scholarly journals A Detector for Premature Atrial and Ventricular Complexes

2021 ◽  
Vol 12 ◽  
Author(s):  
Guadalupe García-Isla ◽  
Luca Mainardi ◽  
Valentina D. A. Corino
Keyword(s):  
Binary Classification ◽  
Morphological Features ◽  
Discrete Wavelet ◽  
Supraventricular Arrhythmia ◽  
Low Sensitivity ◽  
Sensitivity Specificity ◽  
The Relationship ◽  
Ventricular Beat

The relationship between premature atrial complexes (PACs) and atrial fibrillation (AF), stroke and myocardium degradation is unclear. Current PAC detectors are beat classifiers that attain low sensitivity on PAC detection. The lack of a proper PAC detector hinders the study of the implications of this event and its monitoring. In this work a PAC and ventricular detector is presented. Two PhysioNet open-source databases were used: the long-term ST database (LTSTDB) and the supraventricular arrhythmia database (SVDB). A combination of heart rate variability (HRV) and morphological features were used to classify beats. Morphological features were extracted from the ECG as well as on the 4th scale of the discrete wavelet transform (DWT). After feature selection, a random forest algorithm was trained for a binary classification of PAC (S) vs. others and for a multi-labels classification to discriminate between normal (N), S and ventricular (V) beats. The algorithm was tested in a 10-fold cross-validation following a patient-wise train-test division (i.e., no beats belonging to the same patient were included both in the test and train set). The resultant median sensitivity, specificity and positive predictive value (PPV) were 99.29, 99.54, and 100% for (N), 95.83, 99.39, and 35.68% for (S), 100, 99.90, and 79.63% for (V). The proposed method attains a greater PAC and ventricular beat sensitivity and PPV than the state-of-the-art classifiers.

scholarly journals Arrhythmia Classification of ECG Signals Using Hybrid Features

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Syed Muhammad Anwar ◽  
Maheen Gul ◽  
Muhammad Majid ◽  
Majdi Alnowami
Keyword(s):  
Heart Beat ◽  
Morphological Features ◽  
Frequency Resolution ◽  
Discrete Wavelet ◽  
Supraventricular Arrhythmia ◽  
Dynamic Feature ◽  
Dynamic Features ◽  
Hybrid Features ◽  
Rr Interval

Automatic detection and classification of life-threatening arrhythmia plays an important part in dealing with various cardiac conditions. In this paper, a novel method for classification of various types of arrhythmia using morphological and dynamic features is presented. Discrete wavelet transform (DWT) is applied on each heart beat to obtain the morphological features. It provides better time and frequency resolution of the electrocardiogram (ECG) signal, which helps in decoding important information of a quasiperiodic ECG using variable window sizes. RR interval information is used as a dynamic feature. The nonlinear dynamics of RR interval are captured using Teager energy operator, which improves the arrhythmia classification. Moreover, to remove redundancy, DWT subbands are subjected to dimensionality reduction using independent component analysis, and a total of twelve coefficients are selected as morphological features. These hybrid features are combined and fed to a neural network to classify arrhythmia. The proposed algorithm has been tested over MIT-BIH arrhythmia database using 13724 beats and MIT-BIH supraventricular arrhythmia database using 22151 beats. The proposed methodology resulted in an improved average accuracy of 99.75% and 99.84% for class- and subject-oriented scheme, respectively, using three-fold cross validation.

scholarly journals Classification of Normal and Crackles Respiratory Sounds into Healthy and Lung Cancer Groups

2018 ◽  
Vol 8 (3) ◽  
pp. 1530
Author(s):  
N. Abdul Malik ◽  
W. Idris ◽  
T. S. Gunawan ◽  
R. F. Olanrewaju ◽  
S. Noorjannah Ibrahim
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
Respiratory Sounds ◽  
Discrete Wavelet ◽  
Frequency Bands ◽  
Common Cancer ◽  
Testing Stage ◽  
Hidden Layer ◽  
Sensitivity Specificity

