Normal Sinus Rhythm Database Research Articles

Spectral decomposition of heart rate variability using generalized harmonic analysis

In this paper, a method for decomposing harmonics in the spectrum of heart rate variability (HRV) using generalized harmonic analysis (GHA) is introduced. First, a simulated RR interval signal generated by an integral pulse frequency modulation model was decomposed spectra by GHA, Pisarenko harmonic decomposition (PHD), and autoregressive (AR) spectral decomposition model. The spectral profiles were obtained by the GHA, PHD, and AR methods for various numbers of extracted sinusoids and model orders from 1 to 48. The spectral profiles of GHA were the most stable. Of the power values of the sinusoids extracted by each method, it was clear that the power values estimated by GHA were approximately equal to the mean square value and closer than that obtained using the PHD or a fast Fourier transform (FFT). Second, a comparison of the power of the low-frequency (LF) and high-frequency (HF) band components reveals that the values obtained by GHA are similar to those obtained by FFT for the analysis using a real ECG signal. Third, Bland-Altman analysis reveals that LF and HF band power value calculated by the GHA are compatible with ones by the Lomb-Scargle periodogram using MIT-BIH normal sinus rhythm database from short term recordings of 30 min. These results suggest that GHA is a useful tool for calculating the power of a component in the spectral analysis of HRV. The limitations of spectral decomposition by GHA are still discussed.

CSNet: A deep learning approach for ECG compressed sensing

Remote electrocardiogram (ECG) monitoring plays a very important role in the prevention and treatment of cardiovascular diseases. However, the current long-term ECG monitoring generates a large amount of data, which puts great pressure on the bandwidth and transmission systems. Compressed sensing (CS) has great attraction for resource-limited wearable devices used in remote ECG monitoring. Traditional CS reconstruction algorithms often require complex signal processing and prior knowledge, and the reconstruction process is time-consuming, which limits the application of CS in remote ECG monitoring systems. This paper proposes a non-iterative fast reconstruction algorithm based on CS and deep learning, which combines convolutional neural network (CNN) and long short-term memory (LSTM) to directly learn the mapping relationship between the rising dimension signal of the measurements and the original signal, and is validated in the MIT-BIH Arrhythmia Database (MITDB). The experimental results show that the reconstruction error of this method is lower than that of traditional algorithms including basis pursuit (BP), orthogonal matching pursuit (OMP), bound-optimization-based block sparse Bayesian learning (BSBL-BO) and rotate-singular value decomposition + basis pursuit (R-SVD+BP) at high compression ratios (CRs, when CR⩾50%). At the same time, for a 30-min ECG signal, only about 0.12 s is needed to complete the reconstruction, which is at least 45 times faster than the traditional algorithms, which is enough to support real-time applications. In addition, the proposed method is also validated in MIT-BIH Normal Sinus Rhythm Database (NSRDB), MIT-BIH Atrial Fibrillation Database (AFDB) and European ST-T Database (EDB), and the reconstructed signals of NSRDB and AFDB meet the clinical requirements at CR⩽70%, and the reconstructed signals of EDB meet the clinical requirements at CR⩽90%.

