基于Flexible ICA的睡眠EEG及EOG中心电伪差的消除

提出了一种采用自适应非线性函数的ICA学习算法,Flexible ICA算法,并将其应用于睡眠EEG自动分期的前期预处理中,用于消除采集到的各通道信号中的心电伪差.实验结果证明,Flexible ICA算法能够快速有效的消除各通道的心电伪差,为后期的睡眠EEG自动分期打下了良好的基础.     

ECG Enhancement and QRS Detection Based on Sparse Derivatives

Electrocardiography (ECG) signals are often contaminated by various kinds of noise or artifacts, for example, morphological changes due to motion artifact, non-stationary noise due to muscular contraction (EMG), etc. Some of these contaminations severely affect the usefulness of ECG signals, especially when computer aided algorithms are utilized. In this paper, a novel ECG enhancement algorithm is proposed based on sparse derivatives. By solving a convex ?₁ optimization problem, artifacts are reduced by modeling the clean ECG signal as a sum of two signals whose second and third-order derivatives (differences) are sparse respectively. The algorithm is applied to a QRS detection system and validated using the MIT-BIH Arrhythmia database (109,452 anotations), resulting a sensitivity of Se = 99.87% and a positive prediction of +P = 99.88%.     

A wavelet-based adaptive filter for removing ECG interference in EMGdi signals

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.     

A Novel Algorithm for Movement Artifact Removal in ECG Signals Acquired from Wearable Systems Applied to Horses

This study reports on a novel method to detect and reduce the contribution of movement artifact (MA) in electrocardiogram (ECG) recordings gathered from horses in free movement conditions. We propose a model that integrates cardiovascular and movement information to estimate the MA contribution. Specifically, ECG and physical activity are continuously acquired from seven horses through a wearable system. Such a system employs completely integrated textile electrodes to monitor ECG and is also equipped with a triaxial accelerometer for movement monitoring. In the literature, the most used technique to remove movement artifacts, when noise bandwidth overlaps the primary source bandwidth, is the adaptive filter. In this study we propose a new algorithm, hereinafter called Stationary Wavelet Movement Artifact Reduction (SWMAR), where the Stationary Wavelet Transform (SWT) decomposition algorithm is employed to identify and remove movement artifacts from ECG signals in horses. A comparative analysis with the Normalized Least Mean Square Adaptive Filter technique (NLMSAF) is performed as well. Results achieved on seven hours of recordings showed a reduction greater than 40% of MA percentage (between before- and after- the application of the proposed algorithm). Moreover, the comparative analysis with the NLMSAF, applied to the same ECG recordings, showed a greater reduction of MA percentage in favour of SWMAR with a statistical significant difference (p–value < 0.0.5).     

Selecting EEG components using time series analysis in brain death diagnosis

In diagnosis of brain death for human organ transplant, EEG (electroencephalogram) must be flat to conclude the patient’s brain death but it has been reported that the flat EEG test is sometimes difficult due to artifacts such as the contamination from the power supply and ECG (electrocardiogram, the signal from the heartbeat). ICA (independent component analysis) is an effective signal processing method that can separate such artifacts from the EEG signals. Applying ICA to EEG channels, we obtain several separated components among which some correspond to the brain activities while others contain artifacts. This paper aims at automatic selection of the separated components based on time series analysis. In the flat EEG test in brain death diagnosis, such automatic component selection is helpful.     

Empirical mode decomposition based filtering techniques for power line interference reduction in electrocardiogram using various adaptive structures and subtraction methods

Electrocardiogram (ECG) is a vital sign monitoring measurement of the cardiac activity. One of the main problems in biomedical signals like electrocardiogram is the separation of the desired signal from noises caused by power line interference, muscle artifacts, baseline wandering and electrode artifacts. Different types of digital filters are used to separate signal components from unwanted frequency ranges. Adaptive filter is one of the primary methods to filter, because it does not need the signal statistic characteristics. In contrast with Fourier analysis and wavelet methods, a new technique called EMD, a fully data-driven technique is used. It is an adaptive method well suited to analyze biomedical signals. This paper foregrounds an empirical mode decomposition based two-weight adaptive filter structure to eliminate the power line interference in ECG signals. This paper proposes four possible methods and each have less computational complexity compared to other methods. These methods of filtering are fully a signal-dependent approach with adaptive nature, and hence it is best suited for denoising applications. Compared to other proposed methods, EMD based direct subtraction method gives better SNR irrespective of the level of noises.     