Lung cancer is the most common cancer worldwide and the third most common cancer in Malaysia. Due to its high prevalence worldwide and in Malaysia, it is an utmost importance to have the disease detected at an early stage which would result in a higher chance of cure and possibly better survival. The current methods used for lung cancer screening might not be simple, inexpensive and safe and not readily accessible in outpatient clinics. In this paper, we present the classification of normal and crackles sounds acquired from 20 healthy and 23 lung cancer patients, respectively using Artificial Neural Network. Firstly, the sounds signals were decomposed into seven different frequency bands using Discrete Wavelet Transform (DWT) based on two different mother wavelets namely Daubechies 7 (db7) and Haar. Secondly, mean, standard deviation and maximum PSD of the detail coefficients for five frequency bands (D3, D4, D5, D6, and D7) were calculated as features. Fifteen features were used as input to the ANN classifier. The results of classification show that db7 based performed better than Haar with perfect 100% sensitivity, specificity and accuracy for testing and validation stages when using 15 nodes at the hidden layer. While for Haar, only testing stage shows the perfect 100% for sensitivity, specificity, and accuracy when using 10 nodes at the hidden layer.

scholarly journals Voice Analysis to Differentiate the Dopaminergic Response in People With Parkinson's Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Anubhav Jain ◽  
Kian Abedinpour ◽  
Ozgur Polat ◽  
Mine Melodi Çalışkan ◽  
Afsaneh Asaei ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Speech Processing ◽  
Current Knowledge ◽  
Binary Classification ◽  
Prosodic Features ◽  
Quantitative Measurements ◽  
Before And After ◽  
Sensitivity Specificity

Humans' voice offers the widest variety of motor phenomena of any human activity. However, its clinical evaluation in people with movement disorders such as Parkinson's disease (PD) lags behind current knowledge on advanced analytical automatic speech processing methodology. Here, we use deep learning-based speech processing to differentially analyze voice recordings in 14 people with PD before and after dopaminergic medication using personalized Convolutional Recurrent Neural Networks (p-CRNN) and Phone Attribute Codebooks (PAC). p-CRNN yields an accuracy of 82.35% in the binary classification of ON and OFF motor states at a sensitivity/specificity of 0.86/0.78. The PAC-based approach's accuracy was slightly lower with 73.08% at a sensitivity/specificity of 0.69/0.77, but this method offers easier interpretation and understanding of the computational biomarkers. Both p-CRNN and PAC provide a differentiated view and novel insights into the distinctive components of the speech of persons with PD. Both methods detect voice qualities that are amenable to dopaminergic treatment, including active phonetic and prosodic features. Our findings may pave the way for quantitative measurements of speech in persons with PD.

scholarly journals Eltern, Schule, Aushandlung: Chancen und Grenzen demokratischer Schulentwicklung im Kontext von Vielfalt

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Katjuscha von Werthern
Keyword(s):  
Equal Opportunity ◽  
School Development ◽  
Entire Group ◽  
Educationally Disadvantaged ◽  
Starting Point ◽  
The Subject ◽  
Anglo American ◽  
The Relationship

In educational policy and practical school discourse, cooperation between parents and schools is generally considered important in promoting more equal-opportunity education, but is also described as difficult in terms of implementation. The relationship between schools and parents with a so-called migration background (Migrationshintergrund) is the subject of a great deal of discussion, and these parents are frequently assigned responsibility of a lack in cooperation. In this contribution, I will show that the classification of an entire group of parents as bildungsfern (literally “far from education”) is part of the problem. (This problematic term is currently used in Germany to designate those population groups that in Anglo-American discourse are labelled “educationally disadvantaged”.) Classical forms of participation such as parents’ associations are insufficient and do not live up to parental diversity. The concept of democratic school-development (Schütze & Hildebrandt 2006) tries to engage all the parents in a school to minimize exclusion and institutional hierarchies. The starting point of this study, presented here in sections, is whether this can succeed over the long term. Democratic processes of school development offer great potential to approach diversity constructively and to make schools more democratic. But such processes, it seems, can never be considered completed, but need to be seen as an ongoing development in which all participants need to be involved in ever new ways and where the aims require constant renegotiation.

