Alzheimer’s disease detection from optimal EEG channels and Tunable Q-Wavelet Transform

Puri, Digambar; Nalbalwar, Sanjay L.; Nandgaonkar, Anil; Wagh, Abhay

doi:10.11591/ijeecs.v25.i3.pp1420-1428

Cited by 11 publications

(12 citation statements)

References 23 publications

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“…Ieracitano et al [30] proposed Bi-spectrum (BiS) based higher order statistical features with multi-layer perceptron (MLP) to detect the AD from NC. They reported 96.7% accuracy [30], [31]. In other studies [32]- [36]…”

Section: Benchmarking With Previous Studiesmentioning

confidence: 85%

Identification of Alzheimerâ€™s Disease Using Novel Dual Decomposition Technique and Machine Learning Algorithms from EEG Signals

Puri¹,

Nalbalwar²,

Nandgaonkar³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Alzheimer's disease (AD) is one of the neurodegenerative disorders. The rate of AD prevalence is rapidly increasing worldwide. The existing clinical invasive methods and neuro-imaging techniques to detect AD are time-consuming, subjective, and expensive. To overcome these issues, we proposed a new automatic framework for detecting AD at an early stage based on the dual decomposition method. Initially, EEG signals of mild cognitive impairment (MCI), AD, and normal control (NC) patients are divided into five subbands by employing discrete wavelet transform (DWT). Subsequently, a Variational mode decomposition (VMD) is applied to these five EEG subbands for further decomposition into various intrinsic mode functions (IMFs). Afterward, three different multiscale permutation entropy (PE) features, namely Shannon PE (SPE), Tsalli's PE (TPE), and Renyi PE (RPE), have been measured from each IMF. Later, these features have been used to train and test ensemble bagged tree (EBT), k-nearest neighbor, support vector machine (SVM), decision tree (DT), and neural networks with a 10-fold cross-validation scheme. The proposed method has been verified using EEG signals of 59-AD, 7-MCI, and 102-NC subjects. The results obtained from the proposed DWT-VMD method provide 95.20% accuracy for three-class and 97.70% for two-class classification using an EBT classifier with 10-fold cross-validation. It shows a significant ability to distinguish AD from MCI. The proposed dual decomposition method can employ for other neurodegenerative disorders such as Parkinson's disease, epilepsy, various sleep disorders, and major depressive disorders.

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Section: Benchmarking With Previous Studiesmentioning

confidence: 85%

Identification of Alzheimerâ€™s Disease Using Novel Dual Decomposition Technique and Machine Learning Algorithms from EEG Signals

Puri¹,

Nalbalwar²,

Nandgaonkar³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…A total of 40 articles (Table 3 , Ref. [ 20 , 25 , 26 , 27 , 28 , 30 , 31 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ]) published between 2000 and 2023 using entropy measures were included (Fig. 2 ).…”

Section: Resultsmentioning

confidence: 99%

Is EEG Entropy a Useful Measure for Alzheimer's Disease?

Zúñiga,

Acero-González,

Restrepo-Castro

et al. 2024

Actas Esp Psiquiatr

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Background: The number of individuals diagnosed with Alzheimer's disease (AD) has increased, and it is estimated to continue rising in the coming years. The diagnosis of this disease is challenging due to variations in onset and course, its diverse clinical manifestations, and the indications for measuring deposit biomarkers. Hence, there is a need to develop more precise and less invasive diagnostic tools. Multiple studies have considered using electroencephalography (EEG) entropy measures as an indicator of the onset and course of AD. Entropy is deemed suitable as a potential indicator based on the discovery that variations in its complexity can be associated with specific pathologies such as AD. Methodology: Following PRISMA guidelines, a literature search was conducted in 4 scientific databases, and 40 articles were analyzed after discarding and filtering. Results: There is a diversity in entropy measures; however, Sample Entropy (SampEn) and Multiscale Entropy (MSE) are the most widely used (21/40). In general, it is found that when comparing patients with controls, patients exhibit lower entropy (20/40) in various areas. Findings of correlation with the level of cognitive decline are less consistent, and with neuropsychiatric symptoms (2/40) or treatment response less explored (2/40), although most studies show lower entropy with greater severity. Machine learning-based studies show good discrimination capacity. Conclusions: There is significant difficulty in comparing multiple studies due to their heterogeneity; however, changes in Multiscale Entropy (MSE) scales or a decrease in entropy levels are considered useful for determining the presence of AD and measuring its severity.

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“…For ML algorithms, we select the following features [6]: Output (OP), Statistical features viz. mean (µ), standard deviation (σ), kurtosis (β 1 ), skewness (β 2 ) and discrete cosine Transform (DCT) [7].…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

Drowsiness Detection using EEG signals and Machine Learning Algorithms

Joshi,

Kamble,

Parate

et al. 2022

ITM Web Conf.

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Drowsiness is described as a state of reduced consciousness and vigilance accompanied by a desire or want to sleep. Driver tiredness is frequently detected using wearable sensors that track vehicle movement and camera-based systems that track driver behavior. Many alternative EEG-based drowsiness detection systems are developed due to the potential of electroencephalogram (EEG) signals to observe human mood and the ease with which they may be obtained. This paper applies Deep learning architecture like Convolutional Neural networks (CNN) and algorithms for the classification of EEG data for Drowsiness Detection. The key measures of video-based approaches include the detection of physical features; nevertheless, problems such as brightness limitations and practical challenges such as driver attention limits its usefulness. The main measure of video-based methods is the degree of closure of the eyelids; however, its success is limited by constraints like as brightness restrictions and practical challenges such as driver distraction. We have extracted statistical features and trained using various classifiers like Logistic Regression, Naïve Bayes, SVM, and K Nearest Neighbours and compared the accuracy using a deep learning CNN model. Results demonstrate that CNN achieved an accuracy of 94.75% by delegating feature extraction on itself. Upon comparing existing state–of–the–art drowsiness detection systems, the testing results reveal a higher detection capability. The results show that the the suggested method can be used to develop a reliable EEG-based driving drowsiness detection system.

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Alzheimer’s disease detection from optimal EEG channels and Tunable Q-Wavelet Transform

Cited by 11 publications

References 23 publications

Identification of Alzheimerâ€™s Disease Using Novel Dual Decomposition Technique and Machine Learning Algorithms from EEG Signals

Identification of Alzheimerâ€™s Disease Using Novel Dual Decomposition Technique and Machine Learning Algorithms from EEG Signals

Is EEG Entropy a Useful Measure for Alzheimer's Disease?

Drowsiness Detection using EEG signals and Machine Learning Algorithms

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