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Lu W, Gong D, Xue X, Gao L. Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs. Front Bioeng Biotechnol 2024; 12:1367929. [PMID: 38832128 PMCID: PMC11145508 DOI: 10.3389/fbioe.2024.1367929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 04/19/2024] [Indexed: 06/05/2024] Open
Abstract
Introduction: Surface electromyogram (sEMG) signals have been widely used in human upper limb force estimation and motion intention recognition. However, the electrocardiogram(ECG) artifact generated by the beating of the heart is a major factor that reduces the quality of the EMG signal when recording the sEMG signal from the muscle close to the heart. sEMG signals contaminated by ECG artifacts are difficult to be understood correctly. The objective of this paper is to effectively remove ECG artifacts from sEMG signals by a novel method. Methods: In this paper, sEMG and ECG signals of the biceps brachii, brachialis, and triceps muscle of the human upper limb will be collected respectively. Firstly, an improved multi-layer wavelet transform algorithm is used to preprocess the raw sEMG signal to remove the background noise and power frequency interference in the raw signal. Then, based on the theory of blind source separation analysis, an improved Fast-ICA algorithm was constructed to separate the denoising signals. Finally, an ECG discrimination algorithm was used to find and eliminate ECG signals in sEMG signals. This method consists of the following steps: 1) Acquisition of raw sEMG and ECG signals; 2) Decoupling the raw sEMG signal; 3) Fast-ICA-based signal component separation; 4) ECG artifact recognition and elimination. Results and discussion: The experimental results show that our method has a good effect on removing ECG artifacts from contaminated EMG signals. It can further improve the quality of EMG signals, which is of great significance for improving the accuracy of force estimation and motion intention recognition tasks. Compared with other state-of-the-art methods, our method can also provide the guiding significance for other biological signals.
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Affiliation(s)
- Wei Lu
- School of Management, Fujian University of Technology, Fuzhou, China
| | - Dongliang Gong
- School of Mechanical and Automotive Engineering, Fujian University of Technology, Fuzhou, China
| | - Xue Xue
- School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, China
| | - Lifu Gao
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
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2
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Bing P, Liu W, Zhai Z, Li J, Guo Z, Xiang Y, He B, Zhu L. A novel approach for denoising electrocardiogram signals to detect cardiovascular diseases using an efficient hybrid scheme. Front Cardiovasc Med 2024; 11:1277123. [PMID: 38699582 PMCID: PMC11064874 DOI: 10.3389/fcvm.2024.1277123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 03/25/2024] [Indexed: 05/05/2024] Open
Abstract
Background Electrocardiogram (ECG) signals are inevitably contaminated with various kinds of noises during acquisition and transmission. The presence of noises may produce the inappropriate information on cardiac health, thereby preventing specialists from making correct analysis. Methods In this paper, an efficient strategy is proposed to denoise ECG signals, which employs a time-frequency framework based on S-transform (ST) and combines bi-dimensional empirical mode decomposition (BEMD) and non-local means (NLM). In the method, the ST maps an ECG signal into a subspace in the time frequency domain, then the BEMD decomposes the ST-based time-frequency representation (TFR) into a series of sub-TFRs at different scales, finally the NLM removes noise and restores ECG signal characteristics based on structural self-similarity. Results The proposed method is validated using numerous ECG signals from the MIT-BIH arrhythmia database, and several different types of noises with varying signal-to-noise (SNR) are taken into account. The experimental results show that the proposed technique is superior to the existing wavelet based approach and NLM filtering, with the higher SNR and structure similarity index measure (SSIM), the lower root mean squared error (RMSE) and percent root mean square difference (PRD). Conclusions The proposed method not only significantly suppresses the noise presented in ECG signals, but also preserves the characteristics of ECG signals better, thus, it is more suitable for ECG signals processing.
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Affiliation(s)
- Pingping Bing
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
| | - Wei Liu
- College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China
| | - Zhixing Zhai
- College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China
| | - Jianghao Li
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
| | - Zhiqun Guo
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
| | - Yanrui Xiang
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
| | - Binsheng He
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
| | - Lemei Zhu
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China
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3
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Mohguen O. Noise reduction and QRS detection in ECG signal using EEMD with modified sigmoid thresholding. BIOMED ENG-BIOMED TE 2024; 69:61-78. [PMID: 37665599 DOI: 10.1515/bmt-2022-0450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 08/17/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVES Novel noise reduction and QRS detection algorithms in Electrocardiogram (ECG) signal based on Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and the Modified Sigmoid Thresholding Function (MSTF) are proposed in this paper. METHODS EMD and EEMD algorithms are used to decompose the noisy ECG signal into series of Intrinsic Mode Functions (IMFs). Then, these IMFs are thresholded by the MSTF for reduction of noises and preservation of QRS complexes. After that, the thresholded IMFs are used to obtain the clean ECG signal. The characteristic points P, Q, R, S and T peaks are detected using peak detection algorithm. RESULTS The proposed methods are validated through experiments on the MIT-BIH arrhythmia database and Additive White Gaussian Noise (AWGN) is added to the clean ECG signal at different input SNR (SNR in). Standard performance parameters output SNR (SNR out), mean square error (MSE), root mean square error (RMSE), SNR improvement (SNR imp) and percentage root mean square difference (PRD) are employed for evaluation of the efficacy of the proposed methods. The results showed that the proposed methods provide significant quantitative and qualitative improvements in denoising performance, compared with existing state-of-the-art methods such as wavelet denoising, conventional EMD (EMD-Conv), conventional EEMD (EEMD-Conv, Stockwell Transform (ST) and Complete EEMD with Adaptative Noise with hybrid interval thresholding and higher order statistic to select relevant modes (CEEMDAN-HIT). CONCLUSIONS A detail quantitative analysis demonstrate that for abnormal ECG records 207 m and 214 m at input SNR of -2 dB the SNR imp value is 12.22 and 11.58 dB respectively, which indicates that the proposed algorithm can be used as an effective tool for denoising of ECG signals.
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Affiliation(s)
- Ouahiba Mohguen
- Department of Electronics, LIS Laboratory University Ferhat Abbas Setif 1, Setif, Algeria
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Aviles-Espinosa R, Dore H, Rendon-Morales E. An Experimental Method for Bio-Signal Denoising Using Unconventional Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:3527. [PMID: 37050587 PMCID: PMC10098882 DOI: 10.3390/s23073527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/23/2023] [Accepted: 03/25/2023] [Indexed: 06/19/2023]
Abstract
In bio-signal denoising, current methods reported in the literature consider purely simulated environments, requiring high computational powers and signal processing algorithms that may introduce signal distortion. To achieve an efficient noise reduction, such methods require previous knowledge of the noise signals or to have certain periodicity and stability, making the noise estimation difficult to predict. In this paper, we solve these challenges through the development of an experimental method applied to bio-signal denoising using a combined approach. This is based on the implementation of unconventional electric field sensors used for creating a noise replica required to obtain the ideal Wiener filter transfer function and achieve further noise reduction. This work aims to investigate the suitability of the proposed approach for real-time noise reduction affecting bio-signal recordings. The experimental evaluation presented here considers two scenarios: (a) human bio-signals trials including electrocardiogram, electromyogram and electrooculogram; and (b) bio-signal recordings from the MIT-MIH arrhythmia database. The performance of the proposed method is evaluated using qualitative criteria (i.e., power spectral density) and quantitative criteria (i.e., signal-to-noise ratio and mean square error) followed by a comparison between the proposed methodology and state of the art denoising methods. The results indicate that the combined approach proposed in this paper can be used for noise reduction in electrocardiogram, electromyogram and electrooculogram signals, achieving noise attenuation levels of 26.4 dB, 21.2 dB and 40.8 dB, respectively.
