Segments Of ECG Signals Research Articles

An Automated System for Sleep Apnea Detection

Sleep apnea (SA) is a common sleep-related breathing disorder that can occur hundreds of times per night and can lead to catastrophic cardiovascular and neurological disorders if it happens frequently enough. Polysomnography (PSG) is the standard diagnostic tool for sleep apnea. However, it requires suspected patients to spend one to two nights in the lab and capture roughly 16 signals under professional supervision. The broad use of PSG in public health applications is hampered by complex processes. Some researchers have recently advocated using a single-lead ECG signal to detect SA. These techniques are based on the assumption that SA only uses the current ECG signal segment. SA, on the other hand, is time-dependent; that is, the SA of the preceding ECG segment has an impact on the current SA diagnosis. The authors present a time window artificial neural network that takes advantage of the time dependence between ECG signal segments while requiring no prior assumptions regarding training data distribution. The proposed method's performance has been greatly improved when compared to classic on-time window machine learning approaches as well as previous work after being verified on a genuine ECG signal dataset.

Unsupervised Learning-Based Non-Invasive Fetal ECG Muti-Level Signal Quality Assessment.

To monitor fetal health and growth, fetal heart rate is a critical indicator. The non-invasive fetal electrocardiogram is a widely employed measurement for fetal heart rate estimation, which is extracted from the electrodes placed on the surface of the maternal abdomen. The qualities of the fetal ECG recordings, however, are frequently affected by the noises from various interference sources. In general, the fetal heart rate estimates are unreliable when low-quality fetal ECG signals are used for fetal heart rate estimation, which makes accurate fetal heart rate estimation a challenging task. So, the signal quality assessment for the fetal ECG records is an essential step before fetal heart rate estimation. In other words, some low-quality fetal ECG signal segments are supposed to be detected and removed by utilizing signal quality assessment, so as to improve the accuracy of fetal heart rate estimation. A few supervised learning-based fetal ECG signal quality assessment approaches have been introduced and shown to accurately classify high- and low-quality fetal ECG signal segments, but large fetal ECG datasets with quality annotation are required in these methods. Yet, the labeled fetal ECG datasets are limited. Proposed methods: An unsupervised learning-based multi-level fetal ECG signal quality assessment approach is proposed in this paper for identifying three levels of fetal ECG signal quality. We extracted some features associated with signal quality, including entropy-based features, statistical features, and ECG signal quality indices. Additionally, an autoencoder-based feature is calculated, which is related to the reconstruction error of the spectrograms generated from fetal ECG signal segments. The high-, medium-, and low-quality fetal ECG signal segments are classified by inputting these features into a self-organizing map. The experimental results showed that our proposal achieved a weighted average F1-score of 90% in three-level fetal ECG signal quality classification. Moreover, with the acceptable removal of detected low-quality signal segments, the errors of fetal heart rate estimation were reduced to a certain extent.

A multi-label classification system for anomaly classification in electrocardiogram.

Automatic classification of ECG signals has become a research hotspot, and most of the research work in this field is currently aimed at single-label classification. However, a segment of ECG signal may contain more than two cardiac diseases, and single-label classification cannot accurately judge all possibilities. Besides, single-label classification performs classification in units of segmented beats, which destroys the contextual relevance of signal data. Therefore, studying the multi-label classification of ECG signals becomes more critical. This study proposes a method based on the multi-label question transformation method-binary correlation and classifies ECG signals by constructing a deep sequence model. Binary correlation simplifies the learning difficulty of deep learning models and converts multi-label problems into multiple binary classification problems. The experimental results are as follows: F1 score is 0.767, Hamming Loss is 0.073, Coverage is 3.4, and Ranking Loss is 0.262. It performs better than existing work.

A Multi-Class Approach for the Automatic Detection of Congestive Heart Failure in Windowed ECG.

Congestive heart failure (CHF) is a chronic heart disease that causes debilitating symptoms and leads to higher mortality and morbidity. In this paper, we present HARPER, a novel automatic detector of CHF episodes able to distinguish between Normal Sinus Rhythm (NSR), CHF, and no-CHF. The main advantages of HARPER are its reliability and its capability of providing an early diagnosis. Indeed, the method is based on evaluating real-time features and observing a brief segment of ECG signal. HARPER is an independent tool meaning that it does not need any ECG annotation or segmentation algorithms to provide detection. The approach was submitted to complete experimentation by involving both the intra- and inter-patient validation schemes. The results are comparable to the state-of-art methods, highlighting the suitability of HARPER to be used in modern IoMT systems as a multi-class, fast, and highly accurate detector of CHF. We also provide guidelines for configuring a temporal window to be used in the automatic detection of CHF episodes.

