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1.
Biomed Instrum Technol ; 54(5): 346-351, 2020 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-33049766

RESUMO

Electroencephalography (EEG) is a sensitive and weak biosignal that varies from person to person. It is easily affected by noise and artifacts. Hence, maintaining the signal integrity to design an EEG acquisition system is crucial. This article proposes an analog design for acquiring EEG signals. The proposed design consists of eight blocks: (1) a radio-frequency interference filter and electro-static discharge protection, (2) a preamplifier and second-order high-pass filter with feedback topology and an unblocking mechanism, (3) a driven right leg circuit, (4) two-stage main and variable amplifiers, (5) an eight-order anti-aliasing filter, (6) a six-order 50-Hz notch filter (optional), (7) an opto-isolator circuit, and (8) an isolated power supply. The maximum gain of the design is approximately 94 dB, and its bandwidth ranges from approximately 0.18 to 120 Hz. The depth of the 50-Hz notch filter is -35 dB. Using this filter is optional because it causes EEG integrity problems in frequencies ranging from 40 to 60 Hz.


Assuntos
Amplificadores Eletrônicos , Eletroencefalografia , Artefatos , Fontes de Energia Elétrica , Desenho de Equipamento/métodos
2.
Nihon Hoshasen Gijutsu Gakkai Zasshi ; 76(10): 1009-1016, 2020.
Artigo em Japonês | MEDLINE | ID: mdl-33087646

RESUMO

PURPOSE: The purpose of this paper was to determine the optimal imaging conditions for four-dimensional cone-beam computed tomography (4D-CBCT) using an X-ray tube and a flat-panel detector mounted on a radiotherapy device. METHODS: The optimal imaging conditions were examined by changing the gantry speed (GS) parameter that affected the exposure time. Exposed dose during imaging and image quality of moving phantom were compared between examined conditions. RESULTS: The weighted computed tomography dose index (CTDIW) decreased linearly with increasing GS. However, when GS was 180°/min or faster, the image quality degraded, and errors of 1 mm or more were observed regarding the size of mock tumor in the moving phantom. The accuracy of automatic image matching was within 0.1 mm when GS of 120°/min or slower was chosen. CONCLUSION: From the results of this study, we concluded that GS of 120°/min is the optimum imaging condition. Under this imaging condition, the exposure time and CTDIW can be reduced by about 50% without compromising the accuracy of image registration, compared to the conventional GS of 70°/min. In addition, it has been clarified that there is an event that image reconstruction is not performed correctly due to the influence of phantom artifacts without depending on GS.


Assuntos
Tomografia Computadorizada de Feixe Cônico , Tomografia Computadorizada Quadridimensional , Artefatos , Processamento de Imagem Assistida por Computador , Imagens de Fantasmas
3.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3759-3762, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018819

RESUMO

A surface Electromyography (sEMG) contaminant type detector has been developed by using a Recurrent Neural Network (RNN) with Long Short-Term (LSMT) units in its hidden layer. This setup may reduce the contamination detection processing time since there is no need for feature extraction so that the classification occurs directly from the sEMG signal. The publicly available NINAPro (Non-Invasive Adaptive Prosthetics) database sEMG signals was used to train and test the network. Signals were contaminated with White Gaussian Noise, Movement Artifact, ECG and Power Line Interference. Two out of the 40 healthy subjects' data were considered to train the network and the other 38 to test it. Twelve models were trained under a -20dB contamination, one for each channel. ANOVA results showed that the training channel could affect the classification accuracy if SNR = -20dB and 0dB. An overall accuracy of 97.72% has been achieved by one of the models.


Assuntos
Algoritmos , Processamento de Sinais Assistido por Computador , Artefatos , Eletromiografia , Redes Neurais de Computação
4.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4114-4117, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018903

RESUMO

Assessment of pulmonary function is vital for early detection of chronic diseases such as chronic obstructive pulmonary disease (COPD) in home healthcare. However, monitoring of pulmonary function is often omitted owing to the heavy burden that the use of specific medical devices places on the patients. In this study, we developed a non-contact spirometer using a time-of-flight sensor that measures very small displacements caused by chest wall motion during breathing. However, this sensor occasionally failed when estimating the values from breathing waveforms because their shape depends on the subject test experience. As a result, further measurements were required to address motion artifacts. To accomplish high accuracy estimation in the face of these factors, we developed methods to estimate parameters from a part of the waveform and remove outliers from multiple-region measurements. According to laboratory experiments, the proposed system achieved an absolute error of 5.26 % and a correlation coefficient of 0.88. This study also addressed the limitations of depth sensor measurements, thereby contributing to the implementation of high-accuracy COPD screening.