A mathematical algorithm for ECG signal denoising using window analysis

BACKGROUND: The presence of parasite interference signals could cause serious problems in the registration of ECG signals and many works have been done to suppress electromyogram (EMG) artifacts noises and disturbances from electrocardiogram (ECG). Recently, new developed techniques based on global and local transforms have become popular such as wavelet shrinkage approaches (1995) and time-frequency dependent threshold (1998). Moreover, other techniques such as artificial neural networks (2003), energy thresholding and Gaussian kernels (2006) are used to improve previous works. This review summarizes windowed techniques of the concerned issue. METHODS AND RESULTS: We conducted a mathematical method based on two sets of information, which are dominant scale of QRS complexes and their domain. The task is proposed by using a varying-length window that is moving over the whole signals. Both the high frequency (noise) and low frequency (base-line wandering) removal tasks are evaluated for manually corrupted ECG signals and are validated for actual recorded ECG signals. CONCLUSIONS: Although, the simplicity of the method, fast implementation, and preservation of characteristics of ECG waves represent it as a suitable algorithm, there may be some difficulties due to pre-stage detection of QRS complexes and specification of algorithm's parameters for varying morphology cases.     

Effect of electrocardiographic contamination on surface electromyography assessment of back muscles

The purpose of this study was to demonstrate the relative effect of electrocardiography (ECG) on back muscle surface electromyography (SEMG) parameters and their corresponding sensitivity in low back pain (LBP) assessment.Back muscle SEMG activities were recorded from 17 healthy subjects and 18 chronic LBP patients under static postures (straight sitting and upright standing), and dynamic action (flexion–extension). ECG cancellation based on independent component analysis (ICA) method was performed. Root mean square (RMS) and median frequency (MF) of raw and denoised SEMG data were computed respectively. Multiple comparisons were then performed.A consistent trend of change (increased MF and decreased RMS) followed ECG removal was noticed. In particular, in SEMG measurements under static postures, a significant decrease in RMS (p < 0.05) and increase in MF (p < 0.05) were found in all recording muscle groups. Level of corruption by ECG artifacts on SEMG measurements was found to be more serious and prominent in static postures than that in dynamic action. After ECG removal, significant improvements in the ability of SEMG to discriminate LBP patients from healthy subjects were seen in RMS amplitude recorded while standing (p < 0.05) and MF in all measuring conditions (p < 0.05).This study provides a more complete understanding on the relative effect of ECG contamination on back muscles SEMG parameters and LBP assessment.     

A new method for the extraction of fetal ECG from the dependent abdominal signals using blind source separation and adaptive noise cancellation techniques

Background

The electrocardiogram (ECG) is a diagnostic tool that records the electrical activity of the heart, and depicts it as a series of graph-like tracings, or waves. Being able to interpret these details allows diagnosis of a wide range of heart problems. Fetal electrocardiogram (FECG) extraction has an important impact in medical diagnostics during the mother pregnancy period. Since the observed FECG signals are often mixed with the maternal ECG (MECG) and the noise induced by the movement of electrodes or by mother motion, the separation process of the ECG signal sources from the observed data becomes quite complicated. One of its complexity is when the ECG sources are dependent, thus, in this paper we introduce a new approach of blind source separation (BSS) in the noisy context for both independent and dependent ECG signal source. This approach consist in denoising the observed ECG signals using a bilateral total variation (BTV) filter; then minimizing the Kullbak-Leibler divergence between copula densities to separate the FECG signal from the MECG one.

Results

We present simulation results illustrating the performance of our proposed method. We will consider many examples of independent/dependent source component signals. The results will be compared with those of the classical method called independent component analysis (ICA) under the same conditions. The accuracy of source estimation is evaluated through a criterion, called again the signal-to-noise-ratio (SNR). The first experiment shows that our proposed method gives accurate estimation of sources in the standard case of independent components, with performance around 27 dB in term of SNR. In the second experiment, we show the capability of the proposed algorithm to successfully separate two noisy mixtures of dependent source components - with classical criterion devoted to the independent case - fails, and that our method is able to deal with the dependent case with good performance.

Conclusions

In this work, we focus specifically on the separation of the ECG signal sources taken from skin two electrodes located on a pregnant woman’s body. The ECG separation is interpreted as a noisy linear BSS problem with instantaneous mixtures. Firstly, a denoising step is required to reduce the noise due to motion artifacts using a BTV filter as a very effective one-pass filter for denoising. Then, we use the Kullbak-Leibler divergence between copula densities to separate the fetal heart rate from the mother one, for both independent and dependent cases.

     

DETECT: A MATLAB Toolbox for Event Detection and Identification in Time Series,with Applications to Artifact Detection in EEG Signals

Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG) data as an additional illustration.     

