The Heartbeat Classification Based on PCA

2012 ◽  
Vol 195-196 ◽  
pp. 402-406
Author(s):  
Xue Qin Chen ◽  
Rui Ping Wang
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Recognition Task ◽  
Principal Component ◽  
True Positive Rate ◽  
True Positive ◽  
Heartbeat Classification ◽  
Positive Rate ◽  
Electrocardiogram Ecg ◽  
Pattern Recognition Task

Classify the electrocardiogram (ECG) into different pathophysiological categories is a complex pattern recognition task which has been tried in lots of methods. This paper will discuss a method of principal component analysis (PCA) in exacting the heartbeat features, and a new method of classification that is to calculate the error between the testing heartbeat and reconstructed heartbeat. Training and testing heartbeat is taken from the MIT-BIH Arrhythmia Database, in which 8 types of arrhythmia signals are selected in this paper. The true positive rate (TPR) is 83%.

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

scholarly journals Classifying the Biological Status of Honeybee Workers Using Gas Sensors

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 166
Author(s):  
Jakub T. Wilk ◽  
Beata Bąk ◽  
Piotr Artiemjew ◽  
Jerzy Wilde ◽  
Maciej Siuda
Keyword(s):  
Gas Sensors ◽  
Laboratory Tests ◽  
Test Chamber ◽  
Ambient Air ◽  
True Positive Rate ◽  
True Positive ◽  
Positive Rate ◽  
Parameters Of Accuracy ◽  
Selection Of

Honeybee workers have a specific smell depending on the age of workers and the biological status of the colony. Laboratory tests were carried out at the Department of Apiculture at UWM Olsztyn, using gas sensors installed in two twin prototype multi-sensor detectors. The study aimed to compare the responses of sensors to the odor of old worker bees (3–6 weeks old), young ones (0–1 days old), and those from long-term queenless colonies. From the experimental colonies, 10 samples of 100 workers were taken for each group and placed successively in the research chambers for the duration of the study. Old workers came from outer nest combs, young workers from hatching out brood in an incubator, and laying worker bees from long-term queenless colonies from brood combs (with laying worker bee’s eggs, humped brood, and drones). Each probe was measured for 10 min, and then immediately for another 10 min ambient air was given to regenerate sensors. The results were analyzed using 10 different classifiers. Research has shown that the devices can distinguish between the biological status of bees. The effectiveness of distinguishing between classes, determined by the parameters of accuracy balanced and true positive rate, of 0.763 and 0.742 in the case of the best euclidean.1nn classifier, may be satisfactory in the context of practical beekeeping. Depending on the environment accompanying the tested objects (a type of insert in the test chamber), the introduction of other classifiers as well as baseline correction methods may be considered, while the selection of the appropriate classifier for the task may be of great importance for the effectiveness of the classification.

scholarly journals The influence of digital PET/CT on diagnostic certainty and interrater reliability in [68Ga]Ga-PSMA-11 PET/CT for recurrent prostate cancer

2021 ◽  
Author(s):  
Ian Alberts ◽  
Jan-Niklas Hünermund ◽  
Christos Sachpekidis ◽  
Clemens Mingels ◽  
Viktor Fech ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Interrater Reliability ◽  
True Positive Rate ◽  
True Positive ◽  
Recurrent Prostate Cancer ◽  
Pathological Lesions ◽  
Diagnostic Certainty ◽  
Pet Ct ◽  
Positive Rate ◽  
Digital Pet

Abstract Objective To investigate the impact of digital PET/CT on diagnostic certainty, patient-based sensitivity and interrater reliability. Methods Four physicians retrospectively evaluated two matched cohorts of patients undergoing [68Ga]Ga-PSMA-11 PET/CT on a digital (dPET/CT n = 65) or an analogue scanner (aPET/CT n = 65) for recurrent prostate cancer between 11/2018 and 03/2019. The number of equivocal and pathological lesions as well as the frequency of discrepant findings and the interrater reliability for the two scanners were compared. Results dPET/CT detected more lesions than aPET/CT (p < 0.001). A higher number of pathological scans were observed for dPET/CT (83% vs. 57%, p < 0.001). The true-positive rate at follow-up was 100% for dPET/CT compared to 84% for aPET/CT (p < 0.001). The proportion of lesions rated as non-pathological as a total of all PSMA-avid lesions detected for dPET/CT was comparable to aPET/CT (61.8% vs. 57.0%, p = 0.99). Neither a higher rate of diagnostically uncertain lesions (11.5% dPET/CT vs. 13.7% aPET/CT, p = 0.95) nor discrepant scans (where one or more readers differed in opinion as to whether the scan is pathological) were observed (18% dPET/CT vs. 17% aPET/CT, p = 0.76). Interrater reliability for pathological lesions was excellent for both scanner types (Cronbach’s α = 0.923 dPET/CT; α = 0.948 aPET/CT) and interrater agreement was substantial for dPET/CT (Krippendorf’s α = 0.701) and almost perfect in aPET/CT (α = 0.802). Conclusions A higher detection rate for pathological lesions for dPET/CT compared with aPET/CT in multiple readers was observed. This improved sensitivity was coupled with an improved true-positive rate and was not associated with increased diagnostic uncertainty, rate of non-specific lesions, or reduced interrater reliability. Key Points • New generation digital scanners detect more cancer lesions in men with prostate cancer. • When using digital scanners, the doctors are able to diagnose prostate cancer lesions with better certainty • When using digital scanners, the doctors do not disagree with each other more than with other scanner types.

