FIGURE 1 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
Mild Cognitive Impairment (MCI) is an early stage of dementia, which may lead to Alzheimer’s disease (AD) in older adults. Therefore, early detection of MCI and implementation of treatment and intervention can effectively slow down or even inhibit the progression of the disease, thus minimizing the risk of AD. Currently, we know that published work...
Context in source publication
Context 1
... the segment of each subject was evaluated which accounts for a total of 2063 segments for NREM sleep and a total of 768 segments for wakefulness are analyzed. Figure 1 describes a proposed algorithm for classifying EEG segments from MCI and HC. As shown in the figure, the algorithm consists of three steps. ...
Similar publications
Primary care professionals play a critical role in the care of their patients. In clinical practice, early detection and diagnosis of Mild Cognitive Impairment, Alzheimer's disease and related dementia are often missed or delayed. Disclosure of diagnosis is not timely or not revealed. Though the methods that could improve early detection and diagno...
Citations
... However, the hyperparameters of machine learning models were not optimized. The methods reported in [45][46][47][48] investigated the spectral and complexity fea-tures from the EEG signals and managed to obtain the 93.46 % accuracy. The proposed LCADNet model of AD detection improved the performance by 2% than existing methods. ...
Alzheimer’s disease (AD) is a progressive and incurable neurologi-cal disorder with a rising mortality rate, worsened by error-prone, time-intensive, and expensive clinical diagnosis methods. Automatic AD detection methods using hand-crafted Electroencephalogram (EEG) signal features lack accuracy and reliability. A lightweight convolution neural network for AD detection (LCADNet) is investigated to extract disease-specific features while reducing the detection time. The LCADNet uses two convolutional layers for extracting complex EEG features, two fully connected layers for selecting disease-specific features, and a softmax layer for predicting AD detection probability. A max-pooling layer interlaced between convolutional layers decreases the time-domain redundancy in the EEG signal. The efficiency of the LCADNet and four pre-trained models using transfer learning is compared using a publicly available AD detection dataset. The LCADNet shows the lowest computation complexity in terms of both the number of floating point operations and inference time and the highest classification performance across six measures. The generalization of the LCADNet is assessed by cross-testing it with two other publicly available AD detection datasets. It outperforms existing EEG-based AD detection methods with an accuracy of 98.50%. The LCADNet may be a valuable aid for neurologists and its Python implemen- tation can be found at github.com/SandeepSangle12/LCADNet.git.
... To pinpoint MCI [18], used specific features extracted from slow sleep signals and spindles in combination with complexity and spectral values. The study conducted in [19] described a novel method to get features from EEG tests to distinguish between AD subjects, MCI subjects, and normal people. The use of DL methods such as long short-term memory networks (LSTM), Bi-LSTM, Recurrent Neural Networks (RNN), convolutional neural networks (CNN), and gated recurrent units (GRU) in healthcare applications have been briefly discussed by [20]. ...
Mild cognitive impairment (MCI) is a cognitive disease that primarily affects elderly persons.Patients with MCI have impairments in one or more cognitive areas, such as memory, attention,language, and problem-solving. The risk of Alzheimer’s disease development is 10 times higher among individuals who meet the MCI diagnosis than in those who do not have such a diagnosis. Identifying the primary neurophysiological variations between those who are suffering from cognitive impairment and those who are ageing normally may provide helpful techniques to assess the effectiveness of therapies. Event-related Potentials (ERPs) are utilized to investigate the processing of sensory, cognitive, and motor information in the brain. ERPs enable excellent temporal resolution of underlying brain activity. ERP data is complex due to the temporal variation that occurs in the time domain. It is actually a type of electroencephalography (EEG)
signal that is time-locked to a specific event or behavior. To remove artifacts from the data, this work utilizes Independent component analysis, finite impulse response filter, and fast Fourier
transformation as preprocessing techniques. The bidirectional long short-term memory network is utilized to retain the spatial relationships between the ERP data while learning changes in temporal information for a long time. This network performed well both in modeling and information extraction from the signals. To validate the model performance, the proposed framework is tested
on two benchmark datasets. The proposed framework achieved a state-of-the-art accuracy of 96.03% on the SJTU Emotion EEG Dataset dataset and 97.31% on the Chung–Ang University Hospital EEG dataset for the classification tasks.
... Sleep deficit and bad sleeping habits are risk factors for dementia [5][6][7][8]. However, sleep is not the only risk factor for dementia. ...
... Several studies [49,50] have been able to classify, with various algorithms, dementia in patients. One study has measured the EEG whilst the patients are asleep to classify MCI with promising results [7]. ...
... Several studies have examined sleep and its association with dementia [5][6][7][63][64][65][66]. Using REM sleep as a feature to examine dementia has had a good result and showed that it was a high accuracy factor when predicting dementia [6]. ...
