Computer Aided ECG Analysis - State of the Art and Upcoming Challenges

In this paper we present current achievements in computer aided ECG analysis and their applicability in real world medical diagnosis process. Most of the current work is covering problems of removing noise, detecting heartbeats and rhythm-based analysis. There are some advancements in particular ECG segments detection and beat classifications but with limited evaluations and without clinical approvals. This paper presents state of the art advancements in those areas till present day. Besides this short computer science and signal processing literature review, paper covers future challenges regarding the ECG signal morphology analysis deriving from the medical literature review. Paper is concluded with identified gaps in current advancements and testing, upcoming challenges for future research and a bullseye test is suggested for morphology analysis evaluation.Comment: 7 pages, 3 figures, IEEE EUROCON 2013 International conference on computer as a tool, 1-4 July 2013, Zagreb, Croati

Algorithm for Mobile Platform-Based Real-Time QRS Detection

Recent advancements in smart, wearable technologies have allowed the detection of various medical conditions. In particular, continuous collection and real-time analysis of electrocardiogram data have enabled the early identification of pathologic cardiac rhythms. Various algorithms to assess cardiac rhythms have been developed, but these utilize excessive computational power. Therefore, adoption to mobile platforms requires more computationally efficient algorithms that do not sacrifice correctness. This study presents a modified QRS detection algorithm, the AccYouRate Modified Pan–Tompkins (AMPT), which is a simplified version of the well-established Pan–Tompkins algorithm. Using archived ECG data from a variety of publicly available datasets, relative to the Pan–Tompkins, the AMPT algorithm demonstrated improved computational efficiency by 5–20×, while also universally enhancing correctness, both of which favor translation to a mobile platform for continuous, real-time QRS detection

A Review Of R Peak Detection Techniques Of Electrocardiogram (ECG)

Heart disease is one of the trivial issues regarding health problem over the last few decades in India. Numerous methods have been developed with still-ongoing modifications and ideas to observe and evaluate ECG signals based on each heart beat. Majority of research revolves around arrhythmia classification, heart rate monitoring and blood pressure measurements that require highly accurate assessments of rhythm disorders which can be possible by measuring QRS complex of ECG signal, so accurate QRS detection methods are very important to be utilized. There have been proposed many approaches to find out the R peak detection to analyze the ECG signals in past few years. Most recent and efficient techniques of R peak detection have been reviewed in this paper. Techniques which have been reviewed in this paper are Pan and Tompkins, Wavelet Transform, Empirical Mode Decomposition, Hilbert-Huang Transform, Fuzzy logic systems, Artificial neural networks

Research on Baseline Wander Removal and QRS Detection in Automated Analysis of Computerized Electrocardiogram

目前，计算机化心电图自动分析是一个热门的研究领域。它正在使心电仪器变得越来越智能，从而带来了心脏疾病诊断、监护、防控等方面的变革。但是这一领域的进一步发展正受到两个方面因素的制约：⑴心电图在采集的过程中通常会受到各种噪声的干扰；⑵缺少可靠的、稳定的算法来准确检测出心电图上的各个特征点。 因此，本文将围绕两个问题展开研究：⑴滤除心电图信号中最普遍的一种噪声——基线漂移；⑵检测心电图信号中最显著的成分波——QRS波群。这两项研究是心电图自动分析技术中最重要的工作。本文的主要研究工作及创新点归纳如下： 1、提出了基于离散余弦变换的算法以滤除心电图信号中的基线漂移。基线漂移是心电图信号中最...Currently, automated analysis of computerized electrocardiogram (ECG) is an active area of research. It is making the ECG equipments more and more intelligent, therefore evoking revolution in different aspects including cardiac disease diagnosis, cardiac disease monitoring, cardiac disease prevention and control, etc. However, the further development of this area is being restricted by two factors...学位：工程硕士院系专业：信息科学与技术学院智能科学与技术系_计算机技术学号：3152009115283

ECG classification using deep CNN improved by wavelet transform

© 2020 Tech Science Press. All rights reserved. Atrial fibrillation is the most common persistent form of arrhythmia. A method based on wavelet transform combined with deep convolutional neural network is applied for automatic classification of electrocardiograms. Since the ECG signal is easily inferred, the ECG signal is decomposed into 9 kinds of subsignals with different frequency scales by wavelet function, and then wavelet reconstruction is carried out after segmented filtering to eliminate the influence of noise. A 24-layer convolution neural network is used to extract the hierarchical features by convolution kernels of different sizes, and finally the softmax classifier is used to classify them. This paper applies this method of the ECG data set provided by the 2017 PhysioNet/CINC challenge. After cross validation, this method can obtain 87.1% accuracy and the F1 score is 86.46%. Compared with the existing classification method, our proposed algorithm has higher accuracy and generalization ability for ECG signal data classification

Verification and comparison of MIT-BIH arrhythmia database based on number of beats

The ECG signal processing methods are tested and evaluated based on many databases. The most ECG database used for many researchers is the MIT-BIH arrhythmia database. The QRS-detection algorithms are essential for ECG analyses to detect the beats for the ECG signal. There is no standard number of beats for this database that are used from numerous researches. Different beat numbers are calculated for the researchers depending on the difference in understanding the annotation file. In this paper, the beat numbers for existing methods are studied and compared to find the correct beat number that should be used. We propose a simple function to standardize the beats number for any ECG PhysioNet database to improve the waveform database toolbox (WFDB) for the MATLAB program. This function is based on the annotation's description from the databases and can be added to the Toolbox. The function is removed the non-beats annotation without any errors. The results show a high percentage of 71% from the reviewed methods used an incorrect number of beats for this database