scholarly journals Face Recognition Using the SR-CNN Model

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4237 ◽  
Author(s):  
Yu-Xin Yang ◽  
Chang Wen ◽  
Kai Xie ◽  
Fang-Qing Wen ◽  
Guan-Qun Sheng ◽  
...  
Keyword(s):  
Face Recognition ◽  
Target Position ◽  
Operation Time ◽  
True Positive Rate ◽  
Processing Unit ◽  
True Positive ◽  
Face Database ◽  
Acceleration Ratio ◽  
Computational Performance ◽  
Positive Rate

In order to solve the problem of face recognition in complex environments being vulnerable to illumination change, object rotation, occlusion, and so on, which leads to the imprecision of target position, a face recognition algorithm with multi-feature fusion is proposed. This study presents a new robust face-matching method named SR-CNN, combining the rotation-invariant texture feature (RITF) vector, the scale-invariant feature transform (SIFT) vector, and the convolution neural network (CNN). Furthermore, a graphics processing unit (GPU) is used to parallelize the model for an optimal computational performance. The Labeled Faces in the Wild (LFW) database and self-collection face database were selected for experiments. It turns out that the true positive rate is improved by 10.97–13.24% and the acceleration ratio (the ratio between central processing unit (CPU) operation time and GPU time) is 5–6 times for the LFW face database. For the self-collection, the true positive rate increased by 12.65–15.31%, and the acceleration ratio improved by a factor of 6–7.

scholarly journals Electric Source Localization Adds Evidence for Task-Specific CNVs

1998 ◽  
Vol 11 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Ina M. Tarkka ◽  
Luis F. H. Basile
Keyword(s):  
Pattern Recognition ◽  
Source Localization ◽  
Clinical Neurophysiology ◽  
Recognition Task ◽  
Source Analysis ◽  
Visual Spatial ◽  
Spatio Temporal ◽  
Electric Source ◽  
Pattern Recognition Task ◽  
Spatial Recognition

This study was an attempt to replicate recent magnetoencephalographic (MEG) findings on human task-specific CNV sources (Basile et al., Electroencephalography and Clinical Neurophysiology 90, 1994, 157–165) by means of a spatio-temporal electric source localization method (Scherg and von Cramon, Electroencephalography and Clinical Neurophysiology 62, 1985, 32–44; Scherg and von Cramon, Electroencephalography and Clinical Neurophysiology 65, 1986, 344-360; Scherg and Berg, Brain Electric Source Analysis Handbook, Version 2). The previous MEG results showed CNV sources in the prefrontal cortex of the two hemispheres for two tasks used, namely visual pattern recognition and visual spatial recognition tasks. In the right hemisphere, the sources were more anterior and inferior for the spatial recognition task than for the pattern recognition task. In the present study we obtained CNVs in five subjects during two tasks identical to the MEG study. The elicited electric potentials were modeled with four spatio-temporal dipoles for each task, three of which accounted for the visual evoked response and one that accounted for the CNV. For all subjects the dipole explaining the CNV was always localized in the frontal region of the head, however, the dipole obtained during the visual spatial recognition task was more anterior than the one obtained during the pattern recognition task. Thus, task-specific CNV sources were again observed, although the stable model consisted of only one dipole located close to the midline instead of one dipole in each hemisphere. This was a major difference in the CNV sources between the previous MEG and the present electric source analysis results. We discuss the possible basis for the difference between the two methods used to study slow brain activity that is believed to originate from extended cortical patches.

