Single Channel EEG Classification: A Case Study on Prediction of Major Depressive Disorder Treatment Outcome

Hasanzadeh, Fatemeh; Mohebbi, Maryam; Rostami, Reza

doi:10.1109/access.2020.3046993

Cited by 9 publications

(4 citation statements)

References 49 publications

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“…The evaluation elapsed time of the algorithms of literature [ 5 ], literature [ 6 ], literature [ 7 ], literature [ 8 ], literature [ 9 ], and this paper were compared, and the results are shown in Table 3 .…”

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

“…Analysis of the data in Table 3 shows that with the increasing number of samples, the evaluation time of different algorithms shows an upward trend. The evaluation time of the algorithm in literature [ 5 ] varies from 1.25 s to 1.78 s. The evaluation time of the algorithm in literature [ 6 ] varies from 1.33 s to 1.96 s. The evaluation time of the algorithm in literature [ 7 ] varies from 1.33 s to 2.13 s. The evaluation time of the algorithm in literature [ 8 ] varies from 1.64 s to 2.55 s. The evaluation time of the algorithm in literature [ 8 ] varies from 1.14 s to 1.36 s. Compared with these algorithms, the evaluation time of the algorithm in this paper varies from 0.56 s to 0.91 s, indicating that the evaluation time of the algorithm in this paper is shorter and more efficient.…”

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

“…Therefore, based on laparoscopic narrow-band imaging (NBI) and traditional laparoscopic white light imaging (WLI), the effect of rehabilitation treatment is evaluated so as to obtain accurate evaluation results and verify the effectiveness of this method in diagnosing PM and evaluating the efficacy of chemotherapy through experiments. Hasanzadeh et al [ 8 ] evaluated the treatment results of depression based on electrode electroencephalogram (EEG). The experimental dataset is obtained and divided into training and testing datasets, and the minimum redundancy maximum correlation (mRMR) algorithm is adopted.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation Algorithm for the Effectiveness of Stroke Rehabilitation Treatment Using Cross-Modal Deep Learning

Wang

Zhang

Qin-ming

2022

Computational and Mathematical Methods in Medicine

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It is important to study the evaluation algorithm for the stroke rehabilitation treatment effect to make accurate evaluation and optimize the stroke disease treatment plan according to the evaluation results. To address the problems of poor restoration effect of positron emission tomography (PET) image and recognition restoration effect of evaluation data and so on. In the paper, we propose a stroke rehabilitation treatment effect evaluation algorithm based on cross-modal deep learning. Magnetic resonance images (MRI) and PET of stroke patients were collected as evaluation data to construct a multimodal evaluation dataset, and the data were divided into positive samples and negative samples. According to the mapping relationship between MRI and PET, three-dimensional cyclic adversarial is used to generate the neural network model to recover the missing PET data. Using the cross-modal depth learning network model, the RGB image, depth image, gray image, and normal images of MRI and PET are taken as the feature images and the multifeature fusion method is used to fuse the feature images, output the recognition results of MRI and PET, and evaluate the effect of stroke rehabilitation treatment according to the recognition results. The results show that the proposed algorithm can accurately restore PET images, the evaluation data recognition effect is good, and the evaluation data recognition accuracy is higher than 95%. The evaluation accuracy of stroke rehabilitation treatment effect is high, the evaluation time varies between 0.56 s and 0.91 s, and the practical application effect is good.

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Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation Algorithm for the Effectiveness of Stroke Rehabilitation Treatment Using Cross-Modal Deep Learning

Wang

Zhang

Qin-ming

2022

Computational and Mathematical Methods in Medicine

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“…Wearable and point-of-care EEG devices cannot be developed because of the large number of electrodes required. To address this problem, the single-channel electroencephalogram (SCEEG) [32] was created. SCEEG is simple to use, inexpensive, widely available, and even wearable, despite having a lower spatial resolution than multichannel devices.…”

Section: In Depth Review Of Existing Eeg Processing Modelsmentioning

confidence: 99%

A Detailed Analysis & Parametric Comparison Of Eeg Processing Models

2022

pnr

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Electroencephalograms (EEGs) are capable of representing brain signals in terms of numerical vector sets. These signals are used to estimate a wide variety of neurological disorders including Dementias, Epilepsy, Parkinson, Stroke, Transient Ischemic Attack, etc. A wide variety of machine learning based methods are proposed for processing these signals, and each of these are variant in terms of the qualitative nuances, function advantages, application-specific characteristics, qualitative limitations, and deployment-specific future scopes. Thus, it is difficult for researchers to identify optimal EEG processing models for their clinical use cases. These models vary in terms of their performance metrics, which further complicates the process of model selection for clinical use cases. To overcome these issues, this paper initially provides a detailed discussion about existing EEG processing techniques, in terms of their functional details. This will allow readers to identify optimal machine learning techniques, which are suited for the functional use cases. Continuing this discussion, a comparative analysis of these models is done on the basis of their clinical accuracy, precision, computational complexity, delay and scalability metrics, which will assist readers to identify models for their performance-specific use cases. Thus, this text will allow readers to identify optimally performing models for different EEG processing scenarios.

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A Descriptive Analysis on Various Depression Detection Models of Human Brain: A Review Article

Singh,

Sheoran,

Duhan

2024

Communications in Computer and Information Science

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Single Channel EEG Classification: A Case Study on Prediction of Major Depressive Disorder Treatment Outcome

Cited by 9 publications

References 49 publications

Evaluation Algorithm for the Effectiveness of Stroke Rehabilitation Treatment Using Cross-Modal Deep Learning

Evaluation Algorithm for the Effectiveness of Stroke Rehabilitation Treatment Using Cross-Modal Deep Learning

A Detailed Analysis & Parametric Comparison Of Eeg Processing Models

A Descriptive Analysis on Various Depression Detection Models of Human Brain: A Review Article

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