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Liu TC, Chen YC, Chen PL, Tu PH, Yeh CH, Yeap MC, Wu YH, Chen CC, Wu HT. Removal of electrical stimulus artifact in local field potential recorded from subthalamic nucleus by using manifold denoising. J Neurosci Methods 2024; 403:110038. [PMID: 38145720 DOI: 10.1016/j.jneumeth.2023.110038] [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: 08/14/2023] [Revised: 12/11/2023] [Accepted: 12/17/2023] [Indexed: 12/27/2023]
Abstract
BACKGROUND Deep brain stimulation (DBS) is an effective treatment for movement disorders such as Parkinson's disease (PD). However, local field potentials (LFPs) recorded through lead externalization during high-frequency stimulation (HFS) are contaminated by stimulus artifacts, which require to be removed before further analysis. NEW METHOD In this study, a novel stimulus artifact removal algorithm based on manifold denoising, termed Shrinkage and Manifold-based Artifact Removal using Template Adaptation (SMARTA), was proposed to remove artifacts by deriving a template for each stimulus artifact and subtracting it from the signal. Under a low-dimensional manifold assumption, a matrix denoising technique called optimal shrinkage was applied to design a similarity metric such that the template for stimulus artifacts could be accurately recovered. RESULT SMARTA was evaluated using semirealistic signals, which were the combination of semirealistic stimulus artifacts recorded in an agar brain model and LFPs of PD patients with no stimulation, and realistic LFP signals recorded in patients with PD during HFS. The results indicated that SMARTA removes stimulus artifacts with a modest distortion in LFP estimates. COMPARISON WITH EXISTING METHODS SMARTA was compared with moving-average subtraction, sample-and-interpolate technique, and Hampel filtering. CONCLUSION The proposed SMARTA algorithm helps the exploration of the neurophysiological mechanisms of DBS effects.
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Affiliation(s)
- Tzu-Chi Liu
- Neuroscience Research Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Department of Mathematics, National Taiwan University, Taipei, Taiwan
| | - Yi-Chieh Chen
- Neuroscience Research Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Division of Movement Disorders, Department of Neurology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Po-Lin Chen
- Neuroscience Research Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Division of Movement Disorders, Department of Neurology, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Po-Hsun Tu
- College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Neurosurgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; School of Medicine, National Tsing Hua University, Hsinchu, Taiwan
| | - Chih-Hua Yeh
- College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Neuroradiology, Chang Gung Memorial Hospital, Linkou, Taiwan
| | - Mun-Chun Yeap
- Department of Neurosurgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Yi-Hui Wu
- Biomedical Electronics Translational Research Center, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Chiung-Chu Chen
- Neuroscience Research Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Division of Movement Disorders, Department of Neurology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan.
| | - Hau-Tieng Wu
- Courant Institute of Mathematical Sciences, New York University, New York, USA.
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Mihandoost S, Sörnmo L, Doyen M, Oster J. A comparative study of the performance of methods for f-wave extraction. Physiol Meas 2022; 43. [PMID: 36179708 DOI: 10.1088/1361-6579/ac96ca] [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: 03/01/2022] [Accepted: 09/30/2022] [Indexed: 02/07/2023]
Abstract
Objective.This study proposes a novel technique for atrial fibrillatory waves (f-waves) extraction and investigates the performance of the proposed method comparing with different f-wave extraction methods.Approach.We propose a novel technique combining a periodic component analysis (PiCA) and echo state network (ESN) for f-waves extraction, denoted PiCA-ESN. PiCA-ESN benefits from the advantages of using both source separation and nonlinear adaptive filtering. PiCA-ESN is evaluated by comparing with other state-of-the-art approaches, which include template subtraction technique based on principal component analysis, spatiotemporal cancellation, nonlinear adaptive filtering using an echo state neural network, and a source separation technique based on PiCA. Quality assessment is performed on a recently published reference database including a large number of simulated ECG signals in atrial fibrillation (AF). The performance of the f-wave extraction methods is evaluated in terms of signal quality metrics (SNR, ΔSNR) and robustness of f-wave features.Main results.The proposed method offers the best signal quality performance, with a ΔSNR of approximately 22 dB across all 8 sets of the reference database, as well as the most robust extraction of f-wave features, with 75% of all estimates of dominant atrial frequency well below 1 Hz.
