scholarly journals Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wei Yan ◽  
Zhen Zhang
Keyword(s):  
Feature Extraction ◽  
Cardiovascular Diseases ◽  
Hybrid Model ◽  
Median Filter ◽  
Cardiac Activity ◽  
Diagnostic Algorithm ◽  
Diagnostic Efficiency ◽  
Automatic Diagnosis ◽  
Diagnosis System ◽  
Ecg Database

Arrhythmias are a relatively common type of cardiovascular disease. Most cardiovascular diseases are often accompanied by arrhythmias. In clinical practice, an electrocardiogram (ECG) can be used as a primary diagnostic tool for cardiac activity and is commonly used to detect arrhythmias. Based on the hidden and sudden nature of the MIT-BIH ECG database signal and the small-signal amplitude, this paper constructs a hybrid model for the temporal correlation characteristics of the MIT-BIH ECG database data, to learn the deep-seated essential features of the target data, combine the characteristics of the information processing mechanism of the arrhythmia online automatic diagnosis system, and automatically extract the spatial features and temporal characteristics of the diagnostic data. First, a combination of median filter and bandstop filter is used to preprocess the data in the ECG database with individual differences in ECG waveforms, and there are problems of feature inaccuracy and useful feature omission which cannot effectively extract the features implied behind the massive ECG signals. Its diagnostic algorithm integrates feature extraction and classification into one, which avoids some bias in the feature extraction process and provides a new idea for the automatic diagnosis of cardiovascular diseases. To address the problem of feature importance variability in the temporal data of the MIT-BIH ECG database, a hybrid model is constructed by introducing algorithms in deep neural networks, which can enhance its diagnostic efficiency.

scholarly journals Identification of Risk of Occurring Skin Cancer (Melanoma) Using Convolutional Neural Network (CNN)

2021 ◽  
Vol 20 (2) ◽  
pp. 47-51
Author(s):  
Mayinuzzaman Shawon ◽  
Kazi Fakhrul Abedin ◽  
Anik Majumder ◽  
Abir Mahmud ◽  
Md Mahbub Chowdhury Mishu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Skin Cancer ◽  
Convolutional Neural Network ◽  
Cancer Diagnosis ◽  
Detection System ◽  
Median Filter ◽  
Accuracy Score ◽  
Diagnosis System

Skin cancer is one of the most common malignancy in human, has drawn attention from researchers around the world. As skin cancer can turn into fatal if not treated in its earliest stages, the necessity of devising automated skin cancer diagnosis system that can automatically detect skin cancer efficiently in its earliest stage in a faster process than traditional one is of crucial importance. In this paper, a computer aided skin cancer diagnosis system based Convolutional Neural Network method has been shown. Our proposed system consists of five stages namely image acquisition, image preprocessing, image segmentation, feature extraction and classification We remove hair any noise from the images using dull then use median filter to smoothen the images. Next, k-means algorithm was applied for image segmentation on the preprocessed images. Finally, the segmented images were fed into CNN model for feature extraction and classification. The developed system can classify benign and melanoma type skin cancers from Dermoscopic images as accurate as 80.47%. While developing the skin cancer detection system, we compare accuracy score of other models such as Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) and Random Forest with our proposed system. The proposed method has been tested on ‘ISIC Challenge 2016’ test dataset and an accuracy rate of 80.47% was obtained for accurately classifying benign and malignant skin lesions by our proposed model.

scholarly journals PARAMETERS OF ECG DISPERSION MAPPING AMONG STUDENTS OF NORTHERN UNIVERSITY

2019 ◽  
pp. 98-104 ◽  
Author(s):  
I. A Pogonysheva ◽  
D. A Pogonyshev ◽  
I. I Lunyak
Keyword(s):  
Risk Factors ◽  
Cardiovascular Diseases ◽  
High Risk ◽  
Questionnaire Survey ◽  
Cardiac Activity ◽  
State University ◽  
Men And Women ◽  
Factors Associated ◽  
Far North ◽  
Pathological Characteristics

