scholarly journals Spectral features and optimal Hierarchical Attention Networks for Pulmonary abnormality detection from the Respiratory Sound signals

2021 ◽  
Author(s):  
JAWAD AHMAD DAR ◽  
sajaad Ahmad lone ◽  
Kamal Kr Srivast
Keyword(s):  
Water Cycle ◽  
Optimization Technique ◽  
True Positive Rate ◽  
Respiratory Sounds ◽  
Training Procedure ◽  
True Negative ◽  
Important Concern ◽  
Efficient Performance ◽  
Positive Rate ◽  
Pulmonary Abnormalities

Abstract The most important concern in the medical field is to consider the analysis of data and perform accurate diagnosis. However, the analysis of pulmonary abnormalities may depend on the diagnostic experience and the medical skills of the physicians, and is a time-consuming practice. In order to solve such issues, an efficient Water Cycle Swarm Optimizer-based Hierarchical Attention Network (WCSO-based HAN) is developed for detecting the pulmonary abnormalities from the respiratory sounds signals. However, the developed optimization technique named WCSO is devised by incorporating the Water Cycle Algorithm (WCA) with Competitive Swarm Optimizer (CSO). Here, the pre-processing is performed using the Hanning window and Spectral gating-based noise reduction method in order to remove the falsifications or noises from the signal. Thereafter, the process of feature extraction is carried out to extract the significant features, such as Bark frequency Cepstral coefficient (BFCC) and the short term features, such asspectral flux and spectral centroid. Once the significant features are extracted, classification is performed using HAN where the training procedure of HAN is carried out using WCSO. Furthermore, the developed WCSO-based HAN obtained efficient performance using True Positive Rate (TPR), True Negative Rate (TNR) and accuracy with the values of 0.943, 0.913, and 0.923 using dataset 1, respectively.

scholarly journals FrWCSO-DRN: Fractional Water Cycle Swarm Optimizer-based deep residual network for pulmonary abnormality detection from respiratory sound signal

2021 ◽  
Author(s):  
JAWAD AHMAD DAR ◽  
Kamal Kr srivast ◽  
Sajaad Ahmad Lone
Keyword(s):  
Data Augmentation ◽  
Water Cycle ◽  
Superior Performance ◽  
Respiratory Sounds ◽  
Training Procedure ◽  
Abnormality Detection ◽  
Residual Network ◽  
True Negative ◽  
Significant Information ◽  
Pulmonary Abnormality

Abstract Respiratory sounds disclose significant information regarding the lungs of patients. Numerous methods are developed for analyzing the lung sounds. However, clinical approaches require qualified pulmonologists to diagnose such kind of signals appropriately and are also time consuming. Hence, an efficient Fractional Water Cycle Swarm Optimizer-based Deep Residual Network (FrWCSO-based DRN) is developed in this research for detecting the pulmonary abnormalities using respiratory sounds signals. The proposed FrWCSO is newly designed by the incorporation of Fractional Calculus (FC) and Water Cycle Swarm Optimizer WCSO. Meanwhile, WCSO is the combination of Water Cycle Algorithm (WCA) with Competitive Swarm Optimizer (CSO). The respiratory input sound signals are pre-processed and the important features needed for the further processing are effectively extracted. With the extracted features, data augmentation is carried out for minimizing the over fitting issues for improving the overall detection performance. Once data augmentation is done, feature selection is performed using proposed FrWCSO algorithm. Finally, pulmonary abnormality detection is performed using DRN where the training procedure of DRN is performed using the developed FrWCSO algorithm. The developed method achieved superior performance by considering the evaluation measures, namely True Positive Rate (TPR), True Negative Rate (TNR) and testing accuracy with the values of 0.963, 0.932, and 0.948, respectively.

Performance of Fineneedle Aspiration Cytology of the Breast. Clinical Experience in Ravenna (Italy)

1993 ◽  
Vol 79 (6) ◽  
pp. 413-417
Author(s):  
Lauro Bucchi ◽  
Patrizia Schincaglia ◽  
Giangiuseppe Melandri ◽  
Nori Morini ◽  
Carlo Naldoni ◽  
...  
Keyword(s):  
Predictive Value ◽  
Breast Cancer Case ◽  
True Positive Rate ◽  
Cancer Case ◽  
Breast Lesions ◽  
Additional Contribution ◽  
True Positive ◽  
Aspiration Cytology ◽  
True Negative ◽  
Positive Rate

