A survey on ECG analysis

Electrocardiogram (ECG) has extremely discriminative characteristics in the biometric field and has recently received significant interest as a promising biometric trait. However, ECG signals are susceptible to several types of noises, such as baseline wander, powerline interference, and high/low-frequency noises, making it challenging to realize biometric identification systems precisely and robustly. Therefore, ECG signal denoising is a major preprocessing step and plays a crucial role in ECG-based biometric human identification. ECG signal analysis for biometric recognition can combine several steps, such as preprocessing, feature extraction, feature selection, feature transformation, and classification which is a very challenging task. Moreover, the employed success measures and appropriate constitution of the ECG signal database also play significant roles in biometric system analysis, considering that publicly available databases are essential by the research community to evaluate the performance of their proposed algorithms. In this survey, we review most of the techniques employed for the ECG as biometrics for human authentication. Firstly, we present an overview and discussion on ECG signal preprocessing, feature extraction, feature selection, and feature transformation for ECG-based biometric systems. Secondly, we present a survey of the available ECG databases to evaluate and compare the acquisition protocol, acquisition hardware, and acquisition resolution (bits) for ECG-based biometric systems. Thirdly, we also present a survey on different techniques, including deep learning methods: deep supervised learning, deep semi-supervised learning, and deep unsupervised learning, for ECG signal classification. Lastly, we present the state-of-art approaches of information fusion in multimodal biometric systems.

The electrical activity of the heart and the electrocardiogram (ECG) signal are fundamentally related. In the study that has been published, the ECG signal has been examined and used for a number of applications. The monitoring of heart rate and the analysis of heart rhythm patterns, the detection and diagnosis of cardiac diseases, the identification of emotional states, and the use of biometric identification methods are a few examples of applications in the field. Several various phases may be involved in the analysis of electrocardiogram (ECG) data, depending on the type of study being done. Preprocessing, feature extraction, feature selection, feature modification, and classification are frequently included in these stages. Every stage must be finished in order for the analysis to go smoothly. Additionally, accurate success measures and the creation of an acceptable ECG signal database are prerequisites for the analysis of electrocardiogram (ECG) signals. Identification and diagnosis of various cardiac illnesses depend heavily on the ECG segmentation and feature extraction procedure. Electrocardiogram (ECG) signals are frequently obtained for a variety of purposes, including the diagnosis of cardiovascular conditions, the identification of arrhythmias, the provision of physiological feedback, the detection of sleep apnea, routine patient monitoring, the prediction of sudden cardiac arrest, and the creation of systems for identifying vital signs, emotional states, and physical activities. The ECG has been widely used for the diagnosis and prognosis of a variety of heart diseases. Currently, a range of cardiac diseases can be accurately identified by computerized automated reports, which can then generate an automated report. This academic paper aims to provide an overview of the most important problems associated with using deep learning and machine learning to diagnose diseases based on electrocardiography, as well as a review of research on these techniques and methods and a discussion of the major data sets used by researchers.

Electrocardiogram (ECG) diagnosis is a widely-used clinical approach because it has been proven as an efficient way to monitor and diagnose cardiac diseases. However, because of a large amount of the raw ECG signal data, it is time-consuming to analyze the ECG signal. Moreover, ECG signal analysis is a nonlinear problem, which worsens the difficulty to diagnose the ECG signal. Therefore, many Neural Network (NN)-based ECG analysis approaches were proposed to perform ECG diagnosis in time domain in recent years, which can improve the ability of ECG analysis. By decomposing the ECG signal, the P, Q, R, S, and T waves can be acquired for the further analysis based on the information of the features of these waves such as the amplitude and waves interval. However, because of the complex pre-process for the signal purification and feature extraction, this kind of time-domain ECG signal process still suffers from the long computation time. To solve the problem, we propose a Spectral Artificial Neural Network (SANN) approach for the fast ECG diagnosis in this paper. Compared with the conventional time-domain-based approaches, the SANN analyzes the ECG signal in frequency domain. Because most of the noises in the raw ECG signal are high-frequency signals, the proposed SANN focuses to analyze the low-frequency signals in ECG spectrum. By this way, the proposed SANN not only reduces the pre-processing time but the diagnosis time. To obtain the proper window size for the precise ECG diagnosis, we further propose a heuristic window size adjustment in this paper, which helps to extract the suitable features. The experimental results show that the proposed SANN approach can reduce the ECG diagnosis time by 80% compared with the conventional ECG analysis with only 5% average diagnosis accuracy loss of the cardiac diseases.