AN EFFICIENT ALGORITHM FOR R PEAKS DETECTION OF ELECTROCARDIOGRAM SIGNALS

Efficient [Formula: see text] peaks detection is the key to the accurate analysis of electrocardiogram (ECG) signals which is a benefit to the early detection of cardiovascular diseases. In recent years, many effective [Formula: see text] peaks detection methods have been proposed, however, the false detection rate is relatively high when the noisy ECG signal is involved. Based on the property of MTEO that it could enhance the features of signal, a novel [Formula: see text] peaks detection algorithm is proposed in this paper to deal with ECG signals with low SNR. The algorithm includes two stages. In the first stage, a band-pass filter is used for eliminating noise, then the first-order forward differentiation and MTEO are used to transform the ECG signals, at last, the output of MTEO is smoothed with a Moving Averaging filter. In the second stage, the adaptive thresholds method and efficient decision rules are applied to detect the true [Formula: see text] peaks. The efficiency and robustness of the proposed method are substantiated on MIT-BIH Arrhythmia Database (MITDB), Fantasia Database and MIT-BIH Normal Sinus Rhythm Database. The testing of the proposed method on the MITDB showed the following results: Sensitivity [Formula: see text], Positive predictivity [Formula: see text] and Accuracy [Formula: see text]. On Fantasia Database involvement, [Formula: see text], [Formula: see text] and [Formula: see text]. On MIT-BIH Normal Sinus Rhythm Database involvement, [Formula: see text], [Formula: see text] and [Formula: see text]. Compared with other [Formula: see text] peaks detection methods, the proposed algorithm is simple, efficient and robust.

Classification of ECG signals for detection of arrhythmia and congestive heart failure based on continuous wavelet transform and deep neural networks

According to World Health Organization (WHO) report an estimated 17.9 million lives are being lost each year due to cardiovascular diseases (CVDs) and is the top contributor to the death causes. 80% of the cardiovascular cases include heart attacks and strokes. This work is an effort to accurately predict the common heart diseases such as arrhythmia (ARR) and congestive heart failure (CHF) along with the normal sinus rhythm (NSR) based on the integrated model developed using continuous wavelet transform (CWT) and deep neural networks. The proposed method used in this research analyses the time-frequency features of an electrocardiogram (ECG) signal by first converting the 1D ECG signals to the 2D Scalogram images and subsequently the 2D images are being used as an input to the 2D deep neural network model-AlexNet. The reason behind converting the ECG signals to 2D images is that it is easier to extract deep features from images rather than from the raw data for training purposes in AlexNet. The dataset used for this research was obtained from Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database, MIT-BIH normal sinus rhythm database and Beth Israel Deaconess Medical Center (BIDMC) congestive heart failure database. In this work, we have identified the best fit parameters for the AlexNet model that could successfully predict the common heart diseases with an accuracy of 98.7%. This work is also being compared with the recent research done in the field of ECG Classification for detection of heart conditions and proves to be an effective technique for the classification.

Detection of Atrial Fibrillation and Normal Sinus Rhythm Using Multiple Machine Learning Classifiers

In this era of eHealth, healthcare data has gained a significant importance due to having human survival information. Detection of the atrial fibrillation (AF) from electrocardiogram (ECG) rhythm is a promising area of research because of its critical impact on mortality ratio in all over the world. Although, most of the studies have shown more than 90% results accuracy in terms of specificity (Sp) and sensitivity (Se), yet this accuracy is not sufficient and cannot be considered reliable for AF continuous monitoring due to high ratio of false alarms. Existing works scarcely compare the accuracy of the results generated by a classifier with those of other robust classifiers. Further, the results are needed to be verified with more statistical measures. In this paper, a multiple classifiers-based model is proposed in which the transition between normal sinus rhythm (NSR) and AF are performed on the basis of ventricle activities of the heart. The proposed scheme first extracts several features that are pertinent to AF diagnosis from the ECG data. Later, the classification model categorizes rhythms into classes using statistical techniques. The experimental evaluation is performed on five datasets which include AF Challenge 2017 database, NSR database (NSRDB), NSR RR interval database (NSRDB-2), AF database (AFDB) and long-term AF database (LTAFDB). Based on verification of results using different measures, the proposed scheme outperforms in comparison to the existing systems in terms of area under the curve (AUC), Se, Sp, positive predictive value (PPV), accuracy (ACC), and negative predictive value (NPV) for all datasets. Specifically, the decision tree (DT) obtains 99% AUC, 92% Se and 98% Sp for the AF Challenge 2017 database, which are improved than parallel systems.

Variations of Time Irreversibility of Heart Rate Variability Under Normobaric Hypoxic Exposure.

