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Electrocardiogram (ECG) Signal and Its Applications

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 47810

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Dr. Keun-Chang Kwak

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Department of Electronics Engineering, Chosun University, Gwangju 61452, Republic of Korea
Interests: biometrics; computational intelligence; human-robot interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The electrocardiogram (ECG) is used to investigate some types of abnormal heart function, including arrhythmias and conduction disturbances, as well as hear morphology. The classification of ECG signals plays an important role in the diagnoses of heart diseases. In addition, biometrics using the ECG have been successfully performed recently. The variation of ECG signals has unique characteristics, because humans have physically different body shapes. Thus, it is advantageous for security, because the heart-generated signal is concealed inside the body.

This Special Issue is concerned with signal processing, classification, and interpretation from ECG signal information. Furthermore, it includes ECG biometrics (user recognition and authentication) and applications based on deep learning or computational intelligence.

Dr. Keun-Chang Kwak
Guest Editor

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Keywords

ECG signal classification
ECG biometrics
ECG signal processing
ECG interpretation
ECG signal augmentation
ECG application using deep learning
ECG application using computational intelligence

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Published Papers (10 papers)

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Research

19 pages, 1433 KiB

Open AccessArticle

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

by Htet Myet Lynn, Pankoo Kim and Sung Bum Pan

Appl. Sci. 2021, 11(3), 1125; https://doi.org/10.3390/app11031125 - 26 Jan 2021

Cited by 12 | Viewed by 2588

Abstract

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. Full article

(This article belongs to the Special Issue Electrocardiogram (ECG) Signal and Its Applications)

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15 pages, 4366 KiB

Open AccessArticle

Analysis of EEG, Cardiac Activity Status, and Thermal Comfort According to the Type of Cooling Seat during Rest in Indoor Temperature

by Yunchan Shin, Minjung Lee and Honghyun Cho

Appl. Sci. 2021, 11(1), 97; https://doi.org/10.3390/app11010097 - 24 Dec 2020

Cited by 11 | Viewed by 3112

Abstract

In this study, electroencephalogram (EEG) and cardiac activity status of the human body while using various types of seats during rest were analyzed in indoor summer conditions. Thermal comfort was also evaluated through a subjective survey. The EEG, cardiac activity status, and subjective survey during rest indicated that the use of ventilation and cold water-cooling seats was effective. This effectiveness was because of the θ-wave and α-wave activation, sensorimotor rhythm, β-wave reduction, and left hemisphere activation, demonstrating that the conditions applied were suitable for rest. According to the analysis of the subjective questionnaire survey, the use of ventilation and cold water-cooling seats provided a more pleasant state than the basic seat, improving the subject’s warmth and comfort, and also the concentration. In addition, the use of a cold water-cooling seat provided the highest satisfaction level, being the most favorable condition for rest. Full article

(This article belongs to the Special Issue Electrocardiogram (ECG) Signal and Its Applications)

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14 pages, 3968 KiB

Open AccessArticle

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

by Irena Jekova, Ivo Iliev and Serafim Tabakov

Appl. Sci. 2020, 10(21), 7505; https://doi.org/10.3390/app10217505 - 26 Oct 2020

Cited by 3 | Viewed by 2487

Abstract

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. Full article

(This article belongs to the Special Issue Electrocardiogram (ECG) Signal and Its Applications)

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20 pages, 1023 KiB

Open AccessArticle

QRS Differentiation to Improve ECG Biometrics under Different Physical Scenarios Using Multilayer Perceptron

by Paloma Tirado-Martin, Judith Liu-Jimenez, Jorge Sanchez-Casanova and Raul Sanchez-Reillo

