Comparison between K-nearest-neighbor approaches for conditional entropy estimation: Application to the assessment of the cardiac control in amyotrophic lateral sclerosis patients

2016 ◽  
Author(s):  
Alberto Porta ◽  
Beatrice De Maria ◽  
Vlasta Bari ◽  
Andrea Marchi ◽  
Kalliopi Marinou ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Nearest Neighbor ◽  
Conditional Entropy ◽  
K Nearest Neighbor ◽  
Entropy Estimation ◽  
Cardiac Control ◽  
Lateral Sclerosis

scholarly journals Automatic amyotrophic lateral sclerosis detection using tunable Q-factor wavelet transform

2020 ◽  
Vol 9 (4) ◽  
pp. 744
Author(s):  
Abdelouahad Achmamad ◽  
Abdelali Belkhou ◽  
Atman Jbari
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Wavelet Transform ◽  
Nearest Neighbor ◽  
Neuromuscular Disorders ◽  
Computation Time ◽  
Support Vector ◽  
Q Factor ◽  
K Nearest Neighbor ◽  
Emg Signal ◽  
Lateral Sclerosis

Early diagnosis of amyotrophic lateral sclerosis (ALS) based on electromyography (EMG) is crucial. The processing of a non-stationary EMG signal requires powerful multi-resolution methods. Our study analyzes and classifies the EMG signals. In the present work, we introduce a novel flexible method for classification of EMG signals using tunable Q-factor wavelet transform (TQWT). Different sub-bands generated by the TQWT technique were served to extract useful information related to energy and then the calculated features were selected using a filter selection (FS) method. The effectiveness of the feature selection step resulted not only in the improvement of classification performance but also in reducing the computation time of the classification algorithm. The selected feature subsets were used as inputs to multiple classifier algorithms, namely, k-nearest neighbor (k-NN), least squares support vector machine (LS-SVM) and random forest (RF) for automated diagnosis. The experimental results show better classification measures with k-NN classifier compared with LS-SVM and RF. The robustness of the classification task was tested using a ten-fold cross-validation method. The outcomes of our proposed approach can be exploited to aid clinicians in neuromuscular disorders detection.

scholarly journals Conditional Entropy: A Potential Digital Marker for Stress

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 286
Author(s):  
Soheil Keshmiri
Keyword(s):  
Resting State ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Solution Concept ◽  
Conditional Entropy ◽  
K Nearest Neighbor ◽  
Brain Response ◽  
Substantial Progress ◽  
Eeg Recordings ◽  
The Brain

Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.

scholarly journals 3D Point Cloud Simplification Based on k-Nearest Neighbor and Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Abdelaaziz Mahdaoui ◽  
El Hassan Sbai
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
3D Point Cloud ◽  
K Nearest Neighbor ◽  
Test Dataset ◽  
New Approach ◽  
Entropy Estimation ◽  
3D Objects ◽  
Point Cloud Simplification

While the reconstruction of 3D objects is increasingly used today, the simplification of 3D point cloud, however, becomes a substantial phase in this process of reconstruction. This is due to the huge amounts of dense 3D point cloud produced by 3D scanning devices. In this paper, a new approach is proposed to simplify 3D point cloud based on k-nearest neighbor (k-NN) and clustering algorithm. Initially, 3D point cloud is divided into clusters using k-means algorithm. Then, an entropy estimation is performed for each cluster to remove the ones that have minimal entropy. In this paper, MATLAB is used to carry out the simulation, and the performance of our method is testified by test dataset. Numerous experiments demonstrate the effectiveness of the proposed simplification method of 3D point cloud.

scholarly journals Fault Diagnosis Method for Rolling Bearings Based on Composite Multiscale Fluctuation Dispersion Entropy

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 290 ◽  
Author(s):  
Xiong Gan ◽  
Hong Lu ◽  
Guangyou Yang
Keyword(s):  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Coarse Grained ◽  
K Nearest Neighbor ◽  
Rolling Bearings ◽  
Entropy Estimation ◽  
Fault Recognition ◽  
Knn Classifier ◽  
Diagnosis Method ◽  
The Stability