Aims and background Fineneedle aspiration cytology (FNAC) is a routine test in the evaluation of breast lesions. We assessed the diagnostic accuracy of mammography (MG), physical examination (PE), ultrasonography (US) and FNAC in 1064 histologically confirmed breast lesions (638 malignant, 426 benign) observed consecutively at the Cancer Prevention Center of Ravenna (Italy). Methods The performance of each test and the additional contribution of FNAC were determined. Results FNAC was done in 69.6 % of cancers and 39.7 % of benign lesions (P = 0.00000), the frequency of aspiration being significantly associated with severity at MG, PE, and US. For FNAC, the true positive rate was 95.1 % and the true negative rate 67.4 %. Only one breast cancer case was detected by FNAC alone (additional true positive rate 0.2 %). The positive predictive value of FNAC in the absence of other abnormalities was 5 %. The negative predictive value of a benign report at MG, PE, US and FNAC was 100 %. Conclusions All breast lesions should be evaluated by all available techniques, especially FNAC, and open biopsy should be avoided for those reported as benign at all tests.

scholarly journals The Validity of the Tool “statcheck” in Discovering Statistical Reporting Inconsistencies

2017 ◽  
Author(s):  
Michele B. Nuijten ◽  
Marcel A. L. M. van Assen ◽  
Chris Hubertus Joseph Hartgerink ◽  
Sacha Epskamp ◽  
Jelte M. Wicherts
Keyword(s):  
Multiple Testing ◽  
Degrees Of Freedom ◽  
R Package ◽  
True Positive Rate ◽  
P Value ◽  
True Positive ◽  
Test Statistic ◽  
True Negative ◽  
True Negative Rate ◽  
Positive Rate

The R package “statcheck” (Epskamp & Nuijten, 2016) is a tool to extract statistical results from articles and check whether the reported p-value matches the accompanying test statistic and degrees of freedom. A previous study showed high interrater reliabilities (between .76 and .89) between statcheck and manual coding of inconsistencies (.76 - .89; Nuijten, Hartgerink, Van Assen, Epskamp, & Wicherts, 2016). Here we present an additional, detailed study of the validity of statcheck. In Study 1, we calculated its sensitivity and specificity. We found that statcheck’s sensitivity (true positive rate) and specificity (true negative rate) were high: between 85.3% and 100%, and between 96.0% and 100%, respectively, depending on the assumptions and settings. The overall accuracy of statcheck ranged from 96.2% to 99.9%. In Study 2, we investigated statcheck’s ability to deal with statistical corrections for multiple testing or violations of assumptions in articles. We found that the prevalence of corrections for multiple testing or violations of assumptions in psychology was higher than we initially estimated in Nuijten et al. (2016). Although we found numerous reporting inconsistencies in results corrected for violations of the sphericity assumption, we demonstrate that inconsistencies associated with statistical corrections are not what is causing the high estimates of the prevalence of statistical reporting inconsistencies in psychology.

scholarly journals genuMet: distinguish genuine untargeted metabolic features without quality control samples

2019 ◽  
Author(s):  
L Cao ◽  
C Clish ◽  
FB Hu ◽  
MA Martínez-González ◽  
C Razquin ◽  
...  
Keyword(s):  
Quality Control ◽  
Large Scale ◽  
Control Method ◽  
R Package ◽  
True Positive Rate ◽  
Supplementary Information ◽  
True Negative ◽  
True Negative Rate ◽  
Positive Rate ◽  
Measurement Artifacts

AbstractMotivationLarge-scale untargeted metabolomics experiments lead to detection of thousands of novel metabolic features as well as false positive artifacts. With the incorporation of pooled QC samples and corresponding bioinformatics algorithms, those measurement artifacts can be well quality controlled. However, it is impracticable for all the studies to apply such experimental design.ResultsWe introduce a post-alignment quality control method called genuMet, which is solely based on injection order of biological samples to identify potential false metabolic features. In terms of the missing pattern of metabolic signals, genuMet can reach over 95% true negative rate and 85% true positive rate with suitable parameters, compared with the algorithm utilizing pooled QC samples. genu-Met makes it possible for studies without pooled QC samples to reduce false metabolic signals and perform robust statistical analysis.Availability and implementationgenuMet is implemented in a R package and available on https://github.com/liucaomics/genuMet under GPL-v2 license.ContactLiming Liang: [email protected] informationSupplementary data are available at ….

scholarly journals Online asynchronous decoding of error-related potentials during the continuous control of a robot

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Catarina Lopes-Dias ◽  
Andreea I. Sburlea ◽  
Gernot R. Müller-Putz
Keyword(s):  
True Positive Rate ◽  
Robotic Arm ◽  
Continuous Control ◽  
True Positive ◽  
True Negative ◽  
Computer Interfaces ◽  
True Negative Rate ◽  
Positive Rate ◽  
Related Potentials ◽  
Neural Signature