Appl. Sci. 2020, 10(19), 6896; https://doi.org/10.3390/app10196896 - 1 Oct 2020

Cited by 6 | Viewed by 3921

Abstract

Currently, machine learning techniques are successfully applied in biometrics and Electrocardiogram (ECG) biometrics specifically. However, not many works deal with different physiological states in the user, which can provide significant heart rate variations, being these a key matter when working with ECG biometrics. Techniques in machine learning simplify the feature extraction process, where sometimes it can be reduced to a fixed segmentation. The applied database includes visits taken in two different days and three different conditions (sitting down, standing up after exercise), which is not common in current public databases. These characteristics allow studying differences among users under different scenarios, which may affect the pattern in the acquired data. Multilayer Perceptron (MLP) is used as a classifier to form a baseline, as it has a simple structure that has provided good results in the state-of-the-art. This work studies its behavior in ECG verification by using QRS complexes, finding its best hyperparameter configuration through tuning. The final performance is calculated considering different visits for enrolling and verification. Differentiation in the QRS complexes is also tested, as it is already required for detection, proving that applying a simple first differentiation gives a good result in comparison to state-of-the-art similar works. Moreover, it also improves the computational cost by avoiding complex transformations and using only one type of signal. When applying different numbers of complexes, the best results are obtained when 100 and 187 complexes in enrolment, obtaining Equal Error Rates (EER) that range between 2.79–4.95% and 2.69–4.71%, respectively. Full article

(This article belongs to the Special Issue Electrocardiogram (ECG) Signal and Its Applications)

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8 pages, 762 KiB

Open AccessArticle

ECG-Signal Multi-Classification Model Based on Squeeze-and-Excitation Residual Neural Networks

by Junsang Park, Jin-kook Kim, Sunghoon Jung, Yeongjoon Gil, Jong-Il Choi and Ho Sung Son

Appl. Sci. 2020, 10(18), 6495; https://doi.org/10.3390/app10186495 - 17 Sep 2020

Cited by 27 | Viewed by 5109

Abstract

Accurate electrocardiogram (ECG) interpretation is crucial in the clinical ECG workflow because it is most likely associated with a disease that can cause major problems in the body. In this study, we proposed an ECG-signal multi-classification model using deep learning. We used a squeeze-and-excitation residual network (SE-ResNet), which is a residual network(ResNet) with a squeeze-and-excitation block. Experiments were performed for seven different types of lead-II ECG data obtained from the Korea University Anam Hospital in South Korea. These seven types are normal sinus rhythm, atrial fibrillation, atrial flutter, sinus bradycardia, sinus tachycardia, premature ventricular contraction and first-degree atrioventricular block. We compared the SE-ResNet with a ResNet, as a baseline model, for various depths of layer (18/34/50/101/152). We confirmed that the SE-ResNet had better classification performance than the ResNet, for all layers. The SE-ResNet classifier with 152 layers achieved F1 scores of 97.05% for seven-class classifications. Our model surpassed the baseline model, ResNet, by +1.40% for the seven-class classifications. For ECG-signal multi-classification, considering the F1 scores, the SE-ResNet might be better than the ResNet baseline model. Full article

(This article belongs to the Special Issue Electrocardiogram (ECG) Signal and Its Applications)

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23 pages, 4185 KiB

Open AccessArticle

ECG Arrhythmia Classification using High Order Spectrum and 2D Graph Fourier Transform

by Shu Liu, Jie Shao, Tianjiao Kong and Reza Malekian

Appl. Sci. 2020, 10(14), 4741; https://doi.org/10.3390/app10144741 - 9 Jul 2020

Cited by 26 | Viewed by 4963

Abstract

Heart diseases are in the front rank among several kinds of life threats, due to its high incidence and mortality. Regarded as a powerful tool in the diagnosis of the cardiac disorder and arrhythmia detection, analysis of electrocardiogram (ECG) signals has become the focus of numerous researches. In this study, a feature extraction method based on the bispectrum and 2D graph Fourier transform (GFT) was developed. High-order matrix founded on bispectrum are extended into structured datasets and transformed into the eigenvalue spectrum domain by GFT, so that features can be extracted from statistical quantities of eigenvalues. Spectral features have been computed to construct the feature vector. Support vector machine based on the radial basis function kernel (SVM-RBF) was used to classify different arrhythmia heartbeats downloaded from the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) Arrhythmia Database, according to the Association for the Advancement of Medical Instrumentation (AAMI) standard. Based on the cross-validation method, the experimental results depicted that our proposed model, the combination of bispectrum and 2D-GFT, achieved a high classification accuracy of 96.2%. Full article