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Affiliation(s)
- Sara Mihandoost
- IADI, U1254, INSERM and Université de Lorraine, Nancy, France.,Department of of Electrical Engineering, Urmia University of Technology, Urmia, Iran
| | - Leif Sörnmo
- Department of Biomedical Engineering, Lund University, Lund, Sweden
| | - Matthieu Doyen
- IADI, U1254, INSERM and Université de Lorraine, Nancy, France.,Nancyclotep Molecular and Experimental Imaging Platform, Nancy, France
| | - Julien Oster
- IADI, U1254, INSERM and Université de Lorraine, Nancy, France.,CIC-IT 1433, Université de Lorraine, INSERM, CHRU de Nancy, Nancy, France
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F-Wave Extraction from Single-Lead Electrocardiogram Signals with Atrial Fibrillation by Utilizing an Optimized Resonance-Based Signal Decomposition Method. ENTROPY 2022; 24:e24060812. [PMID: 35741533 PMCID: PMC9222312 DOI: 10.3390/e24060812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/07/2022] [Accepted: 06/09/2022] [Indexed: 02/06/2023]
Abstract
(1) Background: A typical cardiac cycle consists of a P-wave, a QRS complex, and a T-wave, and these waves are perfectly shown in electrocardiogram signals (ECG). When atrial fibrillation (AF) occurs, P-waves disappear, and F-waves emerge. F-waves contain information on the cause of atrial fibrillation. Therefore it is essential to extract F-waves from the ECG signal. However, F-waves overlap the QRS complex and T-waves in both the time and frequency domain, causing this matter to be a difficult one. (2) Methods: This paper presents an optimized resonance-based signal decomposition method for detecting F-waves in single-lead ECG signals with atrial fibrillation (AF). It represents the ECG signal utilizing morphological component analysis as a linear combination of a finite number of components selected from the high-resonance and low-resonance dictionaries, respectively. The linear combination of components in the low-resonance dictionary reconstructs the oscillatory part (F-wave) of the ECG signal. In contrast, the linear combination of components in the high-resonance dictionary reconstructs the transient components part (QRST wave). The tunable Q-factor wavelet transform generates the high and low resonance dictionaries, with a high Q-factor producing a high resonance dictionary and a low Q-factor producing a low resonance dictionary. The different Q-factor settings affect the dictionaries’ characteristics, hence the F-wave extraction. A genetic algorithm was used to optimize the Q-factor selection to select the optimal Q-factor. (3) Results: The presented method helps reduce RMSE between the extracted and the simulated F-waves compared to average beat subtraction (ABS) and principal component analysis (PCA). According to the amplitude of the F-wave, RMSE is reduced by 0.24–0.32. Moreover, the dominant frequency of F-waves extracted by the presented method is clearer and more resistant to interference. The presented method outperforms the other two methods, ABS and PCA, in F-wave extraction from AF-ECG signals with the ventricular premature heartbeat. (4) Conclusion: The proposed method can potentially improve the accuracy of F-wave extraction for mobile ECG monitoring equipment, especially those with fewer leads.
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Chiu NT, Huwiler S, Ferster ML, Karlen W, Wu HT, Lustenberger C. Get rid of the beat in mobile EEG applications: A framework towards automated cardiogenic artifact detection and removal in single-channel EEG. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Lu J, Luo J, Xie Z, Xie K, Cheng Y, Xie S. Dual temporal convolutional network for single-lead fibrillation waveform extraction. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06148-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Liu GR, Lo YL, Malik J, Sheu YC, Wu HT. Diffuse to fuse EEG spectra – Intrinsic geometry of sleep dynamics for classification. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101576] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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Alcaraz R, Sörnmo L, Rieta JJ. Reference database and performance evaluation of methods for extraction of atrial fibrillatory waves in the ECG. Physiol Meas 2019; 40:075011. [PMID: 31216525 DOI: 10.1088/1361-6579/ab2b17] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE This study proposes a reference database, composed of a large number of simulated ECG signals in atrial fibrillation (AF), for investigating the performance of methods for extraction of atrial fibrillatory waves (f-waves). APPROACH The simulated signals are produced using a recently published and validated model of 12-lead ECGs in AF. The database is composed of eight signal sets together accounting for a wide range of characteristics known to represent major challenges in f-wave extraction, including high heart rates, high morphological QRST variability, and the presence of ventricular premature beats. Each set contains 30 5 min signals with different f-wave amplitudes. The database is used for the purpose of investigating the statistical association between different indices, designed for use with either real or simulated signals. MAIN RESULTS Using the database, available at the PhysioNet repository of physiological signals, the performance indices unnormalized ventricular residue (uVR), designed for real signals, and the root mean square error, designed for simulated signals, were found to exhibit the strongest association, leading to the recommendation that uVR should be used when characterizing performance in real signals. SIGNIFICANCE The proposed database facilitates comparison of the performance of different f-wave extraction methods and makes it possible to express performance in terms of the error between simulated and extracted f-wave signals.