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Boyer M, Bouyer L, Roy JS, Campeau-Lecours A. Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:2927. [PMID: 36991639 PMCID: PMC10059683 DOI: 10.3390/s23062927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 02/22/2023] [Accepted: 03/01/2023] [Indexed: 06/19/2023]
Abstract
Electromyography (EMG) is gaining importance in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, EMG signals can be contaminated by various types of noise, interference and artifacts, leading to potential data misinterpretation. Even assuming best practices, the acquired signal may still contain contaminants. The aim of this paper is to review methods employed to reduce the contamination of single channel EMG signals. Specifically, we focus on methods which enable a full reconstruction of the EMG signal without loss of information. This includes subtraction methods used in the time domain, denoising methods performed after the signal decomposition and hybrid approaches that combine multiple methods. Finally, this paper provides a discussion on the suitability of the individual methods based on the type of contaminant(s) present in the signal and the specific requirements of the application.
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Affiliation(s)
- Marianne Boyer
- Department of Mechanical Engineering, Université Laval, Québec, QC G1V 0A6, Canada
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, CIUSSS de la Capitale-Nationale, Québec, QC G1M 2S8, Canada
| | - Laurent Bouyer
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, CIUSSS de la Capitale-Nationale, Québec, QC G1M 2S8, Canada
- Department of Rehabilitation, Université Laval, Québec, QC G1 V0A, Canada
| | - Jean-Sébastien Roy
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, CIUSSS de la Capitale-Nationale, Québec, QC G1M 2S8, Canada
- Department of Rehabilitation, Université Laval, Québec, QC G1 V0A, Canada
| | - Alexandre Campeau-Lecours
- Department of Mechanical Engineering, Université Laval, Québec, QC G1V 0A6, Canada
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, CIUSSS de la Capitale-Nationale, Québec, QC G1M 2S8, Canada
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Pu X, Yi P, Chen K, Ma Z, Zhao D, Ren Y. EEGDnet: Fusing non-local and local self-similarity for EEG signal denoising with transformer. Comput Biol Med 2022; 151:106248. [PMID: 36343405 DOI: 10.1016/j.compbiomed.2022.106248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 09/17/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
Electroencephalogram (EEG) has shown a useful approach to produce a brain-computer interface (BCI). One-dimensional (1-D) EEG signal is yet easily disturbed by certain artifacts (a.k.a. noise) due to the high temporal resolution. Thus, it is crucial to remove the noise in received EEG signal. Recently, deep learning-based EEG signal denoising approaches have achieved impressive performance compared with traditional ones. It is well known that the characteristics of self-similarity (including non-local and local ones) of data (e.g., natural images and time-domain signals) are widely leveraged for denoising. However, existing deep learning-based EEG signal denoising methods ignore either the non-local self-similarity (e.g., 1-D convolutional neural network) or local one (e.g., fully connected network and recurrent neural network). To address this issue, we propose a novel 1-D EEG signal denoising network with 2-D transformer, namely EEGDnet. Specifically, we comprehensively take into account the non-local and local self-similarity of EEG signal through the transformer module. By fusing non-local self-similarity in self-attention blocks and local self-similarity in feed forward blocks, the negative impact caused by noises and outliers can be reduced significantly. Extensive experiments show that, compared with other state-of-the-art models, EEGDnet achieves much better performance in terms of both quantitative and qualitative metrics. Specifically, EEGDnet can achieve 18% and 11% improvements in correlation coefficients when removing ocular artifacts and muscle artifacts, respectively.
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Affiliation(s)
- Xiaorong Pu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China
| | - Peng Yi
- School of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China
| | - Kecheng Chen
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, 999077, China.
| | - Zhaoqi Ma
- School of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China
| | - Di Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100080, China
| | - Yazhou Ren
- School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China.
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Yan W, Wu Y. A time-frequency denoising method for single-channel event-related EEG. Front Neurosci 2022; 16:991136. [PMID: 36507356 PMCID: PMC9732370 DOI: 10.3389/fnins.2022.991136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 11/14/2022] [Indexed: 11/27/2022] Open
Abstract
Introduction Electroencephalogram (EEG) acquisition is easily affected by various noises, including those from electrocardiogram (ECG), electrooculogram (EOG), and electromyogram (EMG). Because noise interference can significantly limit the study and analysis of brain signals, there is a significant need for the development of improved methods to remove this interference for more accurate measurement of EEG signals. Methods Based on the non-linear and non-stationary characteristics of brain signals, a strategy was developed to denoise brain signals using a time-frequency denoising algorithm framework of short-time Fourier transform (STFT), bidimensional empirical mode decomposition (BEMD), and non-local means (NLM). Time-frequency analysis can reveal the signal frequency component and its evolution process, allowing the elimination of noise according to the signal and noise distribution. BEMD can be used to decompose the time-frequency signals into sub-time-frequency signals for noise removal at different scales. NLM relies on structural self-similarity to locally smooth an image to remove noise and restore its main geometric structure, making this method appropriate for time-frequency signal denoising. Results The experimental results show that the proposed method can effectively suppress the high-frequency components of brain signals, resulting in a smoother brain signal waveform after denoising. The correlation coefficient of the reference signal, a superposition average of multiple trial signals, and the original single trial signal was determined, and then correlation coefficients were calculated between the reference signal and single trial signals processed by time-frequency denoising, ensemble empirical mode decomposition (EEMD)-independent component analysis (ICA), EEMD-canonical correlation analysis (CCA), and wavelet threshold denoising methods. The correlation coefficient was highest for the signal processed by the time-frequency denoising method and the reference signal, indicating that the single trial signal after time-frequency denoising was most similar to the waveform of the reference signal and suggesting this is a feasible strategy to effectively reduce noise and more accurately determine signals. Discussion The proposed time-frequency denoising method exhibits excellent performance with promising potential for practical application.
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Earley EJ, Berneving A, Zbinden J, Ortiz-Catalan M. Neurostimulation artifact removal for implantable sensors improves signal clarity and decoding of motor volition. Front Hum Neurosci 2022; 16:1030207. [PMID: 36337856 PMCID: PMC9626522 DOI: 10.3389/fnhum.2022.1030207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 09/27/2022] [Indexed: 11/23/2022] Open
Abstract
As the demand for prosthetic limbs with reliable and multi-functional control increases, recent advances in myoelectric pattern recognition and implanted sensors have proven considerably advantageous. Additionally, sensory feedback from the prosthesis can be achieved via stimulation of the residual nerves, enabling closed-loop control over the prosthesis. However, this stimulation can cause interfering artifacts in the electromyographic (EMG) signals which deteriorate the reliability and function of the prosthesis. Here, we implement two real-time stimulation artifact removal algorithms, Template Subtraction (TS) and ε-Normalized Least Mean Squares (ε-NLMS), and investigate their performance in offline and real-time myoelectric pattern recognition in two transhumeral amputees implanted with nerve cuff and EMG electrodes. We show that both algorithms are capable of significantly improving signal-to-noise ratio (SNR) and offline pattern recognition accuracy of artifact-corrupted EMG signals. Furthermore, both algorithms improved real-time decoding of motor intention during active neurostimulation. Although these outcomes are dependent on the user-specific sensor locations and neurostimulation settings, they nonetheless represent progress toward bi-directional neuromusculoskeletal prostheses capable of multifunction control and simultaneous sensory feedback.