In the field of biomedicine, time irreversibility is used to describe how imbalanced and asymmetric biological signals are. As an important feature of signals, the direction of time is always ignored. To find out the variation regularity of time irreversibility of heart rate variability (HRV) in the initial stage of hypoxic exposure, the present study implemented 2 h acute normobaric hypoxic exposure on six young subjects who have no plateau or hypoxia experiences; oxygen concentration was set as 12.9%. Electrocardiogram (ECG) signals were recorded in the whole process and RR interval sequences were extracted. Mathematical operations were executed to transform the difference of adjacent RR intervals into proportion and distance with delay time to conduct time irreversibility analysis of HRV. The same calculating method was implemented on six items randomly picked out from the MIT-BIH normal sinus rhythm database as a control group. Results show that variation of time irreversibility of HRV in a hypoxic environment is different from that in a normoxic environment, time irreversibility indices of a hypoxic group decreases continually at a delay time of 1 and 2, and indices curves of time irreversibility gradually tend to be steady and gather with each other at a delay time of 3 or 4. The control group shows no consistent tendency no matter what the delay time is in the range of 1–4. Our study indicates that in short-time hypoxic exposure, as hypoxic time goes by, regulation of the cardiovascular autonomic nervous system weakens; regulation times and intensity of sympathetic and parasympathetic nerves tend to be equal.

Atrial fibrillation detection with and without atrial activity analysis using lead-I mobile ECG technology

ObjectiveThis paper presents the performance of an atrial fibrillation (AF) detection algorithm in a lead-I mobile ECG setting. Our aim is to demonstrate that false positive cases occurring due to non-AF arrhythmias can be significantly reduced by taking atrial activity into account in addition to heart rhythm. MethodsAF detection was carried out in two ways: only by heart rhythm assessment based on Poincaré plot of RR intervals (A1), and by also performing P wave analysis on the average majority cycle (A2). ResultsThe algorithm was tested on the PhysioNet MIT-BIH Atrial Fibrillation Database, Long-Term AF Database, MIT-BIH Arrhythmia Database, and MIT-BIH Normal Sinus Rhythm Database. A1 produced an average sensitivity (Se) of 97.64 % and specificity (Sp) of 93.06 %. A2 resulted Se = 96.52 % and Sp = 99.12 %. Additionally, clinical data acquisition was performed by the Sanatmetal WIWE mobile ECG system. On the latter records, Se = 100 % and Sp = 95.31 % were obtained with A1, while A2 resulted Se = 100 % and Sp = 100 %. ConclusionAtrial activity analysis significantly increased Sp in records with frequent premature beats (by almost 12 %) and marked sinus arrhythmia (by 7.5 %). Based on our results and data from the literature, we suggest that AF detection methods should be tested more carefully on non-AF arrhythmia cases. Applied in WIWE, the A2 version of our algorithm outperforms the AF detectors of current single-channel mobile ECG systems. SignificanceThe results contribute to the significant reduction of false positive AF detections in lead-I mobile ECG technology.

Denoising of Electrocardiogram Signal Using S-Transform Based Time–Frequency Filtering Approach

Electrocardiogram (ECG) signals are damaged by various types of noise during acquisition and transmission which may mislead the analysis. In this paper, an automated denoising technique based on time–frequency filtering approach is proposed. The S-transform based time–frequency method with morphological processing is employed to visualize the spectrum of the ECG signal. The time–frequency plane is surface fitted to estimate the noise and then a threshold is used to eliminate it. The proposed method has been assessed with numerous abnormal and normal ECG signals selected from the MIT-BIH normal sinus rhythm database. Several noises with varying signal-to-noise ratio are considered for the simulation study. The results showed that the proposed technique is superior to the existing wavelet-based approach. It significantly reduces the mean square error, percentage root mean square difference and improves the signal-to-noise ratio (SNR). Moreover, at lower SNR condition, the proposed approach efficiently suppresses the noise. In the proposed approach, the requirement of the reference signal is eliminated; and at the same time, the structural information is preserved in the denoised signal.