scholarly journals Accuracy of a deep convolutional neural network in the detection of myopic macular diseases using swept-source optical coherence tomography

2019 ◽  
Author(s):  
Takahiro Sogawa ◽  
Hitoshi Tabuchi ◽  
Daisuke Nagasato ◽  
Hiroki Masumoto ◽  
Yasushi Ikuno ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Correct Answer ◽  
High Myopia ◽  
Binary Classification ◽  
Area Under The Curve ◽  
Optical Coherence ◽  
Swept Source ◽  
Study Results ◽  
Sensitivity Specificity

AbstractThis study examined and compared outcomes of deep learning (DL) in identifying swept-source optical coherence tomography (OCT) images without myopic macular lesions [i.e., no high myopia (nHM) vs. high myopia (HM)], and OCT images with myopic macular lesions [e.g., myopic choroidal neovascularization (mCNV) and retinoschisis (RS)]. A total of 796 SS-OCT images were included in the study as follows and analyzed by k-fold cross-validation (k = 5) using DL’s renowned model, Visual Geometry Group-16: nHM, 107 images; HM, 456 images; mCNV, 122 images; and RS, 111 images (n = 796). The binary classification of OCT images with or without myopic macular lesions; the binary classification of HM images and images with myopic macular lesions (i.e., mCNV and RS images); and the ternary classification of HM, mCNV, and RS images were examined. Additionally, sensitivity, specificity, and the area under the curve (AUC) for the binary classifications as well as the correct answer rate for ternary classification were examined.The classification results of OCT images with or without myopic macular lesions were as follows: AUC, 0.983; sensitivity, 0.953; specificity, 0.940. The classification results of HM images and images with myopic macular lesions were as follows: AUC, 0.976; sensitivity, 0.940; specificity, 0.941. The correct answer rate in the ternary classification of HM images, mCNV images, and RS images were as follows: HM images, 93.7%; mCNV images, 82.4%; and RS, 92.3% with mean, 91.4%. Using noninvasive, easy-to-obtain swept-source OCT images, the DL model was able to classify OCT images without myopic macular lesions and OCT images with myopic macular lesions such as mCNV and RS with high accuracy. The study results suggest the possibility of conducting highly accurate screening of ocular diseases using artificial intelligence, which may improve the prevention of blindness and reduce workloads for ophthalmologists.

scholarly journals Empirical study on the correlation of corporate social responsibility with the banks efficiency and stability

2013 ◽  
Vol 10 (4) ◽  
pp. 86-93 ◽  
Author(s):  
Tatiana Vasileva ◽  
Anna Lasukova
Keyword(s):  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
Short Term ◽  
Main Hypothesis ◽  
National Bank ◽  
Corporate Social ◽  
The Impact ◽  
The Relationship

The aim of this paper is to investigate the relationship between the concept of corporate social responsibility and the most important characteristics of banking – the efficiency and stability in a sample of twelve Ukrainian banks, which are the biggest banks in Ukraine according to the classification of the National Bank of Ukraine (NBU). Our research covers the period from 2006 to 2012. Based on the literature review we construct two main hypothesis related to the impact on the corporate social responsibility concept (CSR) of the following independent variables: 1 – efficiency (as a short term period characteristics of banking), 2 – stability (as a long term characteristics of banking).

scholarly journals Comparative study of quality estimation of binary classification

Informatics ◽  
2020 ◽  
Vol 17 (1) ◽  
pp. 87-101
Author(s):  
V. V. Starovoitov ◽  
Yu. I. Golub
Keyword(s):  
Matthews Correlation Coefficient ◽  
Binary Classification ◽  
Confusion Matrix ◽  
Diagnostic Odds Ratio ◽  
Quality Estimation ◽  
Imbalanced Datasets ◽  
Error Matrix ◽  
Classification Errors ◽  
Sensitivity Specificity

