Automatic and Efficient Framework for Identifying Multiple Neurological Disorders From EEG Signals

Tawhid, Md. Nurul Ahad; Siuly, Siuly; Wang, Kate; Wang, Hua

doi:10.1109/tts.2023.3239526

Cited by 21 publications

(14 citation statements)

References 37 publications

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“…This technique divides the original EEG data into short, informative fragments and gives them the same level as the original signal, increasing the data sample size while preserving an equal ratio [9]- [14], [25]. In this study, we have segmented the filtered signals into three-second (3s) time segments similar to the studies [12]- [14], as those studies have obtained better performance using 3s time segments that are computationally less expensive and also contain enough information to perform the automatic classification task [15].…”

Section: Segmentation Of the Eeg Signalsmentioning

confidence: 99%

“…In this study, we have used EEG data for MCI detection as it is non-invasive, cost-effective, widely available, and portable [12], [13]. Moreover, it captures brain electrical activity over time, making it valuable for assessing cognitive concerns [3], [14], [15].…”

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confidence: 99%

“…EEG signal has several rhythm bands like delta (0-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13)(14)(15)(16), beta (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) and gamma (>32 Hz) [21]. Among those rhythm bands, <0.5 Hz and >32 Hz bands are considered noise [1], [18] and can be removed from the classification process.…”

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confidence: 99%

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Exploring Frequency Band-Based Biomarkers of EEG Signals for Mild Cognitive Impairment Detection

Tawhid,

Siuly,

Kabir

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Mild Cognitive Impairment (MCI) is often considered a precursor to Alzheimer's disease (AD), with a high likelihood of progression. Accurate and timely diagnosis of MCI is essential for halting the progression of AD and other forms of dementia. Electroencephalography (EEG) is the prevalent method for identifying MCI biomarkers. Frequency band-based EEG biomarkers are crucial for identifying MCI as they capture neuronal activities and connectivity patterns linked to cognitive functions. However, traditional approaches struggle to identify precise frequency band-based biomarkers for MCI diagnosis. To address this challenge, a novel framework has been developed for identifying important frequency sub-bands within EEG signals for MCI detection. In the proposed scheme, the signals are first denoised using a stationary wavelet transformation and segmented into small time frames. Then, four frequency sub-bands are extracted from each segment, and spectrogram images are generated for each sub-band as well as for the full filtered frequency band signal segments. This process produces five different sets of images for five separate frequency bands. Afterwards, a convolutional neural network is used individually on those image sets to perform the classification task. Finally, the obtained results for the tested four sub-bands are compared with the results obtained using the full bandwidth. Our proposed framework was tested on two MCI datasets, and the results indicate that the 16-32 Hz sub-band range has the greatest impact on MCI detection, followed by 4-8 Hz. Furthermore, our framework, utilizing the full frequency band, outperformed existing state-of-the-art methods, indicating its potential for developing diagnostic tools for MCI detection.

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Section: Segmentation Of the Eeg Signalsmentioning

confidence: 99%

mentioning

confidence: 99%

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Exploring Frequency Band-Based Biomarkers of EEG Signals for Mild Cognitive Impairment Detection

Tawhid,

Siuly,

Kabir

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…While showing and internally calculating three-dimensional graphics, all vertices are expected to be represented by homogeneous four-dimensional coordinates [35][36][37][38][39][40]. The model uses the 3D coordinates (x, y, and z) to the homogeneous D coordinates (X, Y, Z, and P).…”

Section: Structural Roaming Sorting Algorithmmentioning

confidence: 99%

Application of Wireless Sensor Network Integrated With 3-5g Technology in the Design Of Interactive Space in an Urban Landscape

Zhang

2023

EAI Endorsed Scal Inf Syst

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INTRODUCTION: The conventional approaches of urban landscape design and planning cannot incorporate concepts into the complete planning processes for scenes, and they need to effectively, clearly, and coordinately convey the planning impacts of several systems and huge scenes. Virtual Reality (VR) is now a significant part of designing and planning urban landscapes because of the advancement of Wireless Sensor Networks (WSNs) and 5G technologies. OBJECTIVES: This article discusses the current condition and importance of "Three-Dimensional (3D)" portrayed urban landscape design and planning by outlining and evaluating previous works, introducing roaming algorithms and establishing a 3D database, and developing virtual scene manipulation tools. METHODS: In this approach, the 3D model CapsNet (Capsule Network) neural model is implemented to train the data and provide improved results by highlighting the interactive space in the urban landscaper. CapsNet can improve the computer vision tasks of VR and AR applications. RESULTS: The results show that a 3D visualized system with the CapsNet model can quickly distribute a large amount of data through graphics, enabling experts to intuitively notice testing results or interactively process such findings. Additionally, VR provides designers with practical data processing impacts through smart drawing, comprehensive display, and timely reporting, significantly increasing design productivity and effectively facilitating design quality.

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“…Neurological disorders 1 , diabetes 2 and cognitive impairments 3 , particularly the pandemic COVID-19 in recent years have profoundly impacted human being. It is well known that this pandemic has been more than three years since the first confirmed case of COVID-19 caused by SARS-CoV-2 in 2019 4 , during which various strains of the SARS-CoV-2 virus have emerged.…”

Section: Introductionmentioning

confidence: 99%

Clustering analysis for the evolutionary relationships of SARS-CoV-2 strains

Chen,

Wang,

Liu

et al. 2024

Sci Rep

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To explore the differences and relationships between the available SARS-CoV-2 strains and predict the potential evolutionary direction of these strains, we employ the hierarchical clustering analysis to investigate the evolutionary relationships between the SARS-CoV-2 strains utilizing the genomic sequences collected in China till January 7, 2023. We encode the sequences of the existing SARS-CoV-2 strains into numerical data through k-mer algorithm, then propose four methods to select the representative sample from each type of strains to comprise the dataset for clustering analysis. Three hierarchical clustering algorithms named Ward-Euclidean, Ward-Jaccard, and Average-Euclidean are introduced through combing the Euclidean and Jaccard distance with the Ward and Average linkage clustering algorithms embedded in the OriginPro software. Experimental results reveal that BF.28, BE.1.1.1, BA.5.3, and BA.5.6.4 strains exhibit distinct characteristics which are not observed in other types of SARS-CoV-2 strains, suggesting their being the majority potential sources which the future SARS-CoV-2 strains’ evolution from. Moreover, BA.2.75, CH.1.1, BA.2, BA.5.1.3, BF.7, and B.1.1.214 strains demonstrate enhanced abilities in terms of immune evasion, transmissibility, and pathogenicity. Hence, closely monitoring the evolutionary trends of these strains is crucial to mitigate their impact on public health and society as far as possible.

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Automatic and Efficient Framework for Identifying Multiple Neurological Disorders From EEG Signals

Cited by 21 publications

References 37 publications

Exploring Frequency Band-Based Biomarkers of EEG Signals for Mild Cognitive Impairment Detection

Exploring Frequency Band-Based Biomarkers of EEG Signals for Mild Cognitive Impairment Detection

Application of Wireless Sensor Network Integrated With 3-5g Technology in the Design Of Interactive Space in an Urban Landscape

Clustering analysis for the evolutionary relationships of SARS-CoV-2 strains

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