Embedded Algorithm for QRS Detection Based on Signal Shape

Electrocardiogram (ECG) is one of the most useful medical examinations for the monitoring of cardiovascular diseases. The position and the duration of the QRS complex on the ECG signal are very important in the diagnosis of these diseases. Even though several R-peak (and hence QRS complex) detection algorithms are available, most are based on complex computations that require off-line processing on a personal computer (PC). However, with the advances in wearable devices and telemedicine, an algorithm that can run efficiently on a microcontroller (or embedded system) is needed. This article presents the development of an embedded algorithm for the detection of the QRS complex of an ECG signal. The algorithm is based on the shape and appearance of the signal. It extracts certain characteristics like the shape of the QRS complex, its slope, trend, and the duration between two successive QRS complexes, and then use them to increase detection accuracy. First, the R-peak is detected through the application of three levels of tests using adaptive thresholds. Second, from each R position, the positions of Q and S are detected using three other tests. To evaluate the performance of the algorithm, the MIT-BIH database was used and the sensitivity, positive prediction, and F1 score were used as evaluation metrics. The algorithm obtained average F1 scores of 99.67%, 99.73%, and 99.83% for the MIT-BIH Arrhythmia, Pacemaker Rhythm, and the Normal Sinus Rhythm Databases, respectively. Both normal and abnormal ECG signals were used in this performance test. The algorithm was then implemented on a microcontroller system, and its accuracy and run time were evaluated. The obtained F1 score results were the same as on the personal computer (PC) and an average run time of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16.23~\mu \text{s}$ </tex-math></inline-formula> per sample was obtained. The performance of the algorithm was also compared to other commonly used algorithms. The proposed algorithm has great potential in wearable systems for long-term monitoring.

Classification of human electrocardiograms by multi-layer convolutional neural network and hyperparameter optimization

A multi-layer convolutional neural network (MCNN) with hyperparameter optimization (HyperMCNN) is proposed for classifying human electrocardiograms (ECGs). For performance tests of the HyperMCNN, ECG recordings for patients with cardiac arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR) were obtained from three PhysioNet databases: MIT-BIH Arrhythmia Database, BIDMC Congestive Heart Failure Database, and MIT-BIH Normal Sinus Rhythm Database, respectively. The MCNN hyperparameters in convolutional layers included number of filters, filter size, padding, and filter stride. The hyperparameters in max-pooling layers were pooling size and pooling stride. Gradient method was also a hyperparameter used to train the MCNN model. Uniform experimental design approach was used to optimize the hyperparameter combination for the MCNN. In performance tests, the resulting 16-layer CNN with an appropriate hyperparameter combination (16-layer HyperMCNN) was used to distinguish among ARR, CHF, and NSR. The experimental results showed that the average correct rate and standard deviation obtained by the 16-layer HyperMCNN were superior to those obtained by a 16-layer CNN with a hyperparameter combination given by Matlab examples. Furthermore, in terms of performance in distinguishing among ARR, CHF, and NSR, the 16-layer HyperMCNN was superior to the 25-layer AlexNet, which was the neural network that had the best image identification performance in the ImageNet Large Scale Visual Recognition Challenge in 2012.

WITHDRAWN: Early detection of cardiovascular diseases using deep convolutional neural network & fourier wavelet transform

