Prediction of Clinical Practices by Clinical Data of the Previous Day Using Linear Support Vector Machine

2015 ◽  
pp. 3-13
Author(s):  
Takashi Nakai ◽  
Tadamasa Takemura ◽  
Risa Sakurai ◽  
Kenichiro Fujita ◽  
Kazuya Okamoto ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Clinical Data ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Clinical Practices

EEG Classification of Imaginary Left and Right Leg Movements based on Multivariate Empirical Mode Decomposition and Non-Linear Support Vector Machine

2021 ◽  
Vol 39 (11) ◽  
Author(s):  
Sahar Zolfaghari ◽  
Mohammad Hamiruce Marhaban ◽  
Siti Anom Ahmad ◽  
Asnor Juraiza Ishak ◽  
Pegah Khosropanah ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Empirical Mode Decomposition ◽  
Motor Imagery ◽  
Classification Model ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Non Linear ◽  
Left And Right

Motor-imagery brain-computer interfaces, as rehabilitation tools for motor-disabled individuals, could inherently enrich neuroplasticity and subsequently restore mobility. However, this endeavour's significant challenge is classifying left and right leg motor imagery tasks from non-stationary EEG signals. A subject-independent feature extraction method is essential in a BCI system, and this work involves developing a subject-independent algorithm to classify left/right leg motion intention. The Multivariate Empirical Mode Decomposition was used to decompose EEG during left and right foot movements during imagery tasks. We validated our proposed algorithm using open-access motor imagery data to detect the user's mental intention from EEG. Five subjects of various performance categories with almost 150 trials for each left/right leg MI of hand/leg/tongue, HaLT Paradigm, utilizing C3, C4, and Cz channels were examined to generalize this study to all subjects. A set of statistical features were extracted from the intrinsic mode functions, and the most relevant features were selected for classification using Sequential Floating Feature Selection. Different classifiers were trained using extracted features, and their performances' were evaluated. The findings suggest that the non-linear support vector machine is the best classification model, resulting in the mean classification sensitivity, specificity, precision, negative predictive value, F-measure, 98.15%, 90.74%, 91.97%, 98.33%, 94.72%, 94.44%, respectively. The proposed subject-independent signal processing method significantly improved the offline calibration mode by eliminating the frequency selection step, making it the common-used method for different types of MI-based BCI participants. Offline evaluations suggest that it can lead to significant increases in classification accuracy in comparison to current approaches.

scholarly journals Distinction of Students and Expert Therapists Based on Therapeutic Motions on a Robotic Device Using Support Vector Machine

2020 ◽  
Vol 40 (6) ◽  
pp. 790-797
Author(s):  
Koike Yuji ◽  
Suzuki Makoto ◽  
Okino Akihisa ◽  
Takeda Kazuhisa ◽  
Takanami Yasuhiro ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Exercise Therapy ◽  
Upper Limb ◽  
Peak Velocity ◽  
Movement Time ◽  
Elbow Flexion ◽  
Flexion Angle ◽  
Support Vector ◽  
Robot Arm ◽  
Clinical Practices

Abstract Purpose To clarify the feature values of exercise therapy that can differentiate students and expert therapists and use this information as a reference for exercise therapy education. Methods The participants were therapists with 5 or more years of clinical experience and 4th year students at occupational therapist training schools who had completed their clinical practices. The exercise therapy task included Samothrace (code name, SAMO) exercises implemented on the elbow joint based on the elbow flexion angle, angular velocity, and exercise interval recordings. For analyses and student/therapist comparisons, the peak flexion angle, peak velocity, and movement time were calculated using data on elbow angle changes acquired via SAMO. Subsequently, bootstrap data were generated to differentiate between the exercise therapy techniques adopted by therapists and students, and a support vector machine was used to generate four types of data combinations with the peak flexion angle, peak velocity, and movement time values. These data were used to estimate and compare the respective accuracies with the Friedman test. Results The peak flexion angles were significantly smaller in the case of students. Furthermore, the peak velocities were larger, the peak flexion angles were smaller, and the movement times were shorter compared with those of therapists. The combination of peak velocity and peak flexion angle yielded the highest diagnostic accuracies. Conclusion When students and therapists performed upper limb exercise therapy techniques based on the kinematics movement of a robot arm, the movement speeds and joint angles differed. The combination of peak velocity and peak flexion angle was the most effective classifier used for the differentiation of the abilities of students and therapists. The peak velocity and peak flexion angle of the therapist group can be used as a reference for students when they learn upper limb therapeutic exercise techniques.

Engine Fault Diagnostic System Based on Non-Linear Support Vector Machine

2012 ◽  
Vol 229-231 ◽  
pp. 534-537
Author(s):  
Gao Huan Xu ◽  
Jun Xiang Ye
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Diagnostic System ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Diagnosis System ◽  
The Core ◽  
Wavelet Energy ◽  
Non Linear ◽  
Fault Diagnosis System

The car engine failures in the course of time and place have many possibilities. The engine fault diagnosis system developed in .NET platform. The core of the system make use of noise wavelet energy features and non-linear support vector machine classification. After the experiment, the system has fairly good results.

scholarly journals Interpreting linear support vector machine models with heat map molecule coloring

2011 ◽  
Vol 3 (1) ◽  
Author(s):  
Lars Rosenbaum ◽  
Georg Hinselmann ◽  
Andreas Jahn ◽  
Andreas Zell
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Heat Map
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