1
|
Rahman S, Pal S, Yearwood J, Karmakar C. Robustness of Deep Learning models in electrocardiogram noise detection and classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 253:108249. [PMID: 38815528 DOI: 10.1016/j.cmpb.2024.108249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/20/2024] [Accepted: 05/22/2024] [Indexed: 06/01/2024]
Abstract
BACKGROUND AND OBJECTIVE Automatic electrocardiogram (ECG) signal analysis for heart disease detection has gained significant attention due to busy lifestyles. However, ECG signals are susceptible to noise, which adversely affects the performance of ECG signal analysers. Traditional blind filtering methods use predefined noise frequency and filter order, but they alter ECG biomarkers. Several Deep Learning-based ECG noise detection and classification methods exist, but no study compares recurrent neural network (RNN) and convolutional neural network (CNN) architectures and their complexity. METHODS This paper introduces a knowledge-based ECG filtering system using Deep Learning to classify ECG noise types and compare popular computer vision model architectures in a practical Internet of Medical Things (IoMT) framework. Experimental results demonstrate that the CNN-based ECG noise classifier outperforms the RNN-based model in terms of performance and training time. RESULTS The study shows that AlexNet, visual geometry group (VGG), and residual network (ResNet) achieved over 70% accuracy, specificity, sensitivity, and F1 score across six datasets. VGG and ResNet performances were comparable, but VGG was more complex than ResNet, with only a 4.57% less F1 score. CONCLUSIONS This paper introduces a Deep Learning (DL) based ECG noise classifier for a knowledge-driven ECG filtering system, offering selective filtering to reduce signal distortion. Evaluation of various CNN and RNN-based models reveals VGG and Resnet outperform. Further, the VGG model is superior in terms of performance. But Resnet performs comparably to VGG with less model complexity.
Collapse
Affiliation(s)
- Saifur Rahman
- School of Information Technology, Deakin University, Melbourne, Victoria, Australia.
| | - Shantanu Pal
- School of Information Technology, Deakin University, Melbourne, Victoria, Australia.
| | - John Yearwood
- School of Information Technology, Deakin University, Melbourne, Victoria, Australia.
| | - Chandan Karmakar
- School of Information Technology, Deakin University, Melbourne, Victoria, Australia.
| |
Collapse
|
2
|
Xu H, Yan W, Lan K, Ma C, Wu D, Wu A, Yang Z, Wang J, Zang Y, Yan M, Zhang Z. Assessing Electrocardiogram and Respiratory Signal Quality of a Wearable Device (SensEcho): Semisupervised Machine Learning-Based Validation Study. JMIR Mhealth Uhealth 2021; 9:e25415. [PMID: 34387554 PMCID: PMC8391746 DOI: 10.2196/25415] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 01/24/2021] [Accepted: 06/25/2021] [Indexed: 12/18/2022] Open
Abstract
Background With the development and promotion of wearable devices and their mobile health (mHealth) apps, physiological signals have become a research hotspot. However, noise is complex in signals obtained from daily lives, making it difficult to analyze the signals automatically and resulting in a high false alarm rate. At present, screening out the high-quality segments of the signals from huge-volume data with few labels remains a problem. Signal quality assessment (SQA) is essential and is able to advance the valuable information mining of signals. Objective The aims of this study were to design an SQA algorithm based on the unsupervised isolation forest model to classify the signal quality into 3 grades: good, acceptable, and unacceptable; validate the algorithm on labeled data sets; and apply the algorithm on real-world data to evaluate its efficacy. Methods Data used in this study were collected by a wearable device (SensEcho) from healthy individuals and patients. The observation windows for electrocardiogram (ECG) and respiratory signals were 10 and 30 seconds, respectively. In the experimental procedure, the unlabeled training set was used to train the models. The validation and test sets were labeled according to preset criteria and used to evaluate the classification performance quantitatively. The validation set consisted of 3460 and 2086 windows of ECG and respiratory signals, respectively, whereas the test set was made up of 4686 and 3341 windows of signals, respectively. The algorithm was also compared with self-organizing maps (SOMs) and 4 classic supervised models (logistic regression, random forest, support vector machine, and extreme gradient boosting). One case validation was illustrated to show the application effect. The algorithm was then applied to 1144 cases of ECG signals collected from patients and the detected arrhythmia false alarms were calculated. Results The quantitative results showed that the ECG SQA model achieved 94.97% and 95.58% accuracy on the validation and test sets, respectively, whereas the respiratory SQA model achieved 81.06% and 86.20% accuracy on the validation and test sets, respectively. The algorithm was superior to SOM and achieved moderate performance when compared with the supervised models. The example case showed that the algorithm was able to correctly classify the signal quality even when there were complex pathological changes in the signals. The algorithm application results indicated that some specific types of arrhythmia false alarms such as tachycardia, atrial premature beat, and ventricular premature beat could be significantly reduced with the help of the algorithm. Conclusions This study verified the feasibility of applying the anomaly detection unsupervised model to SQA. The application scenarios include reducing the false alarm rate of the device and selecting signal segments that can be used for further research.
