Electrocardiogram Extraction Research Articles

Fetal electrocardiogram signal extraction based on multi-scale residual shrinkage U-Net

In the extraction of fetal electrocardiogram (ECG) signal, due to the unicity of the scale of the U-Net same-level convolution encoder, the size and shape difference of the ECG characteristic wave between mother and fetus are ignored, and the time information of ECG signals is not used in the threshold learning process of the encoder's residual shrinkage module. In this paper, a method of extracting fetal ECG signal based on multi-scale residual shrinkage U-Net model is proposed. First, the Inception and time domain attention were introduced into the residual shrinkage module to enhance the multi-scale feature extraction ability of the same level convolution encoder and the utilization of the time domain information of fetal ECG signal. In order to maintain more local details of ECG waveform, the maximum pooling in U-Net was replaced by Softpool. Finally, the decoder composed of the residual module and up-sampling gradually generated fetal ECG signals. In this paper, clinical ECG signals were used for experiments. The final results showed that compared with other fetal ECG extraction algorithms, the method proposed in this paper could extract clearer fetal ECG signals. The sensitivity, positive predictive value, and F1 scores in the 2013 competition data set reached 93.33%, 99.36%, and 96.09%, respectively, indicating that this method can effectively extract fetal ECG signals and has certain application values for perinatal fetal health monitoring.

MT-MV-KDF: A novel Multi-Task Multi-View Knowledge Distillation Framework for myocardial infarction detection and localization

Myocardial infarction (MI) is a prevalent and life-threatening cardiovascular ailment, diagnosed primarily through the twelve-lead electrocardiogram (ECG). These leads provide unique insights into electrical activity across diverse heart regions, crucial for MI differentiation and assessment. However, prior studies predominantly focus on single-perspective 12-lead ECG analysis, overlooking inter-lead disparities. Moreover, prevailing methods often lack versatility, being tailored to specific tasks. Therefore, this paper presents the Multi-Task Multi-View Knowledge Distillation Framework (MT-MV-KDF) for MI detection and localization. The framework combines multi-view learning, multi-task learning, and knowledge distillation to enhance model performance and efficiency. Specifically, The introduction of multi-view learning aims to learn ECG feature representations from different views. Additionally, multi-task learning enables the model to learn the similarities and differences of multiple related tasks simultaneously. Finally, knowledge distillation is used to transfer latent knowledge from the complex teacher model to the simpler student model to improve efficiency. The MT-MV-KDF consists of MT-MVT-net and MT-SVS-net, which use a multi-layer CNN architecture to extract ECG different scale features. An attention module is used to capture inter-channel information and spatial cues. Through knowledge distillation, the MT-DSVS-net (0.40M) inherits insights from MT-MVT-net while achieving comparable performance with fewer parameters. Evaluation on the PTB-XL database demonstrates MT-DSVS-net has an Acc, AUC of 93.78% and 92.23% for MI detection and 83.45% and 78.26% for MI localization, respectively. Additionally, Robustness validation on CPSC2018 and Chapman datasets further confirms the efficacy of MT-MV-KDF. This study highlights the MT-MV-KDF effectiveness in MI detection and localization, particularly in improving inter-patient MI localization.

SleepECG-Net: explainable deep learning approach with ECG for pediatric sleep apnea diagnosis.