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Affiliation(s)
- Eric J. Earley
- Center for Bionics and Pain Research, Mölndal, Sweden
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Anton Berneving
- Center for Bionics and Pain Research, Mölndal, Sweden
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Jan Zbinden
- Center for Bionics and Pain Research, Mölndal, Sweden
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Max Ortiz-Catalan
- Center for Bionics and Pain Research, Mölndal, Sweden
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Operational Area 3, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Orthopedics, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- *Correspondence: Max Ortiz-Catalan,
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Gu X, Zhao X, Mao Z, Shi Y, Xu M, Cai M, Xie F. Effect of different anesthetic dose of pentobarbital on respiratory activity in rabbits. Comput Biol Med 2022; 145:105501. [DOI: 10.1016/j.compbiomed.2022.105501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 03/04/2022] [Accepted: 04/04/2022] [Indexed: 11/16/2022]
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Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach-Part III: Other Biosignals. SENSORS 2021; 21:s21186064. [PMID: 34577270 PMCID: PMC8469046 DOI: 10.3390/s21186064] [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: 07/26/2021] [Revised: 08/31/2021] [Accepted: 09/07/2021] [Indexed: 01/18/2023]
Abstract
Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).
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Gonzalez H, George R, Muzaffar S, Acevedo J, Hoppner S, Mayr C, Yoo J, Fitzek F, Elfadel I. Hardware Acceleration of EEG-Based Emotion Classification Systems: A Comprehensive Survey. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:412-442. [PMID: 34125683 DOI: 10.1109/tbcas.2021.3089132] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Recent years have witnessed a growing interest in EEG-based wearable classifiers of emotions, which could enable the real-time monitoring of patients suffering from neurological disorders such as Amyotrophic Lateral Sclerosis (ALS), Autism Spectrum Disorder (ASD), or Alzheimer's. The hope is that such wearable emotion classifiers would facilitate the patients' social integration and lead to improved healthcare outcomes for them and their loved ones. Yet in spite of their direct relevance to neuro-medicine, the hardware platforms for emotion classification have yet to fill up some important gaps in their various approaches to emotion classification in a healthcare context. In this paper, we present the first hardware-focused critical review of EEG-based wearable classifiers of emotions and survey their implementation perspectives, their algorithmic foundations, and their feature extraction methodologies. We further provide a neuroscience-based analysis of current hardware accelerators of emotion classifiers and use it to map out several research opportunities, including multi-modal hardware platforms, accelerators with tightly-coupled cores operating robustly in the near/supra-threshold region, and pre-processing libraries for universal EEG-based datasets.
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Seok D, Lee S, Kim M, Cho J, Kim C. Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems. FRONTIERS IN ELECTRONICS 2021. [DOI: 10.3389/felec.2021.685513] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Removal of motion artifacts is a critical challenge, especially in wearable electroencephalography (EEG) and photoplethysmography (PPG) devices that are exposed to daily movements. Recently, the significance of motion artifact removal techniques has increased since EEG-based brain–computer interfaces (BCI) and daily healthcare usage of wearable PPG devices were spotlighted. In this article, the development on EEG and PPG sensor systems is introduced. Then, understanding of motion artifact and its reduction methods implemented by hardware and/or software fashions are reviewed. Various electrode types, analog readout circuits, and signal processing techniques are studied for EEG motion artifact removal. In addition, recent in-ear EEG techniques with motion artifact reduction are also introduced. Furthermore, techniques compensating independent/dependent motion artifacts are presented for PPG.
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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: 7] [Impact Index Per Article: 2.3] [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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Peri E, Xu L, Ciccarelli C, Vandenbussche NL, Xu H, Long X, Overeem S, van Dijk JP, Mischi M. Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram. SENSORS 2021; 21:s21020573. [PMID: 33467431 PMCID: PMC7829983 DOI: 10.3390/s21020573] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 01/04/2021] [Accepted: 01/12/2021] [Indexed: 01/10/2023]
Abstract
A new algorithm based on singular value decomposition (SVD) to remove cardiac contamination from trunk electromyography (EMG) is proposed. Its performance is compared to currently available algorithms at different signal-to-noise ratios (SNRs). The algorithm is applied on individual channels. An experimental calibration curve to adjust the number of SVD components to the SNR (0–20 dB) is proposed. A synthetic dataset is generated by the combination of electrocardiography (ECG) and EMG to establish a ground truth reference for validation. The performance is compared with state-of-the-art algorithms: gating, high-pass filtering, template subtraction (TS), and independent component analysis (ICA). Its applicability on real data is investigated in an illustrative diaphragm EMG of a patient with sleep apnea. The SVD-based algorithm outperforms existing methods in reconstructing trunk EMG. It is superior to the others in the time (relative mean squared error < 15%) and frequency (shift in mean frequency < 1 Hz) domains. Its feasibility is proven on diaphragm EMG, which shows a better agreement with the respiratory cycle (correlation coefficient = 0.81, p-value < 0.01) compared with TS and ICA. Its application on real data is promising to non-obtrusively estimate respiratory effort for sleep-related breathing disorders. The algorithm is not limited to the need for additional reference ECG, increasing its applicability in clinical practice.
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Affiliation(s)
- Elisabetta Peri
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (C.C.); (H.X.); (X.L.); (S.O.); (J.P.v.D.); (M.M.)
- Correspondence:
| | - Lin Xu
- School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China;
| | - Christian Ciccarelli
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (C.C.); (H.X.); (X.L.); (S.O.); (J.P.v.D.); (M.M.)
| | - Nele L. Vandenbussche
- Center for Sleep Medicine, Kempenhaeghe, P.O. Box 61, 5590 AB Heeze, The Netherlands;
| | - Hongji Xu
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (C.C.); (H.X.); (X.L.); (S.O.); (J.P.v.D.); (M.M.)
| | - Xi Long
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (C.C.); (H.X.); (X.L.); (S.O.); (J.P.v.D.); (M.M.)
| | - Sebastiaan Overeem
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (C.C.); (H.X.); (X.L.); (S.O.); (J.P.v.D.); (M.M.)
- Center for Sleep Medicine, Kempenhaeghe, P.O. Box 61, 5590 AB Heeze, The Netherlands;
| | - Johannes P. van Dijk
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (C.C.); (H.X.); (X.L.); (S.O.); (J.P.v.D.); (M.M.)
- Center for Sleep Medicine, Kempenhaeghe, P.O. Box 61, 5590 AB Heeze, The Netherlands;
- Department of Orthodontics, University of Ulm, 89081 Ulm, Germany
| | - Massimo Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (C.C.); (H.X.); (X.L.); (S.O.); (J.P.v.D.); (M.M.)
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Hossain MB, Lazaro J, Noh Y, Chon KH. Denoising Wearable Armband ECG Data Using the Variable Frequency Complex Demodulation Technique. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:592-595. [PMID: 33018058 DOI: 10.1109/embc44109.2020.9175665] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
We propose a novel electrocardiogram (ECG) denoising technique using the variable frequency complex demodulation (VFCDM) algorithm. We used VFCDM to perform the sub-band decomposition of the noise-contaminated ECG to remove the noise components so that accurate QRS complexes could be identified. The ECG quality was further improved by removing baseline drift and smoothing via adaptive mean filtering. The proposed method was validated on the MIT-BIH arrhythmia database (MITDB) and wearable armband ECG data. For the former, we added Gaussian white noise to the ECG signals at different signal-to-noise ratios and the denoising performance of the proposed method was compared with other denoising techniques. The proposed approach showed superior denoising performance compared to the other methods. We compared the QRS complex detection performance of the noisy to the denoised armband ECG. The performance of the proposed denoising method on the armband ECG resulted in comparable QRS complex detection as that obtained when using Holter monitor ECG signals. This demonstrates that the proposed algorithm can significantly increase the amount of usable armband ECG data, which would otherwise have been discarded due to electromyogram contamination especially during arm movements. Hence, the proposed algorithm has the potential to enable long-term monitoring of atrial fibrillation using the armband without the discomfort of skin irritation often experienced with Holter monitors.Clinical Relevance- The proposed ECG denoising method can significantly increase the ECG quality of wearable ECG devices, which are more susceptible to muscle and motion artifacts.