The cardiac activity of students who have been born and live in the territory equated to regions of Far North was assessed. In total, 132 students of Nizhnevartovsk State University were examined using the CardioVisor-06c analyser that helps to diagnose dysfunctions of the cardiovascular system at preclinical level. The authors conducted a questionnaire survey to identify risk factors associated with cardiovascular diseases in students and analyzed the results of ECG dispersion mapping. The deterioration of the functional state of the myocardium was more pronounced among students with a high risk of developing cardiovascular diseases. The young men and women with pre-pathological characteristics of electrophysiological indicators were referred for additional examination and cardiology consultation.

scholarly journals Modern biomarkers in cardiology

2018 ◽  
pp. 118-123 ◽  
Author(s):  
O. V. Dymova
Keyword(s):  
Clinical Practice ◽  
Cardiovascular Diseases ◽  
Natriuretic Peptides ◽  
International Experience ◽  
Diagnostic Efficiency ◽  
Medical Decisions ◽  
Level Of Evidence ◽  
Neurohumoral Regulation ◽  
Myocardial Stress

Modern clinical practice requires obligatory use of results of laboratory researches, international experience demonstrates the need of the clinic in laboratory information for making up to 70% of medical decisions. Described a large number of biomarkers of cardiovascular diseases, high diagnostic efficiency with sufficient level of evidence is shown not for all. In article the possibilities of a modern laboratory in determining markers of myocardium damage (troponins, hFABP), myocardial stress (natriuretic peptides) and neurohumoral regulation (copeptin), the diagnostic and predictive importance of these researches are considered.

Automatic Diagnosis of Cardiovascular Diseases Using Electrocardiogram Data and Artificial Intelligence Algorithms: A Systematic Review

2021 ◽  
pp. 104-116
Author(s):  
Roberto Mario Cadena Vega ◽  
Efrén Gorrostieta Hurtado ◽  
Marco Antonio Aceves Fernández ◽  
Juan Manuel Ramos Arreguin
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Cardiovascular Diseases ◽  
Automatic Diagnosis

ANN-Based Defects' Diagnosis of Industrial Optical Devices

2011 ◽  
pp. 131-137
Author(s):  
Matthieu Voiry ◽  
Véronique Amarger ◽  
Joel Bernier ◽  
Kurosh Madani
Keyword(s):  
Neural Network ◽  
Data Extraction ◽  
Production Quality ◽  
Optical Devices ◽  
Major Step ◽  
Automatic Diagnosis ◽  
Diagnosis System ◽  
Repetitive Nature ◽  
Faults Diagnosis ◽  
Harmful Effects

A major step for high-quality optical devices faults diagnosis concerns scratches and digs defects detection and characterization in products. These kinds of aesthetic flaws, shaped during different manufacturing steps, could provoke harmful effects on optical devices’ functional specificities, as well as on their optical performances by generating undesirable scatter light, which could seriously damage the expected optical features. A reliable diagnosis of these defects becomes therefore a crucial task to ensure products’ nominal specification. Moreover, such diagnosis is strongly motivated by manufacturing process correction requirements in order to guarantee mass production quality with the aim of maintaining acceptable production yield. Unfortunately, detecting and measuring such defects is still a challenging problem in production conditions and the few available automatic control solutions remain ineffective. That’s why, in most of cases, the diagnosis is performed on the basis of a human expert based visual inspection of the whole production. However, this conventionally used solution suffers from several acute restrictions related to human operator’s intrinsic limitations (reduced sensitivity for very small defects, detection exhaustiveness alteration due to attentiveness shrinkage, operator’s tiredness and weariness due to repetitive nature of fault detection and fault diagnosis tasks). To construct an effective automatic diagnosis system, we propose an approach based on four main operations: defect detection, data extraction, dimensionality reduction and neural classification. The first operation is based on Nomarski microscopy issued imaging. These issued images contain several items which have to be detected and then classified in order to discriminate between “false” defects (correctable defects) and “abiding” (permanent) ones. Indeed, because of industrial environment, a number of correctable defects (like dusts or cleaning marks) are usually present beside the potential “abiding” defects. Relevant features extraction is a key issue to ensure accuracy of neural classification system; first because raw data (images) cannot be exploited and, moreover, because dealing with high dimensional data could affect learning performances of neural network. This article presents the automatic diagnosis system, describing the operations of the different phases. An implementation on real industrial optical devices is carried out and an experiment investigates a MLP artificial neural network based items classification.