CVD (Cardiovascular) Diseases is leading cause of human deaths globally. The increasing threats of CVD can be early detected with various medical tests including electrocardiogram (ECG), and also 2D Echo, Stress Test. As ECG is non-invasive, clinical therapeutic agent, so with the help of an ECG signal, early detection of CVD is possible and proper medication can be provided for human life. All these signals from different equipment can, however, be non-stationary and repetitive which takes more time to process and exhausting for physical examination. Moreover, Heart Signal from ECG machine is not a stationary indicator, the discrepancies might not be repeated and may demonstrate up at various periods, hence there is need to adopt computer aided model for fast and accurate prognosis of CVD’s. Also now a day’s various smart devices are available in market which captures the heart rate through pulse detection and also needs fast processing of that heart rate signals for accurately identifying the ECG Signal Wavelengths which further helps in identification of CVD’s. Through this study, we are proposing CVD detection by using Deep Neural Network (DNN) with the help of HRV (Heart Rate Variability). The experimental result had shown that we had achieved 99% accuracy by using our DNN model. Earlier Mental stress detection methods using machine learning methodologies like Support Vector Machine (SVM), Decision Tree (DT) or Random Forest (RF) which gives the accuracy in the range of 80%-85%. In comparison to that we had achieved the highest accuracy rate because we had used Fourier wavelet transform mechanism on ECG signal to detect the variance in HRV (Heart Rate Variability). We had also used 2 additional intermediately layer for training the DNN model and implemented four-fold average accuracy and precision which had given us better result. We had used the combination of MIT-BIH Arrhythmia Database; BIDMC Congestive Heart Failure Database & MIT-BIH Normal Sinus Rhythm Database ECG Dataset. This dataset was resampled with rate of 128 Hz and data length was truncated to a common length of 65,536 samples. This study thus demonstrated the improvement of DNN on CVD detection with the highest accuracy of deep learning model as of now.

ECG based biometric identification using one-dimensional local difference pattern

In this work, an enhanced version of 1D local binary pattern is proposed, for the derivation of the most relevant features for ECG-based human recognition. Generally, ECG signal characteristics by nature impose some notable challenges, mostly related to its sensitivity to noises, artifacts, behavioral and emotional disorders and other variability factors. To deal with this critical issue, we use a One-dimensional Local Difference Pattern (1D-LDP) operator to extract the discriminating statistical features from ECG by using the difference between consecutive neighboring samples to capture both the micro and macro patterns information in the heartbeat activity while reducing the local and global variation occurred in ECG over time. To verify its robustness, K-nearest neighbors (KNN) linear support vector machine (SVM) and neural network were performed as the classifier models in this work. Obtained results show that the 1D-LDP operator clearly outperforms existing 1D-LBP variants on MIT-BIH Normal Sinus Rhythm and ECG-ID database.

Statistical Analysis of HRV Parameters for the Detection of Arrhythmia

The repolarization and depolarization in heart generate electrical signals in the form of an ECG wave. The condition of the heart can be indicated by using Heart Rate Variability (HRV) features. In this work, FIR filter is used at the pre-processing phase for denoising, and then statistical analysis is applied for time-domain HRV feature extraction and selection. This algorithm is evaluated on different records of MIT/BIH Normal Sinus Rhythm and Arrhythmia database. The [Formula: see text]-test implementation in both databases shows that there are significant variations in HRV features, where meanRR and HR have suggestive significant ([Formula: see text]) changes, while maxRR, minRR, maxminRR, and SDNN have strongly significant ([Formula: see text]) changes. To validate the statistical analysis of HRV, feature classification has been done using SVM and kNN classifiers. A significant improvement of 2% and 14.02% has been observed in the overall accuracy of SVM and kNN classifiers after feature selection, respectively. These HRV features can be used for the early prediction of various Cardio-Vascular Diseases (CVD).

Analysis of autonomic nervous pattern in hypertension based on short-term heart rate variability.