Assuntos
Doença Pulmonar Obstrutiva Crônica , Respiração , Artefatos , Humanos , Movimento (Física) , Doença Pulmonar Obstrutiva Crônica/diagnóstico , Espirometria
5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4126-4129, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018906

RESUMO

A surface electromyography (sEMG) detector, that not only removes stimulation artifacts entirely but also increases the recording time, has been developed in this paper. The sEMG detector consists of an sEMG detection circuit and a stimulation isolator. The sEMG detection circuit employs a stimulus isolate switch (SIS), a blanking (BLK) and non-linear feed-back (NFB) circuit to remove the artifacts and to increase the recording time. In the SIS, the connection between stimulator and stimulation electrodes, along with the stimulation electrodes and the ground are controlled by an opto-isolator, and the connection of instrument amplifier and the recording electrodes are controlled by CMOS-based switches. The mode switches of the BLK and the NFB circuit also employs CMOS-based switches. By an accurate timing adjustment, the voluntary EMG can be recorded during electrical stimulation. Two 6 able-bodied experiments have been performed to test the three anti-artifact sEMG detector: BLK, BLK&SIS, BLK&SIS&NFB. The results indicate that the BLK&SIS&NFB proposed in this work effectively removes stimulus artifacts and M-waves, and has a longer recording time compared with BLK and BLK&SIS circuits.


Assuntos
Amplificadores Eletrônicos , Artefatos , Estimulação Elétrica , Eletrodos , Eletromiografia
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4410-4413, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018973

RESUMO

When estimating the heart rate (HR) of an exerciser by using a photoplethysmographic (PPG) sensor, the PPG output is contaminated with motion artifact (MA) induced by his motion, resulting in erroneous HR. To cancel the MA in the PPG output, we have proposed a technique based on adaptive filter algorithm using an MA sensor. On the one hand, we have so far fixed the tap length of the adaptive filter algorithm for the sake of its simple implementation, but on the other hand, we have noticed that the tap length dynamically changes according to the type of sensor wearer, the intensity of exercise and so on. Therefore, in this paper, we propose an MA canceling-PPG HR sensor system based on a serially configured adaptive filter algorithm with variable tap length. Experimental results involving 13 subjects reveal that the MA canceling technique based on the proposed serial configuration outperforms that with a conventional parallel configuration, achieving the minimum root mean square error of 9.97 beats per minute with much less computational complexity.


Assuntos
Artefatos , Fotopletismografia , Algoritmos , Frequência Cardíaca , Processamento de Sinais Assistido por Computador
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4421-4424, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018975

RESUMO

Methods commonly used for reduction of motion artefacts in photoplethysmography employ accelerometry as a reference for adaptive filtering and signal processing. In this paper, we propose the use of an optical flow sensor to measure the relative displacement between a photoplethysmographic sensor and the measurement site. In order to evaluate the performances of this novel method, a wrist-worn device that enables simultaneous acquisition of physiological information and relative motion has been developed. The optical flow sensor provides a two-dimensional information source correlated with artefacts contained in the cardiac frequency band. Preliminary results show a clear correlation between motion recorded by the sensor and artefacts contained in the photoplethysmographic signal. In association with adaptive filtering, the proposed technique shows efficient reduction of motion artefacts during physical activity.


Assuntos
Algoritmos , Artefatos , Movimento (Física) , Fotopletismografia , Processamento de Sinais Assistido por Computador
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4479-4482, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018989

RESUMO

Motion artifacts are arguably the most important issue in the development of wearable ambulatory EEG devices. Designing circuits and systems capable of high-quality EEG recording regardless of these artifacts requires a clear understanding of how the electrode-skin interface is affected by physical motions. In this work, first, we report statistically-significant experimental characterization results of electrodeskin interface impedance for dry contact and non-contact electrodes in the presence of various motions. This leads to a model describing the motion-induced electrode-skin interface impedance variations for these electrodes. Next, a critical review of the possible analog front-end circuits for surface EEG recording is presented, followed by theoretical circuit analysis discussing the effect of electrode movements on the operation of these circuits. Inspired by the developed model and the analytical review, a novel front-end architecture capable of extracting motion from the EEG signal during the amplification stage is presented and experimentally characterized.


