A brain EEG classification system for the mild cognitive impairment analysis

Nancy, A.; Balamurugan, M.; Vijaykumar, Suhasini

doi:10.1109/icaccs.2017.8014655

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…In this study, the number of samples in all dataset is considered relatively small for ML applications. This issue is common among many studies which have less than 50 subjects in the dataset used [23]- [25], [27], [29], [32]. It is understood that data scarcity is a major issue in this field.…”

Section: ) Data Augmentationmentioning

confidence: 99%

“…Some popular linear features used are derivative parameters of: signal discrete wavelet transform [18], signal coherence [19], [20], and signal synchrony [21]. Derivatives of other non-linear features such as: spectral entropy, spectral roll-off, zerocrossing rate, [22], [23] correlation dimension, and Lyapunov exponent [24] had also been used, while some studies had proposed novel feature extraction method themselves such as Integrated Pattern Monitoring [25] and multi-channel deep convolutional neural network which combine feature extraction with classification processes [26]. In terms of ML techniques, a multitude of well-established methods have been employed with supervised learning algorithms being the most widespread.…”

Section: Introductionmentioning

confidence: 99%

“…In terms of ML techniques, a multitude of well-established methods have been employed with supervised learning algorithms being the most widespread. Support Vector Machine (SVM) was the most widely used technique [18], [22], [25], [27]. However, simpler supervised learning algorithms such as Linear and Quadratic Discriminant Analysis (LDA, QDA) [28], [29], logistic regression [23], classification tree and random forest [18], [28] had also been used.…”

Section: Introductionmentioning

confidence: 99%

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A Dementia Classification Framework Using Frequency and Time-Frequency Features Based on EEG Signals

Durongbhan

Zhao

Chen

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

105

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Alzheimer's Disease (AD) accounts for 60-70% of all dementia cases, and clinical diagnosis at its early stage is extremely difficult. As several new drugs aiming to modify disease progression or alleviate symptoms are being developed, to assess their efficacy, novel robust biomarkers of brain function are urgently required. This study aims to explore a routine to gain such biomarkers using the quantitative analysis of Electroencephalography (QEEG). This paper proposes a supervised classification framework which uses EEG signals to classify healthy controls (HC) and AD participants. The framework consists of data augmentation, feature extraction, K-Nearest Neighbour (KNN) classification, quantitative evaluation and topographic visualisation. Considering the human brain either as a stationary or a dynamical system, both frequency-based and time-frequency-based features were tested in 40 participants. Results: a) The proposed method can achieve up to 99% classification accuracy on short (4s) eyes open EEG epochs, with the KNN algorithm that has best performance when compared to alternative machine learning approaches; b) The features extracted using the wavelet transform produced better classification performance in comparison to the features based on FFT; c) In the spatial domain, the temporal and parietal areas offer the best distinction between healthy controls and AD. The proposed framework can effectively classify HC and AD participants with high accuracy, meanwhile offering identification and localisation of significant QEEG features. These important findings and the proposed classification framework could be used for the development of a biomarker for the diagnosis and monitoring of disease progression in AD.

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Section: ) Data Augmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Dementia Classification Framework Using Frequency and Time-Frequency Features Based on EEG Signals

Durongbhan

Zhao

Chen

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

105

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EEG Band Separation Using Multilayer Perceptron for Efficient Feature Extraction and Perfect BCI Paradigm

Sunny

Afroze

Hossain

2020

2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)

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Dementia prediction using novel IOTM (Internet of Things in Medical) architecture framework

Pavitra,

Singh,

Sharma

et al. 2023

IDA

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In the last decades the health care developments highly rise the level of ages of world population. This improvement was accompanied by increasing the diseases related with elder like Dementia, which Alzheimer’s disease represents the most common form. The present studies aim to design and implementation a medical system for improving the life of Alzheimer’s disease persons and ease the burden of their caregivers. AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patient’s future health, and recommend better treatments. AI goes beyond the foundations of deep learning to give you insight into the nuances of applying AI to medical use cases. Diagnosis is about identifying disease. By building an algorithm we can diagnosis chest X-ray and determine whether it contains disease, another algorithm that will look at brain MRIs and identify the location of tumours in those brain MRIs health of the patients lab values and their demographics and use those to predict the risk of an event. A Smart IOT Interactive Assistance is a technological device that continuously monitors the stability of an Alzheimer’s patient, indicates their position on a map, automatically reminds them to take their prescriptions, and has a call button for any emergencies they could experience during the day. The system has two components, one of which the patient wears and the other of which is an IoT platform application utilized by the caregiver. The motion processing unit sensor, GPS, heart rate sensor with microcontrollers, and LCD display were used to construct the wearable device. An Internet of Things (IoT) platform supports this device, allowing the caregiver to communicate with the patient from any location.

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A brain EEG classification system for the mild cognitive impairment analysis

Cited by 3 publications

References 15 publications

A Dementia Classification Framework Using Frequency and Time-Frequency Features Based on EEG Signals

A Dementia Classification Framework Using Frequency and Time-Frequency Features Based on EEG Signals

EEG Band Separation Using Multilayer Perceptron for Efficient Feature Extraction and Perfect BCI Paradigm

Dementia prediction using novel IOTM (Internet of Things in Medical) architecture framework

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