Application of Stockwell Transform and Shannon Energy for Pace Pulses Detection in a Single-Lead ECG Corrupted by EMG Artifacts

Electrocardiogram (ECG) analysis is important for the detection of pace pulse artifacts, since their existence indicates the presence of a pacemaker. ECG gives information on the proper functionality of the device and could help to evaluate the reaction of the heart. Beyond the challenges related to the diversity of ECG arrhythmias and pace pulses, the existence of electromyogram (EMG) noise could cause serious problems for the correct detection of pace pulses. This study reveals the potential of a methodology based on Stockwell transformation (S-transform), subsequent Shannon energy calculation and a threshold-based rule for pace artifact detection in a single-lead ECG corrupted with EMG noise. The design, validation and test are performed on a large, publicly available artificial database acquired with high amplitude and time resolution. It includes various combinations of ECG arrhythmias and pace pulses with different amplitudes, rising edges and total pulse durations, as well as timing that corresponds to different pacemaker modes. The training was done over 312 (ECG + EMG) signals. The method was validated on 390 clean ECGs and independently tested on 312 (ECG + EMG) and 390 clean ECGs. The achieved accuracy over the test dataset was Se = 100%, PPV = 98.0% for ECG corrupted by EMG artifacts and Se = 99.9%, PPV = 98.3% for clean ECG signals. This shows that, despite EMG artifacts, the S-transform could distinctly localize the pace pulse positions and, together with the applied ShE, could provide precise pace pulses detection in the time domain.

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ECG Beats Classification Using Mixture of Features

International Scholarly Research Notices ◽

10.1155/2014/178436 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 21

Author(s):

Manab Kumar Das ◽

Samit Ari

Keyword(s):

Feature Extraction ◽

Extraction Methods ◽

Ectopic Beat ◽

Extraction Techniques ◽

Efficient System ◽

Ecg Signals ◽

Temporal Features ◽

S Transform ◽

Electrocardiogram Ecg

Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.

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Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics

Sensors ◽

10.3390/s19040935 ◽

2019 ◽

Vol 19 (4) ◽

pp. 935 ◽

Cited By ~ 9

Author(s):

Yeong-Hyeon Byeon ◽

Sung-Bum Pan ◽

Keun-Chang Kwak

Keyword(s):

Frequency Domain ◽

Training Transfer ◽

Training Data ◽

Deep Convolutional Neural Networks ◽

Ecg Signals ◽

Electrocardiogram Signals ◽

Noisy Signals ◽

The Time Domain ◽

Electrocardiogram Ecg

This paper conducts a comparative analysis of deep models in biometrics using scalogram of electrocardiogram (ECG). A scalogram is the absolute value of the continuous wavelet transform coefficients of a signal. Since biometrics using ECG signals are sensitive to noise, studies have been conducted by transforming signals into a frequency domain that is efficient for analyzing noisy signals. By transforming the signal from the time domain to the frequency domain using the wavelet, the 1-D signal becomes a 2-D matrix, and it could be analyzed at multiresolution. However, this process makes signal analysis morphologically complex. This means that existing simple classifiers could perform poorly. We investigate the possibility of using the scalogram of ECG as input to deep convolutional neural networks of deep learning, which exhibit optimal performance for the classification of morphological imagery. When training data is small or hardware is insufficient for training, transfer learning can be used with pretrained deep models to reduce learning time, and classify it well enough. In this paper, AlexNet, GoogLeNet, and ResNet are considered as deep models of convolutional neural network. The experiments are performed on two databases for performance evaluation. Physikalisch-Technische Bundesanstalt (PTB)-ECG is a well-known database, while Chosun University (CU)-ECG is directly built for this study using the developed ECG sensor. The ResNet was 0.73%—0.27% higher than AlexNet or GoogLeNet on PTB-ECG—and the ResNet was 0.94%—0.12% higher than AlexNet or GoogLeNet on CU-ECG.