Obstructive sleep apnea (OSA) in children is a prevalent and serious respiratory condition linked to cardiovascular morbidity. Polysomnography, the standard diagnostic approach, faces challenges in accessibility and complexity, leading to underdiagnosis. To simplify OSA diagnosis, deep learning (DL) algorithms have been developed using cardiac signals, but they often lack interpretability. Our study introduces a novel interpretable DL approach (SleepECG-Net) for directly estimating OSA severity in at-risk children. A combination of convolutional and recurrent neural networks (CNN-RNN) was trained on overnight electrocardiogram (ECG) signals. Gradient-weighted Class Activation Mapping (Grad-CAM), an eXplainable Artificial Intelligence (XAI) algorithm, was applied to explain model decisions and extract ECG patterns relevant to pediatric OSA. Accordingly, ECG signals from the semi-public Childhood Adenotonsillectomy Trial (CHAT, n = 1610) and Cleveland Family Study (CFS, n = 64), and the private University of Chicago (UofC, n = 981) databases were used. OSA diagnostic performance reached 4-class Cohen's Kappa of 0.410, 0.335, and 0.249 in CHAT, UofC, and CFS, respectively. The proposal demonstrated improved performance with increased severity along with heightened cardiovascular risk. XAI findings highlighted the detection of established ECG features linked to OSA, such as bradycardia-tachycardia events and delayed ECG patterns during apnea/hypopnea occurrences, focusing on clusters of events. Furthermore, Grad-CAM heatmaps identified potential ECG patterns indicating cardiovascular risk, such as P, T, and U waves, QT intervals, and QRS complex variations. Hence, SleepECG-Net approach may improve pediatric OSA diagnosis by also offering cardiac risk factor information, thereby increasing clinician confidence in automated systems, and promoting their effective adoption in clinical practice.

Empowering AI-Diagnosis: Deep Learning Abilities for Accurate Atrial Fibrillation Classification

Artificial intelligence (AI) is a powerful technology that can enhance clinical decision-making and the efficiency of global health systems. An AI-enabled electrocardiogram (ECG) is an essential tool for diagnosing heart abnormalities such as arrhythmias. The most prevalent arrhythmia globally is atrial fibrillation (AF), which is an irregular heart rhythm that originates in the atria and can lead to other heart-related complications. A trusted AI classification of AF is explored in this study. Deep learning (DL) has been used to analyze large amounts of publicly available ECG datasets in order to classify normal sinus rhythm (NSR), AF, and other types of arrhythmias. A convolutional neural network (CNN) has been proposed to extract ECG features and classify ECG signals. Based on a 10-fold cross-validation strategy, we conducted experiments involving three scenarios for AF classification: (i) a balanced set, an imbalanced set, and an extremely imbalanced set; (ii) a comparison of ECG denoising algorithms; and (iii) the classification of AF, NSR, and other arrhythmia types (15 classes). As a result, we have achieved 100% accuracy, sensitivity, specificity, precision, and F1-score for the AF, NSR, and non-AF classifications, both for balanced and imbalanced sets. In addition, for the classification of AF, NSR, and other types of arrhythmia (15 classes), the performance results achieved an accuracy of 99.77%, sensitivity of 96.48%, specificity of 99.87%, precision of 97.03%, and F1-score of 96.68%. The results can empower AI diagnosis and assist clinicians in classifying AF on routine screening ECGs.

Classification of Electrocardiogram Signals using Deep Learning based on Genetic Algorithm Feature Extraction.

Arrhythmias using electrocardiogram (ECG) signal is important in medical and computer research due to the timely diagnosis of dangerous cardiac conditions. The current study used the ECG to classify cardiac signals into normal heartbeats, congestive heart failure, ventricular arrhythmias, atrial fibrillation arrhythmias, atrial flutter, malignant ventricular arrhythmias, and premature atrial fibrillation. A deep learning algorithm was used to identify and diagnose cardiac arrhythmias. We proposed a new ECG signal classification method to increase signal classification sensitivity. We smoothed the ECG signal with noise removal filters. A discrete wavelet transform based on an arrhythmic database was applied to extract ECG features. Feature vectors were obtained based on wavelet decomposition energy properties and calculated values of PQRS morphological features. We used the genetic algorithm to reduce the feature vector and determine the input layer weights of the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Proposed methods for classifying ECG signals were in different classes of rhythm to diagnose heart rhythm diseases. Training data was with 80% of the data set and test data was with 20% for the whole data set. The learning accuracy for the results of training and test data in the ANN classifier was calculated as 99.9% and 88.92% and in ANFIS as 99.8% and 88.83% respectively. Based on these results, good accuracy was observed.