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Xu L, Peri E, Vullings R, Rabotti C, Van Dijk JP, Mischi M. Comparative Review of the Algorithms for Removal of Electrocardiographic Interference from Trunk Electromyography. SENSORS (BASEL, SWITZERLAND) 2020; 20:E4890. [PMID: 32872470 PMCID: PMC7506664 DOI: 10.3390/s20174890] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 08/23/2020] [Accepted: 08/24/2020] [Indexed: 11/29/2022]
Abstract
Surface electromyogram (EMG) is a noninvasive measure of muscle electrical activity and has been widely used in a variety of applications. When recorded from the trunk, surface EMG can be contaminated by the cardiac electrical activity, i.e., the electrocardiogram (ECG). ECG may distort the desired EMG signal, complicating the extraction of reliable information from the trunk EMG. Several methods are available for ECG removal from the trunk EMG, but a comparative assessment of the performance of these methods is lacking, limiting the possibility of selecting a suitable method for specific applications. The aim of the present study is therefore to review and compare the performance of different ECG removal methods from the trunk EMG. To this end, a synthetic dataset was generated by combining in vivo EMG signals recorded on the biceps brachii and healthy or dysrhythmia ECG data from the Physionet database with a predefined signal-to-noise ratio. Gating, high-pass filtering, template subtraction, wavelet transform, adaptive filtering, and blind source separation were implemented for ECG removal. A robust measure of Kurtosis, i.e., KR2 and two EMG features, the average rectified value (ARV), and mean frequency (MF), were then calculated from the processed EMG signals and compared with the EMG before mixing. Our results indicate template subtraction to produce the lowest root mean square error in both ARV and MF, providing useful insight for the selection of a suitable ECG removal method.
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Affiliation(s)
- Lin Xu
- School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Elisabetta Peri
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (E.P.); (R.V.); (J.P.V.D.); (M.M.)
| | - Rik Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (E.P.); (R.V.); (J.P.V.D.); (M.M.)
| | | | - Johannes P. Van Dijk
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (E.P.); (R.V.); (J.P.V.D.); (M.M.)
- Clinical Physics Department at Kempenhaeghe, 6532 SZ Nijmegen, The Netherlands
| | - Massimo Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (E.P.); (R.V.); (J.P.V.D.); (M.M.)
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17
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Reljin N, Lazaro J, Hossain MB, Noh YS, Cho CH, Chon KH. Using the Redundant Convolutional Encoder-Decoder to Denoise QRS Complexes in ECG Signals Recorded with an Armband Wearable Device. SENSORS (BASEL, SWITZERLAND) 2020; 20:E4611. [PMID: 32824420 PMCID: PMC7472132 DOI: 10.3390/s20164611] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 08/11/2020] [Accepted: 08/14/2020] [Indexed: 12/04/2022]
Abstract
Long-term electrocardiogram (ECG) recordings while performing normal daily routines are often corrupted with motion artifacts, which in turn, can result in the incorrect calculation of heart rates. Heart rates are important clinical information, as they can be used for analysis of heart-rate variability and detection of cardiac arrhythmias. In this study, we present an algorithm for denoising ECG signals acquired with a wearable armband device. The armband was worn on the upper left arm by one male participant, and we simultaneously recorded three ECG channels for 24 h. We extracted 10-s sequences from armband recordings corrupted with added noise and motion artifacts. Denoising was performed using the redundant convolutional encoder-decoder (R-CED), a fully convolutional network. We measured the performance by detecting R-peaks in clean, noisy, and denoised sequences and by calculating signal quality indices: signal-to-noise ratio (SNR), ratio of power, and cross-correlation with respect to the clean sequences. The percent of correctly detected R-peaks in denoised sequences was higher than in sequences corrupted with either added noise (70-100% vs. 34-97%) or motion artifacts (91.86% vs. 61.16%). There was notable improvement in SNR values after denoising for signals with noise added (7-19 dB), and when sequences were corrupted with motion artifacts (0.39 dB). The ratio of power for noisy sequences was significantly lower when compared to both clean and denoised sequences. Similarly, cross-correlation between noisy and clean sequences was significantly lower than between denoised and clean sequences. Moreover, we tested our denoising algorithm on 60-s sequences extracted from recordings from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and obtained improvement in SNR values of 7.08 ± 0.25 dB (mean ± standard deviation (sd)). These results from a diverse set of data suggest that the proposed denoising algorithm improves the quality of the signal and can potentially be applied to most ECG measurement devices.
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Affiliation(s)
- Natasa Reljin
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (N.R.); (J.L.); (M.B.H.); (C.H.C.)
| | - Jesus Lazaro
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (N.R.); (J.L.); (M.B.H.); (C.H.C.)
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, 50009 Zaragoza, Spain
- CIBER in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28029 Madrid, Spain
| | - Md Billal Hossain
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (N.R.); (J.L.); (M.B.H.); (C.H.C.)
| | - Yeon Sik Noh
- College of Nursing, University of Massachusetts Amherst, Amherst, MA 01003, USA;
- Department of Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, MA 01002, USA;
| | - Chae Ho Cho
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (N.R.); (J.L.); (M.B.H.); (C.H.C.)
| | - Ki H. Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (N.R.); (J.L.); (M.B.H.); (C.H.C.)
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18
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van Leuteren RW, Hutten GJ, de Waal CG, Dixon P, van Kaam AH, de Jongh FH. Processing transcutaneous electromyography measurements of respiratory muscles, a review of analysis techniques. J Electromyogr Kinesiol 2019; 48:176-186. [PMID: 31401341 DOI: 10.1016/j.jelekin.2019.07.014] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 07/23/2019] [Accepted: 07/27/2019] [Indexed: 11/28/2022] Open
Abstract
Transcutaneous electromyography (tc-EMG) has been used to measure the electrical activity of respiratory muscles during inspiration in various studies. Processing the raw tc-EMG signal of these inspiratory muscles has shown to be difficult as baseline noise, cardiac interference, cross-talk and motion artefacts can influence the signal quality. In this review we will discuss the most important sources of signal noise in tc-EMG of respiratory muscles and the various techniques described to suppress or reduce this signal noise. Furthermore, we will elaborate on the options available to develop or improve an algorithm that can be used to guide the approach for analysis of tc-EMG signals of inspiratory muscles in future research.
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Affiliation(s)
- R W van Leuteren
- Department of Neonatology, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
| | - G J Hutten
- Department of Neonatology, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Department of Neonatology, Emma Children's Hospital, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - C G de Waal
- Department of Neonatology, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - P Dixon
- Vyaire Medical, Basingstoke, United Kingdom
| | - A H van Kaam
- Department of Neonatology, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Department of Neonatology, Emma Children's Hospital, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - F H de Jongh
- Department of Neonatology, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Faculty of Science and Technology, University of Twente, Enschede, the Netherlands
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Jiang X, Bian GB, Tian Z. Removal of Artifacts from EEG Signals: A Review. SENSORS (BASEL, SWITZERLAND) 2019; 19:E987. [PMID: 30813520 PMCID: PMC6427454 DOI: 10.3390/s19050987] [Citation(s) in RCA: 198] [Impact Index Per Article: 39.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Revised: 02/03/2019] [Accepted: 02/21/2019] [Indexed: 11/28/2022]
Abstract
Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application.
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Affiliation(s)
- Xiao Jiang
- Institute of Automation, Chinese Academy of Science, Beijing 100190, China.
- School of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China.
| | - Gui-Bin Bian
- Institute of Automation, Chinese Academy of Science, Beijing 100190, China.
| | - Zean Tian
- School of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China.