Malaria Screening Using Front-Line Loop-Mediated Isothermal Amplification

2020 ◽  
Author(s):  
Eric McGinnis ◽  
Geoffrey Chan ◽  
Monika Hudoba ◽  
Todd Markin ◽  
Jim Yakimec ◽  
...  
Keyword(s):  
Diagnostic Algorithm ◽  
Blood Film ◽  
Isothermal Amplification ◽  
Diagnostic Efficiency ◽  
Front Line ◽  
Hub And Spoke ◽  
Labor Time ◽  
Health Region ◽  
Loop Mediated Isothermal Amplification ◽  
Time Savings

Abstract Objectives: We implemented front-line loop-mediated isothermal amplification (LAMP)–based malaria screening in our nonendemic multicenter health region to reduce reliance on microscopy without sacrificing diagnostic efficiency. We aimed to evaluate changes in test volumes, positivity rates, turnaround times, and approximate labor time savings resulting from implementation of LAMP-based malaria testing to assess the efficacy of the novel testing algorithm in our regional hub-and-spoke testing model. Methods: We reviewed data generated from institutional malaria testing between 2016 and 2019, having implemented LAMP in October 2018 as a front-line screening test for all malaria investigations from our hub facility and investigations from satellite facilities with negative rapid diagnostic tests (RDTs) and microscopy. Results: Blood film microscopy and RDT workloads decreased substantially in the year following LAMP implementation (by 90% and 46%, respectively,) despite similar numbers of patients tested and positivity rates for malaria compared with historical data. LAMP turnaround times (TATs) were comparable to historical TATs for RDTs, and TATs for RDTs and thick films did not increase with the change in workflow. Conclusions: LAMP was successfully implemented in our multicenter health region malaria diagnostic algorithm, significantly reducing reliance on microscopic evaluations and RDT and providing substantial labor time savings without compromising TATs.

scholarly journals A Fault Diagnosis Method of Bogie Axle Box Bearing Based on Spectrum Whitening Demodulation

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7155
Author(s):  
Zejun Zheng ◽  
Dongli Song ◽  
Xiao Xu ◽  
Lei Lei
Keyword(s):  
Feature Extraction ◽  
Health Status ◽  
Fault Diagnosis ◽  
High Speed ◽  
Energy Operator ◽  
Ar Model ◽  
Diagnostic Efficiency ◽  
Heavy Load ◽  
Feature Extraction Method ◽  
Vibration Data

The axle box bearing of bogie is one of the key components of the rail transit train, which can ensure the rotary motion of wheelsets and make the wheelsets adapt to the conditions of uneven railways. At the same time, the axle box bearing also exposes most of the load of the car body. Long-time high-speed rotation and heavy load make the axle box bearing prone to failure. If the bearing failure occurs, it will greatly affect the safety of the train. Therefore, it is extremely important to monitor the health status of the axle box bearing. At present, the health status of the axle box bearing is mainly monitored by vibration information and temperature information. Compared with the temperature data, the vibration data can more easily detect the early fault of the bearing, and early warning of the bearing state can avoid the occurrence of serious fault in time. Therefore, this paper is based on the vibration data of the axle box bearing to carry out adaptive fault diagnosis of bearing. First, the AR model predictive filter is used to denoise the vibration signal of the bearing, and then the signal is whitened in the frequency domain. Finally, the characteristic value of vibration data is extracted by energy operator demodulation, and the fault type is determined by comparing with the theoretical value. Through the analysis of the constructed simulation signal data, the characteristic parameters of the data can be effectively extracted. The experimental data collected from the bearing testbed of high-speed train are analyzed and verified, which further proves the effectiveness of the feature extraction method proposed in this paper. Compared with other axle box bearing fault diagnosis methods, the innovation of the proposed method is that the signal is denoised twice by using AR filter and spectrum whitening, and the adaptive extraction of fault features is realized by using energy operator. At the same time, the steps of setting parameters in the process of feature extraction are avoided in other feature extraction methods, which improves the diagnostic efficiency and is conducive to use in online monitoring system.

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