Multi-Feature Fusion Method Based on EEG Signal and its Application in Stroke Classification

Li, Fenglian; Fan, Yuzhou; Zhang, Xueying; Wang, Can; Hu, Fengyun; Jia, Wei; Hui, Haisheng

doi:10.1007/s10916-019-1517-9

Cited by 34 publications

(11 citation statements)

References 20 publications

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“…Overall, SVM shows the highest accuracy, the highest AUC, and the highest Gini coefficient. SVM is a benchmark machine learning technique as well as proven to show good results in multi-class classification to discriminate stroke patients and healthy control subjects using EEG signal [34,35]. Though the computational period of the SVM model is longer, the SVM model seems to be the most accurate model to predict stroke prognostics.…”

Section: Discussionmentioning

confidence: 99%

HealthSOS: Real-Time Health Monitoring System for Stroke Prognostics

2020

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Electroencephalography (EEG) is immediate and sensitive to cortical impairment resulting from ischemic stroke and is considered as the potential predictive tool of stroke onset, and post-stroke clinical management. Brainwave monitoring outside the heavily equipped clinical environment demands a low-cost, portable, and wearable EEG system. This study aims to assess the feasibility of using an ambulatory EEG system to classify the stroke patient group with neurological changes due to ischemic stroke and the control healthy adult group. HealthSOS, a real-time health monitoring system for stroke prognostics, is proposed here, which consists of an eye-mask embedded portable EEG device, data analytics, and medical ontology based health advisor service. This system was investigated with 37 stroke patients (mean age 71.6 years, 61% male) admitted in the emergency unit of a hospital and 36 healthy elderly volunteers (mean age 76 years, 28% male). EEG was recorded in resting-state using the portable device with frontal cortical electrodes (Fp1, Fp2) embedded in an eye-mask within 120 h after the onset of symptoms of ischemic stroke (confirmed clinically). The EEG data acquisition of the left and right brain hemispheres was done for at least 15 minutes in the awake resting state while subjects laid down on the bed. The statistical result shows that the revised brain symmetry index (rsBSI), the delta-alpha ratio, and the delta-theta ratio of the stroke group differ significantly from those of the healthy control group. In the machine learning analysis, the support vector machine (SVM) model shows the highest accuracy (Overall accuracy: 92%) and the highest Gini coefficient (95%) in classification performance. This study will be useful for early stroke prognostics and the management of post-stroke treatment.

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Section: Discussionmentioning

confidence: 99%

HealthSOS: Real-Time Health Monitoring System for Stroke Prognostics

2020

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“…UCF101 mainly collects 101 human behaviors on the YouTube website. In general, these 101 human behaviors can be classified into five categories: playing musical instruments, human actions [30], interacting with objects, interacting with people, and sports. Examples of samples in this data set are shown in Figure 2.…”

Section: Human Action Recognition Algorithm Based On Multifeature Fusion Combined With Deep Reinforcement Learningmentioning

confidence: 99%

Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning

2021

Mobile Information Systems

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Through the recognition and analysis of human motion information, the actual motion state of human body can be obtained. However, the multifeature fusion of human behavior has limitations in recognition accuracy and robustness. Combined with deep reinforcement learning, multifeature fusion human behavior recognition is studied and we proposed a multifeature fusion human behavior recognition algorithm using deep reinforcement learning. Firstly, several typical human behavior data sets are selected as the research data in the benchmark data set. In the selected data sets, the behavior category contained in each video is the same behavior, and there are category tags. Secondly, the attention model is constructed. In the deep reinforcement learning network, the small sampling area is used as the model input. Finally, the corresponding position of the next visual area is estimated according to the time series information obtained after the input. The human behavior recognition algorithm based on deep reinforcement learning multifeature fusion is completed. The results show that the average accuracy of multifeature fusion of the algorithm is about 95%, the human behavior recognition effect is good, the identification accuracy rate is as high as about 98% and passed the camera movement impact performance test and the algorithm robustness, and the average time consumption of the algorithm is only 12.7 s, which shows that the algorithm has very broad application prospects.

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“…Thakur et al [6] applied a feed forward neural network with back propagation to diagnose stroke by predicting cerebral ischaemia, which is one of the risk factors for stroke. Li et al [7] presented a new method for diagnosing stroke through EEG signals. They worked on a multi-feature fusion by combining fuzzy entropy, wavelet packet energy and hierarchical theory.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage

Al-Mekhlafi¹,

Senan²,

Rassem³

et al. 2022

Computers, Materials &Amp; Continua

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Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data space. Meanwhile, the Recursive Feature Elimination algorithm (RFE) was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant features. The features are fed into the various classification algorithms, namely, Support Vector Machine (SVM), K Nearest Neighbours (KNN), Decision Tree, Random Forest, and Multilayer Perceptron. All algorithms achieved superior results. The Random Forest algorithm achieved the best performance amongst the algorithms; it reached an overall accuracy of 99%. This algorithm classified stroke cases with Precision, Recall and F1 score of 98%, 100% and 99%, respectively. In the second dataset, the MRI image dataset was evaluated by using the AlexNet model and AlexNet + SVM hybrid technique. The hybrid model AlexNet + SVM performed is better than the AlexNet model; it reached accuracy, sensitivity, specificity and Area Under the Curve (AUC) of 99.9%, 100%, 99.80% and 99.86%, respectively.

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Multi-Feature Fusion Method Based on EEG Signal and its Application in Stroke Classification

Cited by 34 publications

References 20 publications

HealthSOS: Real-Time Health Monitoring System for Stroke Prognostics

HealthSOS: Real-Time Health Monitoring System for Stroke Prognostics

Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning

Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage

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