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20
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Rath M, Vette AH, Ramasubramaniam S, Li K, Burdick J, Edgerton VR, Gerasimenko YP, Sayenko DG. Trunk Stability Enabled by Noninvasive Spinal Electrical Stimulation after Spinal Cord Injury. J Neurotrauma 2018; 35:2540-2553. [PMID: 29786465 DOI: 10.1089/neu.2017.5584] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Electrical neuromodulation of spinal networks improves the control of movement of the paralyzed limbs after spinal cord injury (SCI). However, the potential of noninvasive spinal stimulation to facilitate postural trunk control during sitting in humans with SCI has not been investigated. We hypothesized that transcutaneous electrical stimulation of the lumbosacral enlargement can improve trunk posture. Eight participants with non-progressive SCI at C3-T9, American Spinal Injury Association Impairment Scale (AIS) A or C, performed different motor tasks during sitting. Electromyography of the trunk muscles, three-dimensional kinematics, and force plate data were acquired. Spinal stimulation improved trunk control during sitting in all tested individuals. Stimulation resulted in elevated activity of the erector spinae, rectus abdominis, and external obliques, contributing to improved trunk control, more natural anterior pelvic tilt and lordotic curve, and greater multi-directional seated stability. During spinal stimulation, the center of pressure (COP) displacements decreased to 1.36 ± 0.98 mm compared with 4.74 ± 5.41 mm without stimulation (p = 0.0156) in quiet sitting, and the limits of stable displacement increased by 46.92 ± 35.66% (p = 0.0156), 36.92 ± 30.48% (p = 0.0156), 54.67 ± 77.99% (p = 0.0234), and 22.70 ± 26.09% (p = 0.0391) in the forward, backward, right, and left directions, respectively. During self-initiated perturbations, the correlation between anteroposterior arm velocity and the COP displacement decreased from r = 0.5821 (p = 0.0007) without to r = 0.5115 (p = 0.0039) with stimulation, indicating improved trunk stability. These data demonstrate that the spinal networks can be modulated transcutaneously with tonic electrical spinal stimulation to physiological states sufficient to generate a more stable, erect sitting posture after chronic paralysis.
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Affiliation(s)
- Mrinal Rath
- 1 Department of Biomedical Engineering, University of California , Los Angeles, California.,2 Department of Integrative Biology and Physiology, University of California , Los Angeles, California
| | - Albert H Vette
- 3 Department of Mechanical Engineering, University of Alberta , Donadeo Innovation Centre for Engineering, Edmonton, Alberta, Canada .,4 Glenrose Rehabilitation Hospital , Alberta Health Services, Edmonton, Alberta, Canada
| | | | - Kun Li
- 5 Division of Engineering and Applied Sciences, California Institute of Technology , Pasadena, California
| | - Joel Burdick
- 5 Division of Engineering and Applied Sciences, California Institute of Technology , Pasadena, California
| | - Victor R Edgerton
- 1 Department of Biomedical Engineering, University of California , Los Angeles, California.,2 Department of Integrative Biology and Physiology, University of California , Los Angeles, California.,6 Department of Neurobiology and Neurosurgery, University of California , Los Angeles, California.,7 Institut Guttmann, Hospital de Neurorehabilitació, Institut Universitari adscrit a la Universitat Autònoma de Barcelona , Barcelona, Badalona, Spain .,8 Centre for Neuroscience and Regenerative Medicine, Faculty of Science, University of Technology , Sydney, Australia
| | - Yury P Gerasimenko
- 2 Department of Integrative Biology and Physiology, University of California , Los Angeles, California.,9 Pavlov Institute of Physiology , St. Petersburg, Russia
| | - Dimitry G Sayenko
- 2 Department of Integrative Biology and Physiology, University of California , Los Angeles, California.,10 Department of Neurosurgery, Center for Neuroregeneration, Houston Methodist Research Institute , Houston, Texas
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21
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Separation of electrocardiographic from electromyographic signals using dynamic filtration. Med Eng Phys 2018; 57:1-10. [PMID: 29699890 DOI: 10.1016/j.medengphy.2018.04.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Revised: 04/04/2018] [Accepted: 04/07/2018] [Indexed: 11/21/2022]
Abstract
Trunk muscle electromyographic (EMG) signals are often contaminated by the electrical activity of the heart. During low or moderate muscle force, these electrocardiographic (ECG) signals disturb the estimation of muscle activity. Butterworth high-pass filters with cut-off frequency of up to 60 Hz are often used to suppress the ECG signal. Such filters disturb the EMG signal in both frequency and time domain. A new method based on the dynamic application of Savitzky-Golay filter is proposed. EMG signals of three left trunk muscles and pure ECG signal were recorded during different motor tasks. The efficiency of the method was tested and verified both with the experimental EMG signals and with modeled signals obtained by summing the pure ECG signal with EMG signals at different levels of signal-to-noise ratio. The results were compared with those obtained by application of high-pass, 4th order Butterworth filter with cut-off frequency of 30 Hz. The suggested method is separating the EMG signal from the ECG signal without EMG signal distortion across its entire frequency range regardless of amplitudes. Butterworth filter suppresses the signals in the 0-30 Hz range thus preventing the low-frequency analysis of the EMG signal. An additional disadvantage is that it passes high-frequency ECG signal components which is apparent at equal and higher amplitudes of the ECG signal as compared to the EMG signal. The new method was also successfully verified with abnormal ECG signals.
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22
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Kumar S, Panigrahy D, Sahu P. Denoising of Electrocardiogram (ECG) signal by using empirical mode decomposition (EMD) with non-local mean (NLM) technique. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.01.005] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
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23
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Sbrollini A, Strazza A, Candelaresi S, Marcantoni I, Morettini M, Fioretti S, Di Nardo F, Burattini L. Surface electromyography low-frequency content: Assessment in isometric conditions after electrocardiogram cancellation by the Segmented-Beat Modulation Method. INFORMATICS IN MEDICINE UNLOCKED 2018. [DOI: 10.1016/j.imu.2018.10.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022] Open
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Goh SK, Abbass HA, Tan KC, Al-Mamun A, Wang C, Guan C. Automatic EEG Artifact Removal Techniques by Detecting Influential Independent Components. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2017. [DOI: 10.1109/tetci.2017.2690913] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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25
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A new algorithm for ECG interference removal from single channel EMG recording. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2017; 40:575-584. [DOI: 10.1007/s13246-017-0564-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Accepted: 06/08/2017] [Indexed: 11/26/2022]
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26
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Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data. Crit Care Med 2017; 44:e456-63. [PMID: 26992068 DOI: 10.1097/ccm.0000000000001660] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
OBJECTIVE The use of machine-learning algorithms to classify alerts as real or artifacts in online noninvasive vital sign data streams to reduce alarm fatigue and missed true instability. DESIGN Observational cohort study. SETTING Twenty-four-bed trauma step-down unit. PATIENTS Two thousand one hundred fifty-three patients. INTERVENTION Noninvasive vital sign monitoring data (heart rate, respiratory rate, peripheral oximetry) recorded on all admissions at 1/20 Hz, and noninvasive blood pressure less frequently, and partitioned data into training/validation (294 admissions; 22,980 monitoring hours) and test sets (2,057 admissions; 156,177 monitoring hours). Alerts were vital sign deviations beyond stability thresholds. A four-member expert committee annotated a subset of alerts (576 in training/validation set, 397 in test set) as real or artifact selected by active learning, upon which we trained machine-learning algorithms. The best model was evaluated on test set alerts to enact online alert classification over time. MEASUREMENTS AND MAIN RESULTS The Random Forest model discriminated between real and artifact as the alerts evolved online in the test set with area under the curve performance of 0.79 (95% CI, 0.67-0.93) for peripheral oximetry at the instant the vital sign first crossed threshold and increased to 0.87 (95% CI, 0.71-0.95) at 3 minutes into the alerting period. Blood pressure area under the curve started at 0.77 (95% CI, 0.64-0.95) and increased to 0.87 (95% CI, 0.71-0.98), whereas respiratory rate area under the curve started at 0.85 (95% CI, 0.77-0.95) and increased to 0.97 (95% CI, 0.94-1.00). Heart rate alerts were too few for model development. CONCLUSIONS Machine-learning models can discern clinically relevant peripheral oximetry, blood pressure, and respiratory rate alerts from artifacts in an online monitoring dataset (area under the curve > 0.87).