A novel method based on shifted rank-1 reconstruction for removing EMG artifacts in ECG signals

Electrocardiogram (ECG) signal is an essential feature of human health monitoring and detection. Electromyogram (EMG) artifacts will be involved as the noise distortion and make it difficult for the doctor’s diagnosis. By comparing with the characteristics of these two signals, ECG signals have periodicity while EMG artifacts are random. However, they overlap in the frequency domain. According to the periodicity of ECG signals, several methods of extracting ECG signals from EMG artifacts are proposed. The core of ECG extraction is to form a rank-1 trajectory matrix for reconstruction. However, due to the EMG artifacts, most formed matrices are not strict rank-1 matrices, and the length of traversal segment selected for pure ECG reconstructions is not easily determined. Therefore, a new ECG extraction method based on shifted rank-1 reconstruction is proposed. Given an approximated traversal segment length, the trajectory matrix is constructed. Through the optimization of lambda in the low rank matrix, the constructed approximation matrix is approximated by shift vectors to obtain a strict rank-1 matrix. Pure ECG signals can be reconstructed via the singular values of shifted matrix. The validation of the proposed method is made by applying the algorithm to ECG records from four different databases. The quantitative and qualitative analysis are carried out and compared with other methods. The results indicate that the proposed SR1 method can remove EMG artifacts and does not require any specific traversal segment lengths to extract clean ECG signals. The QRS complexes and ST segments can be retained in reconstructed ECG signals.

Computationally Efficient LOB Algorithm for Digitization of Degraded Paper ECG Reports

Purpose: A less computationally intensive methodology is required to digitise paper Electrocardiogram (ECG) records from scanned photographs so that it can be implemented on mobile devices and used in remote places without the necessary hardware or software requirements. Due to numerous degradations inherent in older resting paper ECG recordings, automated digitization of paper ECG records also becomes challenging. Methods: In the proposed method, we present a robust and simple, Level of Binarization (LOB) characteristic curve to binarize paper records. The binarized image is processed using the discontinuous signal correction (DSC) technique and is vertically scanned to extract 1-D ECG signal. Results: The proposed method is tested for paper ECG records having degradations like variable background colour, steep slope, ink evaporation, and folding. The average RMS error and accuracy for 101 test samples, is 0.01 and 98.34 % respectively. The results show that, the LOB-DSC gives better performance when compared to the existing ECG extraction methods. As proposed, the LOB method does not require pre-processing, and the computational complexity is also reduced. Conclusion: An automatic threshold for binarization and estimation of several types of degradations in old ECG records is obtained using the less computationally intensive LOB characteristic curve. This method can be applied in remote locations with low availability of advanced processing devices for ECG signal extraction.

An Automatic ECG Signal Quality Assessment Method Based on Resnet and Self-Attention

Electrocardiogram (ECG) signals are among the significant physiological signals that indicate the essential properties of the human body. In recent years, the measurement of ECG signals has become more portable thanks to the increasing usage of wearable health testing technology. However, the enormous amount of signal data gathered over a long period of time does impose a heavy load on medical professionals. In addition, false alarms might occur due to the potential for the detected signal to become jumbled with noise and motion perturbations. Therefore, analyzing the quality of the measured raw ECG signal automatically is a valuable task. In this paper, we propose a new single-channel ECG signal quality assessment method that combines the Resnet network structure and the principle of self-attention to extract ECG signal features using the principle of similarity between individual QRS heartbeats within a time slice of ten seconds. In addition, an improved self-attention module is introduced into the deep neural network to learn the similarity between features. Finally, the network distinguishes between acceptable and unacceptable ECG segments. The model test results indicate that the F1-score can approach 0.954, which leads to a more accurate assessment of the ECG signal quality.

