scholarly journals Automatic Detection of Atrial Fibrillation and Other Arrhythmias in ECG Recordings Acquired by a Smartphone Device

Electronics ◽  
2018 ◽  
Vol 7 (9) ◽  
pp. 199 ◽  
Author(s):  
Lucia Billeci ◽  
Magda Costi ◽  
David Lombardi ◽  
Franco Chiarugi ◽  
Maurizio Varanini
Keyword(s):  
Atrial Fibrillation ◽  
Support Vector Machine Classifier ◽  
Least Square ◽  
Support Vector ◽  
Personal Health ◽  
Cardiac Complications ◽  
Linear Discriminant ◽  
Novel Approach ◽  
Health Monitoring Systems

Atrial fibrillation (AF) is the most common cardiac disease and is associated with other cardiac complications. Few attempts have been made for discriminating AF from other arrhythmias and noise. The aim of this study is to present a novel approach for such a classification in short ECG recordings acquired using a smartphone device. The implemented algorithm was tested on the Physionet Computing in Cardiology Challenge 2017 Database and, for the purpose of comparison, on the MIT-BH AF database. After feature extraction, the stepwise linear discriminant analysis for feature selection was used. The Least Square Support Vector Machine classifier was trained and cross-validated on the available dataset of the Challenge 2017. The best performance was obtained with a total of 30 features. The algorithm produced the following performance: F1 Normal rhythm = 0.92; F1 AF rhythm: 0.82; F1 Other rhythm = 0.75; Global F1 = 0.83, obtaining the third best result in the follow-up phase of the Physionet Challenge. On the MIT-BH ADF database the algorithm gave the following performance: F1 Normal rhythm = 0.98; F1 AF rhythm: 0.99; Global F1 = 0.98. Since the algorithm reliably detect AF and other rhythms in smartphone ECG recordings, it could be applied for personal health monitoring systems.

Histogram Based Bacteria Colony Features Analysis

2014 ◽  
Vol 609-610 ◽  
pp. 1448-1452
Author(s):  
Kun Zhang ◽  
Min Rui Fei
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Least Square ◽  
Support Vector ◽  
Least Square Estimation ◽  
Novel Approach ◽  
Traditional Approaches ◽  
Improved Support Vector Machine

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. This paper presents a novel approach for adaptive colony segmentation by classifying the detected peaks of intensity histograms of images. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained support vector machine (USVM) has better recognition accuracy than the other state of the art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.

scholarly journals Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Kun Zhang ◽  
Minrui Fei ◽  
Xin Li ◽  
Huiyu Zhou
Keyword(s):  
Support Vector Machine ◽  
Mean Shift ◽  
Least Square ◽  
Support Vector ◽  
Svm Classifier ◽  
Least Square Estimation ◽  
Unstructured Environments ◽  
Novel Approach ◽  
Traditional Approaches ◽  
Improved Support Vector Machine

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. Unstructured environments also affect the automatic colony screening. This paper presents a novel approach for adaptive colony segmentation in unstructured environments by treating the detected peaks of intensity histograms as a morphological feature of images. In order to avoid disturbing peaks, an entropy based mean shift filter is introduced to smooth images as a preprocessing step. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained least square support vector machine (ULSSVM) has better recognition accuracy than the other state-of-the-art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.

scholarly journals Effects of apixaban compared with warfarin as gain in event-free time – a novel assessment of the results of the ARISTOTLE trial

2019 ◽  
Vol 27 (12) ◽  
pp. 1311-1319 ◽  
Author(s):  
Erik Berglund ◽  
Lars Wallentin ◽  
Jonas Oldgren ◽  
Henrik Renlund ◽  
John H Alexander ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Intracranial Bleeding ◽  
Free Time ◽  
Systemic Embolism ◽  
Bleeding Events ◽  
Double Blind ◽  
Warfarin Group ◽  
Novel Approach ◽  
Atrial Fibrillation Trial