Collapse
Affiliation(s)
- Haoran Xu
- Medical School of Chinese PLA, Beijing, China
| | - Wei Yan
- Department of Hyperbaric Oxygen, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Ke Lan
- Beijing SensEcho Science & Technology Co., Ltd., Beijing, China
| | - Chenbin Ma
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Di Wu
- Beijing SensEcho Science & Technology Co., Ltd., Beijing, China
| | - Anshuo Wu
- University of Washington, Seattle, WA, United States
| | | | | | - Yaning Zang
- Department of Kinesiology, Shanghai University of Sport, Shanghai, China
| | - Muyang Yan
- Department of Hyperbaric Oxygen, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Zhengbo Zhang
- Centre for Artificial Intelligence in Medicine, Medical Innovation Research Department, Chinese PLA General Hospital, Beijing, China
| |
Collapse
|
3
|
Zhu W, Qiu L, Cai W, Yu J, Li D, Li W, Zhong J, Wang Y, Wang L. A novel method to reduce false alarms in ECG diagnostic systems: capture and quantification of noisy signals. Physiol Meas 2021; 42. [PMID: 33878739 DOI: 10.1088/1361-6579/abf9f4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 04/20/2021] [Indexed: 11/11/2022]
Abstract
Objective. Muscle artifacts (MA) and electrode motion artifacts (EMA) in electrocardiogram (ECG) signals lead to a large number of false alarms from cardiac diagnostic systems. To reduce false alarms, it is necessary to improve the performance of the diagnostic algorithm in noisy environments by removing excessively noisy signals. However, existing methods focus on signal quality assessment and contain too many artificial features. Here, we present a novel method to flexibly eliminate noisy signals without any artificial features.Approach. Our method contains an improved lightweight deep neural network (DNN) to capture the signal portions damaged by EMA and MA, uses the sample entropy to quantize noisy portions, and discards those portions that exceed a defined threshold. Our method was tested in conjunction with Pan-Tompkins (PT), Filter Bank (FB), and 'UNSW' R-peak detection algorithms along with two heartbeat classification algorithms on datasets synthesized from the MIT-BIH Noise Stress Test Database, the China Physiological Signal Challenge 2018 Database and the MIT-BIH Arrhythmia Database.Main results. For PT R-peak detection algorithms, the sensitivity (Se) increased noticeably from 89.01% to 99.42% in the synthesized datasets with a signal-to-noise ratio of 6 dB. With the same datasets, the Se of the FB algorithm increased about 9.29%, and a 3.64% increase occurred in the Se of the 'UNSW' algorithm. For heartbeat classification algorithms, the overall F1-score increased about 6% in the synthesized one-heartbeat datasets. It is the first study to utilize a DNN to capture noisy segments of the ECG signal.Significance. Too many false alarms can cause alarm fatigue. Our method can be utilized as the preprocessing before signal analysis, thereby reducing false alarms from the ECG diagnostic systems.