AbstractError-related potentials (ErrPs) are the neural signature of error processing. Therefore, the detection of ErrPs is an intuitive approach to improve the performance of brain-computer interfaces (BCIs). The incorporation of ErrPs in discrete BCIs is well established but the study of asynchronous detection of ErrPs is still in its early stages. Here we show the feasibility of asynchronously decoding ErrPs in an online scenario. For that, we measured EEG in 15 participants while they controlled a robotic arm towards a target using their right hand. In 30% of the trials, the control of the robotic arm was halted at an unexpected moment (error onset) in order to trigger error-related potentials. When an ErrP was detected after the error onset, participants regained the control of the robot and could finish the trial. Regarding the asynchronous classification in the online scenario, we obtained an average true positive rate (TPR) of 70% and an average true negative rate (TNR) of 86.8%. These results indicate that the online asynchronous decoding of ErrPs was, on average, reliable, showing the feasibility of the asynchronous decoding of ErrPs in an online scenario.

Mastoid, middle ear and inner ear analysis in CT scan – a possible contribution for the identification of remains

2020 ◽  
Vol 60 (2) ◽  
pp. 102-111
Author(s):  
Henrique Rodrigues ◽  
Rosa Ramos ◽  
Leoni Fagundes ◽  
Orlando Galego ◽  
David Navega ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Visual Assessment ◽  
True Positive Rate ◽  
Observer Agreement ◽  
True Positive ◽  
True Negative ◽  
True Negative Rate ◽  
Negative Rate ◽  
Positive Rate

Objective We aimed to evaluate whether the internal structures of the human ear have anatomical characteristics that are sufficiently distinctive to contribute to human identification and use in a forensic context. Materials and methods After data anonymisation, a dataset containing temporal bone CT scans of 100 subjects was processed by a radiologist who was not involved in the study. Four reference images were selected for each subject. Of the original sample, 10 examinations were used for visual comparison, case by case, against the dataset of 100 patients. This visual assessment was performed independently by four observers, who evaluated the anatomical agreement using a Likert scale (1–5). Inter-observer agreement, true positive rate, positive predictive value, true negative rate, negative predictive value, false positive rate, false negative rate and positive likelihood ratio (LR+) were evaluated. Results Inter-observer agreement obtained an overall Cohen’s Kappa = 99.59%. True positive rate, positive predictive value, true negative rate and negative predictive value were all 100%. Conclusion Visual assessment of the mastoid examinations was shown to be a robust and reliable approach to identify unique osseous features and contribute to human identification. The statistical analysis indicates that regardless of the examiner’s background and training, the approach has a high degree of accuracy.

scholarly journals Prevalence, statistical thresholds, and accuracy assessment for species distribution models

Web Ecology ◽  
2013 ◽  
Vol 13 (1) ◽  
pp. 13-19 ◽  
Author(s):  
B. B. Hanberry ◽  
H. S. He
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Accuracy Assessment ◽  
Area Under The Curve ◽  
True Positive Rate ◽  
Distribution Models ◽  
True Negative ◽  
Entire Study ◽  
Predicted Probability ◽  
Positive Rate

Abstract. For species distribution models, species frequency is termed prevalence and prevalence in samples should be similar to natural species prevalence, for unbiased samples. However, modelers commonly adjust sampling prevalence, producing a modeling prevalence that has a different frequency of occurrences than sampling prevalence. The separate effects of (1) use of sampling prevalence compared to adjusted modeling prevalence and (2) modifications necessary in thresholds, which convert continuous probabilities to discrete presence or absence predictions, to account for prevalence, are unresolved issues. We examined effects of prevalence and thresholds and two types of pseudoabsences on model accuracy. Use of sampling prevalence produced similar models compared to use of adjusted modeling prevalences. Mean correlation between predicted probabilities of the least (0.33) and greatest modeling prevalence (0.83) was 0.86. Mean predicted probability values increased with increasing prevalence; therefore, unlike constant thresholds, varying threshold to match prevalence values was effective in holding true positive rate, true negative rate, and species prediction areas relatively constant for every modeling prevalence. The area under the curve (AUC) values appeared to be as informative as sensitivity and specificity, when using surveyed pseudoabsences as absent cases, but when the entire study area was coded, AUC values reflected the area of predicted presence as absent. Less frequent species had greater AUC values when pseudoabsences represented the study background. Modeling prevalence had a mild impact on species distribution models and accuracy assessment metrics when threshold varied with prevalence. Misinterpretation of AUC values is possible when AUC values are based on background absences, which correlate with frequency of species.

scholarly journals Automating areas of interest analysis in mobile eye tracking experiments based on machine learning