The paper describes results of analytical and experimental analysis of seventeen functions used for evaluation of binary classification results of arbitrary data. The results are presented by 2×2 error matrices. The behavior and properties of the main functions calculated by the elements of such matrices are studied.  Classification options with balanced and imbalanced datasets are analyzed. It is shown that there are linear dependencies between some functions, many functions are invariant to the transposition of the error matrix, which allows us to calculate the estimation without specifying the order in which their elements were written to the matrices.It has been proven that all classical measures such as Sensitivity, Specificity, Precision, Accuracy, F1, F2, GM, the Jacquard index are sensitive to the imbalance of classified data and distort estimation of smaller class objects classification errors. Sensitivity to imbalance is found in the Matthews correlation coefficient and Kohen’s kappa. It has been experimentally shown that functions such as the confusion entropy, the discriminatory power, and the diagnostic odds ratio should not be used for analysis of binary classification of imbalanced datasets. The last two functions are invariant to the imbalance of classified data, but poorly evaluate results with approximately equal common percentage of classification errors in two classes.We proved that the area under the ROC curve (AUC) and the Yuden index calculated from the binary classification confusion matrix are linearly dependent and are the best estimation functions of both balanced and imbalanced datasets.

Classification of Emotional Responses and their Relation to Dominance-Submission in Adult Beagle Dyads

1965 ◽  
Vol 16 (3_suppl) ◽  
pp. 1253-1258 ◽  
Author(s):  
Andrew K. Solarz
Keyword(s):  
Emotional Response ◽  
Emotional Responses ◽  
Intermediate Position ◽  
Dyadic Interaction ◽  
Emotional Displays ◽  
Emotional Display ◽  
Relationship Of ◽  
The Relationship

Ss were 298 10-yr.-old female beagles. The relationship of each dog's emotional response display toward a human stimulus was related to its dominance-submission status within a long term dyad. Emotional display was classified into four main categories, friendly, stay-behavior, wary, and aggressive. Forms of dyadic interaction were categorized into three main categories, dominant-submissive, combat, and parallel-possession. The parallel-possession category was related to a significantly greater frequency of displayed friendly responses while the combat category was related to a significantly greater frequency of displayed stay-behavior. Dominant and submissive dogs obtained an intermediate position on both emotions and did not differ from each other. A similar emotional response occurred in each of both dogs of a pair with greater than chance frequency; this held for both friendly and stay-behavior emotional displays. Explanatory hypotheses of “reinforced parallel approach” and “interpersonal avoidance” were offered.

scholarly journals Symbols and world politics: Towards a long-term perspective on historical trends and contemporary challenges

2018 ◽  
Vol 25 (3) ◽  
pp. 931-954 ◽  
Author(s):  
Andrew Linklater
Keyword(s):  
International Relations ◽  
World Politics ◽  
Historical Trends ◽  
Historical Trend ◽  
The Relationship ◽  
Preliminary Classification ◽  
Empirical Analyses

The role of symbols in world politics remains on the margins of the study of international relations. There has been no systematic discussion of how to promote theoretically informed empirical analyses of their role in earlier epochs and in the current era. This article defends a long-term perspective on symbols that emphasises their relationship with the overall historical trend towards societies of greater magnitude and destructive power. It advances a preliminary classification of analytically distinguishable core symbols in order to support future inquiries into symbols in state-organised societies and symbols that have been central to attempts to create wider solidarities. A long-term perspective on symbolic realms is important in order to understand the relationship between ‘national’ and ‘cosmopolitan symbols’ in the current era. Current challenges in the symbolic sphere illustrate more general trends in human societies, namely, problems in constructing wider symbolic frameworks that permit closer cooperation between groups in the context of increasing levels of interconnectedness.

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