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Affiliation(s)
- Raúl Alcaraz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, Cuenca, Spain
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Alagapan S, Shin HW, Fröhlich F, Wu HT. Diffusion geometry approach to efficiently remove electrical stimulation artifacts in intracranial electroencephalography. J Neural Eng 2018; 16:036010. [PMID: 30523899 DOI: 10.1088/1741-2552/aaf2ba] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Cortical oscillations, electrophysiological activity patterns, associated with cognitive functions and impaired in many psychiatric disorders can be observed in intracranial electroencephalography (iEEG). Direct cortical stimulation (DCS) may directly target these oscillations and may serve as therapeutic approaches to restore functional impairments. However, the presence of electrical stimulation artifacts in neurophysiological data limits the analysis of the effects of stimulation. Currently available methods suffer in performance in the presence of nonstationarity inherent in biological data. APPROACH Our algorithm, shape adaptive nonlocal artifact removal (SANAR) is based on unsupervised manifold learning. By estimating the Euclidean median of k-nearest neighbors of each artifact in a nonlocal fashion, we obtain a faithful representation of the artifact which is then subtracted. This approach overcomes the challenges presented by nonstationarity. MAIN RESULTS SANAR is effective in removing stimulation artifacts in the time domain while preserving the spectral content of the endogenous neurophysiological signal. We demonstrate the performance in a simulated dataset as well as in human iEEG data. Using two quantitative measures, that capture how much of information from endogenous activity is retained, we demonstrate that SANAR's performance exceeds that of one of the widely used approaches, independent component analysis, in the time domain as well as the frequency domain. SIGNIFICANCE This approach allows for the analysis of iEEG data, single channel or multiple channels, during DCS, a crucial step in advancing our understanding of the effects of periodic stimulation and developing new therapies.
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Affiliation(s)
- Sankaraleengam Alagapan
- Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America. Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
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MacCannell ADV, Jackson EC, Mathers KE, Staples JF. An improved method for detecting torpor entrance and arousal in a mammalian hibernator using heart rate data. ACTA ACUST UNITED AC 2018; 221:jeb.174508. [PMID: 29361606 DOI: 10.1242/jeb.174508] [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: 11/16/2017] [Accepted: 01/10/2018] [Indexed: 12/22/2022]
Abstract
We used electrocardiogram (ECG) telemeters to measure the heart rate of hibernating Ictidomys tridecemlineatus (thirteen-lined ground squirrel). An increase in heart rate from 2.2 to 5 beats min-1 accurately identified arousal from torpor before any change in body temperature was detected. Variability in raw heart rate data was significantly reduced by a forward-backward Butterworth low-pass filter, allowing for discrete differential analysis. A decrease in filtered heart rate to 70% of maximum values in interbout euthermia (from approximately 312 to 235 beats min-1) accurately detected entrance into torpor bouts. At this point, body temperature had fallen from 36.1°C to only 34.7°C, much higher than the 30°C typically used to identify entrance. Using these heart rate criteria allowed advanced detection of entrance and arousal (detected 51.9 and 76 min earlier, respectively), compared with traditional body temperature criteria. This method will improve our ability to detect biochemical and molecular markers underlying these transition periods, during which many physiological changes occur.
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Affiliation(s)
| | - Ethan C Jackson
- Department of Computer Science, University of Western Ontario, London ON, N6A5B7, Canada
| | - Katherine E Mathers
- Department of Biology, University of Western Ontario, London ON, N6A5B8, Canada
| | - James F Staples
- Department of Biology, University of Western Ontario, London ON, N6A5B8, Canada
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