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Miljković N, Popović N, Djordjević O, Konstantinović L, Šekara TB. ECG artifact cancellation in surface EMG signals by fractional order calculus application. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 140:259-264. [PMID: 28254082 DOI: 10.1016/j.cmpb.2016.12.017] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Revised: 12/22/2016] [Accepted: 12/29/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE New aspects for automatic electrocardiography artifact removal from surface electromyography signals by application of fractional order calculus in combination with linear and nonlinear moving window filters are explored. Surface electromyography recordings of skeletal trunk muscles are commonly contaminated with spike shaped artifacts. This artifact originates from electrical heart activity, recorded by electrocardiography, commonly present in the surface electromyography signals recorded in heart proximity. For appropriate assessment of neuromuscular changes by means of surface electromyography, application of a proper filtering technique of electrocardiography artifact is crucial. METHODS A novel method for automatic artifact cancellation in surface electromyography signals by applying fractional order calculus and nonlinear median filter is introduced. The proposed method is compared with the linear moving average filter, with and without prior application of fractional order calculus. 3D graphs for assessment of window lengths of the filters, crest factors, root mean square differences, and fractional calculus orders (called WFC and WRC graphs) have been introduced. For an appropriate quantitative filtering evaluation, the synthetic electrocardiography signal and analogous semi-synthetic dataset have been generated. The examples of noise removal in 10 able-bodied subjects and in one patient with muscle dystrophy are presented for qualitative analysis. RESULTS The crest factors, correlation coefficients, and root mean square differences of the recorded and semi-synthetic electromyography datasets showed that the most successful method was the median filter in combination with fractional order calculus of the order 0.9. Statistically more significant (p < 0.001) ECG peak reduction was obtained by the median filter application compared to the moving average filter in the cases of low level amplitude of muscle contraction compared to ECG spikes. CONCLUSIONS The presented results suggest that the novel method combining a median filter and fractional order calculus can be used for automatic filtering of electrocardiography artifacts in the surface electromyography signal envelopes recorded in trunk muscles.
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Affiliation(s)
- Nadica Miljković
- University of Belgrade, School of Electrical Engineering, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia.
| | - Nenad Popović
- University of Belgrade, School of Electrical Engineering, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia.
| | - Olivera Djordjević
- Rehabilitation Clinic "Dr Miroslav Zotović", Sokobanjska 13, 11000 Belgrade, Serbia; University of Belgrade, School of Medicine, Dr Subotića 8, 11000 Belgrade, Serbia.
| | - Ljubica Konstantinović
- Rehabilitation Clinic "Dr Miroslav Zotović", Sokobanjska 13, 11000 Belgrade, Serbia; University of Belgrade, School of Medicine, Dr Subotića 8, 11000 Belgrade, Serbia.
| | - Tomislav B Šekara
- University of Belgrade, School of Electrical Engineering, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia.
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Extended Kalman smoother with differential evolution technique for denoising of ECG signal. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2016; 39:783-95. [PMID: 27542170 DOI: 10.1007/s13246-016-0468-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Accepted: 08/01/2016] [Indexed: 10/21/2022]
Abstract
Electrocardiogram (ECG) signal gives a lot of information on the physiology of heart. In reality, noise from various sources interfere with the ECG signal. To get the correct information on physiology of the heart, noise cancellation of the ECG signal is required. In this paper, the effectiveness of extended Kalman smoother (EKS) with the differential evolution (DE) technique for noise cancellation of the ECG signal is investigated. DE is used as an automatic parameter selection method for the selection of ten optimized components of the ECG signal, and those are used to create the ECG signal according to the real ECG signal. These parameters are used by the EKS for the development of the state equation and also for initialization of the parameters of EKS. EKS framework is used for denoising the ECG signal from the single channel. The effectiveness of proposed noise cancellation technique has been evaluated by adding white, colored Gaussian noise and real muscle artifact noise at different SNR to some visually clean ECG signals from the MIT-BIH arrhythmia database. The proposed noise cancellation technique of ECG signal shows better signal to noise ratio (SNR) improvement, lesser mean square error (MSE) and percent of distortion (PRD) compared to other well-known methods.
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30
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Complexity Analysis of Surface EMG for Overcoming ECG Interference toward Proportional Myoelectric Control. ENTROPY 2016. [DOI: 10.3390/e18040106] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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31
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Niegowski M, Zivanovic M. Wavelet-based unsupervised learning method for electrocardiogram suppression in surface electromyograms. Med Eng Phys 2016; 38:248-56. [DOI: 10.1016/j.medengphy.2015.12.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2015] [Revised: 10/26/2015] [Accepted: 12/20/2015] [Indexed: 11/28/2022]
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32
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Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data. J Clin Monit Comput 2015; 30:875-888. [PMID: 26438655 DOI: 10.1007/s10877-015-9788-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Accepted: 09/30/2015] [Indexed: 10/23/2022]
Abstract
Huge hospital information system databases can be mined for knowledge discovery and decision support, but artifact in stored non-invasive vital sign (VS) high-frequency data streams limits its use. We used machine-learning (ML) algorithms trained on expert-labeled VS data streams to automatically classify VS alerts as real or artifact, thereby "cleaning" such data for future modeling. 634 admissions to a step-down unit had recorded continuous noninvasive VS monitoring data [heart rate (HR), respiratory rate (RR), peripheral arterial oxygen saturation (SpO2) at 1/20 Hz, and noninvasive oscillometric blood pressure (BP)]. Time data were across stability thresholds defined VS event epochs. Data were divided Block 1 as the ML training/cross-validation set and Block 2 the test set. Expert clinicians annotated Block 1 events as perceived real or artifact. After feature extraction, ML algorithms were trained to create and validate models automatically classifying events as real or artifact. The models were then tested on Block 2. Block 1 yielded 812 VS events, with 214 (26 %) judged by experts as artifact (RR 43 %, SpO2 40 %, BP 15 %, HR 2 %). ML algorithms applied to the Block 1 training/cross-validation set (tenfold cross-validation) gave area under the curve (AUC) scores of 0.97 RR, 0.91 BP and 0.76 SpO2. Performance when applied to Block 2 test data was AUC 0.94 RR, 0.84 BP and 0.72 SpO2. ML-defined algorithms applied to archived multi-signal continuous VS monitoring data allowed accurate automated classification of VS alerts as real or artifact, and could support data mining for future model building.
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Niegowski M, Zivanovic M. ECG-EMG separation by using enhanced non-negative matrix factorization. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:4212-5. [PMID: 25570921 DOI: 10.1109/embc.2014.6944553] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
We present a novel approach to single-channel ECG-EMG signal separation by means of enhanced non-negative matrix factorization (NMF). The approach is based on a linear decomposition of the input signal spectrogram in two non-negative components, which represent the ECG and EMG spectrogram estimates. As ECG and EMG have different time-frequency (TF) patterns, the decomposition is enhanced by reshaping the input mixture spectrogram in order to emphasize a sparse ECG over a noisy-like EMG. Moreover, initialization of the classical NMF algorithm with accurately designed ECG and EMG structures further increases its separation performance. The comparative study suggests that the proposed method outperforms two reference methods for both synthetic and real signal mixture scenarios.