Physiological studies have found that the autonomic nervous system plays an important role in controlling blood pressure values. This paper, based on machine learning approaches, analysed short-term heart rate variability to determine differences in autonomic nervous function between hypertensive patients and normal population. The electrocardiogram (ECG) of hypertensive patients are 137 ECG recordings provided by Smart Health for Assessing the Risk of Events via ECG (SHAREE database). The RR intervals of healthy subjects include the data of 18 subjects from the MIT-BIH Normal Sinus Rhythm Database (nsrdb) and 54 subjects from the Normal Sinus Rhythm RR Interval Database (nsr2db). In this paper, each RR segment includes continuous 500 beats. Seventeen features were extracted to distinguish the hypertensive heart beat rhythms from the normal ones, and Kolmogorov-Smirnov test and sequential backward selection (SBS) were applied to get the best feature combinations. In addition, support vector machine (SVM), k-nearest neighbor (KNN) and random forest (RF) were applied as classifiers in the study. The performance of each classifier was evaluated independently using the leave-one-subject-out validation method. The best predictive model was based on RF and enabled to identify hypertensive patients by five features with an accuracy of 86.44%. The best five HRV features are sample entropy (SampEn), very low frequency spectral powers (VLF), root mean square of successful differences (RMSSD), ratio of low frequency spectral powers and high frequency spectral powers (LF/HF) and vector angle index (VAI). The results of the study show sympathetic overactivity and decreased parasympathetic tone in hypertensive patients.

ECG Classification using Machine Learning

Recently, the obvious increasing number of cardiovascular disease, the automatic classification research of Electrocardiogram signals (ECG) has been playing a important part in the clinical diagnosis of cardiovascular disease. Convolution neural network (CNN) based method is proposed to classify ECG signals. The proposed CNN model consists of five layers in addition to the input layer and the output layer, i.e., two convolution layers, two down sampling layers and one full connection layer, extracting the effective features from the original data and classifying the features using wavelet .The classification of ARR (Arrhythmia), CHF (Congestive Heart Failure), and NSR (Normal Sinus Rhythm) signals. The experimental results contains on ARR signals from the MIT-BIH arrhythmia,CHF signals from the BIDMC Congestive Heart Failure and NSR signals from the MIT-BIH Normal Sinus Rhythm Databases show that the proposed method achieves a promising classification accuracy of 90.63%, significantly outperforming several typical ECG classification methods.

Recovery of the fetal electrocardiogram for morphological analysis from two trans-abdominal channels via optimal shrinkage

Objective : We propose a novel algorithm to recover fetal electrocardiogram (ECG) for both the fetal heart rate analysis and morphological analysis of its waveform from two or three trans-abdominal maternal ECG channels. Approach: We design an algorithm based on the optimal-shrinkage under the wave-shape manifold model. For the fetal heart rate analysis, the algorithm is evaluated on publicly available database, 2013 PhyioNet/Computing in Cardiology Challenge, set A (CinC2013). For the morphological analysis, we analyze CinC2013 and another publicly available database, non-invasive fetal ECG arrhythmia database (nifeadb), and propose to simulate semi-real databases by mixing the MIT-BIH normal sinus rhythm database and MITDB arrhythmia database. Main results: For the fetal R peak detection, the proposed algorithm outperforms all algorithms under comparison. For the morphological analysis, the algorithm provides an encouraging result in recovery of the fetal ECG waveform, including PR, QT and ST intervals, even when the fetus has arrhythmia, both in real and simulated databases. Significance: To the best of our knowledge, this is the first work focusing on recovering the fetal ECG for morphological analysis from two or three channels with an algorithm potentially applicable for continuous fetal electrocardiographic monitoring, which creates the potential for long term monitoring purpose.

Missing RRI Interpolation Algorithm based on Locally Weighted Partial Least Squares for Precise Heart Rate Variability Analysis.

The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV), which reflects activities of the autonomic nervous system (ANS) and has been used for various health monitoring services. Accurate R wave detection is crucial for success in HRV-based health monitoring services; however, ECG artifacts often cause missing R waves and deteriorate the accuracy of HRV analysis. The present work proposes a new missing RRI interpolation technique based on Just-In-Time (JIT) modeling. In the JIT modeling framework, a local regression model is built by weighing samples stored in the database according to the distance from a query and output is estimated only when an estimate is requested. The proposed method builds a local model and estimates missing RRI only when an RRI detection error is detected. Locally weighted partial least squares (LWPLS) is adopted for local model construction. The proposed method is referred to as LWPLS-based RRI interpolation (LWPLS-RI). The performance of the proposed LWPLS-RI was evaluated through its application to RRI data with artificial missing RRIs. We used the MIT-BIH Normal Sinus Rhythm Database for nominal RRI dataset construction. Missing RRIs were artificially introduced and they were interpolated by the proposed LWPLS-RI. In addition, MEAN that replaces the missing RRI by a mean of the past RRI data was compared as a conventional method. The result showed that the proposed LWPLS-RI improved root mean squared error (RMSE) of RRI by about 70% in comparison with MEAN. In addition, the proposed method realized precise HRV analysis. The proposed method will contribute to the realization of precise HRV-based health monitoring services.