(This article belongs to the Special Issue Electrocardiogram (ECG) Signal and Its Applications)

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17 pages, 3186 KiB

Open AccessArticle

P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images

by Rana N. Costandy, Safa M. Gasser, Mohamed S. El-Mahallawy, Mohamed W. Fakhr and Samir Y. Marzouk

Appl. Sci. 2020, 10(3), 976; https://doi.org/10.3390/app10030976 - 3 Feb 2020

Cited by 10 | Viewed by 5944

Abstract

Electrocardiogram (ECG) signal analysis is a critical task in diagnosing the presence of any cardiac disorder. There are limited studies on detecting P-waves in various atrial arrhythmias, such as atrial fibrillation (AFIB), atrial flutter, junctional rhythm, and other arrhythmias due to P-wave variability and absence in various cases. Thus, there is a growing need to develop an efficient automated algorithm that annotates a 2D printed version of P-waves in the well-known ECG signal databases for validation purposes. To our knowledge, no one has annotated P-waves in the MIT-BIH atrial fibrillation database. Therefore, it is a challenge to manually annotate P-waves in the MIT-BIH AF database and to develop an automated algorithm to detect the absence and presence of different shapes of P-waves. In this paper, we present the manual annotation of P-waves in the well-known MIT-BIH AF database with the aid of a cardiologist. In addition, we provide an automatic P-wave segmentation for the same database using a fully convolutional neural network model (U-Net). This algorithm works on 2D imagery of printed ECG signals, as this type of imagery is the most commonly used in developing countries. The proposed automatic P-wave detection method obtained an accuracy and sensitivity of 98.56% and 98.78%, respectively, over the first 5 min of the second lead of the MIT-BIH AF database (a total of 8280 beats). Moreover, the proposed method is validated using the well-known automatically and manually annotated QT database (a total of 11,201 and 3194 automatically and manually annotated beats, respectively). This results in accuracies of 98.98 and 98.9%, and sensitivities of 98.97 and 97.24% for the automatically and manually annotated QT databases, respectively. Thus, these results indicate that the proposed automatic method can be used for analyzing long-printed ECG signals on mobile battery-driven devices using only images of the ECG signals, without the need for a cardiologist. Full article

(This article belongs to the Special Issue Electrocardiogram (ECG) Signal and Its Applications)

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24 pages, 4362 KiB

Open AccessArticle

Pre-Configured Deep Convolutional Neural Networks with Various Time-Frequency Representations for Biometrics from ECG Signals

by Yeong-Hyeon Byeon and Keun-Chang Kwak

Appl. Sci. 2019, 9(22), 4810; https://doi.org/10.3390/app9224810 - 10 Nov 2019

Cited by 19 | Viewed by 4505

Abstract

We evaluated electrocardiogram (ECG) biometrics using pre-configured models of convolutional neural networks (CNNs) with various time-frequency representations. Biometrics technology records a person’s physical or behavioral characteristics in a digital signal via a sensor and analyzes it to identify the person. An ECG signal is obtained by detecting and amplifying a minute electrical signal flowing on the skin using a noninvasive electrode when the heart muscle depolarizes at each heartbeat. In biometrics, the ECG is especially advantageous in security applications because the heart is located within the body and moves while the subject is alive. However, a few body states generate noisy biometrics. The analysis of signals in the frequency domain has a robust effect on the noise. As the ECG is noise-sensitive, various studies have applied time-frequency transformations that are robust to noise, with CNNs achieving a good performance in image classification. Studies have applied time-frequency representations of the 1D ECG signals to 2D CNNs using transforms like MFCC (mel frequency cepstrum coefficient), spectrogram, log spectrogram, mel spectrogram, and scalogram. CNNs have various pre-configured models such as VGGNet, GoogLeNet, ResNet, and DenseNet. Combinations of the time-frequency representations and pre-configured CNN models have not been investigated. In this study, we employed the PTB (Physikalisch-Technische Bundesanstalt)-ECG and CU (Chosun University)-ECG databases. The MFCC accuracies were 0.45%, 2.60%, 3.90%, and 0.25% higher than the spectrogram, log spectrogram, mel spectrogram, and scalogram accuracies, respectively. The Xception accuracies were 3.91%, 0.84%, and 1.14% higher than the VGGNet-19, ResNet-101, and DenseNet-201 accuracies, respectively. Full article