Complexity Analysis in the PR, QT, RR and ST Segments of ECG for Early Assessment of Severity in Cardiac Autonomic Neuropathy

Early-stage detection of cardiac autonomic neuropathy (CAN) is important for better management of the disease and prevents hospitalization. This study has investigated the complex nature of PR, QT, RR, and ST time segments of ECG signals by computing the fractal dimension (FD) of all segments from 20 min ECG recordings of people with different severity of the disease and healthy individuals. The mean computed for each ECG time segment to distinguish between subjects was insufficient for an early diagnosis. Statistical analysis shows that the change of FD in various time segments of ECG throughout the recording was most suitable to assess the steps for severity in symptoms of CAN between the healthy and the subjects with early symptoms of CAN. The complexity of ECG features was evaluated using various classifier models, namely, support vector machine (SVM), naïve Bayes, random forest, K-nearest neighbor (KNN), AdaBoost, and neural networks. Performance measures were computed on all models, with a maximum neural network classifier having an accuracy of 96.9%. Feature ranking results show that fractal features have more significance than the time segments of ECG in differentiating the subjects. The results of statistical validation show that all the selected features based on ECG physiology proved to have an evident complexity change between normal and severity stages of CAN. Thus, this work reports the complexity analysis in all the selected time segments of ECG that can be an effective tool for early diagnostics for CAN.

Inter-patient arrhythmia classification with improved deep residual convolutional neural network

Background and Objective: Early detection of arrhythmias has become critical due to the increased mortality from cardiovascular disease, and ECG is an effective tool for diagnosing cardiovascular disease and detecting arrhythmias. Classification based on ECG signal segments is more suitable for clinical application.Methods: An improved deep residual convolutional neural network is proposed to classify arrhythmias automatically. Firstly, the overlapping segmentation method is used to segment the ECG signals in the MIT-BIH database into segments of 5 seconds in length to overcome the imbalance between classes, and these segments of the ECG signals are re-labeled. Then the discrete wavelet transform (DWT) is used to denoise these segments and the improved deep residual convolutional neural network is used for arrhythmia classification. In addition, the focal loss function is used to overcome the imbalanced classification difficulty between classes.Results: The proposed method gives 94.54% sensitivity, 93.33% positive predictivity, and 80.80% specificity for normal segments. For the supraventricular ectopic segment, the proposed method gives 35.22% sensitivity, 65.88% positive predictivity, and 98.83% specificity. For the ventricular ectopic segment, the proposed method gives 88.35% sensitivity, 79.86% positive predictivity, and 94.92% specificity.Conclusion: The results of this study indicate that the proposed improved deep residual convolutional neural network model trained by the training set obtained by using the overlapping segmentation method is comparable to a classical method and three state-of-art methods. In addition, the classification performance of the network model trained by focal loss as the loss function is further improved.

A Novel Lossless ECG Compression Algorithm for Active Implants.

A low complexity lossless ECG compression algorithm for active implants is proposed in this paper. The algorithm is based on adaptive length encoding by combining linear prediction with delta encoding. The algorithm is tested on forty-eight segments of 30-min ECG signals obtained from MIT-BIH Arrhythmia Database. The results show that with the data segment length of 33 and the predictor order of 2, the average compression rate of the algorithm reaches 2.43 and there is no difference between the reconstructed signal and the original one. It implies that it can realize the lossless compression with a high compression ratio. Meanwhile, the low complexity makes this novel algorithm suitable for ECG monitoring applications of active implants.

Parallel classification model of arrhythmia based on DenseNet-BiLSTM

In order to improve the classification performance of the model for different kinds of arrhythmias, a parallel classification model of arrhythmia based on DenseNet-BiLSTM is researched and proposed. Firstly, the model adopts a parallel structure. After wavelet denoising and heartbeat segmentation of ECG signals, this model can simultaneously capture the waveform features of small-scale heartbeat and large-scale heartbeat containing RR interval; Then, based on deep learning, Densely connected convolutional network (DenseNet) is applied to improve the model's ability to extract local features of ECG signals, and bidirectional long short-term memory network (BiLSTM) is introduced to improve the performance of the model in extracting time series features of ECG signals; Finally, weighted cross entropy loss function is used to alleviate the class imbalance of arrhythmia, and Softmax function is applied to achieve 4 classifications of arrhythmia. Experiments based on MIT-BIH arrhythmia database show that under the intra-patient paradigm, training time for each epoch, overall accuracy, F1 and specificity are 42 s, 99.44%, 95.89% and 99.32%, respectively; Under the inter-patient paradigm, training time for each epoch, overall accuracy, F1 and specificity are 23 s, 92.37%, 63.49% and 94.51%, respectively. Compared with other classification models, the model proposed in this paper has a good classification effect and is expected to be used in clinical auxiliary diagnosis.