This paper describes about the analysis of electrocardiogram (ECG) signals using neural network approach. Heart structure is a unique system that can generate ECG signals independently via heart contraction. Basically, an ECG signal consists of PQRST wave. All these waves are represented respective heart functions. Normal healthy heart can be simply recognized by normal ECG signal while heart disorder or arrhythmias signals contain differences in terms of features and morphological attributes in their corresponding ECG waveform. Some major important features will be extracted from ECG signals such as amplitude, duration, pre-gradient, post-gradient and so on. These features will then be fed as an input to neural network system. The target output represented real peaks of the signals is also being defined using a binary number. Result obtained showing that neural network pattern recognition is able to classify and recognize the real peaks accordingly with overall accuracy of 81.6% although there might be limitations and misclassification happened. Future recommendations have been highlighted to improve network's performance in order to get better and more accurate result.

In this paper, a new approach for automatic analysis of single lead ECG for human recognition is proposed and evaluated. Following the pre-processing step, the ECG stream is partitioned into separate windows where each window includes single beat of ECG signal. After successful QRS detection, various temporal, amplitude and AR coefficients are extracted and used as an input to a classifier in order to identify the individuals. In this work, proposed system has been tested using records from three different publicly available ECG databases. Signal pre-processing techniques, applied parameter extraction methods and some intermediate and final classification results are presented in this paper. that PAW yields equivalent performance in terms of accuracy compared to conventional temporal and amplitude feature extraction methods. Even though, PAW is complicated process which needs powerful digital signal processors to overcome the time delay. In this paper, a new approach for automatic analysis of single lead electrocardiogram (ECG) for human recognition and individual identification is proposed. This approach depends on on analytic (Amplitude, Time and Width) and modelling (AR) features extracted from the ECG beat. Obtained results indicate high level of accuracy and shorter processing time needed to identify the individuals. Eighteen analytic and modelling features are extracted to identify individuals and k nearest neighbour (knn) classification algorithm applied in order to classify those features and evaluate the proposed approach. ECG feature selection and extraction using AR modelling has recently been used (9) resulting in accurate classification of various arrhythmia and ventricular arrhythmia conditions. The remainder of this paper is organized as follows. Section II gives a brief description of the techniques used in the pre-processing phase to clean ECG signals of noise and other artefacts. Section III provides a review of QRS detection methods used in this work. Feature selection and extraction methods are discussed in Section IV whilst Section V contains experimental results and discussion of those results. Conclusions are presented in Section VI.

During last few years, lot of study has provided on analysis and diagnosis of Electrocardiogram (ECG) signal. In this world, human is suffering from plenty of diseases. Arrhythmia is one of the diseases which cause by electrical malfunction in cardiac signal of heart. In arrhythmia condition victim loses consciousness and has no pulse which occur death within a minute, leads to sudden cardiac arrest. The P, QRS complex and T wave of ECG signal triggered and generates improper electrical signal that provide clinical information to diagnose. A new technologies and algorithms are introduced as a good approach towards detection and analysis of ECG. This paper aim at recognition of ST segment detection and QRS complex or R peak detection to diagnose arrhythmia. We propose novel method to detect arrhythmia from ECG signal using different concepts as Discrete Wavelet Transform (DWT), Adaptive Least Mean Square (ALMS) and Support Vector Machine (SVM).

This paper describes about the analysis of electrocardiogram (ECG) signals using neural network approach. Heart structure is a unique system that can generate ECG signals independently via heart contraction. Basically, an ECG signal consists of PQRST wave. All these waves are represented respective heart functions. Normal healthy heart can be simply recognized by normal ECG signal while heart disorder or arrhythmias signals contain differences in terms of features and morphological attributes in their corresponding ECG waveform. Some major important features will be extracted from ECG signals such as amplitude, duration, pre-gradient, post-gradient and so on. These features will then be fed as an input to neural network system. The target output represented real peaks of the signals is also being defined using a binary number. Result obtained showing that neural network pattern recognition is able to classify and recognize the real peaks accordingly with overall accuracy of 81.6% although there might be limitations and misclassification happened. Future recommendations have been highlighted to improve network’s performance in order to get better and more accurate result.