Assuntos
Eletroencefalografia , Dispositivos Eletrônicos Vestíveis , Artefatos , Eletrodos , Movimento (Física)
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4563-4566, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019009

RESUMO

Wearable sensors enable the simultaneous recording of several electrophysiological signals from the human body in a non-invasive and continuous manner. Textile sensors are garnering substantial interest in the wearable technology because they can be knitted directly into the daily-used objects like underwear, bra, dress, etc. However, accurate processing of signals recorded by textile sensors is extremely challenging due to the very low signal-to-noise ratio (SNR). Systematic classification of textile sensor noise (TSN) is necessary to: (i) identify different types of noise and their statistical characteristics, (ii) explore how each type of noise influences the electrophysiological signal, (iii) develop optimal textile-specific electronics that suppress TSN, and (iv) reproduce TSN and create large dataset of textile sensors to validate various machine learning and signal processing algorithms. In this paper, we develop a novel technique to classify textile sensor artifacts in ECG signals. By simultaneously recording signals from the waist (textile sensors) and chest (gel electrode), we extract TSN by removing the chest ECG signal from the recorded textile data. We classify TSN based on its morphological and statistical features in two main categories, namely, slow and fast. Linear prediction coding (LPC) is utilized to model each class of TSN by auto-regression coefficients and residues. The residual signal can be approximated by Gaussian distribution which enables reproducing slow and fast artifacts that not only preserve the similar morphological features but also fulfill the statistical properties of TSN. By reproducing TSN and adding them to clean ECG signals, we create a textile-like ECG signal which can be used to develop and validate different signal processing algorithms.


Assuntos
Dispositivos Eletrônicos Vestíveis , Artefatos , Humanos , Processamento de Sinais Assistido por Computador , Razão Sinal-Ruído , Têxteis
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5347-5352, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019191

RESUMO

Heart rate (HR) monitoring under real-world activities of daily living conditions is challenging, particularly, using peripheral wearable devices integrated with simple optical and acceleration sensors. The study presents a novel technique, named as CurToSS: CURve Tracing On Sparse Spectrum, for continuous HR estimation in daily living activity conditions using simultaneous photoplethysmogram (PPG) and triaxial-acceleration signals. The performance validation of HR estimation using the CurToSS algorithm is conducted in four public databases with distinctive study groups, sensor types, and protocols involving intense physical and emotional exertions. The HR performance of this time-frequency curve tracing method is also compared to that of contemporary algorithms. The results suggest that the CurToSS method offers the best performance with significantly (P<0.01) lowest HR error compared to spectral filtering and multi-channel PPG correlation methods. The current HR performances are also consistently better than a deep learning approach in diverse datasets. The proposed algorithm is powerful for reliable long-term HR monitoring under ambulatory daily life conditions using wearable biosensor devices.


Assuntos
Fotopletismografia , Processamento de Sinais Assistido por Computador , Atividades Cotidianas , Artefatos , Frequência Cardíaca , Humanos
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 138-141, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017949

RESUMO

This paper introduces a simple approach combining deep learning and histogram contour processing for automatic detection of various types of artifact contaminating the raw electroencephalogram (EEG). The proposed method considers both spatial and temporal information of raw EEG, without additional need for reference signals like ECG or EOG. The proposed method was evaluated with data including 785 EEG sequences contaminated by artifacts and 785 artifact-free EEG sequences collected from 15 intensive care patients. The obtained results showed an overall accuracy of 0.98, representing high reliability of proposed technique in detecting different types of artifacts and being comparable or outperforming the approaches proposed earlier in the literature.


Assuntos
Artefatos , Aprendizado Profundo , Eletroencefalografia , Humanos , Reprodutibilidade dos Testes
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 200-203, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017964

