scholarly journals Visualizing and Quantifying Irregular Heart Rate Irregularities to Identify Atrial Fibrillation Events

2021 ◽  
Vol 12 ◽  
Author(s):  
Noam Keidar ◽  
Yonatan Elul ◽  
Assaf Schuster ◽  
Yael Yaniv
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Visual Presentation ◽  
Detection Methods ◽  
Screening Methods ◽  
Screening Programs ◽  
Machine Learning Classification ◽  
Rr Interval ◽  
Learning Classifier ◽  
The Difference

BackgroundScreening the general public for atrial fibrillation (AF) may enable early detection and timely intervention, which could potentially decrease the incidence of stroke. Existing screening methods require professional monitoring and involve high costs. AF is characterized by an irregular irregularity of the cardiac rhythm, which may be detectable using an index quantifying and visualizing this type of irregularity, motivating wide screening programs and promoting the research of AF patient subgroups and clinical impact of AF burden.MethodsWe calculated variability, normality and mean of the difference between consecutive RR interval series (denoted as modified entropy scale—MESC) to quantify irregular irregularities. Based on the variability and normality indices calculated for long 1-lead ECG records, we created a plot termed a regularogram (RGG), which provides a visual presentation of irregularly irregular rates and their burden in a given record. To inspect the potency of these indices, they were applied to train and test a machine learning classifier to identify AF episodes in gold-standard, publicly available databases (PhysioNet) that include recordings from both patients with AF and/or other rhythm disturbances, and from healthy volunteers. The classifier was trained and validated on one database and tested on three other databases.ResultsIrregular irregularities were identified using normality, variability and mean MESC indices. The RGG displayed visually distinct differences between patients with vs. without AF and between patients with different levels of AF burden. Training a simple, explainable machine learning tool integrating these three indices enabled AF detection with 99.9% accuracy, when trained on the same person, and 97.8%, when trained on patients from a different database. Comparison to other RR interval-based AF detection methods that utilize signal processing, classic machine learning and deep learning techniques, showed superiority of our suggested method.ConclusionVisualizing and quantifying irregular irregularities will be of value for both rapid visual inspection of long Holter recordings for the presence and the burden of AF, and for machine learning classification to identify AF episodes. A free online tool for calculating the indices, drawing RGGs and estimating AF burden, is available.

scholarly journals Sentiment Analysis for Customer Opinion on Hotel using Machine Learning Techniques

2019 ◽  
Vol 8 (12) ◽  
pp. 5211-5213
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Computational Study ◽  
Machine Learning Techniques ◽  
Main Task ◽  
Product Portfolio ◽  
Machine Learning Classification ◽  
The Difference ◽  
Clear Idea

Opinions from others play a significant part to take our own decision, The people’s opinions, attitudes and emotions are a computational study toward an entity is called as Sentiment Analysis (SA) or Opinion Mining (OM). In today's world, everything like business, organization and even individuals wants to know opinion from public or customers about their presentation, products and about their services which will give clear idea about their product, portfolio in the market and if these services is not up to the mark how their services they improve, so that their business will perform better. To give output as positive, negative or neutral and find the difference of a specified user text or data from the dataset is the main task of the sentiment or opinion analysis. The opinions, sentiments and subjectivity of text are computational treatment in text mining with Sentiment Analysis (SA). With the help of sentiment analysis this paper describe the machine learning classification techniques for hotel reviews for which dataset obtained from Trip advisor hotel reviews website. System got 99.07 % accuracy for MAXENT Classifier with Train size and Test size 80% and 20% respectively.

scholarly journals The Vista of Application of Specific Anaphylaxis Accurate Diagnosis Based on DNA Single-Nucleotide Methylation Sites

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiangjie Guo ◽  
Yaqin Bai ◽  
Hualin Guo ◽  
Peng Wu ◽  
Hao Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dna Methylation ◽  
Pollen Season ◽  
Accurate Diagnosis ◽  
The Other ◽  
Single Nucleotide ◽  
Machine Learning Classification ◽  
The World ◽  
On Line ◽  
The Difference

