Principal component analysis of neural population response of knee joint proprioceptors in cat

1978 ◽  
Vol 156 (1) ◽  
pp. 51-65 ◽  
Author(s):  
W.J. Heetderks
Keyword(s):  
Principal Component Analysis ◽  
Knee Joint ◽  
Principal Component ◽  
Component Analysis ◽  
Neural Population ◽  
Population Response

scholarly journals Demixed principal component analysis of neural population data

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Dmitry Kobak ◽  
Wieland Brendel ◽  
Christos Constantinidis ◽  
Claudia E Feierstein ◽  
Adam Kepecs ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Population Data ◽  
Component Analysis ◽  
Neural Representation ◽  
Neural Population ◽  
Population Activity ◽  
Cortical Areas ◽  
Dimensionality Reduction Technique ◽  
Single Figure

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.

scholarly journals Author response: Demixed principal component analysis of neural population data

2016 ◽  
Author(s):  
Dmitry Kobak ◽  
Wieland Brendel ◽  
Christos Constantinidis ◽  
Claudia E Feierstein ◽  
Adam Kepecs ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Population Data ◽  
Component Analysis ◽  
Author Response ◽  
Neural Population

scholarly journals Feature Extraction and Classification for the Detection of Knee Joint Disorders using Random Forest Classifier

2021 ◽  
Vol 9 (10) ◽  
pp. 1348-1356
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Random Forest ◽  
Knee Joint ◽  
Principal Component ◽  
Approximate Entropy ◽  
Component Analysis ◽  
Random Forest Classifier ◽  
Invasive Technique ◽  
Entropy Measures

A non-invasive technique using knee joint vibroarthrographic (VAG) signals can be used for the early diagnosis of knee joint disorders. Among the algorithms devised for the detection of knee joint disorders using VAG signals, algorithms based on entropy measures can provide better performance. In this work, the VAG signal is preprocessed using wavelet decomposition into sub band signals. Features of the decomposed sub bands such as approximate entropy, sample entropy and wavelet energy are extracted as a quantified measure of complexity of the signal. A feature selection based on Principal Component Analysis (PCA) is performed in order to select the significant features. The extracted features are then used for classification of VAG signal into normal and abnormal VAG using random forest classifier. It is observed that the classifier provides a better accuracy with feature selection using principal component analysis. And the result shows that the classifier is able to classify the signal with an accuracy of 87%, error rate of 0.13, sensitivity of 0.874 and specificity of 0.777.

scholarly journals Detection of Knee Joint Disorders using SVM Classifier

2021 ◽  
pp. 261-271
Author(s):  
Alphonsa Salu S. J. ◽  
Jeraldin Auxillia D
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Knee Joint ◽  
Principal Component ◽  
Approximate Entropy ◽  
Component Analysis ◽  
Invasive Technique ◽  
Support Vector ◽  
Svm Classifier ◽  
Entropy Measures

A non-invasive technique using knee joint vibroarthographic (VAG) signals can be used for the early diagnosis of knee joint disorders. Among the algorithms devised for the detection of knee joint disorders using VAG signals, algorithms based on entropy measures can provide better performance. In this work, the VAG signal is preprocessed using wavelet decomposition into sub band signals. Features of the decomposed sub bands such as approximate entropy, sample entropy & wavelet energy are extracted as a quantified measure of complexity of the signal. A feature selection based on Principal Component Analysis (PCA) is performed in order to select the significant features. The extracted features are then used for classification of VAG signal into normal and abnormal VAG using support vector machine. It is observed that the classifier provides a better accuracy with feature selection using principal component analysis. And the results show that the classifier was able to classify the signal with an accuracy of 82.6%, error rate of 0.174, sensitivity of 1.0 and specificity of 0.888.

Principal Component Analysis of Knee Joint Differences Between Bilateral and Unilateral Total Knee Replacement Patients During Level Walking

2021 ◽  
Author(s):  
Derek Yocum ◽  
Jeffrey Reinbolt ◽  
Joshua T. Weinhandl ◽  
Tyler Standifird ◽  
Eugene Fitzhugh ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Knee Joint ◽  
Total Knee Replacement ◽  
Knee Replacement ◽  
Frontal Plane ◽  
Principal Component ◽  
Component Analysis ◽  
Level Walking ◽  
Peak Knee ◽  
Total Knee

Abstract Many unilateral total knee replacement (TKR) patients will need a contralateral TKR. Differences in knee joint biomechanics between bilateral patients and unilateral patients are not well established. The purpose of this study was to examine knee joint differences in level walking between bilateral and unilateral patients, and asymptomatic controls, using principal component analysis. Knee joints of 1st replaced limbs of 15 bilateral patients (69.40±5.04 years), 15 replaced limbs of unilateral patients (66.47±6.15 years), and 15 asymptomatic controls (63.53±9.50 years) were analyzed during level walking. Principal component analysis examined knee joint sagittal- and frontal-plane kinematics and moments, and vertical GRF. A one-way analysis of variance analyzed differences between principal component scores of each group. TKR patients exhibited more flexed and abducted knees throughout stance, decreased sagittal knee range of motion (ROM), increased early-stance adduction ROM, decreased loading-response knee extension and push-off knee flexion moments, decreased loading-response and push-off peak knee abduction moment (KAbM), increased KAbM at midstance, increased midstance vertical ground reaction force (GRF), and decreased loading-response and push-off vertical GRF. Additionally, bilateral patients exhibited reduced sagittal knee ROM, increased adduction ROM, decreased sagittal knee moments throughout stance, decreased KAbM throughout stance, an earlier loading-response peak vertical GRF, and a decreased push-off vertical GRF, compared to unilateral patients. TKR patients, especially bilateral patients had stiff knee motion in the sagittal-plane, increased frontal-plane joint laxity, and a quadriceps avoidance gait.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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