Detection of Healthy and Unhealthy Brain States from Local Field Potentials Using Machine Learning

Fabietti, Marcos; Mahmud, Murni; Lotfi, Ahmad; Leparulo, Alessandro; Fontana, Roberto; Vassanelli, Stefano; Fassolato, Cristina

doi:10.1007/978-3-031-15037-1_3

Cited by 16 publications

(6 citation statements)

References 14 publications

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“…The aim of this work is the development of a deep learning model for early and accurate diagnosis of Alzheimer's disease and dementia, potentially advancing early intervention and treatment. Febietti et al [25] delve into early detection by utilizing cortical and hippocampal Local Field Potentials (LFPs) and ensemble machine learning models. By incorporating electrophysiological data, this study explores an alternative approach to Alzheimer's disease detection.…”

Section: Literature Surveymentioning

confidence: 99%

“…The proposed Dual-3DM 3 -AD model is compared with several state-ofthe-art approaches to demonstrate the proposed model efficacy for AD classification. EPEE [22], Novel-CNN [23], DEMNET [24], EMLM [25], RELS-TSVM [26] and THS-GAN are the approaches utilized for the comparison purpose. The comparison of Dual-3DM 3 -AD model performance metrics with state-of-the-art approaches is unveiled in Table [i] EPEE: A deep learning based approach using EPEE is proposed for Alzheimer diagnosis using MRI images, which performs better.…”

Section: (Ii) Comparison With Diverse State-of-art Approachesmentioning

confidence: 99%

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Dual-3DM³AD: Mixed Transformer Based Semantic Segmentation and Triplet Pre-Processing for Early Multi-Class Alzheimer’s Diagnosis

Khan,

Mahendran,

Perumal

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Alzheimer's Disease (AD) is a widespread, chronic, irreversible, and degenerative condition, and its early detection during the prodromal stage is of utmost importance. Typically, AD studies rely on single data modalities, such as MRI or PET, for making predictions. Nevertheless, combining metabolic and structural data can offer a comprehensive perspective on AD staging analysis. To address this goal, this paper introduces an innovative multi-modal fusion-based approach named as Dual-3DM 3 -AD. This model is proposed for an accurate and early Alzheimer's diagnosis by considering both MRI and PET image scans. Initially, we pre-process both images in terms of noise reduction, skull stripping and 3D image conversion using Quaternion Non-local Means Denoising Algorithm (QNLM), Morphology function and Block Divider Model (BDM), respectively, which enhances the image quality. Furthermore, we have adapted Mixed-transformer with Furthered U-Net for performing semantic segmentation and minimizing complexity. Dual-3DM 3 -AD model is consisted of multi-scale feature extraction module for extracting appropriate features from both segmented images. The extracted features are then aggregated using Densely Connected Feature Aggregator Module (DCFAM) to utilize both features. Finally, a multi-head attention mechanism is adapted for feature dimensionality reduction, and then the softmax layer is applied for multi-class Alzheimer's diagnosis. The proposed Dual-3DM 3 -AD model is compared with several baseline approaches with the help of several performance metrics. The final results unveil that the proposed work achieves 98% of accuracy, 97.8% of sensitivity, 97.5% of specificity, 98.2% of f-measure, and better ROC curves, which outperforms other existing models in multiclass Alzheimer's diagnosis.

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Section: Literature Surveymentioning

confidence: 99%

Section: (Ii) Comparison With Diverse State-of-art Approachesmentioning

confidence: 99%

Dual-3DM³AD: Mixed Transformer Based Semantic Segmentation and Triplet Pre-Processing for Early Multi-Class Alzheimer’s Diagnosis

Khan,

Mahendran,

Perumal

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Diagnosis of DR can be performed through either manual examination by an ophthalmologist or by utilising an automated system. With the advancements in Artificial Intelligence (AI) techniques, automated system development has been facilitated in many application areas including anomaly detection [ 3 ], brain signal analysis [ 4 ], neurodevelopmental disorder assessment and classification focusing on autism [ 5 , 6 , 7 ], neurological disorder detection and management [ 8 ], supporting the detection and management of the COVID-19 pandemic [ 9 ], cyber security and trust management [ 10 , 11 , 12 , 13 ], various disease diagnosis [ 14 , 15 , 16 , 17 ], smart healthcare service delivery [ 18 , 19 ], text and social media mining [ 20 , 21 ], understanding student engagement [ 22 , 23 ], etc. As can be seen in the literature, automated systems for early disease detection have been a major area of development.…”

Section: Introductionmentioning

confidence: 99%

Classification of Diabetic Retinopathy Disease Levels by Extracting Spectral Features Using Wavelet CNN