scholarly journals Markerless tracking of an entire insect colony

2020 ◽  
Author(s):  
Katarzyna Bozek ◽  
Laetitia Hebert ◽  
Yoann Portugal ◽  
Greg J. Stephens
Keyword(s):  
Cell Activity ◽  
True Positive Rate ◽  
Computational Method ◽  
Visual Features ◽  
Brood Cell ◽  
True Positive ◽  
Body Width ◽  
Markerless Tracking ◽  
Positive Rate ◽  
Fast Motion

AbstractWe present a comprehensive, computational method for tracking an entire colony of the honey bee Apis mellifera using high-resolution video on a natural honeycomb background. We adapt a convolutional neural network (CNN) segmentation architecture to automatically identify bee and brood cell positions, body orientations and within-cell states. We achieve high accuracy (~10% body width error in position, ~10° error in orientation, and true positive rate > 90%) and demonstrate months-long monitoring of sociometric colony fluctuations. We combine extracted positions with rich visual features of organism-centered images to track individuals over time and through challenging occluding events, recovering ~79% of bee trajectories from five observation hives over a span of 5 minutes. The resulting trajectories reveal important behaviors, including fast motion, comb-cell activity, and waggle dances. Our results provide new opportunities for the quantitative study of collective bee behavior and for advancing tracking techniques of crowded systems.

scholarly journals Finding COVID-19 from Chest X-rays using Deep Learning on a Small Dataset

2020 ◽  
Author(s):  
Lawrence Hall ◽  
Dmitry Goldgof ◽  
Rahul Paul ◽  
Gregory M. Goldgof
Keyword(s):  
False Negative ◽  
True Positive Rate ◽  
Small Data ◽  
Good Resolution ◽  
X Rays ◽  
True Positive ◽  
Data Set ◽  
X Ray ◽  
Negative Rate ◽  
Positive Rate

<p>Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We have obtained 135 chest X-rays of COVID-19 and 320 chest X-rays of viral and bacterial pneumonia. </p><p> A pre-trained deep convolutional neural network, Resnet50 was tuned on 102 COVID-19 cases and 102 other pneumonia cases in a 10-fold cross validation. The results were </p><p> an overall accuracy of 89.2% with a COVID-19 true positive rate of 0.8039 and an AUC of 0.95. Pre-trained Resnet50 and VGG16 plus our own small CNN were tuned or trained on a balanced set of COVID-19 and pneumonia chest X-rays. An ensemble of the three types of CNN classifiers was applied to a test set of 33 unseen COVID-19 and 218 pneumonia cases. The overall accuracy was 91.24% with the true positive rate for COVID-19 of 0.7879 with 6.88% false positives for a true negative rate of 0.9312 and AUC of 0.94. </p><p> This preliminary study has flaws, most critically a lack of information about where in the disease process the COVID-19 cases were and the small data set size. More COVID-19 case images at good resolution will enable a better answer to the question of how useful chest X-rays can be for diagnosing COVID-19.</p>

Considerations on the region of interest in the ROC space

2021 ◽  
pp. 096228022110605
Author(s):  
Luigi Lavazza ◽  
Sandro Morasca
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Region Of Interest ◽  
True Positive Rate ◽  
True Positive ◽  
Receiver Operating Characteristic Curves ◽  
Positive Rate ◽  
Receiver Operating

Receiver Operating Characteristic curves have been widely used to represent the performance of diagnostic tests. The corresponding area under the curve, widely used to evaluate their performance quantitatively, has been criticized in several respects. Several proposals have been introduced to improve area under the curve by taking into account only specific regions of the Receiver Operating Characteristic space, that is, the plane to which Receiver Operating Characteristic curves belong. For instance, a region of interest can be delimited by setting specific thresholds for the true positive rate or the false positive rate. Different ways of setting the borders of the region of interest may result in completely different, even opposing, evaluations. In this paper, we present a method to define a region of interest in a rigorous and objective way, and compute a partial area under the curve that can be used to evaluate the performance of diagnostic tests. The method was originally conceived in the Software Engineering domain to evaluate the performance of methods that estimate the defectiveness of software modules. We compare this method with previous proposals. Our method allows the definition of regions of interest by setting acceptability thresholds on any kind of performance metric, and not just false positive rate and true positive rate: for instance, the region of interest can be determined by imposing that [Formula: see text] (also known as the Matthews Correlation Coefficient) is above a given threshold. We also show how to delimit the region of interest corresponding to acceptable costs, whenever the individual cost of false positives and false negatives is known. Finally, we demonstrate the effectiveness of the method by applying it to the Wisconsin Breast Cancer Data. We provide Python and R packages supporting the presented method.

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