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ELECTROCARDIOGRAM BEAT CLASSIFICATION USING S-TRANSFORM BASED FEATURE SET

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519414500663 ◽

2014 ◽

Vol 14 (05) ◽

pp. 1450066 ◽

Cited By ~ 6

Author(s):

MANAB KUMAR DAS ◽

SAMIT ARI

Keyword(s):

Feature Extraction ◽

Support Vector ◽

Svm Classifier ◽

Massachusetts Institute Of Technology ◽

Ecg Signals ◽

Beat Classification ◽

Temporal Features ◽

S Transform ◽

Institute Of Technology ◽

Stockwell Transform

In this paper, the conventional Stockwell transform is effectively used to classify the ECG arrhythmias. The performance of ECG classification mainly depends on feature extraction based on an efficient formation of morphological and temporal features and the design of the classifier. Feature extraction is the important component of designing the system based on pattern recognition since even the best classifier will not perform better if the good features are not selected properly. Here, the S-transform (ST) is used to extract the morphological features which is appended with temporal features. This feature set is independently classified using artificial neural network (NN) and support vector machine (SVM). In this work, five classes of ECG beats (normal, ventricular, supra ventricular, fusion and unknown beats) from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database are classified according to AAMI EC57 1998 standard (Association for the Advancement of Medical Instrumentation). Performance is evaluated on several normal and abnormal ECG signals of MIT-BIH arrhythmias database using two classifier techniques: ST with NN classifier (ST-NN) and other proposed ST with SVM classifier (ST-SVM). The proposed method achieves accuracy of 98.47%. The performance of the proposed technique is compared with ST-NN and earlier reported technique.

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Analysis and Synthesis of Electrocardiogram (ECG) using Fourier and Wavelet Transform

International Journal of Thermal and Environmental Engineering ◽

10.5383/ijtee.17.02.002 ◽

2020 ◽

Vol 17 (02) ◽

Keyword(s):

Wavelet Transform ◽

Wavelet Analysis ◽

Wavelet Transforms ◽

Great Accuracy ◽

Ecg Signal ◽

Ecg Signals ◽

Analysis And Synthesis ◽

The Time Domain ◽

Transformation Methods ◽

Electrocardiogram Ecg

Electrocardiogram (ECG) is the study of the electrical signals of the human heart that are generated by the pumping action of the heart caused by the polarization and depolarization of the nodes of the heart. These signals must be interpreted with great accuracy and efficiency as they are paramount in prognosis and subsequent diagnosis of the condition of the patient. The goal of this project is to analyze the ECG signals following Fourier and Wavelet transforms, and to highlight and demonstrate the advantages of the Wavelet transform. Firstly, it involves simulating the temporal digital ECG signal and explaining the signal constituents, i.e., P, Q, R, S, T waves while staying in the time domain. Secondly, the ECG signal will be transferred into the frequency domain for quick, fast, and compressed analysis and carry out signal processing using Fourier analysis and highlight the pros and cons of this technique. Thirdly, wavelet analysis will be explored and demonstrated to mitigate the shortcoming of the former tool, i.e., Fourier. At this stage, various ECG signals, mimicking abnormalities, will be analyzed. This work will highlight the effectiveness of wavelet analysis as a tool to examine ECG signals. This work, hence, will entail, comparison of both transformation methods by utilizing the computational power of MATLAB.

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Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

npj Digital Medicine ◽

10.1038/s41746-021-00447-y ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Elisa Mejía-Mejía ◽

James M. May ◽

Mohamed Elgendi ◽

Panayiotis A. Kyriacou

Keyword(s):

Blood Pressure ◽

Heart Rate ◽

Heart Rate Variability ◽

Pulse Rate ◽

Pulse Transit Time ◽

Pulse Rate Variability ◽

Physiological Factors ◽

Ecg Signals ◽

Non Invasive ◽

Electrocardiogram Ecg

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

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A new wearable multichannel magnetocardiogram system with a SERF atomic magnetometer array

Scientific Reports ◽

10.1038/s41598-021-84971-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yanfei Yang ◽

Mingzhu Xu ◽

Aimin Liang ◽

Yan Yin ◽

Xin Ma ◽

...