(This article belongs to the Special Issue Electrocardiogram (ECG) Signal and Its Applications)

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20 pages, 8499 KiB

Open AccessArticle

Efficient Real-Time R and QRS Detection Method Using a Pair of Derivative Filters and Max Filter for Portable ECG Device

by Tae Wuk Bae and Kee Koo Kwon

Appl. Sci. 2019, 9(19), 4128; https://doi.org/10.3390/app9194128 - 2 Oct 2019

Cited by 18 | Viewed by 4813

Abstract

Recently, with the active development of wearable electrocardiogram (ECG) devices such as smart-bands or portable ECG devices, efficient ECG signal processing technology that can be applied in real-time has been actively studied. However, a wearable ECG device is exposed to various noise situations, thereby reducing the reliability of the detected R point or QRS interval. In addition, as early warning techniques in healthcare systems have been studied, real-time ECG signal processing techniques have become very important in wearable ECG devices. In this paper, we propose an efficient real-time R and QRS detection method using two kinds of first-order derivative filters and a max filter to analyze ECG signals measured from wearable ECG devices in real-time. The proposed method detects the R point and QRS interval in units of a sliding window for real-time processing and combines the detected R points in each sliding window. Also, the reliability of the detected R points and RR intervals is examined through noise region analysis using the histogram characteristic of a sample point. The performance of the proposed method was verified by the MIT-BIH database (DB), CYBHi DB and real ECG data measured from the developed wearable ECG patch. The proposed method achieves Se = 99.80%, +P = 99.80%, and DER = 0.36% against MIT-BIH DB. In addition, the proposed method enables accurate R point detection and heart rate variability (HRV) analysis even with noisy ECG signals. Full article

(This article belongs to the Special Issue Electrocardiogram (ECG) Signal and Its Applications)

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17 pages, 4472 KiB

Open AccessArticle

An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique

by Siti Nurmaini, Radiyati Umi Partan, Wahyu Caesarendra, Tresna Dewi, Muhammad Naufal Rahmatullah, Annisa Darmawahyuni, Vicko Bhayyu and Firdaus Firdaus

Appl. Sci. 2019, 9(14), 2921; https://doi.org/10.3390/app9142921 - 22 Jul 2019

Cited by 68 | Viewed by 6870

Abstract

An automated classification system based on a Deep Learning (DL) technique for Cardiac Disease (CD) monitoring and detection is proposed in this paper. The proposed DL architecture is divided into Deep Auto-Encoders (DAEs) as an unsupervised form of feature learning and Deep Neural Networks (DNNs) as a classifier. The objective of this study is to improve on the previous machine learning technique that consists of several data processing steps such as feature extraction and feature selection or feature reduction. It is also noticed that the previously used machine learning technique required human interference and expertise in determining robust features, yet was time-consuming in the labeling and data processing steps. In contrast, DL enables an embedded feature extraction and feature selection in DAEs pre-training and DNNs fine-tuning process directly from raw data. Hence, DAEs is able to extract high-level of features not only from the training data but also from unseen data. The proposed model uses 10 classes of imbalanced data from ECG signals. Since it is related to the cardiac region, abnormality is usually considered for an early diagnosis of CD. In order to validate the result, the proposed model is compared with the shallow models and DL approaches. Results found that the proposed method achieved a promising performance with 99.73% accuracy, 91.20% sensitivity, 93.60% precision, 99.80% specificity, and a 91.80% F1-Score. Moreover, both the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR) curve from the confusion matrix showed that the developed model is a good classifier. The developed model based on unsupervised feature extraction and deep neural network is ready to be used on a large population before its installation for clinical usage. Full article

(This article belongs to the Special Issue Electrocardiogram (ECG) Signal and Its Applications)

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