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Abstract
This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts. We first introduce background knowledge on the characteristics of EEG activity, of the artifacts and of the EEG measurement model. Then, we present algorithms commonly employed in the literature and describe their key features. Lastly, principally on the basis of the results provided by various researchers, but also supported by our own experience, we compare the state-of-the-art methods in terms of reported performance, and provide guidelines on how to choose a suitable artifact removal algorithm for a given scenario. With this review we have concluded that, without prior knowledge of the recorded EEG signal or the contaminants, the safest approach is to correct the measured EEG using independent component analysis-to be precise, an algorithm based on second-order statistics such as second-order blind identification (SOBI). Other effective alternatives include extended information maximization (InfoMax) and an adaptive mixture of independent component analyzers (AMICA), based on higher order statistics. All of these algorithms have proved particularly effective with simulations and, more importantly, with data collected in controlled recording conditions. Moreover, whenever prior knowledge is available, then a constrained form of the chosen method should be used in order to incorporate such additional information. Finally, since which algorithm is the best performing is highly dependent on the type of the EEG signal, the artifacts and the signal to contaminant ratio, we believe that the optimal method for removing artifacts from the EEG consists in combining more than one algorithm to correct the signal using multiple processing stages, even though this is an option largely unexplored by researchers in the area.
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Affiliation(s)
- Jose Antonio Urigüen
- Deustotech-Life (eVida Research Group), University of Deusto, Facultad de Ingeniería, 4a Planta Avda/Universidades 24, 48007 Bilbao, Spain
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Taralunga DD, Ungureanu M, Hurezeanu B, Gussi I, Strungaru R. Empirical mode decomposition applied for non-invasive electrohysterograhic signals denoising. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2015:4134-4137. [PMID: 26737204 DOI: 10.1109/embc.2015.7319304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The electrical activity of the uterus, i.e. the electrohysterogram (EHG), is one of the most prominent tool for preterm labour. There is no standard acquisition set up and often the EHG is corrupted with different types of noise: maternal and fetal electrocardiogram (mECG, fECG), electrical activity of the skeletal muscles, movement artifacts, power line interference (PLI) etc. Moreover, some of these noises overlap in frequency domain with the EHG. Thus, simple linear filtering approaches are not adequate. In this paper the empirical mode decomposition (EMD), a simple and data driven method, is proposed for EHG denoising. The method is evaluated on simulated data having different signal to noise ratios (SNRs) obtaining promising results.
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36
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Abbaspour S, Fallah A. A combination method for electrocardiogram rejection from surface electromyogram. Open Biomed Eng J 2014; 8:13-9. [PMID: 24772195 PMCID: PMC3999703 DOI: 10.2174/1874120701408010013] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2013] [Revised: 01/24/2014] [Accepted: 01/27/2014] [Indexed: 11/22/2022] Open
Abstract
The electrocardiogram signal which represents the electrical activity of the heart provides interference in the
recording of the electromyogram signal, when the electromyogram signal is recorded from muscles close to the heart.
Therefore, due to impurities, electromyogram signals recorded from this area cannot be used. In this paper, a new method
was developed using a combination of artificial neural network and wavelet transform approaches, to eliminate the electrocardiogram
artifact from electromyogram signals and improve results. For this purpose, contaminated signal is initially
cleaned using the neural network. With this process, a large amount of noise can be removed. However, low-frequency
noise components remain in the signal that can be removed using wavelet. Finally, the result of the proposed method is
compared with other methods that were used in different papers to remove electrocardiogram from electromyogram. In
this paper in order to compare methods, qualitative and quantitative criteria such as signal to noise ratio, relative error,
power spectrum density and coherence have been investigated for evaluation and comparison. The results of signal to
noise ratio and relative error are equal to 15.6015 and 0.0139, respectively.
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Affiliation(s)
- Sara Abbaspour
- Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Ali Fallah
- Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
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37
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Automatic identification of motion artifacts in EHG recording for robust analysis of uterine contractions. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2014; 2014:470786. [PMID: 24523828 PMCID: PMC3912778 DOI: 10.1155/2014/470786] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2013] [Accepted: 10/14/2013] [Indexed: 11/17/2022]
Abstract
Electrohysterography (EHG) is a noninvasive technique for monitoring uterine electrical activity. However, the presence of artifacts in the EHG signal may give rise to erroneous interpretations and make it difficult to extract useful information from these recordings. The aim of this work was to develop an automatic system of segmenting EHG recordings that distinguishes between uterine contractions and artifacts. Firstly, the segmentation is performed using an algorithm that generates the TOCO-like signal derived from the EHG and detects windows with significant changes in amplitude. After that, these segments are classified in two groups: artifacted and nonartifacted signals. To develop a classifier, a total of eleven spectral, temporal, and nonlinear features were calculated from EHG signal windows from 12 women in the first stage of labor that had previously been classified by experts. The combination of characteristics that led to the highest degree of accuracy in detecting artifacts was then determined. The results showed that it is possible to obtain automatic detection of motion artifacts in segmented EHG recordings with a precision of 92.2% using only seven features. The proposed algorithm and classifier together compose a useful tool for analyzing EHG signals and would help to promote clinical applications of this technique.
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38
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Comparison of non-invasive electrohysterographic recording techniques for monitoring uterine dynamics. Med Eng Phys 2013; 35:1736-43. [DOI: 10.1016/j.medengphy.2013.07.008] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2012] [Revised: 05/30/2013] [Accepted: 07/23/2013] [Indexed: 11/18/2022]
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39
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Benosman MM, Bereksi-Reguig F, Salerud EG. Analysis of ECG-trunk muscle signal amplitude and heart rate relationship. J Med Eng Technol 2013; 37:449-55. [PMID: 23964696 DOI: 10.3109/03091902.2013.828107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
The objective of this paper is to investigate if bioelectrical signals, generated from trunk muscles identified in an electrocardiogram (ECG) signal presented in this paper as ECG-Trunk Muscles Signals amplitude (Ecg-TMSA) are correlated with Heart rate (HR) during different levels of physical activity and also if Ecg-TMSA is not influenced by mental activity. HR and Ecg-TMSA were derived from ECG in 14 subjects when walking and jogging at different treadmill velocities from 4-10 (km h(-1)). The mean relationship for all 14 subjects was HR = (42.3 ± 0.2) + (45.3 ± 2.8) Ecg-TMSA, r(2 )= 0.91. The result of one individual data points example for a 21 min experiment was (r(2 )= 0.93, p < 0.0001, n = 336). The obtained results show a linear relationship between Ecg-TMSA and HR. Moreover, the Ecg-TMSA was not affected by mental activity.
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Affiliation(s)
- Mourad-M Benosman
- Biomedical Engineering Department, Tlemcen University , Tlemcen, 13000 , Algeria and
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40
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Galiana-Merino JJ, Ruiz-Fernandez D, Martinez-Espla JJ. Power line interference filtering on surface electromyography based on the stationary wavelet packet transform. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 111:338-346. [PMID: 23726363 DOI: 10.1016/j.cmpb.2013.04.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2012] [Revised: 04/23/2013] [Accepted: 04/30/2013] [Indexed: 06/02/2023]
Abstract
Power line interference is one of the main problems in surface electromyogram signals (EMG) analysis. In this work, a new method based on the stationary wavelet packet transform is proposed to estimate and remove this kind of noise from EMG data records. The performance has been quantitatively evaluated with synthetic noisy signals, obtaining good results independently from the signal to noise ratio (SNR). For the analyzed cases, the obtained results show that the correlation coefficient is around 0.99, the energy respecting to the pure EMG signal is 98-104%, the SNR is between 16.64 and 20.40dB and the mean absolute error (MAE) is in the range of -69.02 and -65.31dB. It has been also applied on 18 real EMG signals, evaluating the percentage of energy respecting to the noisy signals. The proposed method adjusts the reduction level to the amplitude of each harmonic present in the analyzed noisy signals (synthetic and real), reducing the harmonics with no alteration of the desired signal.
