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Krasteva V, Stoyanov T, Schmid R, Jekova I. Delineation of 12-Lead ECG Representative Beats Using Convolutional Encoder-Decoders with Residual and Recurrent Connections. SENSORS (BASEL, SWITZERLAND) 2024; 24:4645. [PMID: 39066042 PMCID: PMC11280871 DOI: 10.3390/s24144645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 07/11/2024] [Accepted: 07/16/2024] [Indexed: 07/28/2024]
Abstract
The aim of this study is to address the challenge of 12-lead ECG delineation by different encoder-decoder architectures of deep neural networks (DNNs). This study compares four concepts for encoder-decoders based on a fully convolutional architecture (CED-Net) and its modifications with a recurrent layer (CED-LSTM-Net), residual connections between symmetrical encoder and decoder feature maps (CED-U-Net), and sequential residual blocks (CED-Res-Net). All DNNs transform 12-lead representative beats to three diagnostic ECG intervals (P-wave, QRS-complex, QT-interval) used for the global delineation of the representative beat (P-onset, P-offset, QRS-onset, QRS-offset, T-offset). All DNNs were trained and optimized using the large PhysioNet ECG database (PTB-XL) under identical conditions, applying an advanced approach for machine-based supervised learning with a reference algorithm for ECG delineation (ETM, Schiller AG, Baar, Switzerland). The test results indicate that all DNN architectures are equally capable of reproducing the reference delineation algorithm's measurements in the diagnostic PTB database with an average P-wave detection accuracy (96.6%) and time and duration errors: mean values (-2.6 to 2.4 ms) and standard deviations (2.9 to 11.4 ms). The validation according to the standard-based evaluation practices of diagnostic electrocardiographs with the CSE database outlines a CED-Net model, which measures P-duration (2.6 ± 11.0 ms), PQ-interval (0.9 ± 5.8 ms), QRS-duration (-2.4 ± 5.4 ms), and QT-interval (-0.7 ± 10.3 ms), which meet all standard tolerances. Noise tests with high-frequency, low-frequency, and power-line frequency noise (50/60 Hz) confirm that CED-Net, CED-Res-Net, and CED-LSTM-Net are robust to all types of noise, mostly presenting a mean duration error < 2.5 ms when compared to measurements without noise. Reduced noise immunity is observed for the U-net architecture. Comparative analysis with other published studies scores this research within the lower range of time errors, highlighting its competitive performance.
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Affiliation(s)
- Vessela Krasteva
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl. 105, 1113 Sofia, Bulgaria; (V.K.); (T.S.)
| | - Todor Stoyanov
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl. 105, 1113 Sofia, Bulgaria; (V.K.); (T.S.)
| | - Ramun Schmid
- Signal Processing, Schiller AG, Altgasse 68, CH-6341 Baar, Switzerland;
| | - Irena Jekova
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl. 105, 1113 Sofia, Bulgaria; (V.K.); (T.S.)
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Guedri H, Bajahzar A, Belmabrouk H. ECG compression with Douglas-Peucker algorithm and fractal interpolation. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:3502-3520. [PMID: 34198398 DOI: 10.3934/mbe.2021176] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In this paper, we propose a new ECG compression method using the fractal technique. The proposed approaches utilize the fact that ECG signals are a fractal curve. This algorithm consists of three steps: First, the original ECG signals are processed and they are converted into a 2-D array. Second, the Douglas-Peucker algorithm (DP) is used to detect critical points (compression phase). Finally, we used the fractal interpolation and the Iterated Function System (IFS) to generate missing points (decompression phase). The proposed (suggested) methodology is tested for different records selected from PhysioNet Database. The obtained results showed that the proposed method has various compression ratios and converges to a high value. The average compression ratios are between 3.19 and 27.58, and also, with a relatively low percentage error (PRD), if we compare it to other methods. Results depict also that the ECG signal can adequately retain its detailed structure when the PSNR exceeds 40 dB.