Background A novel approach to determine the effect of a treatment is to calculate the delay of event, which estimates the gain of event-free time. The aim of this study was to estimate gains in event-free time for stroke or systemic embolism, death, bleeding events, and the composite of these events, in patients with atrial fibrillation randomized to either warfarin or apixaban in the Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation trial (ARISTOTLE). Design The ARISTOTLE study was a randomized double-blind trial comparing apixaban with warfarin. Methods Laplace regression was used to estimate the delay in time to the outcomes between the apixaban and the warfarin group in 6, 12, 18 and 22 months of follow-up. Results The gain in event-free time for apixaban versus warfarin was 181 (95% confidence interval 76 to 287) days for stroke or systemic embolism and 55 (–4 to 114) days for death after 22 months of follow-up. The corresponding gains in event-free times for major and intracranial bleeding were 206 (130 to 281) and 392 (249 to 535) days, respectively. The overall gain for the composite of all these events was a gain of 116 (60 to 171) days. Conclusions In patients with atrial fibrillation, 22 months of treatment with apixaban, as compared with warfarin, provided gains of approximately 6 months in event-free time for stroke or systemic embolism, 7 months for major bleeding and 13 months for intracranial bleeding.

scholarly journals Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Mahmood I. Alhusseini ◽  
Firas Abuzaid ◽  
Albert J. Rogers ◽  
Junaid A.B. Zaman ◽  
Tina Baykaner ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Unique Identifier ◽  
Multiple Systems ◽  
Linear Discriminant ◽  
Link Type ◽  
Decision Logic ◽  
The Hilbert Transform

Background: Advances in ablation for atrial fibrillation (AF) continue to be hindered by ambiguities in mapping, even between experts. We hypothesized that convolutional neural networks (CNN) may enable objective analysis of intracardiac activation in AF, which could be applied clinically if CNN classifications could also be explained. Methods: We performed panoramic recording of bi-atrial electrical signals in AF. We used the Hilbert-transform to produce 175 000 image grids in 35 patients, labeled for rotational activation by experts who showed consistency but with variability (kappa [κ]=0.79). In each patient, ablation terminated AF. A CNN was developed and trained on 100 000 AF image grids, validated on 25 000 grids, then tested on a separate 50 000 grids. Results: In the separate test cohort (50 000 grids), CNN reproducibly classified AF image grids into those with/without rotational sites with 95.0% accuracy (CI, 94.8%–95.2%). This accuracy exceeded that of support vector machines, traditional linear discriminant, and k-nearest neighbor statistical analyses. To probe the CNN, we applied gradient-weighted class activation mapping which revealed that the decision logic closely mimicked rules used by experts (C statistic 0.96). Conclusions: CNNs improved the classification of intracardiac AF maps compared with other analyses and agreed with expert evaluation. Novel explainability analyses revealed that the CNN operated using a decision logic similar to rules used by experts, even though these rules were not provided in training. We thus describe a scaleable platform for robust comparisons of complex AF data from multiple systems, which may provide immediate clinical utility to guide ablation. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02997254. Graphic Abstract: A graphic abstract is available for this article.

scholarly journals Anticoagulation in patients with cardiac manifestations of Chagas disease and cardioembolic ischemic stroke

2018 ◽  
Vol 76 (1) ◽  
pp. 22-25 ◽  
Author(s):  
Jean M.C. Monteiro ◽  
Daniel L. San-Martin ◽  
Beatriz C.G. Silva ◽  
Pedro A.P. de Jesus ◽  
Jamary Oliveira Filho
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Chagas Disease ◽  
Retrospective Cohort ◽  
Cardiac Complications ◽  
Minor Bleeding ◽  
Warfarin Group ◽  
Cardiac Manifestations ◽  
Cis Methods