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Affiliation(s)
- J J Galiana-Merino
- Dept. Physics, Systems Engineering and Signal Theory, University of Alicante, P.O. Box 99, E-03080 Alicante, Spain.
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41
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Nitzken M, Bajaj N, Aslan S, Gimel'farb G, El-Baz A, Ovechkin A. Local Wavelet-Based Filtering of Electromyographic Signals to Eliminate the Electrocardiographic-Induced Artifacts in Patients with Spinal Cord Injury. ACTA ACUST UNITED AC 2013; 6. [PMID: 24307920 DOI: 10.4236/jbise.2013.67a2001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Surface Electromyography (EMG) is a standard method used in clinical practice and research to assess motor function in order to help with the diagnosis of neuromuscular pathology in human and animal models. EMG recorded from trunk muscles involved in the activity of breathing can be used as a direct measure of respiratory motor function in patients with spinal cord injury (SCI) or other disorders associated with motor control deficits. However, EMG potentials recorded from these muscles are often contaminated with heart-induced electrocardiographic (ECG) signals. Elimination of these artifacts plays a critical role in the precise measure of the respiratory muscle electrical activity. This study was undertaken to find an optimal approach to eliminate the ECG artifacts from EMG recordings. Conventional global filtering can be used to decrease the ECG-induced artifact. However, this method can alter the EMG signal and changes physiologically relevant information. We hypothesize that, unlike global filtering, localized removal of ECG artifacts will not change the original EMG signals. We develop an approach to remove the ECG artifacts without altering the amplitude and frequency components of the EMG signal by using an externally recorded ECG signal as a mask to locate areas of the ECG spikes within EMG data. These segments containing ECG spikes were decomposed into 128 sub-wavelets by a custom-scaled Morlet Wavelet Transform. The ECG-related sub-wavelets at the ECG spike location were removed and a de-noised EMG signal was reconstructed. Validity of the proposed method was proven using mathematical simulated synthetic signals and EMG obtained from SCI patients. We compare the Root-mean Square Error and the Relative Change in Variance between this method, global, notch and adaptive filters. The results show that the localized wavelet-based filtering has the benefit of not introducing error in the native EMG signal and accurately removing ECG artifacts from EMG signals.
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Affiliation(s)
- Matthew Nitzken
- BioImaging laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
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42
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Zhou P, Lowery M, Weir R, Kuiken T. Elimination of ECG Artifacts from Myoelectric Prosthesis Control Signals Developed by Targeted Muscle Reinnervation. CONFERENCE PROCEEDINGS : ... ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL CONFERENCE 2012; 2005:5276-9. [PMID: 17281440 DOI: 10.1109/iembs.2005.1615670] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
We investigated elimination of electrocardiogram (ECG) artifacts from the myoelectric prosthesis control signals, taken from the reinnervated pectoralis muscles of a patient with bilateral amputations at shoulder disarticulation level. The performance of various ECG artifact removal methods including high pass filtering, spike clipping, template subtracting, wavelet thresholding and adaptive filtering was presented. In particular, considering the clinical requirements and memory limitation of commercial prosthesis controllers, we further explored suitable means of ECG artifact removal for clinical application.
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Affiliation(s)
- Ping Zhou
- Neural Eng. Center for Artificial Limbs, Rehabilitation Inst. of Chicago, IL
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43
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Zivanovic M, Gonzalez-Izal M. Nonstationary Harmonic Modeling for ECG Removal in Surface EMG Signals. IEEE Trans Biomed Eng 2012; 59:1633-40. [DOI: 10.1109/tbme.2012.2191287] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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44
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Willigenburg NW, Daffertshofer A, Kingma I, van Dieën JH. Removing ECG contamination from EMG recordings: A comparison of ICA-based and other filtering procedures. J Electromyogr Kinesiol 2012; 22:485-93. [PMID: 22296869 DOI: 10.1016/j.jelekin.2012.01.001] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2011] [Revised: 12/07/2011] [Accepted: 01/03/2012] [Indexed: 11/26/2022] Open
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45
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Sweeney KT, Ward TE, McLoone SF. Artifact removal in physiological signals--practices and possibilities. ACTA ACUST UNITED AC 2012; 16:488-500. [PMID: 22361665 DOI: 10.1109/titb.2012.2188536] [Citation(s) in RCA: 133] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an aging population. This, in turn, places an ever-increasing burden on healthcare due to the increasing prevalence of patients with chronic illnesses and the reducing income-generating population base needed to sustain them. The need to urgently address this healthcare "time bomb" has accelerated the growth in ubiquitous, pervasive, distributed healthcare technologies. The current move from hospital-centric healthcare toward in-home health assessment is aimed at alleviating the burden on healthcare professionals, the health care system and caregivers. This shift will also further increase the comfort for the patient. Advances in signal acquisition, data storage and communication provide for the collection of reliable and useful in-home physiological data. Artifacts, arising from environmental, experimental and physiological factors, degrade signal quality and render the affected part of the signal useless. The magnitude and frequency of these artifacts significantly increases when data collection is moved from the clinic into the home. Signal processing advances have brought about significant improvement in artifact removal over the past few years. This paper reviews the physiological signals most likely to be recorded in the home, documenting the artifacts which occur most frequently and which have the largest degrading effect. A detailed analysis of current artifact removal techniques will then be presented. An evaluation of the advantages and disadvantages of each of the proposed artifact detection and removal techniques, with particular application to the personal healthcare domain, is provided.
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Affiliation(s)
- Kevin T Sweeney
- Department of Electronic Engineering, National University of Ireland, Maynooth, Ireland.
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46
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Removal of the electrocardiogram signal from surface EMG recordings using non-linearly scaled wavelets. J Electromyogr Kinesiol 2011; 21:683-8. [DOI: 10.1016/j.jelekin.2011.03.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2010] [Revised: 03/12/2011] [Accepted: 03/12/2011] [Indexed: 11/23/2022] Open
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47
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An automated ECG-artifact removal method for trunk muscle surface EMG recordings. Med Eng Phys 2010; 32:840-8. [DOI: 10.1016/j.medengphy.2010.05.007] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2009] [Revised: 05/19/2010] [Accepted: 05/23/2010] [Indexed: 11/20/2022]
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48
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49
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Hargrove L, Zhou P, Englehart K, Kuiken TA. The Effect of ECG Interference on Pattern-Recognition-Based Myoelectric Control for Targeted Muscle Reinnervated Patients. IEEE Trans Biomed Eng 2009; 56:2197-201. [PMID: 19692302 DOI: 10.1109/tbme.2008.2010392] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Levi Hargrove
- Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA.
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50
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Zhan C, Yeung LF, Yang Z. A wavelet-based adaptive filter for removing ECG interference in EMGdi signals. J Electromyogr Kinesiol 2009; 20:542-9. [PMID: 19692270 DOI: 10.1016/j.jelekin.2009.07.007] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2009] [Revised: 05/27/2009] [Accepted: 07/22/2009] [Indexed: 10/20/2022] Open
Abstract
Diaphragmatic electromyogram (EMGdi) signals convey important information on respiratory diseases. In this paper, an adaptive filter for removing the electrocardiographic (ECG) interference in EMGdi signals based on wavelet theory is proposed. Power spectrum analysis was performed to evaluate the proposed method. Simulation results show that the power spectral density (PSD) of the extracted EMGdi signal from an ECG corrupted signal is within 1.92% average error relative to the original EMGdi signal. Testing on clinical EMGdi data confirm that this method is also efficient in removing ECG artifacts from the corrupted clinical EMGdi signal.
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Affiliation(s)
- Choujun Zhan
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.
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