Background:
The most common degenerative condition in older adults is dementia, which can be predicted using a number of indicators and whose progression can be slowed down. One of the indicators of an increased risk of dementia is sleep disturbances. This study aims to examine if machine learning can predict dementia and which sleep disturbance factors impact dementia.
Methods:
This study uses five machine learning algorithms (gradient boosting, logistic regression, gaussian naive Bayes, random forest and support vector machine) and data on the older population (60+) in Sweden from the Swedish National Study on Ageing and Care-Blekinge (= 4175). Each algorithm uses 10-fold stratified cross-validation to obtain the results, which consist of the Brier score for checking accuracy and the feature importance for examining the factors which impact dementia. The algorithms use 16 features which are on personal and sleep disturbance factors.
Results:
Logistic regression found an association between dementia and sleep disturbances. However, it is slight for the features in the study. Gradient boosting was the most accurate algorithm with 92.9% accuracy, 0.926 f1-score, 0.974 ROC AUC and 0.056 Brier score. The significant factors were different in each machine learning algorithm. If the person sleeps more than two hours during the day, their sex, education level, age, waking up during the night and if the person snores are the variables that most consistently have the highest feature importance in all algorithms.
Conclusion:
There is an association between sleep disturbances and dementia, which machine learning algorithms can predict. Furthermore, the risk factors for dementia are different across the algorithms, but sleep disturbances can predict dementia.
... 123 Studies have shown lower overall δ in non-REM (NREM) sleep and lower overall power in both NREM and REM sleep in aMCI compared with controls. 124 Pathological changes in NREM and REM sleep may predict the trajectory of cognitive decline in older adults. The advantages of neuroimaging techniques, electrophysiological examinations and retinal imaging technologies lie in their relatively low cost, rapidity and non-invasiveness to allow massive screening in the context of large-scale screening applications, but their current applications are still limited due to a lack of validation studies with biomarker evidence. ...
Alzheimer’s disease (AD) is a common cause of dementia, characterised by cerebral amyloid-β deposition, pathological tau and neurodegeneration. The prodromal stage of AD (pAD) refers to patients with mild cognitive impairment (MCI) and evidence of AD’s pathology. At this stage, disease-modifying interventions should be used to prevent the progression to dementia. Given the inherent heterogeneity of MCI, more specific biomarkers are needed to elucidate the underlying AD’s pathology. Although the uses of cerebrospinal fluid and positron emission tomography are widely accepted methods for detecting AD’s pathology, their clinical applications are limited by their high costs and invasiveness, particularly in low-income areas in China. Therefore, to improve the early detection of Alzheimer's disease (AD) pathology through cost-effective screening methods, a panel of 45 neurologists, psychiatrists and gerontologists was invited to establish a formal consensus on the screening of pAD in China. The supportive evidence and grades of recommendations are based on a systematic literature review and focus group discussion. National meetings were held to allow participants to review, vote and provide their expert opinions to reach a consensus. A majority (two-thirds) decision was used for questions for which consensus could not be reached. Recommended screening methods are presented in this publication, including neuropsychological assessment, peripheral biomarkers and brain imaging. In addition, a general workflow for screening pAD in China is established, which will help clinicians identify individuals at high risk and determine therapeutic targets.
... As a result, the model of the ECP feature combined with CKF-SVM achieved the highest accuracy reaching 90.19% accuracy. Geng et al. (2022) investigated whether sleep EEG-based signals can be used to diagnose MCI by using a dataset that consists of 20 MCI and 20 HC. They have extracted spindle features and sleep slow waves from sleep EEG signals and then they have fused with spectral and complexity features. ...
Mild cognitive impairment (MCI) is a neuropsychological syndrome that is characterized by cognitive impairments. It typically affects adults 60 years of age and older. It is a noticeable decline in the cognitive function of the patient, and if left untreated it gets converted to Alzheimer’s disease (AD). For that reason, early diagnosis of MCI is important as it slows down the conversion of the disease to AD. Early and accurate diagnosis of MCI requires recognition of the clinical characteristics of the disease, extensive testing, and long-term observations. These observations and tests can be subjective, expensive, incomplete, or inaccurate. Electroencephalography (EEG) is a powerful choice for the diagnosis of diseases with its advantages such as being non-invasive, based on findings, less costly, and getting results in a short time. In this study, a new EEG-based model is developed which can effectively detect MCI patients with higher accuracy. For this purpose, a dataset consisting of EEG signals recorded from a total of 34 subjects, 18 of whom were MCI and 16 control groups was used, and their ages ranged from 40 to 77. To conduct the experiment, the EEG signals were denoised using Multiscale Principal Component Analysis (MSPCA), and to increase the size of the dataset Data Augmentation (DA) method was performed. The tenfold cross-validation method was used to validate the model, moreover, the power spectral density (PSD) of the EEG signals was extracted from the EEG signals using three spectral analysis methods, the periodogram, welch, and multitaper. The PSD graphs of the EEG signals showed signal differences between the subjects of control and the MCI group, indicating that the signal power of MCI patients is lower compared to control groups. To classify the subjects, one of the best classifiers of deep learning algorithms called the Bi-directional long-short-term-memory (Bi-LSTM) was used, and several machine learning algorithms, such as decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN). These algorithms were trained and tested using the extracted feature vectors from the control and the MCI groups. Additionally, the values of the coefficient matrix of those algorithms were compared and evaluated with the performance evaluation matrix to determine which one performed the best overall. According to the experimental results, the proposed deep learning model of multitaper spectral analysis approach with Bi-LSTM deep learning algorithm attained the highest number of correctly classified samples for diagnosing MCI patients and achieved a remarkable accuracy compared to the other proposed models. The achieved classification results of the deep learning model are reported to be 98.97% accuracy, 98.34% sensitivity, 99.67% specificity, 99.70% precision, 99.02% f1 score, and 97.94% Matthews correlation coefficient (MCC).
