Adaptive classification of EEG for dementia diagnosis

Cejnek, Matouš; Bukovský, Ivo; Vyšata, Oldřich

doi:10.1109/iwcim.2015.7347075

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…This might indicate the brain atrophy in the AD group. The previously displayed results [35] obtained with different datasets yield a better classification rate (sensitivity and specificity of 95%). This may be caused by multiple reasons: bigger and more balanced dataset in the previous study, origin of all data from one measuring device, absence of MCI patients, classification over all channels.…”

Section: Discussionmentioning

confidence: 77%

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Novelty detection-based approach for Alzheimer’s disease and mild cognitive impairment diagnosis from EEG

Cejnek

Vyšata

Vališ

et al. 2021

Med Biol Eng Comput

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Alzheimer’s disease is diagnosed via means of daily activity assessment. The EEG recording evaluation is a supporting tool that can assist the practitioner to recognize the illness, especially in the early stages. This paper presents a new approach for detecting Alzheimer’s disease and potentially mild cognitive impairment according to the measured EEG records. The proposed method evaluates the amount of novelty in the EEG signal as a feature for EEG record classification. The novelty is measured from the parameters of EEG signal adaptive filtration. A linear neuron with gradient descent adaptation was used as the filter in predictive settings. The extracted feature (novelty measure) is later classified to obtain Alzheimer’s disease diagnosis. The proposed approach was cross-validated on a dataset containing EEG records of 59 patients suffering from Alzheimer’s disease; seven patients with mild cognitive impairment (MCI) and 102 controls. The results of cross-validation yield 90.73% specificity and 89.51% sensitivity. The proposed method of feature extraction from EEG is completely new and can be used with any classifier for the diagnosis of Alzheimer’s disease from EEG records.

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Section: Discussionmentioning

confidence: 77%

“…Different entropy measures show a decline in patients with Alzheimer's disease [33,34]. This study is an extension of previous conference paper [35]. In this study, we use a different dataset containing also MCI patients and data measured on two different machines -in order to determine whether this approach is independent on a device or not.…”

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confidence: 99%

Novelty detection-based approach for Alzheimer’s disease and mild cognitive impairment diagnosis from EEG

Cejnek

Vyšata

Vališ

et al. 2021

Med Biol Eng Comput

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“…The electroencephalogram (EEG) is a record of the human brain's electrical activity and is obtained from voltage fluctuations (electrical fields) acquired with electrodes (generally, noninvasive), which are the net result of excitatory and inhibitory synaptic potentials occurring in pyramidal neurons [1]. It can be used for patient monitoring/management, condition assessment, and it can even assist in the diagnosis of neurological and psychiatric disorders, such as Alzheimer [2], dementia [3], epileptic seizure [4], bipolar depression, and schizophrenia [5].…”

Section: Introductionmentioning

confidence: 99%

Electroencephalographic Signal Compression Based on Adaptive Segmentation and Video Encoders

Melo¹,

Filho²,

Júnior³

2017

Anais De XXXV Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Traditionally, biological signals are generated as one-dimensional arrays (even if acquired with many channels) and consequently encoded through one-dimensional techniques. Nonetheless, some researchers have addressed the encoding of biological records as two-dimensional arrays, in such a way that signal dependencies are exploited by two-dimensional encoders (e.g., video and image encoders), which are preceded by adaptation steps. The main goal of the latter is to reshape input signals and make their structures more suitable to target encoders, in order to favor dependency exploration and then provide higher performance. The present work employs a similar approach for electroencephalograms, but with the use of a new preprocessing technique, named as percentage difference segmentation, which is combined with the H.264 and high efficiency video coding compressors. Simulation results show that the proposed methodology is effective and outperforms state-of-the-art schemes present in the literature, in terms of P RD × compression ratio.

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Critical Evaluation of Different Biomarkers and Machine-Learning-Based Approaches to Identify Dementia Disease in Early Stages

Gimhani

Aponso

Krishnarajah

2019

Advances in Intelligent Systems and Computing

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Adaptive classification of EEG for dementia diagnosis

Cited by 8 publications

References 12 publications

Novelty detection-based approach for Alzheimer’s disease and mild cognitive impairment diagnosis from EEG

Novelty detection-based approach for Alzheimer’s disease and mild cognitive impairment diagnosis from EEG

Electroencephalographic Signal Compression Based on Adaptive Segmentation and Video Encoders

Critical Evaluation of Different Biomarkers and Machine-Learning-Based Approaches to Identify Dementia Disease in Early Stages

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