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Affiliation(s)
- Hichem Guedri
- Electronics and Microelectronics Laboratory, Physics Department, Faculty of Sciences, Monastir University, Monastir 5019, Tunisia
| | - Abdullah Bajahzar
- Department of Computer Science and Information, College of Science, Majmaah University, Zulfi 11932, Saudi Arabia
| | - Hafedh Belmabrouk
- Department of Physics, College of Science Zulfi, Majmaah University, Zulfi 11932, Saudi Arabia
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An Automated High-Accuracy Detection Scheme for Myocardial Ischemia Based on Multi-Lead Long-Interval ECG and Choi-Williams Time-Frequency Analysis Incorporating a Multi-Class SVM Classifier. SENSORS 2021; 21:s21072311. [PMID: 33810211 PMCID: PMC8037073 DOI: 10.3390/s21072311] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Revised: 02/12/2021] [Accepted: 02/15/2021] [Indexed: 11/29/2022]
Abstract
Cardiovascular Disease (CVD) is a primary cause of heart problems such as angina and myocardial ischemia. The detection of the stage of CVD is vital for the prevention of medical complications related to the heart, as they can lead to heart muscle death (known as myocardial infarction). The electrocardiogram (ECG) reflects these cardiac condition changes as electrical signals. However, an accurate interpretation of these waveforms still calls for the expertise of an experienced cardiologist. Several algorithms have been developed to overcome issues in this area. In this study, a new scheme for myocardial ischemia detection with multi-lead long-interval ECG is proposed. This scheme involves an observation of the changes in ischemic-related ECG components (ST segment and PR segment) by way of the Choi-Williams time-frequency distribution to extract ST and PR features. These extracted features are mapped to a multi-class SVM classifier for training in the detection of unknown conditions to determine if they are normal or ischemic. The use of multi-lead ECG for classification and 1 min intervals instead of beats or frames contributes to improved detection performance. The classification process uses the data of 92 normal and 266 patients from four different databases. The proposed scheme delivered an overall result with 99.09% accuracy, 99.49% sensitivity, and 98.44% specificity. The high degree of classification accuracy for the different and unknown data sources used in this study reflects the flexibility, validity, and reliability of this proposed scheme. Additionally, this scheme can assist cardiologists in detecting signal abnormality with robustness and precision, and can even be used for home screening systems to provide rapid evaluation in emergency cases.
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Hossain MB, Bashar SK, Lazaro J, Reljin N, Noh Y, Chon KH. A robust ECG denoising technique using variable frequency complex demodulation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105856. [PMID: 33309076 PMCID: PMC7920915 DOI: 10.1016/j.cmpb.2020.105856] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 11/16/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Electrocardiogram (ECG) is widely used for the detection and diagnosis of cardiac arrhythmias such as atrial fibrillation. Most of the computer-based automatic cardiac abnormality detection algorithms require accurate identification of ECG components such as QRS complexes in order to provide a reliable result. However, ECGs are often contaminated by noise and artifacts, especially if they are obtained using wearable sensors, therefore, identification of accurate QRS complexes often becomes challenging. Most of the existing denoising methods were validated using simulated noise added to a clean ECG signal and they did not consider authentically noisy ECG signals. Moreover, many of them are model-dependent and sampling-frequency dependent and require a large amount of computational time. METHODS This paper presents a novel ECG denoising technique using the variable frequency complex demodulation (VFCDM) algorithm, which considers noises from a variety of sources. We used the sub-band decomposition of the noise-contaminated ECG signals using VFCDM to remove the noise components so that better-quality ECGs could be reconstructed. An adaptive automated masking is proposed in order to preserve the QRS complexes while removing the unnecessary noise components. Finally, the ECG was reconstructed using a dynamic reconstruction rule based on automatic identification of the severity of the noise contamination. The ECG signal quality was further improved by removing baseline drift and smoothing via adaptive mean filtering. RESULTS Evaluation results on the standard MIT-BIH Arrhythmia database suggest that the proposed denoising technique provides superior denoising performance compared to studies in the literature. Moreover, the proposed method was validated using real-life noise sources collected from the noise stress test database (NSTDB) and data from an armband ECG device which contains significant muscle artifacts. Results from both the wearable armband ECG data and NSTDB data suggest that the proposed denoising method provides significantly better performance in terms of accurate QRS complex detection and signal to noise ratio (SNR) improvement when compared to some of the recent existing denoising algorithms. CONCLUSIONS The detailed qualitative and quantitative analysis demonstrated that the proposed denoising method has been robust in filtering varieties of noises present in the ECG. The QRS detection performance of the denoised armband ECG signals indicates that the proposed denoising method has the potential to increase the amount of usable armband ECG data, thus, the armband device with the proposed denoising method could be used for long term monitoring of atrial fibrillation.
