Net convolutional neural network

Shen,

Yang,

Wen

et al. 2024

Heliyon

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“…The generated energy-time-frequency representations are used to visualize 2D heatmaps that map the signal's energy across time-frequency instants. Accordingly, computer vision and DL techniques are leveraged where these heatmaps serve as input images [200]- [208], [212], [217], [222], [223], [227], [228], [230], [232], [233], [238], [239], [241]. 3) Transformation coefficients as signal representations: In these approaches, the generated mappings are treated as transformed representations of the signal.…”

Section: B Transform-based Methodsmentioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing. This assumption creates a significant obstacle for a wide range of readers. To address these challenges, this paper takes an integrated article approach. It begins with a detailed tutorial on the fundamentals of signal processing, providing the reader with the necessary background knowledge. Following this, it explores the key stages of a standard signal processing-based ML pipeline, offering an in-depth review of feature extraction techniques, their inherent challenges, and solutions. Differing from existing literature, this work offers an application-independent review and introduces a novel classification taxonomy for feature extraction techniques. Furthermore, it aims at linking theoretical concepts with practical applications, and demonstrates this through two specific use cases: a spectral-based method for condition monitoring of rolling bearings and a wavelet energy analysis for epilepsy detection using EEG signals. In addition to theoretical contributions, this work promotes a collaborative research culture by providing a public repository of relevant Python and MATLAB signal processing codes. This effort is intended to support collaborative research efforts and ensure the reproducibility of the results presented.

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“…Traditional object detection methods can be broadly divided into three categories. Firstly, methods such as Local Binary Patterns (LBP) [10], Histogram of Oriented Gradients (HOG) [11], and Gray Level Co-occurrence Matrix (GLCM) [12] extract the features of surface defects by manually designing parameters [13]. The second category comprises techniques based on statistical and spectral methods, such as Fourier Transform [14], Wavelet Transform [15], and Gabor Filters [16].…”

Section: Introductionmentioning

confidence: 99%

SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects

Wu,

Chen,

et al. 2024

Metals

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Flat-rolled steel sheets are one of the major products of the metal industry. Strip steel’s production quality is crucial for the economic and safety aspects of humanity. Addressing the challenges of identifying the surface defects of strip steel in real production environments and low detection efficiency, this study presents an approach for strip defect detection based on YOLOv5s, termed SDD-YOLO. Initially, this study designs the Convolution-GhostNet Hybrid module (CGH) and Multi-Convolution Feature Fusion block (MCFF), effectively reducing computational complexity and enhancing feature extraction efficiency. Subsequently, CARAFE is employed to replace bilinear interpolation upsampling to improve image feature utilization; finally, the Bidirectional Feature Pyramid Network (BiFPN) is introduced to enhance the model’s adaptability to targets of different scales. Experimental results demonstrate that, compared to the baseline YOLOv5s, this method achieves a 6.3% increase in mAP50, reaching 76.1% on the Northeastern University Surface Defect Database for Detection (NEU-DET), with parameters and FLOPs of only 3.4MB and 6.4G, respectively, and FPS reaching 121, effectively identifying six types of defects such as Crazing and Inclusion. Furthermore, under the conditions of strong exposure, insufficient brightness, and the addition of Gaussian noise, the model’s mAP50 still exceeds 70%, demonstrating the model’s strong robustness. In conclusion, the proposed SDD-YOLO in this study features high accuracy, efficiency, and lightweight characteristics, making it applicable in actual production to enhance strip steel production quality and efficiency.

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Automatic seizure detection based on Gray Level Co-occurrence Matrix of STFT imaged-EEG

Cited by 17 publications

References 38 publications

A real-time epilepsy seizure detection approach based on EEG using short-time Fourier transform and Google-Net convolutional neural network

A real-time epilepsy seizure detection approach based on EEG using short-time Fourier transform and Google-Net convolutional neural network

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects

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