Classification of mild cognitive impairment based on handwriting dynamics and qEEG

Chai, Jiali; Wu, Ruixuan; Li, Aoyu; Xue, Chen; Qiang, Yan; Zhao, Juanjuan; Zhao, Qinghua; Yang, Qian

doi:10.1016/j.compbiomed.2022.106418

Cited by 20 publications

(7 citation statements)

References 61 publications

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“…The data is typically stored on the E4 and then transferred to the computer for processing. The MUSE 2 is a wire-free, portable, wearable, and flexible EEG headband widely used in meditation and research ( Cannard et al, 2021 ; Hunkin et al, 2021 ; Chai et al, 2023 ). It contains five electrodes, two of which are frontal electrodes (AF7 and AF8), two others are temporal electrodes (TP9 and TP10) and a reference electrode located at the Fpz position.…”

Section: Methodsmentioning

confidence: 99%

Synergy through integration of digital cognitive tests and wearable devices for mild cognitive impairment screening

Zhang

et al. 2023

Front. Hum. Neurosci.

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IntroductionAdvances in mobile computing platforms and the rapid development of wearable devices have made possible the continuous monitoring of patients with mild cognitive impairment (MCI) and their daily activities. Such rich data can reveal more subtle changes in patients’ behavioral and physiological characteristics, providing new ways to detect MCI anytime, anywhere. Therefore, we aimed to investigate the feasibility and validity of digital cognitive tests and physiological sensors applied to MCI assessment.MethodsWe collected photoplethysmography (PPG), electrodermal activity (EDA) and electroencephalogram (EEG) signals from 120 participants (61 MCI patients, 59 healthy controls) during rest and cognitive testing. The features extracted from these physiological signals involved the time domain, frequency domain, time-frequency domain and statistics. Time and score features during the cognitive test are automatically recorded by the system. In addition, selected features of all modalities were classified by tenfold cross-validation using five different classifiers.ResultsThe experimental results showed that the weighted soft voting strategy combining five classifiers achieved the highest classification accuracy (88.9%), precision (89.9%), recall (88.2%), and F1 score (89.0%). Compared to healthy controls, the MCI group typically took longer to recall, draw, and drag. Moreover, during cognitive testing, MCI patients showed lower heart rate variability, higher electrodermal activity values, and stronger brain activity in the alpha and beta bands.DiscussionIt was found that patients’ classification performance improved when combining features from multiple modalities compared to using only tablet parameters or physiological features, indicating that our scheme could reveal MCI-related discriminative information. Furthermore, the best classification results on the digital span test across all tasks suggest that MCI patients may have deficits in attention and short-term memory that came to the fore earlier. Finally, integrating tablet cognitive tests and wearable sensors would provide a new direction for creating an easy-to-use and at-home self-check MCI screening tool.

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Section: Methodsmentioning

confidence: 99%

Synergy through integration of digital cognitive tests and wearable devices for mild cognitive impairment screening

Zhang

et al. 2023

Front. Hum. Neurosci.

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“…We used the SVM machine learning method (Cortes & Vapnik, 1995) because it proved to perform well in previous AD studies compared to other classification algorithms (Chai et al, 2023; Khatri & Kwon, 2022; Menagadevi et al, 2023).…”

Section: Methodsmentioning

confidence: 99%

Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer’s disease and neurotypical aging

Liao,

Martin,

Fakorede

et al. 2023

Preprint

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There is increasing evidence of the usefulness of electroencephalography (EEG) as an early neurophysiological marker of preclinical AD. Our objective was to apply machine learning approaches on event-related oscillations to discriminate preclinical AD from neurotypical controls. Twenty-two preclinical AD participants who were cognitively normal with elevated amyloid and 21 cognitively normal with no elevated amyloid controls completed n-back working memory tasks (n= 0, 1, 2). EEG signals were recorded through a high-density sensor net. The event-related spectral changes were extracted using the discrete wavelet transform in the delta, theta, alpha, and beta bands. The support vector machine (SVM) machine learning method was employed to classify participants, and classification performance was assessed using the Area Under the Curve (AUC) metric. The relative power of the beta and delta bands outperformed other frequency bands with higher AUC values. The 2-back task obtained higher AUC values than the 0 and 1-back tasks. The highest AUC values were from the 2-back task beta band (AUC = 0.86) and delta bands (AUC = 0.85) nontarget data. This study demonstrates the promise of using machine learning on EEG event-related oscillations from working memory tasks to detect preclinical AD.

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“…Electroencephalogram (EEG) signals can assist in the diagnosis of neurodegenerative disorders by providing information on the neural activity of the brain. There were 3 AD studies [ 55 , 60 , 70 ] and 1 for PD [ 66 ] related to EEG data. EEG signals are collected using a cap-type electrode device placed on the participant’s head.…”

Section: Narrative Synthesis Of Relevant Findings From the Evidencementioning

confidence: 99%

Multimodal learning of clinically accessible tests to aid diagnosis of neurodegenerative disorders: a scoping review

Huang

Bai

et al. 2023

Health Inf Sci Syst

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With ageing populations around the world, there is a rapid rise in the number of people with Alzheimer’s disease (AD) and Parkinson’s disease (PD), the two most common types of neurodegenerative disorders. There is an urgent need to find new ways of aiding early diagnosis of these conditions. Multimodal learning of clinically accessible data is a relatively new approach that holds great potential to support early precise diagnosis. This scoping review follows the PRSIMA guidelines and we analysed 46 papers, comprising 11,750 participants, 3569 with AD, 978 with PD, and 2482 healthy controls; the recency of this topic was highlighted by nearly all papers being published in the last 5 years. It highlights the effectiveness of combining different types of data, such as brain scans, cognitive scores, speech and language, gait, hand and eye movements, and genetic assessments for the early detection of AD and PD. The review also outlines the AI methods and the model used in each study, which includes feature extraction, feature selection, feature fusion, and using multi-source discriminative features for classification. The review identifies knowledge gaps around the need to validate findings and address limitations such as small sample sizes. Applying multimodal learning of clinically accessible tests holds strong potential to aid the development of low-cost, reliable, and non-invasive methods for early detection of AD and PD.

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Classification of mild cognitive impairment based on handwriting dynamics and qEEG

Cited by 20 publications

References 61 publications

Synergy through integration of digital cognitive tests and wearable devices for mild cognitive impairment screening

Synergy through integration of digital cognitive tests and wearable devices for mild cognitive impairment screening

Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer’s disease and neurotypical aging

Multimodal learning of clinically accessible tests to aid diagnosis of neurodegenerative disorders: a scoping review

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