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

ECG Signal Analysis: Enhancement and R-Peak Detection

The project has been inspired by the need to find an efficient method for ECG Signal Analysis which is simple and has good accuracy and less computation time. The initial task for efficient analysis is the removal of noise. It actually involves the extraction of the required cardiac components by rejecting the background noise. Enhancement of signal is achieved by the use of Empirical Mode Decomposition method. The use of EMD was inspired by its adaptive nature. The second task is that of R peak detection which is achieved by the use of Continuous Wavelet Transform. Efficiency of the method is measured in terms of detection error rate. Various other methods of R peak detection like Hilbert Transform and Difference Operation Method are implemented and the results when compared with the Continuous Wavelet Transform prove that CWT is a better method. The simulation is done in MATLAB environment. The experiments are carried out on MIT-BIH database. The results show that our proposed method is very effective and an efficient method for fast computation of R peak detection

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-Waves: Analysis and Detection by Continuous Wavelet Transform

In this work, we have developed a new algorithm for electrocardiogram (ECG) features extraction. This algorithm was based on continuous wavelet transform (CWT). The core of the process involved analyzing the signal using the CWT coefficients with a selection of scale parameter corresponding to each ECG wave. The entry point of our method was the R peak detection. The next step was the Q and S point localization, after we identified the P and T waves. We evaluated our algorithm on apnea and MIT-BIH databases recording. The algorithm achieved a good performance with the sensitivity of 99.84 % and the positive predictive value of 99.53 %

Peak misdetection in heart-beat-based security: Characterization and tolerance

Abstract — The Inter-Pulse-Interval (IPI) of heart beats has previously been suggested for security in mobile health (mHealth) applications. In IPI-based security, secure communi-cation is facilitated through a security key derived from the time difference between heart beats. However, there currently exists no work which considers the effect on security of imperfect heart-beat (peak) detection. This is a crucial aspect of IPI-based security and likely to happen in a real system. In this paper, we evaluate the effects of peak misdetection on the security performance of IPI-based security. It is shown that even with a high peak detection rate between 99.9 % and 99.0%, a significant drop in security performance may be observed (between-70 % and-303%) compared to having perfect peak detection. We show that authenticating using smaller keys yields both stronger keys as well as potentially faster authentication in case of imperfect heart beat detection. Finally, we present an algorithm which tolerates the effect of a single misdetected peak and increases the security performance by up to 155%. I

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

A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm

Background and objectives - Detection of the R-peak pertaining to the QRS complex of an ECG signal plays an important role for the diagnosis of a patient's heart condition. To accurately identify the QRS locations from the acquired raw ECG signals, we need to handle a number of challenges, which include noise, baseline wander, varying peak amplitudes, and signal abnormality. This research aims to address these challenges by developing an efficient lightweight algorithm for QRS (i.e., R-peak) detection from raw ECG signals. Methods - A lightweight real-time sliding window-based Max-Min Difference (MMD) algorithm for QRS detection from Lead II ECG signals is proposed. Targeting to achieve the best trade-off between computational efficiency and detection accuracy, the proposed algorithm consists of five key steps for QRS detection, namely, baseline correction, MMD curve generation, dynamic threshold computation, R-peak detection, and error correction. Five annotated databases from Physionet are used for evaluating the proposed algorithm in R-peak detection. Integrated with a feature extraction technique and a neural network classifier, the proposed ORS detection algorithm has also been extended to undertake normal and abnormal heartbeat detection from ECG signals. Results - The proposed algorithm exhibits a high degree of robustness in QRS detection and achieves an average sensitivity of 99.62% and an average positive predictivity of 99.67%. Its performance compares favorably with those from the existing state-of-the-art models reported in the literature. In regards to normal and abnormal heartbeat detection, the proposed QRS detection algorithm in combination with the feature extraction technique and neural network classifier achieves an overall accuracy rate of 93.44% based on an empirical evaluation using the MIT-BIH Arrhythmia data set with 10-fold cross validation. Conclusions - In comparison with other related studies, the proposed algorithm offers a lightweight adaptive alternative for R-peak detection with good computational efficiency. The empirical results indicate that it not only yields a high accuracy rate in QRS detection, but also exhibits efficient computational complexity at the order of O(n), where n is the length of an ECG signal

Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems

Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.Mohamed Elgendi, Björn Eskofier, Socrates Dokos, Derek Abbot

Análise da regulação autonômica cardiovascular por modelamento MVAR de variáveis fisiológicas não invasivamente amostradas

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2013.A regulação cardiovascular de curto prazo mediada pelos ramos simpático e parassimpático do sistema nervoso autônomo tem sido investigada por modelagem multivariada autoregressiva (MVAR), tendo se sedimentado como uma poderosa ferramenta de análise nas pesquisas clínicas. Modelos MVAR empregam a frequência cardíaca (FC), a pressão arterial sistólica (PAS) e a forma de onda do fluxo respiratório (RESP). ECG (a partir do qual é obtido a série FC) e a atividade respiratória podem ser facilmente captados dos pacientes. No entanto, os métodos envolvidos na aquisição da PAS (batimento a batimento) dificultam a utilização dos modelos MVAR durante exames clínicos. Estudos recentes mostram uma correlação inversa entre o tempo de trânsito da onda de pulso (PWTT) e as flutuações da PAS. PWTT é o intervalo de tempo decorrido entre a onda R do ECG e a base da onda de pulso captada com fotopletismografia no mesmo ciclo cardíaco. Este trabalho investiga a viabilidade do uso do inverso da série PWTT (IPWTT) como uma alternativa para medidas invasivas de PAS em modelagem MVAR da regulação cardiovascular. Para isso, as séries FC, RESP e IPWTT amostradas de voluntários durante alterações posturais e bloqueio autônomico foram aplicadas a modelos MVAR. Os resultados obtidos mostram que a série IPWTT pode ser utilizada em substituição das medições de PAS, a fim de superar as dificuldades práticas relacionadas com a amostragem contínua da PAS durante os exames clínicos. Isto abre a perspectiva de ampla utilização de modelagem MVAR para avaliação da função autonômica em exames clínicos de rotina, possibilitando uma melhor caracterização da enfermidade ou condição física do indivíduo. Abstract : Short-term cardiovascular regulation mediated by the sympathetic and parasympathetic branches of the autonomic nervous system has been investigated by multivariate autoregressive (MVAR) modeling, providing insightful analysis. MVAR models employ heart rate (HR), systolic blood pressure (SBP) and respiratory waveforms. ECG (from which HR series is obtained) and respiratory flow waveform (RFW) can be easily sampled from the patients. Nevertheless, the available methods for acquisition of beat-to-beat SBP measurements during exams hamper the wider use of MVAR models in clinical research. Recent studies show an inverse correlation between pulse wave transit time (PWTT) series and SBP fluctuations. PWTT is the time interval between the ECG R-wave peak and photoplethysmography waveform (PPG) base point within the same cardiac cycle. This work investigates the feasibility of using inverse PWTT (IPWTT) series as an alternative input to SBP for MVAR modeling of the cardiovascular regulation. For that, HR, RFW and IPWTT series acquired from volunteers during postural changes and autonomic blockade were used applied to MVAR models. Obtained results show that IPWTT series can be used in place of SBP measurements in order to overcome practical difficulties related to the continuous sampling of the SPB during clinical exams. This opens the prospect of widespread use of MVAR modeling for assessment of autonomic function in clinical routine, allowing a better characterization of disease or physical condition of the individual