Classification of electrocardiogram signal using an ensemble of deep learning models

PurposeAccording to the World Health Organization, arrhythmia is one of the primary causes of deaths across the globe. In order to reduce mortality rate, cardiovascular disease should be properly identified and the proper treatment for the same should be immediately provided to the patients. The objective of this paper was to implement a better heartbeat classification model which will work better than the other implemented heartbeat classification methods.Design/methodology/approachIn this paper, the ensemble of two deep learning models is proposed to classify the MIT-BIH arrhythmia database into four different classes according to ANSI-AAMI standards. First, a convolutional neural network (CNN) model is used to classify heartbeats on a raw data set. Secondly, four features (wavelets, R-R intervals, morphological and higher-order statistics) are extracted from the data set and then applied to a long short-term memory (LSTM) model to classify the heartbeats. Finally, the ensemble of CNN and LSTM model with sum rule, product rule and majority voting has been used to identify the heartbeat classes.FindingsAmong these, the highest accuracy obtained is 98.58% using ensemble method with product rule. The results show that the ensemble of CNN and BLSTM has offered satisfactory performance compared to other techniques discussed in this study.Originality/valueIn this study, we have developed a new combination of two deep learning models to enhance the performance of arrhythmia classification using segmentation of input ECG signals. The contributions of this study are as follows: First, a deep CNN model is built to classify ECG heartbeat using a raw data set. Second, four types of features (R-R interval, HOS, morphological and wavelet) were extracted from the raw data set and then applied to the bidirectional LSTM model to classify the ECG heartbeat. Third, combination rules (sum rules, product rules and majority voting rules) were tested to ensure the accumulated probabilities of the CNN and LSTM models.

Анализ биомедицинских изображений в кардиологии на основе машинного обучения

In this work presents a system of an automatic algorithm for analyzing images and other signals that can significantly help cardiologists in processing large amounts of data or data that require significant processing time). The analysis of heart rate variability, as well as the ECG waveform allows you to diagnose various diseases, including at an early stage, and determine specific physiological conditions, for example, the presence of physical activity now. The aim of this study was to develop a new effective method for detecting arrhythmias using image segments of ECG signals. At the first stage, image processing takes place, to identify noise in the image, data categories are allocated. At the second stage, the identification of the norm, a deviation from the norm and a critical deviation from the norm, which will allow predicting the early development of the disease and identifying diseases in patients.

Detection of Sleep Apnea from Single-Lead ECG Signal Using a Time Window Artificial Neural Network.

Sleep apnea (SA) is a ubiquitous sleep-related respiratory disease. It can occur hundreds of times at night, and its long-term occurrences can lead to some serious cardiovascular and neurological diseases. Polysomnography (PSG) is a commonly used diagnostic device for SA. But it requires suspected patients to sleep in the lab for one to two nights and records about 16 signals through expert monitoring. The complex processes hinder the widespread implementation of PSG in public health applications. Recently, some researchers have proposed using a single-lead ECG signal for SA detection. These methods are based on the hypothesis that the SA relies only on the current ECG signal segment. However, SA has time dependence; that is, the SA of the ECG segment at the previous moment has an impact on the current SA diagnosis. In this study, we develop a time window artificial neural network that can take advantage of the time dependence between ECG signal segments and does not require any prior assumptions about the distribution of training data. By verifying on a real ECG signal dataset, the performance of our method has been significantly improved compared to traditional non-time window machine learning methods as well as previous works.