Collapse
Affiliation(s)
- Wenliang Zhu
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, People's Republic of China.,Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, People's Republic of China
| | - Lishen Qiu
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, People's Republic of China.,Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, People's Republic of China
| | - Wenqiang Cai
- School of Electronics and Information Technology, Soochow University, People's Republic of China
| | - Jie Yu
- School of Electronics and Information Technology, Soochow University, People's Republic of China
| | - Deyin Li
- School of Electronics and Information Technology, Soochow University, People's Republic of China
| | - Wanyue Li
- School of Electronics and Information Technology, Soochow University, People's Republic of China
| | - Jun Zhong
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, People's Republic of China
| | - Yan Wang
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, People's Republic of China
| | - Lirong Wang
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, People's Republic of China.,School of Electronics and Information Technology, Soochow University, People's Republic of China
| |
Collapse
|
4
|
Assessing the signal quality of electrocardiograms from varied acquisition sources: A generic machine learning pipeline for model generation. Comput Biol Med 2020; 130:104164. [PMID: 33360108 DOI: 10.1016/j.compbiomed.2020.104164] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 11/27/2020] [Accepted: 12/01/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND AND OBJECTIVE Long-term electrocardiogram monitoring comes at the expense of signal quality. During unconstrained movements, the electrocardiogram is often corrupted by motion artefacts, which can lead to inaccurate physiological information. In this situation, automated quality assessment methods are useful to increase the reliability of the measurements. A generic machine learning pipeline that generates classification models for electrocardiogram quality assessment is presented in this article. The presented pipeline is tested on signals from varied acquisition sources, towards selecting segments that can be used for heart rate analysis in lifestyle applications. METHODS Electrocardiogram recordings from traditional, wearable and ubiquitous devices, are segmented in 10 s windows and manually labeled by experienced researchers into two quality classes. To capture the electrocardiogram dynamics, a comprehensive set of 43 features is extracted from each segment, based on the time-domain signal, its Fast Fourier Transform, the Autocorrelation function and the Stationary Wavelet Transform. To select the most relevant features for each acquisition source we employ both a customized hybrid approach and the state-of-the-art Neighborhood Component Analysis method and compare them. Support Vector Machines (SVM), Decision Trees, K-Nearest-Neighbors and supervised ensemble methods are tested as possible binary classifiers. RESULTS The results for the best performing models on traditional, wearable and ubiquitous electrocardiogram datasets are, respectively: balanced-accuracy: 89%, F1-score: 93% with the Fine Gaussian SVM model and 10 features; balanced-accuracy: 93%, F1-score: 93% with the Fine Gaussian SVM model and 11 features; balanced-accuracy: 95%, F1-score: 86%, with the Fine Gaussian SVM model and 8 features. CONCLUSIONS According to the results, our generic pipeline can generate classification models tailored to individual acquisition sources, provided that a standard Lead I or Lead II is available. Such models accurately establish whether the electrocardiogram quality is good or bad for heart rate analysis. Furthermore, removing bad quality segments decreases errors in heart rate calculation.
Collapse
|
5
|
A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices. ENTROPY 2020; 22:e22070733. [PMID: 33286505 PMCID: PMC7517279 DOI: 10.3390/e22070733] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 06/27/2020] [Accepted: 06/28/2020] [Indexed: 01/03/2023]
Abstract
Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring of the patient’s electrocardiogram (ECG). Although recent portable and wearable devices may become very useful in this context, they often record ECG signals strongly corrupted with noise and artifacts. This impairs automatized ulterior analyses that could only be conducted reliably through a previous stage of automatic identification of high-quality ECG intervals. So far, a variety of techniques for ECG quality assessment have been proposed, but poor performances have been reported on recordings from patients with AF. This work introduces a novel deep learning-based algorithm to robustly identify high-quality ECG segments within the challenging environment of single-lead recordings alternating sinus rhythm, AF episodes and other rhythms. The method is based on the high learning capability of a convolutional neural network, which has been trained with 2-D images obtained when turning ECG signals into wavelet scalograms. For its validation, almost 100,000 ECG segments from three different databases have been analyzed during 500 learning-testing iterations, thus involving more than 320,000 ECGs analyzed in total. The obtained results have revealed a discriminant ability to detect high-quality and discard low-quality ECG excerpts of about 93%, only misclassifying around 5% of clean AF segments as noisy ones. In addition, the method has also been able to deal with raw ECG recordings, without requiring signal preprocessing or feature extraction as previous stages. Consequently, it is particularly suitable for portable and wearable devices embedding, facilitating early detection of AF as well as other automatized diagnostic facilities by reliably providing high-quality ECG excerpts to further processing stages.