This study presents a simple electrocardiogram (ECG) signal analyzer for homecare system among the elderly. It can transmit ECG signals of patient around his/her house through Bluetooth to computers in house. ECG signals are analyzed by the computer. If abnormal case of heartbeat is found, the emergency call is automatically dialed. Meanwhile, the determined heartbeat case of ECG signals will be forwarded to patient's MD through internet. Therefore, the patient can do whatever he/she wants around his/her house with our proposed simple cardiac arrhythmias signal analyzer. The proposed consists of five major processing stages: (i) preprocessing stage for enlarging ECG signals' amplitude and eliminating noises; (ii) ECG signal transmitter/receiver stage, ECG signals are transmitted through Bluetooth to the signal receiver in patient's house; (iii) QRS extraction stage for detecting QRS waveform using the Difference Operation Method (DOM) method; (iv) qualitative features stage for qualitative feature selection on ECG signals; and (v) classification stage for determining patient's heartbeat cases using the Principal Component Analysis (PCA) method. In the experiment, the total classification accuracy (TCA) was approximately 93.19% in average.

The distortion of the electrocardiogram by capacitance

Electrocardiogram signal classification for automated delineation using bidirectional long short-term memory

Analysis of electrocardiogram (ECG) signals is one of the major research interests in bio-medical signal processing. ECG signals have been acquired from MIT-BIH Arrhythmia database is used for a new method for analysis of ECG signals with Features extraction techniques using filtering based on scores of the features selected. The fact that ECG signals comprises of time series, frequency patterns and complex features in extraction phase, the feature selection algorithm relies on the ranks delivered by the objective function defined by correlation criterion and Fisher criterion. A variation in the score of features between 0 to 1 fits the best for cardiac arrhythmia classification. The evaluated parameters provide an accurate classification of the ventricular arrhythmia with accuracy of 96.41%, sensitivity 95% and specificity 97%.

The electrocardiogram (ECG) signal has often been reported to play an important role in the primary diagnosis, prognosis, and survival analysis of heart diseases. Electrocardiography has brought several valuable impacts on the practice of medicine. This paper deals with the feature extraction and automatic analysis of different ECG signal waves using derivative based/ Pan-Tompkins based algorithms. The ECG signal contains an important amount of information that can be exploited in different way. It allows for the analysis of cardiac health condition. The discrimination of ECG signals using the Data Mining Decision Tree techniques is of crucial importance in the cardiac disease therapy and control of cardiac arrhythmias. Different ECG signals from MIT/BIH Arrhythmia data base are used for ECG features extraction and analysis. Two pathologies are considered: atrial fibrillation and right bundle branch block. Some decision tree classification algorithms currently in use, including C4.5, Improved C4.5, CHAID (Chi square Automatic Interaction Detector) and Improved CHAID are performed for performance analysis. Promising results have been achieved using the C4.5 classifier, with an overall accuracy of 96.87%.

The goal of this article is to present an algorithm for QRS complex detection in an electrocardiogram (ECG) signal, realized in Matlab software. The algorithm was tested on real ECG signals acquired with a commercial monitoring system Alive Heart Monitor and also for reference on signals from MIT-BIH online ECG signal database. The goal of our research is to import signals from the monitoring system to a PDA or a Smart Phone via wireless network for signal processing and use in telehealthcare and telemonitoring. Therefore, we examine the concept of wireless acquisition and digital signal processing of ECG (electrocardiogram) signal, which, in addition to traditional medicine is increasingly used in the field of telemedicine as well as in completely non-medical areas, such as sport, entertainment, marketing, etc. Furthermore, we describe the fundamental architecture of an electrocardiograph and describe and overview the basic methods for ECG signal processing.

Detection and delineation of QRS-complexes, P and T-waves, are important issues in the analysis and interpretation of Electrocardiogram (ECG) signals. In this paper, a classifier motivated from statistical learning theory, i.e., Support Vector Machine (SVM), has been explored for detection and delineation of these wave components. Digital filtering techniques are used to remove interference present in ECG signal. The feature extraction is done using a modified definition of slope of the ECG signals. The performance of the proposed algorithm is validated using ECG recordings from dataset-3 of the CSE multi-lead measurement library. The results in terms of accuracy, i.e., 94.4%, obtained clearly indicate a high degree of agreement with the manual annotations made by the referees of CSE dataset-3.

The electrocardiogram (ECG) signal is used to assess electrical abnormalities and provides vital information about of heart health. One problem in ECG analysis is the feature extraction due to the intrinsic noise. This paper presents a ECG feature extraction method that consists of the morphology analysis, the fiducial point localization, and time intervals measurements by using both continuous and discrete wavelets transform (CWT and DWT, respectively). From ECG signals obtained from CardioExpress SL3 medical device, we obtained the principal features by using the Wavelet theory. The results were validated and compared with the established values by the International Committees, the Common Standards for quantitative Electrocardiography (CSE), with a Sensitivity (Se) of 96% and with a standard deviation (std) less than 8%.