Noise ECG generation method based on generative adversarial network

Over the years, significant hospitals have scanned paper electrocardiograms and saved them into electronic health records to form digital management of diagnosis and treatment records. Therefore, the electrocardiogram(ECG) scans have substantial sample diversity, correlate with the patient's past medical records, and have significant research value. However, the scanned ECG is noisy, and it is difficult to directly use it as the training data of the intelligent diagnosis algorithm, so it is necessary to preprocess it. In recent years, some researchers have proposed methods to extract ECG signals from noisy ECG using neural networks, but the results are not good enough due to the lack of paired noise ECG and noiseless ECG to train neural networks. This paper proposes an unsupervised noise ECG image generation method to overcome this difficulty. These generated images have the same ECG signals as the input ECG image, and the noise is similar to the actual ECG image. These two kinds of input images do not need to be paired. To evaluate the global and local quality of the generated images, our method uses a two-discriminator generative adversarial network and calls them global discriminator and local discriminator separately. The output vector of the global discriminator is fed into the generator when training the model, thereby helping the generator to improve the generated results in a targeted manner. Using the data generated by this method to train the neural network for ECG signal extraction can significantly improve its extraction performance. The Dice coefficient of the proposed network reaches 0.868, which is higher than 0.798 of the robust baseline model. Therefore, the proposed method can effectively solve the problem of lacking training data in the current ECG signal extraction network.

Detection of arrhythmia in 12-lead varied-length ECG using multi-branch signal fusion network

Objective. Automatic detection of arrhythmia based on electrocardiogram (ECG) plays a critical role in early prevention and diagnosis of cardiovascular diseases. With the increase in widely available digital ECG data and the development of deep learning, multi-class arrhythmia classification based on automatic feature extraction of ECG has become increasingly attractive. However, the majority of studies cannot accept varied-length ECG signals and have limited performance in detecting multi-class arrhythmias. Approach. In this study, we propose a multi-branch signal fusion network (MBSF-Net) for multi-label classification of arrhythmia in 12-lead varied-length ECG. Our model utilizes the complementary power between different structures, which include Inception with depthwise separable convolution (DWS-Inception), spatial pyramid pooling (SPP) Layer, and multi-scale fusion Resnet (MSF-Resnet). The proposed method can extract features from each lead of 12-lead ECG recordings separately and then effectively fuse the features of each lead by integrating multiple convolution kernels with different receptive fields, which can achieve the information of complementation between different angles of the ECG signal. In particular, our model can accept 12-lead ECG signals of arbitrary length. Main results. The experimental results show that our model achieved an overall classification F1 score of 83.8% in the 12-lead ECG data of CPSC-2018. In addition, the F1 score of the MBSF-Net performed best among the MBF-Nets which are removed the SPP layer from MBSF-Net. In comparison with the latest ECG classification algorithms, the proposed model can be applied in varied-length signals and has an excellent performance, which not only can fully retain the integrity of the original signals, but also eliminates the cropping/padding signal beforehand when dealing with varied-length signal database. Significance. MBSF-Net provides an end-to-end multi-label classification model with outperfom performance, which allows detection of disease in varied-length signals without any additional cropping/padding. Moreover, our research is beneficial to the development of computer-aided diagnosis.

Extraction of Fetal ECG From Abdominal and Thorax ECG Using a Non-Causal Adaptive Filter Architecture.

Extracting the Electrocardiogram (ECG) of a fetus from the ECG signal of the maternal abdomen is a challenging task due to different artifacts. The paper proposes a N-tap non-causal adaptive filter (NC-AF) that update the weight by considering the N number of past weights and N-1 number of the reference signal and error signal samples after the processing sample number n. Using the maternal abdominal signal as the primary signal and thorax signal as the reference input, the output e(n) is obtained from the mean of N number of errors. The filtering performance of NC-AF was evaluated using the Synthetic dataset and Daisy dataset with the metrics such as correlation coefficient ( γ), peak root mean square difference (PRD), the output signal to noise ratio (SNR), root mean square error (RMSE), and fetal R-peak detection accuracy (FRPDA). The NC-AF provides a maximum correlation coefficient, PRD, SNR, RMSE and FRPDA of 0.9851, 83.04%, 8.52 dB, 0.208 and 97.09% respectively with filter length N=38. The paper also proposes the architecture of NC-AF that can be implemented in hardware like FPGA. Further, the NC-AF was implemented on Virtex-7 FPGA and its performance is evaluated in terms of resource utilization, throughput, and power consumption. For filter length N=38 and wordlength L=24, the maximum performance of the filter can be attained with a power consumption of [Formula: see text] and a maximum clock frequency of 139.47 MHz.