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Affiliation(s)
- Md-Billal Hossain
- Department of Biomedical Engineering, University of Connecticut, 260 Glenbrook Road, Unit 3247 Storrs, CT 06269-3247, USA
| | - Syed Khairul Bashar
- Department of Biomedical Engineering, University of Connecticut, 260 Glenbrook Road, Unit 3247 Storrs, CT 06269-3247, USA
| | - Jesus Lazaro
- Aragon Institute for Engineering Research, University of Zaragoza, Spain
| | - Natasa Reljin
- Department of Biomedical Engineering, University of Connecticut, 260 Glenbrook Road, Unit 3247 Storrs, CT 06269-3247, USA
| | - Yeonsik Noh
- College of Nursing/Department of Electrical and Computer Engineering, University of Massachusetts Amherst, USA
| | - Ki H Chon
- Department of Biomedical Engineering, University of Connecticut, 260 Glenbrook Road, Unit 3247 Storrs, CT 06269-3247, USA.
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Zhou Y, Hong S, Shang J, Wu M, Wang Q, Li H, Xie J. Addressing Noise and Skewness in Interpretable Health-Condition Assessment by Learning Model Confidence. SENSORS (BASEL, SWITZERLAND) 2020; 20:E7307. [PMID: 33352690 PMCID: PMC7765787 DOI: 10.3390/s20247307] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 12/10/2020] [Accepted: 12/15/2020] [Indexed: 11/17/2022]
Abstract
Assessing the health condition has a wide range of applications in healthcare, military, aerospace, and industrial fields. Nevertheless, traditional feature-engineered techniques involve manual feature extraction, which are too cumbersome to adapt to the changes caused by the development of sensor network technology. Recently, deep-learning-based methods have achieved initial success in health-condition assessment research, but insufficient considerations for problems such as class skewness, noisy segments, and result interpretability make it difficult to apply them to real-world applications. In this paper, we propose a K-margin-based Interpretable Learning approach for health-condition assessment. In detail, a skewness-aware RCR-Net model is employed to handle problems of class skewness. Furthermore, we present a diagnosis model based on K-margin to automatically handle noisy segments by naturally exploiting expected consistency among the segments associated with each record. Additionally, a knowledge-directed interpretation method is presented to learn domain knowledge-level features automatically without the help of human experts which can be used as an interpretable decision-making basis. Finally, through experimental validation in the field of both medical and aerospace, the proposed method has a better generality and high efficiency with 0.7974 and 0.8005 F1 scores, which outperform all state-of-the-art deep learning methods for health-condition assessment task by 3.30% and 2.99%, respectively.
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Affiliation(s)
- Yuxi Zhou
- School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China; (Y.Z.); (J.S.); (M.W.); (Q.W.)
- Key Laboratory of Machine Perception, Ministry of Education, Peking University, Beijing 100871, China
| | - Shenda Hong
- National Institute of Health Data Science at Peking University, Beijing 100191, China;
- Institute of Medical Technology, Health Science Center of Peking University, Beijing 100191, China
| | - Junyuan Shang
- School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China; (Y.Z.); (J.S.); (M.W.); (Q.W.)
- Key Laboratory of Machine Perception, Ministry of Education, Peking University, Beijing 100871, China
| | - Meng Wu
- School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China; (Y.Z.); (J.S.); (M.W.); (Q.W.)
- Key Laboratory of Machine Perception, Ministry of Education, Peking University, Beijing 100871, China
| | - Qingyun Wang
- School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China; (Y.Z.); (J.S.); (M.W.); (Q.W.)
- Key Laboratory of Machine Perception, Ministry of Education, Peking University, Beijing 100871, China
| | - Hongyan Li
- School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China; (Y.Z.); (J.S.); (M.W.); (Q.W.)
- Key Laboratory of Machine Perception, Ministry of Education, Peking University, Beijing 100871, China
| | - Junqing Xie
- Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, Oxford OX3 7LD, UK;
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