Collapse
|
6
|
Asgharzadeh-Bonab A, Amirani MC, Mehri A. Spectral entropy and deep convolutional neural network for ECG beat classification. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.02.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
7
|
Kumar P, Sharma VK. Detection and classification of ECG noises using decomposition on mixed codebook for quality analysis. Healthc Technol Lett 2020; 7:18-24. [PMID: 32190336 PMCID: PMC7067057 DOI: 10.1049/htl.2019.0096] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Revised: 09/29/2019] [Accepted: 01/16/2020] [Indexed: 11/19/2022] Open
Abstract
In this Letter, a robust technique is presented to detect and classify different electrocardiogram (ECG) noises including baseline wander (BW), muscle artefact (MA), power line interference (PLI) and additive white Gaussian noise (AWGN) based on signal decomposition on mixed codebooks. These codebooks employ temporal and spectral-bound waveforms which provide sparse representation of ECG signals and can extract ECG local waves as well as ECG noises including BW, PLI, MA and AWGN simultaneously. Further, different statistical approaches and temporal features are applied on decomposed signals for detecting the presence of the above mentioned noises. The accuracy and robustness of the proposed technique are evaluated using a large set of noise-free and noisy ECG signals taken from the Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database, MIT-BIH polysmnographic database and Fantasia database. It is shown from the results that the proposed technique achieves an average detection accuracy of above 99% in detecting all kinds of ECG noises. Furthermore, average results show that the technique can achieve an average sensitivity of 98.55%, positive productivity of 98.6% and classification accuracy of 97.19% for ECG signals taken from all three databases.
Collapse
Affiliation(s)
- Pramendra Kumar
- Department of Computer and Communication Engineering, SCIT, Manipal University Jaipur, India
| | - Vijay Kumar Sharma
- Department of Computer and Communication Engineering, SCIT, Manipal University Jaipur, India
| |
Collapse
|
8
|
Booth BM, Mundnich K, Feng T, Nadarajan A, Falk TH, Villatte JL, Ferrara E, Narayanan S. Multimodal Human and Environmental Sensing for Longitudinal Behavioral Studies in Naturalistic Settings: Framework for Sensor Selection, Deployment, and Management. J Med Internet Res 2019; 21:e12832. [PMID: 31432781 PMCID: PMC6719486 DOI: 10.2196/12832] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 06/02/2019] [Accepted: 06/19/2019] [Indexed: 01/17/2023] Open
Abstract
Background Recent advances in mobile technologies for sensing human biosignals are empowering researchers to collect real-world data outside of the laboratory, in natural settings where participants can perform their daily activities with minimal disruption. These new sensing opportunities usher a host of challenges and constraints for both researchers and participants. Objective This viewpoint paper aims to provide a comprehensive guide to aid research teams in the selection and management of sensors before beginning and while conducting human behavior studies in the wild. The guide aims to help researchers achieve satisfactory participant compliance and minimize the number of unexpected procedural outcomes. Methods This paper presents a collection of challenges, consideration criteria, and potential solutions for enabling researchers to select and manage appropriate sensors for their research studies. It explains a general data collection framework suitable for use with modern consumer sensors, enabling researchers to address many of the described challenges. In addition, it provides a description of the criteria affecting sensor selection, management, and integration that researchers should consider before beginning human behavior studies involving sensors. On the basis of a survey conducted in mid-2018, this paper further illustrates an organized snapshot of consumer-grade human sensing technologies that can be used for human behavior research in natural settings. Results The research team applied the collection of methods and criteria to a case study aimed at predicting the well-being of nurses and other staff in a hospital. Average daily compliance for sensor usage measured by the presence of data exceeding half the total possible hours each day was about 65%, yielding over 355,000 hours of usable sensor data across 212 participants. A total of 6 notable unexpected events occurred during the data collection period, all of which had minimal impact on the research project. Conclusions The satisfactory compliance rates and minimal impact of unexpected events during the case study suggest that the challenges, criteria, methods, and mitigation strategies presented as a guide for researchers are helpful for sensor selection and management in longitudinal human behavior studies in the wild.