Deep learning assessment of left ventricular hypertrophy based on electrocardiogram.

BackgroundCurrent electrocardiogram (ECG) criteria of left ventricular hypertrophy (LVH) have low sensitivity. Deep learning (DL) techniques have been widely used to detect cardiac diseases due to its ability of automatic feature extraction of ECG. However, DL was rarely applied in LVH diagnosis. Our study aimed to construct a DL model for rapid and effective detection of LVH using 12-lead ECG.MethodsWe built a DL model based on convolutional neural network-long short-term memory (CNN-LSTM) to detect LVH using 12-lead ECG. The echocardiogram and ECG of 1,863 patients obtained within 1 week after hospital admission were analyzed. Patients were evenly allocated into 3 sets at 3:1:1 ratio: the training set (n = 1,120), the validation set (n = 371) and the test set 1 (n = 372). In addition, we recruited 453 hospitalized patients into the internal test set 2. Different DL model of each subgroup was developed according to gender and relative wall thickness (RWT).ResultsThe LVH was predicted by the CNN-LSTM model with an area under the curve (AUC) of 0.62 (sensitivity 68%, specificity 57%) in the test set 1, which outperformed Cornell voltage criteria (AUC: 0.57, sensitivity 48%, specificity 72%) and Sokolow-Lyon voltage (AUC: 0.51, sensitivity 14%, specificity 96%). In the internal test set 2, the CNN-LSTM model had a stable performance in predicting LVH with an AUC of 0.59 (sensitivity 65%, specificity 57%). In the subgroup analysis, the CNN-LSTM model predicted LVH by 12-lead ECG with an AUC of 0.66 (sensitivity 72%, specificity 60%) for male patients, which performed better than that for female patients (AUC: 0.59, sensitivity 50%, specificity 71%).ConclusionOur study established a CNN-LSTM model to diagnose LVH by 12-lead ECG with higher sensitivity than current ECG diagnostic criteria. This CNN-LSTM model may be a simple and effective screening tool of LVH.

Fetal ECG denoising using dynamic time warping template subtraction.

The analysis of the fetal electrocardiogram (ECG) requires to remove the mother ECG (mECG) from the abdominal ECG signals. Template subtraction is a method that consists in modeling and removing the mECG's mean period i.e. the signal waveform defined as the Euclidean mean of all periods. This mean period is then subtracted to all periods to extract the fetal ECG (fECG). Such a method is not accurate because each mECG's period is not correctly aligned with the mean period. We propose to take account of the diffeomorphism of each period to improve the precision of the model and remove the mECG more efficiently. The soft-dynamic time warping (DTW) algorithm is used to compute the mean mECG period and the alignment between the mean period and all periods. Our approach is compared to a classic template subtraction on synthetic and real databases. Results show that considering the dynamic time warping allows a better removal of the mECG. Clinical relevance - The template subtraction is modified in this paper to consider the time warping for each mother ECG's period in order to improve the fetal ECG extraction from the abdominal ECG.

A novel fetal ecg signal extraction from maternal ecg signal using conditional generative adversarial networks (CGAN)

Fetal Electrocardiogram (ECG) signal extraction from non-invasive abdominal ECG signal is one of the important clinical practices followed to observe the fetal health state. Information about heart growth and health conditions of a fetus can be observed from fetal ECG signals. However, acquiring fetal ECG from abdominal ECG signals is still considered as a challenging task in biomedical analysis. This is mainly due to corrupted high amplitude maternal ECG signals, low signal to noise ratio of fetal ECG signal, difficulties in reduction of QRS (Q wave, R wave, S wave) complexities, fetal ECG signal superimposed characteristics, other motion, and electromyography artifacts. To reduce these conventional challenges, in fetal ECG analysis of a novel Conditional Generative adversarial network (CGAN) is introduced in this research work to extract the fetal ECG signal. The proposed classification model was classified efficiently in fetal ECG signals from non-invasive abdominal ECG signals. The experimental analysis demonstrates that the proposed network model provides better results in terms of sensitivity, specificity, and accuracy compared to the conventional fetal ECG extraction models like singular value decomposition, periodic component analysis, and Adaptive neuro-fuzzy inference system.

