Control of a prosthetic leg based on walking intentions for gait rehabilitation: an fNIRS study

Khan, Rayyan Azam; Naseer, Noman; Nazeer, Hammad; Khan, M. N. Afzal

doi:10.3389/conf.fnhum.2018.227.00144

Cited by 9 publications

(3 citation statements)

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“…For factor selection, various techniques have been adopted in previous studies. 11 , 21 , 34 We applied a combination of three separate techniques to obtain the most optimal factors that would subsequently be used to train the SVM model. To the best of our knowledge, a supervised ML algorithm with optimal feature selection has not been previously used to prognosticate survival in pancreatic cancer.…”

Section: Discussionmentioning

confidence: 99%

“…This resulted in 8 categorical features that were significantly different between the two groups at a significance level of p <.05. 20 , 21 To select prognostic factors from continuous features, a two-sided student's t-test was utilized. This resulted in 1 prognostic factor, tumour size on MRI, that was statistically significant between group 1 and 2 at p <.05.…”

Section: Methodsmentioning

confidence: 99%

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Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm

Baig

Khan

Verdiales

et al. 2021

Technol Cancer Res Treat

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Background: The purpose of this project is to identify prognostic features in resectable pancreatic head adenocarcinoma and use these features to develop a machine learning algorithm that prognosticates survival for patients pursuing pancreaticoduodenectomy. Methods: A retrospective cohort study of 93 patients who underwent a pancreaticoduodenectomy was performed. The patients were analyzed in 2 groups: Group 1 (n = 38) comprised of patients who survived < 2 years, and Group 2 (n = 55) comprised of patients who survived > 2 years. After comparing the two groups, 9 categorical features and 2 continuous features (11 total) were selected to be statistically significant (p < .05) in predicting outcome after surgery. These 11 features were used to train a machine learning algorithm that prognosticates survival. Results: The algorithm obtained 75% accuracy, 41.9% sensitivity, and 97.5% specificity in predicting whether survival is less than 2 years after surgery. Conclusion: A supervised machine learning algorithm that prognosticates survival can be a useful tool to personalize treatment plans for patients with pancreatic cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm

Baig

Khan

Verdiales

et al. 2021

Technol Cancer Res Treat

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“…Feature Selection. For the consistency between extracted data across all paradigms, six statistical features (SV, KR, SS, SM, SK, and SP) were used to extract information across data [28,43,46,54,55]. SM is calculated in the following equation as…”

Section: Finite Impulse Responsementioning

confidence: 99%

Cortical Tasks-Based Optimal Filter Selection: An fNIRS Study

Khan

Naseer

Saleem

et al. 2020

Journal of Healthcare Engineering

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Functional near-infrared spectroscopy (fNIRS) is one of the latest noninvasive brain function measuring technique that has been used for the purpose of brain-computer interfacing (BCI). In this paper, we compare and analyze the effect of six most commonly used filtering techniques (i.e., Gaussian, Butterworth, Kalman, hemodynamic response filter (hrf), Wiener, and finite impulse response) on classification accuracies of fNIRS-BCI. To conclude with the best optimal filter for a specific cortical task owing to a specific cortical region, we divided our experimental tasks according to the three main cortical regions: prefrontal, motor, and visual cortex. Three different experiments were performed for prefrontal and motor execution tasks while one for visual stimuli. The tasks performed for prefrontal include rest (R) vs mental arithmetic (MA), R vs object rotation (OB), and OB vs MA. Similarly, for motor execution, R vs left finger tapping (LFT), R vs right finger tapping (RFT), and LFT vs RFT. Likewise, for the visual cortex, R vs visual stimuli (VS) task. These experiments were performed for ten trials with five subjects. For consistency among extracted data, six statistical features were evaluated using oxygenated hemoglobin, namely, slope, mean, peak, kurtosis, skewness, and variance. Combination of these six features was used to classify data by the nonlinear support vector machine (SVM). The classification accuracies obtained from SVM by using hrf and Gaussian were significantly higher for R vs MA, R vs OB, R vs RFT, and R vs VS and Wiener filter for OB vs MA. Similarly, for R vs LFT and LFT vs RFT, hrf was found to be significant p<0.05. These results show the feasibility of using hrf for effective removal of noises from fNIRS data.

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Machine learning based liver disease diagnosis: A systematic review

Khan

Luo

2022

Neurocomputing

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Control of a prosthetic leg based on walking intentions for gait rehabilitation: an fNIRS study

Cited by 9 publications

References 0 publications

Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm

Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm

Cortical Tasks-Based Optimal Filter Selection: An fNIRS Study

Machine learning based liver disease diagnosis: A systematic review

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