Keyword(s):

Independent Component Analysis ◽

Data Acquisition ◽

Current Distribution ◽

Spin Exchange ◽

Healthy Human ◽

Ecg Signals ◽

Mode Decomposition ◽

Magnetometer Array ◽

Data Acquisition And Processing ◽

Electrocardiogram Ecg

AbstractIn this study, a wearable multichannel human magnetocardiogram (MCG) system based on a spin exchange relaxation-free regime (SERF) magnetometer array is developed. The MCG system consists of a magnetically shielded device, a wearable SERF magnetometer array, and a computer for data acquisition and processing. Multichannel MCG signals from a healthy human are successfully recorded simultaneously. Independent component analysis (ICA) and empirical mode decomposition (EMD) are used to denoise MCG data. MCG imaging is realized to visualize the magnetic and current distribution around the heart. The validity of the MCG signals detected by the system is verified by electrocardiogram (ECG) signals obtained at the same position, and similar features and intervals of cardiac signal waveform appear on both MCG and ECG. Experiments show that our wearable MCG system is reliable for detecting MCG signals and can provide cardiac electromagnetic activity imaging.

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Novel Stable Capacitive Electrocardiogram Measurement System

Sensors ◽

10.3390/s21113668 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3668

Author(s):

Chi-Chun Chen ◽

Shu-Yu Lin ◽

Wen-Ying Chang

Keyword(s):

Measurement System ◽

Input Resistance ◽

Voltage Divider ◽

Motion Artifact ◽

Guard Ring ◽

Capacitive Sensing ◽

Ecg Signals ◽

Optimal Input ◽

Electrocardiogram Ecg ◽

Mode Interference

This study presents a noncontact electrocardiogram (ECG) measurement system to replace conventional ECG electrode pads during ECG measurement. The proposed noncontact electrode design comprises a surface guard ring, the optimal input resistance, a ground guard ring, and an optimal voltage divider feedback. The surface and ground guard rings are used to reduce environmental noise. The optimal input resistor mitigates distortion caused by the input bias current, and the optimal voltage divider feedback increases the gain. Simulated gain analysis was subsequently performed to determine the most suitable parameters for the design, and the system was combined with a capacitive driven right leg circuit to reduce common-mode interference. The present study simulated actual environments in which interference is present in capacitive ECG signal measurement. Both in the case of environmental interference and motion artifact interference, relative to capacitive ECG electrodes, the proposed electrodes measured ECG signals with greater stability. In terms of R–R intervals, the measured ECG signals exhibited a 98.6% similarity to ECGs measured using contact ECG systems. The proposed noncontact ECG measurement system based on capacitive sensing is applicable for use in everyday life.

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Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication

Applied Sciences ◽

10.3390/app11031125 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1125

Author(s):

Htet Myet Lynn ◽

Pankoo Kim ◽

Sung Bum Pan

Keyword(s):

Short Term Memory ◽

Feature Learning ◽

Biometric Authentication ◽

Qrs Complex ◽

Data Types ◽

Ecg Signals ◽

Data Independent Acquisition ◽

Deep Networks ◽

Classification Tasks ◽

Electrocardiogram Ecg

In this report, the study of non-fiducial based approaches for Electrocardiogram(ECG) biometric authentication is examined, and several excessive techniques are proposed to perform comparative experiments for evaluating the best possible approach for all the classification tasks. Non-fiducial methods are designed to extract the discriminative information of a signal without annotating fiducial points. However, this process requires peak detection to identify a heartbeat signal. Based on recent studies that usually rely on heartbeat segmentation, QRS detection is required, and the process can be complicated for ECG signals for which the QRS complex is absent. Thus, many studies only conduct biometric authentication tasks on ECG signals with QRS complexes, and are hindered by similar limitations. To overcome this issue, we proposed a data-independent acquisition method to facilitate highly generalizable signal processing and feature learning processes. This is achieved by enhancing random segmentation to avoid complicated fiducial feature extraction, along with auto-correlation to eliminate the phase difference due to random segmentation. Subsequently, a bidirectional recurrent neural network (RNN) with long short-term memory (LSTM) deep networks is utilized to automatically learn the features associated with the signal and to perform an authentication task. The experimental results suggest that the proposed data-independent approach using a BLSTM network achieves a relatively high classification accuracy for every dataset relative to the compared techniques. Moreover, it exhibited a significantly higher accuracy rate in experiments using ECG signals without the QRS complex. The results also revealed that data-dependent methods can only perform well for specified data types and amendments of data variations, whereas the presented approach can also be considered for generalization to other quasi-periodical biometric signal-based classification tasks in future studies.

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