Patient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networks

Atrial Fibrillation (AF) is the most common cardiac arrhythmia worldwide. It is associated with reduced quality of life and increases the risk of stroke and myocardial infarction. Unfortunately, many cases of AF are asymptomatic and undiagnosed, which increases the risk for the patients. Due to its paroxysmal nature, the detection of AF requires the evaluation, by a cardiologist, of long-term ECG signals. In Colombia, it is difficult to have access to an early diagnosis of AF because of the associated costs to the detection and the geographical distribution of cardiologists. This work is part of a macro project that aims to develop a specific-patient portable device for the detection of AF. This device will be based on a Convolutional Neural Network (CNN). We aim to find a suitable CNN model, which later could be implemented in hardware. Diverse techniques were applied to improve the answer regarding accuracy, sensitivity, specificity, and precision. The final model achieves an accuracy of , a specificity of , a sensitivity of and a precision of . During the development of the model, the computational cost and memory resources were taking into account in order to obtain an efficient hardware model in a future implementation of the device.

Application of Dispersion Entropy to Healthy and Pathological Heartbeat ECG Segments.

Entropy quantification algorithms are a prominent tool for the quantification of irregularity in biological signal segments towards the characterization of the physiological state of individuals. This paper investigates the potential of Dispersion Entropy (DisEn) as a non-linear method to quantify the uncertainty of ECG signal segments for different types of heartbeats and the stratification of healthy heartbeats for the potential detection of developing pathologies in individuals. Our results indicate that the DisEn algorithm produces distributions with significant differences for the considered types of heartbeats, with higher DisEn values being more prominent in pathological heartbeats and normal heartbeats preceding them. This suggests that, with further research, DisEn algorithms can be integrated with heartbeat detection and classification algorithms for the improvement of medical prognosis through ECG signal processing.

RETRACTED ARTICLE: Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals

The heart disease is one of the most serious health problems in today’s world. Over 50 million persons have cardiovascular diseases around the world. Our proposed work based on 744 segments of ECG signal is obtained from the MIT-BIH Arrhythmia database (strongly imbalanced data) for one lead (modified lead II), from 29 people. In this work, we have used long-duration (10 s) ECG signal segments (13 times less classifications/analysis). The spectral power density was estimated based on Welch’s method and discrete Fourier transform to strengthen the characteristic ECG signal features. Our main contribution is the design of a novel three-layer (48 + 4 + 1) deep genetic ensemble of classifiers (DGEC). Developed method is a hybrid which combines the advantages of: (1) ensemble learning, (2) deep learning, and (3) evolutionary computation. Novel system was developed by the fusion of three normalization types, four Hamming window widths, four classifiers types, stratified tenfold cross-validation, genetic feature (frequency components) selection, layered learning, genetic optimization of classifiers parameters, and new genetic layered training (expert votes selection) to connect classifiers. The developed DGEC system achieved a recognition sensitivity of 94.62% (40 errors/744 classifications), accuracy = 99.37%, specificity = 99.66% with classification time of single sample = 0.8736 (s) in detecting 17 arrhythmia ECG classes. The proposed model can be applied in cloud computing or implemented in mobile devices to evaluate the cardiac health immediately with highest precision.

Automated Electrocardiogram Signals Based Risk Marker for Early Sudden Cardiac Death Prediction

Sudden cardiac death (SCD) is one of the cardiovascular diseases that lead to millions of deaths worldwide every year. The aim of the present work is to propose a method for reducing the mortality rate of the SCD patients by an early prediction for SCD from the ECG signal. Normal and SCD MIT databases were used in this research work. One minute segments of ECG signals were segmented from MIT databases where these segments are ten minutes before sudden cardiac arrest (SCA) onset. The collected raw ECG signals were subjected to filter to remove the noise and then normalized. A frequency-domain feature and time-domain features were extracted from the Q-T segment, Q-T interval, R-R interval and QRS interval. The features were normalized to improve the performance of the classifier. Artificial intelligence classifiers; namely, K-nearest neighbor (KNN) and linear discriminant analysis (LDA) were used separately on SCD and normal ECG signals. The highest classification accuracy obtained for KNN and LDA are 97% and 95.5% respectively.