ABSTRACT Objectives To describe anticoagulation characteristics in patients with cardiac complications from Chagas disease and compare participants with and without cardioembolic ischemic stroke (CIS). Methods A retrospective cohort of patients with Chagas disease, using anticoagulation, conducted from January 2011 to December 2014. Results Forty-two patients with Chagas disease who were using anticoagulation were studied (age 62.9±12.4 years), 59.5% female and 47.6% with previous CIS, 78.6% with non-valvular atrial fibrillation and 69.7% with dilated cardiomyopathy. Warfarin was used in 78.6% of patients and dabigatran (at different times) in 38%. In the warfarin group, those with CIS had more medical appointments per person-years of follow-up (11.7 vs 7.9), a higher proportion of international normalized ratios within the therapeutic range (57% vs 42% medical appointments, p = 0.025) and an eight times higher frequency of minor bleeding (0.64 vs 0.07 medical appointments). Conclusion Patients with Chagas disease and previous CIS had better control of INR with a higher frequency of minor bleeding.

Image Steganalysis in High-Dimensional Feature Spaces with Proximal Support Vector Machine

2019 ◽  
Vol 11 (1) ◽  
pp. 78-89 ◽  
Author(s):  
Ping Zhong ◽  
Mengdi Li ◽  
Kai Mu ◽  
Juan Wen ◽  
Yiming Xue
Keyword(s):  
Support Vector Machine ◽  
Ridge Regression ◽  
Optimization Method ◽  
Least Square ◽  
Computational Time ◽  
Support Vector ◽  
Image Steganalysis ◽  
Linear Discriminant ◽  
Feature Sets ◽  
Proximal Support Vector Machine

This article presents the linear Proximal Support Vector Machine (PSVM) to the image steganalysis, and further generates a very efficient method called PSVM-LSMR through implementing PSVM by the state-of-the-art optimization method Least Square Minimum-Residual (LSMR). Also, motivated by extreme learning machine (ELM), a nonlinear algorithm PSVM-ELM is proposed for the image steganalysis. It is shown by the experiments with the wide stego schemes and rich steganalysis feature sets in both the spatial and JPEG domains that the PSVM can achieve comparable performance with Fisher Linear Discriminant (FLD) and ridge regression, and its computational time is far more less than that of them on large feature sets. The PSVM-LSMR is comparable to Ridge Regression implemented by LSMR (RR-LSMR), and both of them require the least computational time among all the competitions when dealing with medium or large feature sets. The nonlinear PSVM-ELM performs comparably or even better than FLD and ridge regression for the spatial domain steganographic schemes, and its computational time is apparently less than that of them on large feature sets.

Monitoring of drill runout using Least Square Support Vector Machine classifier

Measurement ◽  
2019 ◽  
Vol 146 ◽  
pp. 24-34 ◽  
Author(s):  
J. Susai Mary ◽  
M.A. Sai Balaji ◽  
A. Krishnakumari ◽  
R.S. Nakandhrakumar ◽  
D. Dinakaran
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Least Square ◽  
Support Vector

scholarly journals Cumulant Features based Classification of Brain MR Images using ANN and LS-SVM Algorithm

2019 ◽  
Vol 8 (11) ◽  
pp. 4008-4012
Keyword(s):  
Brain Tumour ◽  
Automatic Classification ◽  
Machine Learning Algorithms ◽  
Least Square ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Mr Images ◽  
Mr Image ◽  
Linear Discriminant

Automatic classification of magnetic resonance (MR) brain images using machine learning algorithms has a significant role in clinical diagnosis of brain tumour. The higher order spectra cumulant features are powerful and competent tool for automatic classification. The study proposed an effective cumulant-based features to predict the severity of brain tumour. The study at first stage implicates the one-level classification of 2-D discrete wavelet transform (DWT) of taken brain MR image. The cumulants of every sub-bands are then determined to calculate the primary feature vector. Linear discriminant analysis is adopted to extract the discriminative features derived from the primary ones. A three layer feed-forward artificial neural network (ANN) and least square based support vector machine (LS-SVM) algorithms are considered to compute that the brain MR image is either belongs to normal or to one of seven other diseases (eight-class scenario). Furthermore, in one more classification problem, the input MR image is categorized as normal or abnormal (two-class scenario). The correct classification rate (CCR) of LS-SVM is superior than the ANN algorithm thereby the proposed study with LS-SVM attains higher accuracy rate in both classification scenarios of MR images.

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