RESUMO

A central question in neuroscience is how the brain processes real-world sensory input. For decades most classical studies focus on carefully controlled artificial stimuli. More recently researchers started to investigate brain activity under more realistic conditions. The main challenge in this setting is the analysis of the complex signals obtained with modern neuroimaging methods in response to natural stimuli. Inter-subject correlations (ISCs) have become a popular paradigm to study brain activation under natural stimulation. The underlying assumption of this analysis is that features of natural stimuli that are perceived and processed by all subjects exposed to the same stimulus result in similar activation patterns across subjects. Higher degrees of realism in stimulation, for instance audiovisual stimulation is more realistic than auditory stimulation, is usually associated with higher ISC values. We can confirm these findings in experiments in which we present a movie stimulus with varying degrees of realism. Extending previous findings we highlight the importance of artifact removal when evaluating ISCs and show that the impact of realism in natural stimulation on ISCs is frequency-dependent. A major challenge associated with this type of analysis is that it can be difficult to attribute the correlation strength to the physiological process of interest. In this study, we demonstrate that ISCs of neural activation as measured by electroencephalogram (EEG) recordings are influenced significantly by non-neural artifacts such as occulograms. Our findings highlight the potential of inter-subject correlations as a biomarker for immersion: If more realistic stimuli consistently lead to higher inter-subject correlations, then inter-subject correlations can serve as a quantitative marker for how engaging audiovisual stimuli are perceived.Clinical relevance- Future research will evaluate if correlation levels among subjects, who are exposed to natural stimuli are affected by neurological diseases such as Alzheimers, Parkinsons, and Schizophrenia among others.


Assuntos
Artefatos , Eletroencefalografia , Estimulação Acústica , Encéfalo , Neuroimagem
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 217-220, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017968

RESUMO

Independent Component Analysis (ICA) has became the most popular method to remove eye-blinking artifacts from electroencephalogram (EEG) recording. For long term EEG recording, ICA was commonly considered to costing a lot of computation time. Furthermore, with no ground truth, the discussion about the quality of ICA decomposition in a nonstationary environment was specious. In this study, we investigated the "signal" (P300 waveform) and the "noise" (averaged eye-blinking artifacts) on a cross-modal long-term EEG recording to evaluate the efficiency and effectiveness of different methods on ICA eye-blinking artifacts removal. As a result, it was found that, firstly, down sampling is an effective way to reduce the computation time in ICA. Appropriate down sampling ratio could speed up ICA computation 200 times and keep the decomposition performance stable, in which the computation time of ICA decomposition on a 2800 s EEG recording was less than 5 s. Secondly, dimension reduction by PCA was also a way to improve the efficiency and effectiveness of ICA. Finally, the comparison by cropping the dataset indicated that performing ICA on each run of the experiment separately would achieve a better result for eye-blinking artifacts removal than using all the EEG data input for ICA.


Assuntos
Artefatos , Piscadela , Eletroencefalografia
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 240-243, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017973

RESUMO

The vestibulo-ocular reflex (VOR) is a dynamic system of the human brain that helps to maintain balance and to stabilize vision during head movement. The video head impulse test (vHIT) is a clinical test that uses lightweight, high-speed video goggles to examine the VOR function by calculating the ratio of eye-movement to head-movement velocities. The main problem with a patient's vHIT is that data coming from the goggles may have artifacts and other noise. This paper proposes an impulse classification network (ICN) using a one-dimensional convolutional neural network that can detect noisy data and classify human VOR impulses. Our ICN found actual classes of a patient's impulses with 95% accuracy.Clinical Relevance-ICN is a high-performance classification method that works on a patient's vHIT impulse data by identifying abnormalities and artifacts. This method is an advanced clinical decision support system that can help doctors quickly make decisions.


Assuntos
Teste do Impulso da Cabeça , Reflexo Vestíbulo-Ocular , Artefatos , Movimentos Oculares , Movimentos da Cabeça , Humanos
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 402-405, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018013

RESUMO

A new approach of pole-zero modeling in the presence of white noise is proposed. While the model estimate is calculated through the conventional least square estimation, the choice of number of poles and zeros in this scenario is critical and a challenging task. A wrong choice can overfit the additive noise in larger orders or underfit and discard parts of the noiseless data in smaller orders. To overcome this issue, we choose the order through RE Minimization (REM). RE is the error between the observed noisy data and the unavailable noiseless output. Using the available output error, the method provides a probabilistic worst case upperbound for RE and optimizes it. Simulation results on generated synthetic data show advantages of REM compared to existing order selection methods such as AIC and BIC. The results show that the proposed method avoids over or under parametrizing of AIC and BIC. The results in a practical application of EOG artifacts removal of eye blinks from EEG data provides an efficient modeling of the true background EEG with optimal eye blink removal.