Shock advisory tool: Detection of life-threatening cardiac arrhythmias and shock success prediction by means of a common parameter set

The aim of the present work was to study the possibility of a parameter set to assure both reliable detection of shockable rhythms and adequate shock success prediction. A set of 10 parameters, reflecting the frequency characteristics, the variations, the complexity, the periodicity and the symmetry of the ECG signals was subjected to discriminant analysis. The reliability of the derived parameters to provide an adequate shock advisory decision, which accounts the arrhythmia type (shockable or non-shockable) and the possibility for shock success, was studied. Moreover, the influence of different types of artifacts on the accuracy for shockable and non-shockable rhythms classification was evaluated. The shockable rhythm detection ability was estimated towards the AHA recommendations for reliable automatic external defibrillator algorithm performance, while the accuracy for prediction of the shock outcome was compared with the possibility of other proposed methods to differentiate between ventricular fibrillation episodes amenable and non-amenable to defibrillation. The direct comparison of the shockable rhythm detection results with the AHA recommendations for defined rhythm categories proved the adequacy of the processed ECG features to provide accuracy, which meets the AHA performance goal. Besides this, the proposed parameter set proved its adequacy for shock success prediction and the attained prediction accuracy (above 80%) could be considered as acceptable for possible practical application in automatic external defibrillators.The combination of reliable detection and prediction, as well as the fact that the decision for defibrillation will account not only the rhythm type but also the possibility for successful defibrillation, makes the proposed parameter set a reliable tool for automatic external defibrillator shock-advisory algorithms.     

Implantation of Radiotelemetry Transmitters Yielding Data on ECG, Heart Rate, Core Body Temperature and Activity in Free-moving Laboratory Mice

The laboratory mouse is the animal species of choice for most biomedical research, in both the academic sphere and the pharmaceutical industry. Mice are a manageable size and relatively easy to house. These factors, together with the availability of a wealth of spontaneous and experimentally induced mutants, make laboratory mice ideally suited to a wide variety of research areas.In cardiovascular, pharmacological and toxicological research, accurate measurement of parameters relating to the circulatory system of laboratory animals is often required. Determination of heart rate, heart rate variability, and duration of PQ and QT intervals are based on electrocardiogram (ECG) recordings. However, obtaining reliable ECG curves as well as physiological data such as core body temperature in mice can be difficult using conventional measurement techniques, which require connecting sensors and lead wires to a restrained, tethered, or even anaesthetized animal. Data obtained in this fashion must be interpreted with caution, as it is well known that restraining and anesthesia can have a major artifactual influence on physiological parameters^{1, 2}.Radiotelemetry enables data to be collected from conscious and untethered animals. Measurements can be conducted even in freely moving animals, and without requiring the investigator to be in the proximity of the animal. Thus, known sources of artifacts are avoided, and accurate and reliable measurements are assured. This methodology also reduces interanimal variability, thus reducing the number of animals used, rendering this technology the most humane method of monitoring physiological parameters in laboratory animals^{3, 4}. Constant advancements in data acquisition technology and implant miniaturization mean that it is now possible to record physiological parameters and locomotor activity continuously and in realtime over longer periods such as hours, days or even weeks^{3, 5}.Here, we describe a surgical technique for implantation of a commercially available telemetry transmitter used for continuous measurements of core body temperature, locomotor activity and biopotential (i.e. onelead ECG), from which heart rate, heart rate variability, and PQ and QT intervals can be established in freeroaming, untethered mice. We also present pre-operative procedures and protocols for post-operative intensive care and pain treatment that improve recovery, well-being and survival rates in implanted mice^{5, 6}.     

Phantom based qualitative and quantitative evaluation of artifacts in MR images of metallic hip prostheses

PurposeTo develop methods for qualitative and quantitative evaluation of MRI artifacts near metallic prostheses, and to compare the efficiency of different artifact suppression techniques with different types of hip prostheses.MethodsThree hip prostheses of cobalt-chromium, stainless steel, and titanium were embedded in agarose gel together with a rectilinear grid. Coronal MR images of the prostheses were acquired on a 1.5T scanner. Three pulse sequences were evaluated; TSE: a high-bandwidth turbo spin echo; VAT: TSE with view angle tilting, SEMAC: TSE with both VAT and slice distortion correction (6, 10 or 16 z-phase-encoding steps). Through-plane distortions were assessed as the length of visible gridlines, in-plane artifacts as the artifact area, and total artifacts by subtraction of an ideal, undistorted image from the actual image.ResultsVAT reduced in-plane artifacts by up to 50% compared to TSE, but did not reduce through-plane artifacts. SEMAC reduced through-plane artifacts by 60–80% compared to TSE and VAT. SEMAC in-plane artifacts were from 20% higher (6 encoding steps) to 50% lower (16 steps) than VAT. Total artifacts were reduced by 60–80% in the best sequence (SEMAC, 16 steps) compared to the worst (TSE). The titanium prosthesis produced 3–4 times lower artifact scores than the other prostheses.ConclusionsA rectilinear grid phantom is useful for qualitative and quantitative evaluation of artifacts provoked by different MRI protocols and prosthesis models. VAT and SEMAC were superior to TSE with high bandwidth. A proper number of z-encoding steps in SEMAC was critical. The titanium prosthesis caused least artifacts.     