LMS 적응필터를 이용한 직교 함수 기반의CPR 잡음 제거 알고리즘 설계

This study proposes an algorithm for removal of CPR artifact in order that automated external defibrillator (AED) can effectively diagnose ECG rhythm during cardiopulmonary resuscitation (CPR). Current AED required to interrupt chest compression for reliable rhythm analysis to avoid the effect of artifacts produced by CPR. However even temporarily interruption of chest compression during CPR adversely affects the probability of restoration of spontaneous circulation (ROSC) and survival after the delivery of the shock. Therefore, we proposed a method for removal of CPR artifacts using least mean square (LMS) filter. The removal of the CPR artifacts would enable compressions to continue during AED rhythm analysis, thereby increasing the likelihood of resuscitation success. It was tested on 31 segments of shockable and 300 segments of non-shockable ECG signals recorded from three pigs during CPR. In the result, sensitivity (Se) and specificity (Sp) analysis on the test segments showed values of Se = 3.2%, Sp = 66.0% and Se = 96.8%, Sp = 98.7% in the case of unfiltered and filtered signals during CPR. In conclusion, it was shown that the proposed method can be a useful tool to exactly diagnose the ECG rhythm during the CPR.

Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting

Abstract Automatic sleep apnea detection using single lead ECG is a precondition for the implementation of a sleep apnea monitoring device. Computerized sleep apnea screening is also essential for expediting sleep apnea research and alleviating the onus of physicians of analyzing a large volume of data by visual inspection. However, most of the state-of-the-art works on automated sleep apnea identification are either based on multiple leads and multiple physiological signals or yield poor performance. In this article, normal inverse Gaussian (NIG) pdf modeling in the recently proposed tunable-Q factor wavelet transform (TQWT) domain is introduced for computer-assisted sleep apnea diagnosis from single-lead ECG signals. First, ECG signal segments are decomposed into sub-bands using TQWT. Afterwards, the corresponding NIG parameters are computed from each of the sub-bands. These parameters are used as features in the proposed apnea detection algorithm. Adaptive boosting (AdaBoost), an eminent ensemble learning based classification scheme is employed to perform classification. The suitability of TQWT is analyzed. The effectiveness of the selected features is validated by intuitive, statistical, and graphical analyses. The performance of the proposed feature extraction scheme is evaluated for various choices of classifiers. Optimal choices of TQWT and AdaBoost parameters are also determined. The performance of the proposed method, as compared to the state-of-the-art algorithms, is comparable or superior in terms of various performance metrics.

Smoothening and Segmentation of ECG Signals Using Total Variation Denoising –Minimization-Majorization and Bottom-Up Approach

An ECG Signal records electrical activity of heart. It includes information on heart's rhythm and is useful for diagnosis of heart related diseases. It encounters with various artifacts during acquisition and transmission. The unwanted signals/noises present in ECG signals disturb the clinical information present in it. This paper tries to reduce unwanted signals through Majorization-Minorization approach to optimize total variation in the signals. The denoised signal is then segmented using bottom up approach. The results show significant improvement in signal to noise ratio and successful segmentation of sections of ECG signals.

Classification of ECG Signals By the Neighborhood Feature Extraction Method

In this study, non-linear dimension reduction methods were applied to ECG signals and success of such dimension reduction techniques for the classification and segmentation of ECG signals were discussed. Also, segmentation of data through neighbourhood feature extraction (NFE) method were enabled by transiting from high dimensioned space to low dimension space by considering the longitudinal combination of ECG signals. Results classification results of NFE algorithm performed through longitudinal combination and as a newly developed method were compared with classification results of ECG signals obtained through dimension reduction by taking one pixel. Results of NFE dimension reduction technique performed by considering the neighbour pixels, advantage of effect on segmentation of ECG signals were presented at empirical results section and the success of suggested method was indicated. Results obtained by performed study are promising for the studies to be conducted in further period.

Numerical Estimation of Ejection Fraction from 12-lead ECG

One of the key factors in assessing the health of heart muscles is measuring their ability to pump the blood out of ventricles in each contraction, which is called ejection fraction (EF). This factor is commonly estimated by a cardiologist when analyzing an echocardiogram, which is a time-consuming and expensive procedure that can also cause some inaccurate results. In this paper, we propose a numerical, short-time, and simple methodology to estimate the EF by calculating the areas and volume under ECG signals, which is a non-invasive, bedside heart monitoring procedure. We computed the areas and volume under the segments of normal ECG signals for 50 subjects by employing trapezoidal, Simpson’s, and Boole’s rules on three orthogonal planes of 12-lead ECG signal directions and five groups of leads for sagittal, frontal and transverse planes. We found a significant correlation (p<.001) between the values for EF parameters reported by cardiologists and our results, which we evaluated by calculating the Wilcoxon Signed-Ranks test, Canberra distance, Paired samples t-test, and Root Mean Square Error (RMSE). The results showed a significant correlation between the reported EF factor and the values derived by our proposed method.