Assuntos
Artefatos , Eletroencefalografia , Algoritmos , Piscadela , Projetos de Pesquisa
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 502-505, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018037

RESUMO

Electroencephalogram (EEG) signals are important to study the activities of human brains. The independent component analysis (ICA) algorithm is a practical blind source separation (BSS) technique that can separate EEG sources from artifacts effectively. However, most traditional ICA algorithms assume that the mixing process is instantaneous and off-line. In this paper, a novel framework based on the extension of the online recursive ICA algorithm (ORICA) is proposed to apply for motor imagery (MI) EEG recording. The contributions are as follows. Firstly, we show ORICA's adaptability to accurate and effective source separation used for artifact-contaminated MI EEG recording. Secondly, to identify EOG signals on the output of source separation, the topographic map is presented to distinguish the target signals. The experimental results show that the proposed framework is able to be applied to process MI EEG recording in real-time situations.


Assuntos
Algoritmos , Eletroencefalografia , Artefatos , Encéfalo , Humanos , Imagens, Psicoterapia
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 616-620, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018063

RESUMO

Despite the enormous potential applications, non-invasive recordings have not yet made enough satisfaction for fetal disease detection. This is mainly due to the fetal ECG signal is contaminated by the maternal electrocardiograph (ECG) interference, muscle contractions, and motion artifacts. In this paper, we propose a joint multiple subspace-based blind source separation (BSS) approach to extract the fetal heart rate (HR), so that it could greatly reduce the effect of maternal ECG and motion artifacts. The approach relies on the estimation of the coefficient matrix formulated as the tensor decomposition in terms of multiple datasets. Since the objective function takes the coupling information from the stacking of the covariance matrix for multiple datasets into account, estimating the coefficient matrices is fulfilled not only on dependence across multiple datasets, but also can combine the extracted components across four different datasets. Numerical results demonstrate that the proposed method can achieve a high extracted HR accuracy for each dataset, when compared to some conventional methods.


Assuntos
Frequência Cardíaca Fetal , Processamento de Sinais Assistido por Computador , Artefatos , Eletrocardiografia , Feminino , Feto , Humanos , Gravidez
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 932-935, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018137

RESUMO

Artifact removal is important for EEG signal processing because artifacts adversely affect analysis results. To preserve normal EEG signal, several methods based on independent component analysis (ICA) have been studied and artifacts are removed by discarding independent components (ICs) classified as artifacts. In this study, a method using Bayesian deep learning and attention module is presented to improve performance of the classifier for ICs. Probability value is computed through the method to predict if a component is artifact and to treat ambiguous inputs. The attention module achieves increasing classification accuracy and shows the map of the region where the classifier concentrates on.


Assuntos
Artefatos , Eletroencefalografia , Algoritmos , Teorema de Bayes , Encéfalo , Aprendizado Profundo
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 936-939, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018138

RESUMO

Vibroarthrographic (VAG) signals are sounds or vibrations caused when a knee joint is flexed or stretched. VAG signal collection is noninvasive and can be performed using an accelerometer or microphone attached to the skin. However, the sensor attached to the skin will move with the soft tissue caused by flexion and extension, causing the baseline of the VAG signal to drift. We call these interferences soft tissue movement artifacts (STMAs). In this study, an algorithm is proposed to filter out STMAs. We compare the proposed method's results with noises collected by an accelerometer. The noise reduction effect is evaluated, revealing an 11.85% increase in the peak signal-to-noise ratio and a 28.18% increase in signal-to-noise ratio compared with the case in which STMA noise was not removed.Clinical Relevance-This study focuses on a proposed post-processing method that can remove soft tissue movement artifacts that cause baseline wander and could thus improve the accuracy of clinical applications of VAG signals.


Assuntos
Artefatos , Processamento de Sinais Assistido por Computador , Algoritmos , Articulação do Joelho , Movimento
20.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 940-943, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018139

RESUMO

Motion artifact contamination may adversely affect the interpretation of biological signals. The development of algorithms to detect, identify, quantify, and mitigate motion artifact is typically performed using a ground truth signal contaminated with previously recorded motion artifact, or simulated motion artifact. The diversity of available motion artifact recordings is limited, and the rationales for existing models of motion artifact are poorly described. In this paper we developed an autoregressive (AR) model of motion artifact based on data collected from 6 subjects walking at slow, medium, and fast paces. The AR model was evaluated for its ability to generate diverse data that replicated the properties of the experimental data. The simulated motion artifact data was successful at learning key time domain and frequency domain properties, including the mean, variance, and power spectrum of the data, but was ineffective for imitating the morphology and probability distribution of the motion artifact data (kurtosis % error of 100.9-103.6%). More sophisticated models of motion artifact may be necessary to develop simulations of motion artifact.


Assuntos
Artefatos , Processamento de Sinais Assistido por Computador , Algoritmos , Movimento (Física) , Caminhada
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