This paper proposes a new method named composite multiscale fluctuation dispersion entropy (CMFDE), which measures the complexity of time series under different scale factors and synthesizes the information of multiple coarse-grained sequences. A simulation validates that CMFDE could improve the stability of entropy estimation. Meanwhile, a fault recognition method for rolling bearings based on CMFDE, the minimum redundancy maximum relevancy (mRMR) method, and the k nearest neighbor (kNN) classifier (CMFDE-mRMR-kNN) is developed. For the CMFDE-mRMR-kNN method, the CMFDE method is introduced to extract the fault characteristics of the rolling bearings. Then, the sensitive features are obtained by utilizing the mRMR method. Finally, the kNN classifier is used to recognize the different conditions of the rolling bearings. The effectiveness of the proposed CMFDE-mRMR-kNN method is verified by analyzing the standard experimental dataset. The experimental results show that the proposed fault diagnosis method can effectively classify the conditions of rolling bearings.

MO15 Autonomic cardiac control in patients with amyotrophic lateral sclerosis

2008 ◽  
Vol 119 ◽  
pp. S9
Author(s):  
Sanja Pavlovic ◽  
Zorica Stevic ◽  
Branislav Milovanovic ◽  
Vidosava Rakocevic-Stojanovic ◽  
Dragana Lavrnic ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Cardiac Control ◽  
Lateral Sclerosis

scholarly journals k-Nearest Neighbor Based Consistent Entropy Estimation for Hyperspherical Distributions

Entropy ◽  
2011 ◽  
Vol 13 (3) ◽  
pp. 650-667 ◽  
Author(s):  
Shengqiao Li ◽  
Robert M. Mnatsakanov ◽  
Michael E. Andrew
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Entropy Estimation

Automated Acoustic Analysis of Oral Diadochokinesis to Assess Bulbar Motor Involvement in Amyotrophic Lateral Sclerosis

2020 ◽  
Vol 63 (1) ◽  
pp. 59-73 ◽  
Author(s):  
Panying Rong
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Acoustic Analysis ◽  
Speaking Rate ◽  
Disease Stage ◽  
Healthy Controls ◽  
Tongue Movement ◽  
Major Barrier ◽  
Syllable Repetition ◽  
Oral Diadochokinesis ◽  
Lateral Sclerosis

Purpose The purpose of this article was to validate a novel acoustic analysis of oral diadochokinesis (DDK) in assessing bulbar motor involvement in amyotrophic lateral sclerosis (ALS). Method An automated acoustic DDK analysis was developed, which filtered out the voice features and extracted the envelope of the acoustic waveform reflecting the temporal pattern of syllable repetitions during an oral DDK task (i.e., repetitions of /tɑ/ at the maximum rate on 1 breath). Cycle-to-cycle temporal variability (cTV) of envelope fluctuations and syllable repetition rate (sylRate) were derived from the envelope and validated against 2 kinematic measures, which are tongue movement jitter (movJitter) and alternating tongue movement rate (AMR) during the DDK task, in 16 individuals with bulbar ALS and 18 healthy controls. After the validation, cTV, sylRate, movJitter, and AMR, along with an established clinical speech measure, that is, speaking rate (SR), were compared in their ability to (a) differentiate individuals with ALS from healthy controls and (b) detect early-stage bulbar declines in ALS. Results cTV and sylRate were significantly correlated with movJitter and AMR, respectively, across individuals with ALS and healthy controls, confirming the validity of the acoustic DDK analysis in extracting the temporal DDK pattern. Among all the acoustic and kinematic DDK measures, cTV showed the highest diagnostic accuracy (i.e., 0.87) with 80% sensitivity and 94% specificity in differentiating individuals with ALS from healthy controls, which outperformed the SR measure. Moreover, cTV showed a large increase during the early disease stage, which preceded the decline of SR. Conclusions This study provided preliminary validation of a novel automated acoustic DDK analysis in extracting a useful measure, namely, cTV, for early detection of bulbar ALS. This analysis overcame a major barrier in the existing acoustic DDK analysis, which is continuous voicing between syllables that interferes with syllable structures. This approach has potential clinical applications as a novel bulbar assessment.

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