Anaphylaxis has rapidly spread around the world in the last several decades. Environmental factors seem to play a major role, and epigenetic marks, especially DNA methylation, get more attention. We discussed several GEO opening data classifications with TOP 100 specific methylation region values (normalized M-values on line) by machine learning, which are remarkable to classify specific anaphylaxis after monoallergen exposure. Then, we sequenced the whole-genome DNA methylation of six people (3 wormwood monoallergen atopic rhinitis patients and 3 normal-immune people) during the pollen season and analyzed the difference of the single nucleotide and DNA region. The results’ divergences were obvious (the differential single nucleotides were mostly distributed in nongene regions but the differential DNA regions of GWAS, on the other hand), which may have caused most single nucleotides to be concealed in the regions’ sequences. Therefore, we suggest that we should conduct more “pragmatic” and directly find special single-nucleotide changes after exposure to atopic allergens instead of complex correlativity. It is possible to try to use DNA methylation marks to accurately diagnose anaphylaxis and form a machine learning classification based on the single methylated CpGs.

scholarly journals Machine learning Classification of Dyslexic Children based on EEG Local Network Features

2019 ◽  
Author(s):  
Z. Rezvani ◽  
M. Zare ◽  
G. Žarić ◽  
M. Bonte ◽  
J. Tijms ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Performance ◽  
Local Network ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Phase Lag ◽  
Frequency Bands ◽  
Machine Learning Classification ◽  
Learning Classifier ◽  
Grade 3

AbstractMachine learning can be used to find meaningful patterns characterizing individual differences. Deploying a machine learning classifier fed by local features derived from graph analysis of electroencephalographic (EEG) data, we aimed at designing a neurobiologically-based classifier to differentiate two groups of children, one group with and the other group without dyslexia, in a robust way. We used EEG resting-state data of 29 dyslexics and 15 typical readers in grade 3, and calculated weighted connectivity matrices for multiple frequency bands using the phase lag index (PLI). From the connectivity matrices, we derived weighted connectivity graphs. A number of local network measures were computed from those graphs, and 37 False Discovery Rate (FDR) corrected features were selected as input to a Support Vector Machine (SVM) and a common K Nearest Neighbors (KNN) classifier. Cross validation was employed to assess the machine-learning performance and random shuffling to assure the performance appropriateness of the classifier and avoid features overfitting. The best performance was for the SVM using a polynomial kernel. Children were classified with 95% accuracy based on local network features from different frequency bands. The automatic classification techniques applied to EEG graph measures showed to be both robust and reliable in distinguishing between typical and dyslexic readers.

scholarly journals Cost-Sensitive Machine Learning Classification for Mass Tuberculosis Screening

2019 ◽  
Author(s):  
Ali Akbar Septiandri ◽  
Aditiawarman ◽  
Roy Tjiong ◽  
Erlina Burhan ◽  
Anuraj H. Shankar
Keyword(s):  
Machine Learning ◽  
Tuberculosis Screening ◽  
Case Identification ◽  
Machine Learning Classification ◽  
Primary Health Centres ◽  
Positive Class ◽  
Data Points ◽  
The Difference ◽  
The Cost ◽  
And Control

AbstractActive screening for Tuberculosis (TB) is needed to optimize detection and treatment. However, current algorithms for verbal screening perform poorly, causing misclassification that leads to missed cases and unnecessary and costly laboratory tests for false positives. We investigated the role of machine learning to improve the predefined one-size-fits-all algorithm used for scoring the verbal screening questionnaire. We present a cost-sensitive machine learning classification for mass tuberculosis screening. We compared score-based classification defined by clinicians to machine learning classification such as SVM-RBF, logistic regression, and XGBoost. We restricted our analyses to data from adults, the population most affected by TB, and investigated the difference between untuned and unweighted classifiers to the cost-sensitive ones. Predictions were compared with the corresponding GeneXpert MTB/Rif results. After adjusting the weight of the positive class to 40 for XGBoost, we achieved 96.64% sensitivity and 35.06% specificity. As such, sensitivity of our identifier increased by 1.26% while specificity increased by 13.19% in absolute value compared to the traditional score-based method defined by our clinicians. Our approach further demonstrated that only 2000 data points were sufficient to enable the model to converge. Our results indicate that even with limited data we can actually devise a better method to identify TB suspects from verbal screening. This approach may be a stepping stone towards more effective TB case identification, especially in primary health centres, and foster better detection and control of TB.

