kNN and SVM Classification for EEG: A Review

Sha’abani, Mnah; Fuad, N.; Jamal, Norezmi; Ismail, Muhammad

doi:10.1007/978-981-15-2317-5_47

Cited by 67 publications

(24 citation statements)

References 32 publications

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“…In this way, a demonstration of the continuous duration of the patient’s stay in the hospital is described as a series of sequential steps that the patient leaves until the hospital leaves ( 30 ). A conditional multi-stage distribution was used for the model of patient length of stay ( 31 ). Time Slicing Cox regression, an extended form of Cox regression, was used to predict mortality in the ICU ( 32 ).…”

Section: Discussionmentioning

confidence: 99%

A Rule Based Intelligent Software to Predict Length of Stay and the Mortality Rate in Trauma Patients in the Intensive Care Unit

Montazeri¹,

Ahmadinejad²,

Montazeri³

et al. 2023

ijph

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Background: Intensive Care Unit (ICU) has the highest mortality rate in the world. ICU has special equipment that leads to the hospital's most costly parts. The length of stay in the ICU is a special issue, and reducing this time is a practical approach. We aimed to use artificial intelligence to help early and timely diagnosis of the disease to help with health. Methods: We designed a rule-based intelligent system to predict the length of stay and the mortality rate of trauma patients in ICU. A neuro-Fuzzy and eight machine learning models were used to predict the mortality rate in trauma patients in ICU. The performances of these techniques were evaluated with accuracy, sensitivity, specificity, and area under the ROC curve. Decision-Table was used to predict the length of stay in trauma patients in ICU. For comparison, eight machine learning models were used. The method is compared based on Mean absolute error and relative absolute error (%). Results: Neuro-Fuzzy expert system and Decision-Table showed better results than other techniques. Accuracy, sensitivity, specificity, and ROC Area of Nero-Fuzzy are 83.6735, 0.9744, 0.3000, 0.8379, and 1, respectively. The mean absolute error and Relative absolute error (%) of the Decision-Table model are 4.5426 and 65.4391, respectively. Conclusion: Neuro-Fuzzy expert system with the highest level of accuracy and a Decision-Table with the lowest Mean absolute error, which are rule-based models, are the best models. Therefore, these models are recommended as a valuable tool for prediction parameters of ICU as well as medical decision-making.

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Section: Discussionmentioning

confidence: 99%

A Rule Based Intelligent Software to Predict Length of Stay and the Mortality Rate in Trauma Patients in the Intensive Care Unit

Montazeri¹,

Ahmadinejad²,

Montazeri³

et al. 2023

ijph

View full text Add to dashboard Cite

show abstract

“…The SVM maps data into a higher dimensional space and separates classes using an optimal hyperplane. In this study, the scikit-learn library [ 77 ] was used to implement the SVM with a Radial Basis Function (RBF), and the parameters were determined heuristically [ 78 ].…”

Section: Methodsmentioning

confidence: 99%

Classification of Activities of Daily Living Based on Grasp Dynamics Obtained from a Leap Motion Controller

Sharif

Ahmadreza

Chembrammel

et al. 2022

Sensors

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Stroke is one of the leading causes of mortality and disability worldwide. Several evaluation methods have been used to assess the effects of stroke on the performance of activities of daily living (ADL). However, these methods are qualitative. A first step toward developing a quantitative evaluation method is to classify different ADL tasks based on the hand grasp. In this paper, a dataset is presented that includes data collected by a leap motion controller on the hand grasps of healthy adults performing eight common ADL tasks. Then, a set of features with time and frequency domains is combined with two well-known classifiers, i.e., the support vector machine and convolutional neural network, to classify the tasks, and a classification accuracy of over 99% is achieved.

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“…There are numerous machine learning classifiers [ 69 ] such as k-nearest neighbor (kNN), SVM, Bayesian, as well as deep learning networks [ 70 ], such as the convolutional neural network (CNN) and long short-term memory (LSTM), though we chose the SVM with the radial basis function kernel, due to its popular use and in consideration of our relatively small sample data size for each participant (2250 samples). To classify each 0.8 s EEG segment as “in the zone” or “out of the zone”, a different SVM model was trained for each participant.…”

Section: Methodsmentioning

confidence: 99%

Moment-to-Moment Continuous Attention Fluctuation Monitoring through Consumer-Grade EEG Device

Zhang

Yan

Sapkota

et al. 2021

Sensors

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While numerous studies have explored using various sensing techniques to measure attention states, moment-to-moment attention fluctuation measurement is unavailable. To bridge this gap, we applied a novel paradigm in psychology, the gradual-onset continuous performance task (gradCPT), to collect the ground truth of attention states. GradCPT allows for the precise labeling of attention fluctuation on an 800 ms time scale. We then developed a new technique for measuring continuous attention fluctuation, based on a machine learning approach that uses the spectral properties of EEG signals as the main features. We demonstrated that, even using a consumer grade EEG device, the detection accuracy of moment-to-moment attention fluctuations was 73.49%. Next, we empirically validated our technique in a video learning scenario and found that our technique match with the classification obtained through thought probes, with an average F1 score of 0.77. Our results suggest the effectiveness of using gradCPT as a ground truth labeling method and the feasibility of using consumer-grade EEG devices for continuous attention fluctuation detection.

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kNN and SVM Classification for EEG: A Review

Cited by 67 publications

References 32 publications

A Rule Based Intelligent Software to Predict Length of Stay and the Mortality Rate in Trauma Patients in the Intensive Care Unit

A Rule Based Intelligent Software to Predict Length of Stay and the Mortality Rate in Trauma Patients in the Intensive Care Unit

Classification of Activities of Daily Living Based on Grasp Dynamics Obtained from a Leap Motion Controller

Moment-to-Moment Continuous Attention Fluctuation Monitoring through Consumer-Grade EEG Device

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