Collapse
Affiliation(s)
- Brandon M Booth
- Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, CA, United States
| | - Karel Mundnich
- Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, CA, United States
| | - Tiantian Feng
- Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, CA, United States
| | - Amrutha Nadarajan
- Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, CA, United States
| | - Tiago H Falk
- Multimedia/Multimodal Signal Analysis and Enhancement Lab, Institut National de la Recherche Scientifique, University of Québec, Montréal, QC, Canada
| | - Jennifer L Villatte
- Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, United States
| | - Emilio Ferrara
- Information Sciences Institute, University of Southern California, Marina del Rey, CA, United States
| | - Shrikanth Narayanan
- Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, CA, United States
| |
Collapse
|
9
|
Nabian M, Yin Y, Wormwood J, Quigley KS, Barrett LF, Ostadabbas S. An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2018; 6:2800711. [PMID: 30443441 PMCID: PMC6231905 DOI: 10.1109/jtehm.2018.2878000] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 09/05/2018] [Accepted: 10/22/2018] [Indexed: 11/09/2022]
Abstract
Electrocardiogram, electrodermal activity, electromyogram, continuous blood pressure, and impedance cardiography are among the most commonly used peripheral physiological signals (biosignals) in psychological studies and healthcare applications, including health tracking, sleep quality assessment, disease early-detection/diagnosis, and understanding human emotional and affective phenomena. This paper presents the development of a biosignal-specific processing toolbox (Bio-SP tool) for preprocessing and feature extraction of these physiological signals according to the state-of-the-art studies reported in the scientific literature and feedback received from the field experts. Our open-source Bio-SP tool is intended to assist researchers in affective computing, digital and mobile health, and telemedicine to extract relevant physiological patterns (i.e., features) from these biosignals semi-automatically and reliably. In this paper, we describe the successful algorithms used for signal-specific quality checking, artifact/noise filtering, and segmentation along with introducing features shown to be highly relevant to category discrimination in several healthcare applications (e.g., discriminating patterns associated with disease versus non-disease). Further, the Bio-SP tool is a publicly-available software written in MATLAB with a user-friendly graphical user interface (GUI), enabling future crowd-sourced modification to these tools. The GUI is compatible with MathWorks Classification Learner app for inference model development, such as model training, cross-validation scheme farming, and classification result computation.
Collapse
Affiliation(s)
- Mohsen Nabian
- Augmented Cognition LabElectrical and Computer Engineering DepartmentNortheastern UniversityBostonMA02115USA
- Harvard Medical SchoolBostonMA02115USA
| | - Yu Yin
- Augmented Cognition LabElectrical and Computer Engineering DepartmentNortheastern UniversityBostonMA02115USA
| | | | | | - Lisa F. Barrett
- Department of PsychologyNortheastern UniversityBostonMA02115USA
| | - Sarah Ostadabbas
- Augmented Cognition LabElectrical and Computer Engineering DepartmentNortheastern UniversityBostonMA02115USA
| |
Collapse
|
10
|
A Comparative Analysis of Methods for Evaluation of ECG Signal Quality after Compression. BIOMED RESEARCH INTERNATIONAL 2018; 2018:1868519. [PMID: 30112363 PMCID: PMC6077674 DOI: 10.1155/2018/1868519] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Revised: 06/04/2018] [Accepted: 06/27/2018] [Indexed: 11/17/2022]
Abstract
The assessment of ECG signal quality after compression is an essential part of the compression process. Compression facilitates the signal archiving, speeds up signal transmission, and reduces the energy consumption. Conversely, lossy compression distorts the signals. Therefore, it is necessary to express the compression performance through both compression efficiency and signal quality. This paper provides an overview of objective algorithms for the assessment of both ECG signal quality after compression and compression efficiency. In this area, there is a lack of standardization, and there is no extensive review as such. 40 methods were tested in terms of their suitability for quality assessment. For this purpose, the whole CSE database was used. The tested signals were compressed using an algorithm based on SPIHT with varying efficiency. As a reference, compressed signals were manually assessed by two experts and classified into three quality groups. Owing to the experts' classification, we determined corresponding ranges of selected quality evaluation methods' values. The suitability of the methods for quality assessment was evaluated based on five criteria. For the assessment of ECG signal quality after compression, we recommend using a combination of these methods: PSim SDNN, QS, SNR1, MSE, PRDN1, MAX, STDERR, and WEDD SWT.
Collapse
|
11
|
Satija U, Ramkumar B, Manikandan MS. A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment. IEEE Rev Biomed Eng 2018; 11:36-52. [PMID: 29994590 DOI: 10.1109/rbme.2018.2810957] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Electrocardiogram (ECG) signal quality assessment (SQA) plays a vital role in significantly improving the diagnostic accuracy and reliability of unsupervised ECG analysis systems. In practice, the ECG signal is often corrupted with different kinds of noises and artifacts. Therefore, numerous SQA methods were presented based on the ECG signal and/or noise features and the machine learning classifiers and/or heuristic decision rules. This paper presents an overview of current state-of-the-art SQA methods and highlights the practical limitations of the existing SQA methods. Based upon past and our studies, it is noticed that a lightweight ECG noise analysis framework is highly demanded for real-time detection, localization, and classification of single and combined ECG noises within the context of wearable ECG monitoring devices which are often resource constrained.
Collapse
|