A self-driven approach for multi-class discrimination in Alzheimer's disease based on wearable EEG

Pérez-Valero, Eduardo; López-Gordo, M. A.; Gutiérrez, Christian Morillas; Carrera-Muñoz, Ismael; Vílchez-Carrillo, Rosa

doi:10.1016/j.cmpb.2022.106841

Cited by 21 publications

(19 citation statements)

References 42 publications

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“…As an alternative, researchers have been exploring Alzheimer's disease detection methods utilizing machine learning and EEG in recent years. [ 168 ] A study presents a preliminary assessment of a self‐driven multi‐class discrimination approach based on a commercially available EEG acquisition system with sixteen channels. The process involved the evaluation of a multi‐class classification problem using a MLP with leave‐one‐subject‐out cross‐validation.…”

Section: Machine Learning‐assisted Wearable Biosensorsmentioning

confidence: 99%

Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

Kim,

Min,

2023

Advanced Sensor Research

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This review article explores the transformative advancements in wearable biosignal sensors powered by machine learning, focusing on four notable biosignals: electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), and photoplethysmogram (PPG). The integration of machine learning with these biosignals has led to remarkable breakthroughs in various medical monitoring and human–machine interface applications. For ECG, machine learning enables automated heartbeat classification and accurate disease detection, improving cardiac healthcare with early diagnosis and personalized interventions. EMG technology, combined with machine learning, facilitates real‐time prediction and classification of human motions, revolutionizing applications in sports medicine, rehabilitation, prosthetics, and virtual reality interfaces. EEG analysis powered by machine learning goes beyond traditional clinical applications, enabling brain activity understanding in psychology, neurology, and human–computer interaction, and holds promise in brain–computer interfaces. PPG, augmented with machine learning, has shown exceptional progress in diagnosing and monitoring cardiovascular and respiratory disorders, offering non‐invasive and accurate healthcare solutions. These integrated technologies, powered by machine learning, open new avenues for medical monitoring and human–machine interaction, shaping the future of healthcare.

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Section: Machine Learning‐assisted Wearable Biosensorsmentioning

confidence: 99%

Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

Kim,

Min,

2023

Advanced Sensor Research

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“…Developing economically viable assessment tools and biomarkers that are highly sensitive to cognitive decline and neural dysfunction, before frank Alzheimer's disease (AD) pathology, is critical for the of AD and mild cognitive impairment (MCI) has attracted increased research attention. [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] The results in the existing literature, however, are mixed and call for newer analytic methods. Notably, in several recent papers, [20][21][22] it was reported that very high accuracy (>96%) may be achieved over modest sample sizes (22-34 participants).…”

Section: Introductionmentioning

confidence: 99%

“…Our analysis indicates that these biomarkers are closely related to the resting-state EEG biomarkers for AD as identified in most consistent findings. [10][11][12][13][14] Second, unlike existing work which generally relies only on one detection approach, [19][20][21]23,24 our soft discrimination of HC and MCI is obtained through weighted majority voting of a selected group of reliable discrimination approaches. This combination of diversified approaches takes the discrepancies between HC and MCI from different perspectives into consideration, and greatly improves the accuracy, stability, and reliability of the proposed model.…”

Section: Introductionmentioning

confidence: 99%

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Novel methodology for detection and prediction of mild cognitive impairment using resting‐state EEG

Deng

Sun

Kavcic

et al. 2023

Alzheimer's & Dementia

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BACKGROUNDEarly discrimination and prediction of cognitive decline are crucial for the study of neurodegenerative mechanisms and interventions to promote cognitive resiliency.METHODSOur research is based on resting‐state electroencephalography (EEG) and the current dataset includes 137 consensus‐diagnosed, community‐dwelling Black Americans (ages 60–90 years, 84 healthy controls [HC]; 53 mild cognitive impairment [MCI]) recruited through Wayne State University and Michigan Alzheimer's Disease Research Center. We conducted multiscale analysis on time‐varying brain functional connectivity and developed an innovative soft discrimination model in which each decision on HC or MCI also comes with a connectivity‐based score.RESULTSThe leave‐one‐out cross‐validation accuracy is 91.97% and 3‐fold accuracy is 91.17%. The 9 to 18 months’ progression trend prediction accuracy over an availability‐limited subset sample is 84.61%.CONCLUSIONThe EEG‐based soft discrimination model demonstrates high sensitivity and reliability for MCI detection and shows promising capability in proactive prediction of people at risk of MCI before clinical symptoms may occur.

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“…Consequently, it represents a promising approach for the detection of neurological diseases. Indeed, researchers have recently combined EEG signal processing and machine learning algorithms to discriminate AD and MCI patients from age matched controls (Trambaiolli et al, 2011 , 2017 ; Aghajani et al, 2013 ; McBride et al, 2013 ; Wang et al, 2015 ; Kashefpoor et al, 2016 ; Cassani et al, 2017 ; Fiscon et al, 2018 ; Ruiz-Gomez et al, 2018 ; Durongbhan et al, 2019 ; Khatun et al, 2019 ; Ieracitano et al, 2020 ; Perez-Valero et al, 2022 ). These works typically analyze the EEG signals in terms of spectral content, complexity, and synchronization, since previous studies have found these features are affected in AD patients.…”

Section: Introductionmentioning

confidence: 99%

An Automated Approach for the Detection of Alzheimer's Disease From Resting State Electroencephalography

Pérez-Valero

Morillas

López-Gordo

et al. 2022

Front. Neuroinform.

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Early detection is crucial to control the progression of Alzheimer's disease and to postpone intellectual decline. Most current detection techniques are costly, inaccessible, or invasive. Furthermore, they require laborious analysis, what delays the start of medical treatment. To overcome this, researchers have recently investigated AD detection based on electroencephalography, a non-invasive neurophysiology technique, and machine learning algorithms. However, these approaches typically rely on manual procedures such as visual inspection, that requires additional personnel for the analysis, or on cumbersome EEG acquisition systems. In this paper, we performed a preliminary evaluation of a fully-automated approach for AD detection based on a commercial EEG acquisition system and an automated classification pipeline. For this purpose, we recorded the resting state brain activity of 26 participants from three groups: mild AD, mild cognitive impairment (MCI-non-AD), and healthy controls. First, we applied automated data-driven algorithms to reject EEG artifacts. Then, we obtained spectral, complexity, and entropy features from the preprocessed EEG segments. Finally, we assessed two binary classification problems: mild AD vs. controls, and MCI-non-AD vs. controls, through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best reported in literature, what suggests that AD detection could be automatically detected through automated processing and commercial EEG systems. This is promising, since it may potentially contribute to reducing costs related to AD screening, and to shortening detection times, what may help to advance medical treatment.

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A self-driven approach for multi-class discrimination in Alzheimer's disease based on wearable EEG

Cited by 21 publications

References 42 publications

Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

Novel methodology for detection and prediction of mild cognitive impairment using resting‐state EEG

An Automated Approach for the Detection of Alzheimer's Disease From Resting State Electroencephalography

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