Frequency-Based Maternal Electrocardiogram Attenuation for Fetal Electrocardiogram Analysis

Fetal electrocardiogram (ECG) waveform analysis along with cardiac time intervals (CTIs) measurements are critical for the management of high-risk pregnancies. Currently, there is no system that can consistently and accurately measure fetal ECG. In this work, we present a new automatic approach to attenuate the maternal ECG in the frequency domain and enhance it with measurable CTIs. First, the coherent components between the maternal ECG and abdominal ECG were identified and subtracted from the latter in the frequency domain. The residual was then converted into the time domain using the inverse Fourier transform to yield the fetal ECG. This process was improved by averaging multiple beats. Two fetal cardiologists, blinded to the method, assessed the quality of fetal ECG based on a grading system and measured the CTIs. We evaluated the fetal ECG quality of our method and time-based methods using one synthetic dataset, one human dataset available in the public domain, and 37 clinical datasets. Among the 37 datasets analyzed, the mean average (± standard deviation) grade was 3.49 ± 1.22 for our method vs. 2.64 ± 1.26 for adaptive interference cancellation (p-value < 0.001), thus showing the frequency-based fetal ECG extraction was the superior method, as assessed from our clinicians’ perspectives. This method has the potential for use in clinical settings.

Predicting Driver Stress Levels with a Sensor-Equipped Steering Wheel and a Quality-Aware Heart Rate Measurement Algorithm.

Unobtrusive monitoring of driver mental states has been regarded as an important element in improving the safety of existing transportation systems. While many solutions exist relying on camera-based systems for e.g., drowsiness detection, these can be sensitive to varying lighting conditions and to driver facial accessories, such as eye/sunglasses. In this work, we evaluate the use of physiological signals derived from sensors embedded directly into the steering wheel. In particular, we are interested in monitoring driver stress levels. To achieve this goal, we first propose a modulation spectral signal representation to reliably extract electrocardiogram (ECG) signals from the steering wheel sensors, thus allowing for heart rate and heart rate variability features to be computed. When input to a simple logistic regression classifier, we show that up to 72% accuracy can be achieved when discriminating between stressful and non-stressful driving conditions. In particular, the proposed modulation spectral signal representation allows for direct quality assessment of the obtained heart rate information, thus can provide additional intelligence to autonomous driver monitoring systems.

BioSignal modelling for prediction of cardiac diseases using intra group selection method

The Autonomous Nervous System (ANS) controls the nervous system and Heart Rate Variability (HRV) can be used as a diagnostic tool to diagnose heart defects. HRV can be classified into linear and nonlinear HRV indices which are used mostly to measure the efficiency of the model. For prediction of cardiac diseases, the selection and extraction features of machine learning model are effective. The available model used till date is based on HRV indices to predict the cardiac diseases accurately. The model could hardly throw light on specifics of indices, selection process and stability of the model. The proposed model is developed considering all facet electrocardiogram amplitude (ECG), frequency components, sampling frequency, extraction methods and acquisition techniques. The machine learning based model and its performance shall be tested using the standard BioSignal method, both on the data available and on the data obtained by the author. This is unique model developed by considering the vast number of mixtures sets and more than four complex cardiac classes. The statistical analysis is performed on a variety of databases such as MIT/BIH Normal Sinus Rhythm (NSR), MIT/BIH Arrhythmia (AR) and MIT/BIH Atrial Fibrillation (AF) and Peripheral Pule Analyser using feature compatibility techniques. The classifiers are trained for prediction with approximately 40000 sets of parameters. The proposed model reaches an average accuracy of 97.87 percent and is sensitive and précised. The best features are chosen from the different HRV features that will be used for classification. The present model was checked under all possible subject scenarios, such as the raw database and the non-ECG signal. In this sense, robustness is defined not only by the specificity parameter, but also by other measuring output parameters. Support Vector Machine (SVM), K-nearest Neighbour (KNN), Ensemble Adaboost (EAB) with Random Forest (RF) are tested in a 5% higher precision band and a lower band configuration. The Random Forest has produced better results, and its robustness has been established.