Sundar,

Subramanian,

Mahmud

2024

Diagnostics

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Diabetic retinopathy (DR) arises from blood vessel damage and is a leading cause of blindness on a global scale. Clinical professionals rely on examining fundus images to diagnose the disease, but this process is frequently prone to errors and is tedious. The usage of computer-assisted techniques offers assistance to clinicians in detecting the severity levels of the disease. Experiments involving automated diagnosis employing convolutional neural networks (CNNs) have produced impressive outcomes in medical imaging. At the same time, retinal image grading for detecting DR severity levels has predominantly focused on spatial features. More spectral features must be explored for a more efficient performance of this task. Analysing spectral features plays a vital role in various tasks, including identifying specific objects or materials, anomaly detection, and differentiation between different classes or categories within an image. In this context, a model incorporating Wavelet CNN and Support Vector Machine has been introduced and assessed to classify clinically significant grades of DR from retinal fundus images. The experiments were conducted on the EyePACS dataset and the performance of the proposed model was evaluated on the following metrics: precision, recall, F1-score, accuracy, and AUC score. The results obtained demonstrate better performance compared to other state-of-the-art techniques.

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“…Of late, Artificial Intelligence(AI) techniques involving machine learning (ML) and deep learning (DL) algorithms have contributed in diverse application domains including: anomaly detection [7,8,9], biosignal and image analysis [10,11,12,13,14,15,16,17,18,19,20 The AI-driven AD prediction is based on the concept that systems can identify stages of dementia by learning patterns through the input data so that optimal decisions can be made with minimal human intervention [79,80]. The contemporary ML and DL algorithms for AD detection have achieved highly admirable results on various scales of metrics [34,78].…”

Section: Introductionmentioning

confidence: 99%

Application of Explainable Artificial Intelligence in Alzheimer's Disease Classification: A Systematic Review

Vimbi¹,

Shaffi²,

Mahmud

et al. 2023

Preprint

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Context: Artificial Intelligence (AI) in the medical domain has achieved remarkable results on various metrics primarily due to recent advancements in computational capabilities. However, AI models used for Computer Aided Diagnosis (CAD) have limited acceptance and trust due to their typical {Blackbox} nature. Consequently, explainable AI (XAI) is needed to justify the trustworthiness of the machine's predictions. Approach: In this work, we undertake a systematic review based on PRISMA guidelines on research articles investigating Alzheimer's Disease (AD) prediction with XAI for the past decade. The review was driven by carefully formulated research questions (RQ) which helped categorize AI models into different XAI methods(Post-hoc, Ante-hoc, Model-Agnostic, Model-Specific, etc) and frameworks (LIME, SHAP, GradCAM, LRP, etc.). Summary: This categorisation provides broad coverage of the interpretation spectrum from intrinsic (model-specific, ante-hoc models) to complex patterns (model-agnostic, post-hoc models) and by taking local explanations to a global scope. Additionally, the study categorizes merit in terms of different forms of interpretation to provide in-depth insight into the factors that support the clinical diagnosis of AD. Finally, limitations and open research challenges are outlined, and possible prospects are presented.

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Detection of Healthy and Unhealthy Brain States from Local Field Potentials Using Machine Learning

Cited by 16 publications

References 14 publications

Dual-3DM³AD: Mixed Transformer Based Semantic Segmentation and Triplet Pre-Processing for Early Multi-Class Alzheimer’s Diagnosis

Dual-3DM³AD: Mixed Transformer Based Semantic Segmentation and Triplet Pre-Processing for Early Multi-Class Alzheimer’s Diagnosis

Classification of Diabetic Retinopathy Disease Levels by Extracting Spectral Features Using Wavelet CNN

Application of Explainable Artificial Intelligence in Alzheimer's Disease Classification: A Systematic Review

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Detection of Healthy and Unhealthy Brain States from Local Field Potentials Using Machine Learning

Cited by 16 publications

References 14 publications

Dual-3DM3AD: Mixed Transformer Based Semantic Segmentation and Triplet Pre-Processing for Early Multi-Class Alzheimer’s Diagnosis

Dual-3DM3AD: Mixed Transformer Based Semantic Segmentation and Triplet Pre-Processing for Early Multi-Class Alzheimer’s Diagnosis

Classification of Diabetic Retinopathy Disease Levels by Extracting Spectral Features Using Wavelet CNN

Application of Explainable Artificial Intelligence in Alzheimer's Disease Classification: A Systematic Review

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Product

Resources

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Dual-3DM³AD: Mixed Transformer Based Semantic Segmentation and Triplet Pre-Processing for Early Multi-Class Alzheimer’s Diagnosis

Dual-3DM³AD: Mixed Transformer Based Semantic Segmentation and Triplet Pre-Processing for Early Multi-Class Alzheimer’s Diagnosis