An Efficient Teager Energy Operator-Based Automated QRS Complex Detection.

Database The efficiency and robustness of the proposed method has been tested on Fantasia Database (FTD), MIT-BIH Arrhythmia Database (MIT-AD), and MIT-BIH Normal Sinus Rhythm Database (MIT-NSD). Aim Because of the importance of QRS complex in the diagnosis of cardiovascular diseases, improvement in accuracy of its measurement has been set as a target. The present study provides an algorithm for automatic detection of QRS complex on the ECG signal, with the benefit of energy and reduced impact of noise on the ECG signal. Method The method is basically based on the Teager energy operator (TEO), which facilitates the detection of the baseline threshold and extracts QRS complex from the ECG signal. Results The testing of the undertaken method on the Fanatasia Database showed the following results: sensitivity (Se) = 99.971%, positive prediction (P+) = 99.973%, detection error rate (DER) = 0.056%, and accuracy (Acc) = 99.944%. On MIT-AD involvement, Se = 99.74%, P+ = 99.97%, DER = 0.291%, and Acc = 99.71%. On MIT-NSD involvement, Se = 99.878%, P+ = 99.989%, DER = 0.134%, and Acc = 99.867%. Conclusion Despite the closeness of the recorded peaks which inflicts a constraint in detection of the two consecutive QRS complexes, the proposed method, by applying 4 simple and quick steps, has effectively and reliably detected the QRS complexes which make it suitable for practical purposes and applications.

Classification of heart signal using wavelet haar and backpropagation neural network

Researchers used many methods to extract and classify heart signals. In this study wavelet haar is use to extract characteristics of heart signals. Artificial neural networks Backpropagation for the classification of heart signals. The data is taken from Physiobank namely MIT-BIH Arrhythmia Database and MIT-BIH Normal Sinus Rhythm Database. The data is processed using Haar wavelet method for its extraction. The results of feature extraction methods will be use for the classification process. The research found that by using Wavelet Haar feature extraction and classification using Backpropagation obtained classification accuracy rate of 92%.

Automatic detection and classification of atrial fibrillation using RR intervals and multi-eigenvalue

Atrial fibrillation (AF) is a common arrhythmia disease. Detection of atrial fibrillation based on electrocardiogram (ECG) is of great significance for clinical diagnosis. Due to the non-linearity and complexity of ECG signals, the procedure to manually diagnose the ECG signals takes a lot of time and is prone to errors. In order to overcome the above problems, a feature extraction method based on RR interval is proposed in this paper. The discrete degree of RR interval is described with the robust coefficient of variation (RCV), the distribution shape of RR interval is described with the skewness parameter (SKP), and the complexity of RR interval is described with the Lempel-Ziv complexity (LZC). Finally, the feature vectors of RCV, SKP, and LZC are input into the support vector machine (SVM) classifier model to achieve automatic classification and detection of atrial fibrillation. To verify the validity and practicability of the proposed method, the MIT-BIH atrial fibrillation database was used to verify the data. The final classification results show that the sensitivity is 95.81%, the specificity is 96.48%, the accuracy is 96.09%, and the specificity of 95.16% is achieved in the MIT-BIH normal sinus rhythm database. The experimental results show that the proposed method is an effective classification method for atrial fibrillation.