Prediction of cardiovascular diseases by integrating multi-modal features with machine learning methods

Electrocardiogram (ECG) and phonocardiogram (PCG) play important roles in early prevention and diagnosis of cardiovascular diseases (CVDs). As the development of machine learning techniques, detection of CVDs by them from ECG and PCG has attracted much attention. However, current available methods are mostly based on single source data. It is desirable to develop efficient multi-modal machine learning methods to predict and diagnose CVDs. In this study, we propose a novel multi-modal method for predicting CVDs based both on ECG and PCG features. By building up conventional neural networks, we extract ECG and PCG deep-coding features respectively. The genetic algorithm is used to screen the combined features and obtain the best feature subset. Then we employ a support vector machine to implement classifications. Experimental results demonstrate the performance of our method is superior to those of single modal methods and alternatives. Our method reaches an AUC value of 0.936 when we use multi-modal features of ECG and PCG.

Recovery and noise cancelling of the fetal heart signal using a combination of NLMS algorithm and S-G filter

PurposeThe purpose of this paper is to provide appropriate methods for reducing the abnormalities on the extracted fetal heart signal from the maternal electrocardiogram (ECG).Design/methodology/approachIn this regard, the extracted signal of the fetal heart from the mother, improved using an active noise cancelation (ANC) system. It uses commonly adaptive algorithms of normalized least mean squares (NLMS). In the present paper, fetal extraction and denoising methodology are proposed. This methodology uses a combination of the NLMS algorithm with Savitzky–Golay (S-G) filter.FindingsThe obtained results show that a combination of NLMS algorithm with filter coefficient of 15 and µ = 0.02 and S-G filter has a better qSNR (qSNR = 3.6727) and good performance for fetal ECG extraction in comparison with the other works for average fmSNR in the range of −30 to −15 dB. Also, with considering the SNR value of −24.7 dB before filtering and SNR = 3.1861 dB after filtering; the SNR improvement of 27.8861 dB has been obtained.Originality/valueA new method in the extract and noise reduction of fetal ECG from maternal ECG by the combination of NLMS algorithm and S-G filter is proposed.

Statistical n-Best AFD-Based Sparse Representation for ECG Biometric Identification

Electrocardiogram (ECG) biometric recognition as a personal identification method is receiving more and more attention because it can support live verification results. Compared with other biometric-based methods, it can provide higher security performance. The difficulty of the problem lies in how to stably extract ECG signal features and achieve real-time verification. In this study, a new type of sparse representation learning framework called statistical $n$ -best adaptive Fourier decomposition (SAFD) originated by Qian is adopted in ECG biometric identification. Adaptive Fourier decomposition (AFD) is a recently developed combination of transform-based signal decomposition and sparse representation method, which can adaptively select the atoms from a redundant dictionary through orthogonal processing. The advantage of the AFD-type methods is that each atom in the dictionary has a precise mathematical formula with good analytic properties. This characteristic is significantly distinguished it from other existing sparse representations, where the atoms learned are usually matrix data and cannot be described mathematically. The proposed SAFD extends the existing $n$ -best AFD from processing single signal to multi-signals and implements the $n$ -best AFD in the stochastic Hardy space. Therefore, the small number of learned atoms by SAFD is sufficient to capture internal structure and robustness of the signal and generate a discriminative representation that reflects the time–frequency characteristics of signals. It is very suitable for non-stationary signals like ECG. The proof of convergence of the algorithm is presented. Extensive experiments are conducted on five public databases collected in different realistic conditions, and an average identification accuracy of 98.0% is achieved. In addition, less than 1 ms for one matching process makes it possible to be implemented in real time. Experimental results demonstrate that the proposed method can achieve superior performance compared to other state-of-the-art ECG biometric identification methods.