2018 ◽  
Vol 11 (6) ◽  
Author(s):  
Julian Wolf ◽  
Stephan Hess ◽  
David Bachmann ◽  
Quentin Lohmeyer ◽  
Mirko Meboldt
Keyword(s):  
Machine Learning ◽  
Eye Tracking ◽  
Ground Truth ◽  
True Positive Rate ◽  
True Negative ◽  
Art Object ◽  
Positive Rate ◽  
Mapping Process ◽  
Break Even Point ◽  
Mobile Eye Tracking

For an in-depth, AOI-based analysis of mobile eye tracking data, a preceding gaze assignment step is inevitable. Current solutions such as manual gaze mapping or marker-based approaches are tedious and not suitable for applications manipulating tangible objects. This makes mobile eye tracking studies with several hours of recording difficult to analyse quantitatively. We introduce a new machine learning-based algorithm, the computational Gaze-Object Mapping (cGOM), that automatically maps gaze data onto respective AOIs. cGOM extends state-of-the-art object detection and segmentation by mask R-CNN with a gaze mapping feature. The new algorithm’s performance is validated against a manual fixation-by-fixation mapping, which is considered as ground truth, in terms of true positive rate (TPR), true negative rate (TNR) and efficiency. Using only 72 training images with 264 labelled object representations, cGOM is able to reach a TPR of approx. 80% and a TNR of 85% compared to the manual mapping. The break-even point is reached at 2 hours of eye tracking recording for the total procedure, respectively 1 hour considering human working time only. Together with a real-time capability of the mapping process after completed training, even hours of eye tracking recording can be evaluated efficiently. (Code and video examples have been made available at: https://gitlab.ethz.ch/pdz/cgom.git)

scholarly journals A semi-automated technique for adenoma quantification in the ApcMin mouse using FeatureCounter

2019 ◽  
Author(s):  
Amy L. Shepherd ◽  
A. Alexander T. Smith ◽  
Kirsty A. Wakelin ◽  
Sabine Kuhn ◽  
Jianping Yang ◽  
...  
Keyword(s):  
True Positive Rate ◽  
Image Features ◽  
Tumour Burden ◽  
True Negative ◽  
Experimenter Bias ◽  
Intestinal Neoplasia ◽  
True Negative Rate ◽  
Positive Rate ◽  
Convenient Route ◽  
Automated Technique

ABSTRACTColorectal cancer is a major contributor to death and disease worldwide. The ApcMin mouse is a widely used model of intestinal neoplasia, as it carries a mutation also found in human colorectal cancers. However, the method most commonly used to quantify tumour burden in these mice is manual adenoma counting, which is time consuming and poorly suited to standardization across different laboratories. We describe a method to produce suitable photographs of the small intestine, process them with an ImageJ macro, FeatureCounter, which automatically locates image features potentially corresponding to adenomas, and a machine learning pipeline to identify and quantify them. Compared to a manual method, the specificity (or True Negative Rate, TNR) and sensitivity (or True Positive Rate, TPR) of this method in detecting adenomas are similarly high at about 80% and 87%, respectively. Importantly, total adenoma area measures derived from the automatically-called tumours were just as capable of distinguishing high-burden from low-burden mice as those established manually. Overall, our strategy is quicker, helps control experimenter bias and yields a greater wealth of information about each tumour, thus providing a convenient route to getting consistent and reliable results from a study.

scholarly journals Automated Bearing Fault Diagnosis using Packet Features of Vibration Signal and Gaussian Support Vector Machine

2020 ◽  
Vol 8 (6S) ◽  
pp. 72-81
Keyword(s):  
Support Vector Machine ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
True Positive Rate ◽  
Support Vector ◽  
True Negative ◽  
Bearing Fault ◽  
Dimensional Reduction Method ◽  
Positive Rate

Effective detection of the bearing fault and, specifically performance dilapidation assessment of a bearing is the topic of intensive analysis that may scale back prices and therefore the nonscheduled down time. This article presents an adaptive approach that is based on Bhattacharya space ranking method and dimensional reduction method as general discriminate analysis (GDA) with Gaussian support vector machine (GSVM) to accurately detect the defects of rolling bearing. For this investigation, first, vibration signal generated by rolling bearing was disintegrated to five levels employing wavelet packet (WP) method. Sixty three logarithmic wavelet packet features (LWPFs) were taken out from five level disintegrated vibration signals. After this, sixty three features were ranked by Bhattacharya space and top ten LWPFs were chosen. The top ten features were reduced to a new feature using GDA for effective detection and then applied to GSVM for detection of bearing fault. The experimental results show that new automated diagnosing approach attained classifier performance parameters as sensitivity (SE) or true positive rate, specificity (SP) or true negative rate, accuracy (AC) and positive prediction value (PPV) of 100, 98.50, 100 and 99.67 % for inner raceway (IR) and, AC: 99.49, SE: 100, SP: 98.78 and PPV: 99.87 for ball bearing (BB) at 0.18 mm diameter faults.

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