... Recently, improving sleeping health has emerged as an intervention strategy for various diseases, including metabolic diseases, all-cause mortality, and Alzheimer's diseases [7,8,9,10]. Sleep EEG data (less noisy than daytime EEG recording) has long been used for investigating memory functions [11,12]. For example, normal aging effects on cognition were reliably estimated from the sleep EEG data [13]; sleep EEG-based brain age index has been studied to find the association between sleep and dementia [14]. ...
Medical data are often multi-modal, which are collected from different sources with different formats, such as text, images, and audio. They have some intrinsic connections in meaning and semantics while manifesting disparate appearances. Polysomnography (PSG) datasets are multi-modal data that include hypnogram, electrocardiogram (ECG), and electroencephalogram (EEG). It is hard to measure the associations between different modalities. Previous studies have used PSG datasets to study the relationship between sleep disorders and quality and sleep architecture. We leveraged a new method of deep learning manifold alignment to explore the relationship between sleep architecture and EEG features. Our analysis results agreed with the results of previous studies that used PSG datasets to diagnose different sleep disorders and monitor sleep quality in different populations. The method could effectively find the associations between sleep architecture and EEG datasets, which are important for understanding the changes in sleep stages and brain activity. On the other hand, the Spearman correlation method, which is a common statistical technique, could not find the correlations between these datasets.KeywordsDeep LearningManifold AlignmentEEGSleep Architecture
... It was also verified that the performance parameters of VST combined with the EEG signal are more beneficial for the detection of cognitive impairment. Geng et al. [17] proposed a sleep EEG-based MCI detection method that extracted sleep slow waves and spindles features from 40 participants and obtained a 93.46% classification accuracy in a gate recurrent unit (GRU) network. This demonstrated that sleep EEG signals are more accurate than waking EEG signals in MCI detection, and again proved that sleep slow waves and spindles features can be used as early biomarkers of AD. ...
Significant advances in sensor technology and virtual reality (VR) offer new possibilities for early and effective detection of mild cognitive impairment (MCI), and this wealth of data can improve the early detection and monitoring of patients. In this study, we proposed a non-invasive and effective MCI detection protocol based on electroencephalogram (EEG), speech, and digitized cognitive parameters. The EEG data, speech data, and digitized cognitive parameters of 86 participants (44 MCI patients and 42 healthy individuals) were monitored using a wearable EEG device and a VR device during the resting state and task (the VR-based language task we designed). Regarding the features selected under different modality combinations for all language tasks, we performed leave-one-out cross-validation for them using four different classifiers. We then compared the classification performance under multimodal data fusion using features from a single language task, features from all tasks, and using a weighted voting strategy, respectively. The experimental results showed that the collaborative screening of multimodal data yielded the highest classification performance compared to single-modal features. Among them, the SVM classifier using the RBF kernel obtained the best classification results with an accuracy of 87%. The overall classification performance was further improved using a weighted voting strategy with an accuracy of 89.8%, indicating that our proposed method can tap into the cognitive changes of MCI patients. The MCI detection scheme based on EEG, speech, and digital cognitive parameters proposed in this study provides a new direction and support for effective MCI detection, and suggests that VR and wearable devices will be a promising direction for easy-to-perform and effective MCI detection, offering new possibilities for the exploration of VR technology in the field of language cognition.
The electroencephalogram (EEG) signal is a general reflection of the neurophysiological activity of the brain, which has the advantages of being safe, efficient, real-time and dynamic. With the development and advancement of machine learning research, automatic diagnosis of Alzheimer's diseases based on deep learning is becoming a research hotspot. Started from feedforward neural networks, this paper compared and analysed the structural properties of neural network models such as recurrent neural networks, convolutional neural networks and deep belief networks and their performance in the diagnosis of Alzheimer's disease. It also discussed the possible challenges and research trends of this research in the future, expecting to provide a valuable reference for the clinical application of neural networks in the EEG diagnosis of Alzheimer's disease.