FEATURE SELECTION AND MACHINE LEARNING CLASSIFICATION FOR MALWARE DETECTION

2015 ◽  
Vol 77 (1) ◽  
Author(s):  
Ban Mohammed Khammas ◽  
Alireza Monemi ◽  
Joseph Stephen Bassi ◽  
Ismahani Ismail ◽  
Sulaiman Mohd Nor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Computer Security ◽  
Malware Detection ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Minimum Number

Malware is a computer security problem that can morph to evade traditional detection methods based on known signature matching. Since new malware variants contain patterns that are similar to those in observed malware, machine learning techniques can be used to identify new malware. This work presents a comparative study of several feature selection methods with four different machine learning classifiers in the context of static malware detection based on n-grams analysis. The result shows that the use of Principal Component Analysis (PCA) feature selection and Support Vector Machines (SVM) classification gives the best classification accuracy using a minimum number of features.

scholarly journals MACHINE LEARNING CLASSIFICATION OF VERIFIED HEAD IMPACT EXPOSURE REVEALS ASSOCIATIONS WITH LONGITUDINAL WHITE MATTER CHANGES IN FEMALE HIGH SCHOOL SOCCER PLAYERS

2021 ◽  
Vol 9 (7_suppl3) ◽  
pp. 2325967121S0015
Author(s):  
Christopher A. DiCesare ◽  
Brittany Green ◽  
Weihong Yuan ◽  
Kim D. Barber Foss ◽  
Jon Dudley ◽  
...  
Keyword(s):  
Machine Learning ◽  
White Matter ◽  
False Positives ◽  
Cumulative Exposure ◽  
Head Impacts ◽  
Machine Learning Classification ◽  
Learning Classifier ◽  
Female High School ◽  
Accurate Quantification

Background: Longitudinal changes in white matter (WM) integrity have been reported following cumulative exposure to sub-concussive head impacts (SCI) incurred during sports. SCI exposure is typically quantified using accelerometers that use an arbitrary g-force threshold for impact detection; however, this approach does not differentiate true impacts from non-physiological events, such as high-frequency cutaneous vibrations. This leads to a high false positive rate and the tendency to overestimate SCI exposure. Hypothesis/Purpose: To examine whether machine learning classification trained on video-verified impacts can produce more accurate quantification of SCI exposure and whether this can reveal associations between cumulative exposure and diffusion tensor imaging (DTI)-measured longitudinal WM changes in athletes. Methods: Pre- and post-season brain MRI scans were collected from 46 female high school soccer athletes. Athletes’ SCI exposure was recorded during their competitive season using head-mounted accelerometers. 24 athletes were assigned to a “Treatment Group” (TG) and were also video recorded during 17 of their games, while the remaining 22 athletes were assigned to a “Control Group” (CG) and were unrecorded. The TG video was used to verify whether the sensor-recorded impacts during those games were true head impacts or false positives. This dataset, along with the corresponding true/false labels, was used to train a machine learning classifier to learn a mapping between the accelerometer data and their respective labels. The trained model was then used to classify and filter the impacts recorded for the CG athletes by removing their predicted false positives. The CG athletes’ SCI exposure in both the unfiltered and filtered conditions was correlated with their pre- to post-season changes in DTI measures of mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and fractional anisotropy (FA). Results: During the TG athletes’ 17 video-recorded games, 5146 impacts were recorded and 1128 were confirmed as true positives (22.0% accuracy). After training the machine learning classifier, the model was able to achieve 83.5% accuracy on this dataset. Associating the CG athletes’ unfiltered SCI exposure to pre- to post-season WM changes revealed no significant associations based on voxel-wise analysis over the whole brain WM network; however, after removing predicted false positives, the filtered SCI exposure revealed significant associations with changes in MD, RD, and FA (all p < 0.05). Conclusion: Machine learning classification of sensor-recorded SCI exposure exhibits superior accuracy and sensitivity to threshold-based detection used in standard accelerometry. Accurate quantification of SCI exposure also reveals associations with longitudinal WM changes. [Figure: see text][Figure: see text]

scholarly journals A Review of Atrial Fibrillation Detection Methods as a Service

2020 ◽  
Vol 17 (9) ◽  
pp. 3093 ◽  
Author(s):  
Oliver Faust ◽  
Edward J. Ciaccio ◽  
U. Rajendra Acharya
Keyword(s):  
Atrial Fibrillation ◽  
Automated System ◽  
Detection Methods ◽  
Ecg Signals ◽  
Rr Interval ◽  
Health Goals ◽  
Patient Morbidity ◽  
System Establishment ◽  
Data Volume ◽  
Monitoring Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals.

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