黄河中游晋陕峡谷陕西侧龙门至壶口段新发现的石制品

2019-2020年,在黄河中游晋陕峡谷陕西一侧龙门至壶口段新发现9处旷野旧石器地点,采集石制品136件,部分石制品直接采自地层剖面上。康家岭地点石制品的埋藏地层为马兰黄土底部的洪积碎屑层,时代可能为晚更新世早期。苏家岭地点有1件石制品出自MIS3阶段弱古土壤层之下的洪积碎屑层,光释光年龄为距今7.2±0.7万年。其余大部分石制品的埋藏地层为马兰黄土层,时代为晚更新世中、晚期,其中凉泉沟地点埋藏石制品的黄土地层光释光年龄大于距今5万年。古人类加工石制品的原料主要为砾石,岩性以石英岩为主,其次为石英,此外还有少量燧石、细砂岩和硅质岩。石制品类型包括石核、石片、石器和断块。个体大小以中小型为主。石核和石片以自然台面者为主,剥片主要采用硬锤锤击法直接剥片,少量应用砸击法。石器有中小型的刮削器、凹缺器和大型的砍砸器,均属简单石核-石片技术产品。本次新发现的旧石器地点进一步扩大了晋陕峡谷地区旧石器时代遗存的时空分布范围,有助于后续相关研究工作的开展。     

Development of SVM based classification techniques for the delineation of wave components in 12-lead electrocardiogram

The automatic detection of electrocardiogram (ECG) waves namely P, QRS and T-wave is important to cardiac disease diagnosis. This paper presents an application of support vector machine (SVM) as a classifier for the delineation of ECG wave components in the 12-lead ECG signal. Digital filtering techniques are used to remove power line interference and baseline wander present in the ECG signal. Gradient of the filtered ECG signal is used as a feature for the detection of QRS-complexes, P- and T-waves. The performance of the algorithm is validated using original 12-lead ECG recordings from the standard CSE ECG database. Significant detection rate is achieved. The percentage of false positive and false negative detection is low. The method successfully detects all kind of morphologies of QRS-complexes, P- and T-waves. The onsets and offsets of the detected QRS-complexes, P- and T-waves are found to be within the tolerance limits given in CSE library.     

ML—2型心电导联转换仪的研制及其实践效果

目的：市售SYD－4228生理学学生实验系统没有配置心电图导联电缆及导联选择装置,不能进行观察心电图的实验,为此研制本心电导联转换仪。方法：设计并制作兼容网络与导联输入电缆,组装成新型的转换仪。结果：本转换仪配合SYD系统在高血钾实验中使用,满足了SYD系统进行心电图实验的需要。结论：本转换仪既适用于SYD系统,也适用于各型心电图机,可以同步对比观察不同导联的心电图。     

Techniques et activités des hommes du Paléolithique supérieur sur le site de Wangfujing - Oriental Plaza, Pékin, Chine

More than 2000 pieces of cultural relics were unearthed from the Oriental Plaza site, including stone artifacts, bone artifacts, fossils, hematite powder, fire use remains and plant root and foliage. By analyzing these remains in refitting stone and bone artifacts, in experimentally producing some artifacts, and comparing with ethnologic data, it is concluded that the Oriental Plaza site is a seasonal human activity site, that humans had been making a life of hunting and collecting food, can make stone artifacts, bone artifacts to kill their quarries, use fire to cook their food, and at the same time they also conducted some religion activities.     

Evaluation of lead concentrations in 18th-century Omaha Indian skeletons using ICP-MS.

The analysis of skeletal remains of Omaha Indians buried between AD 1780 and 1820 indicated that lead was incorporated in cortical bone. The diagenetic or biogenetic origin of the lead was evaluated by examination of lead isotope ratios of the bones and artifacts, and comparison of lead concentrations in burial soils with those of the bones. The isotopic values of the lead artifacts demonstrate that the lead was mined in the Missouri region. Although the isotope ratios in the bones are not identical with that from the lead artifacts, there is a strong relationship between them. This finding indicates that the lead in the bone was at least partly derived from the artifacts. Because lead artifacts rarely accompanied the burials but lead was ubiquitous in the bones, we suggest a biogenetic origin for the lead. There is also the possibility that some of the lead may have been derived from pigments applied to the corpse during mortuary ritual.     

Analysis of ECG signal denoising method based on S-transform

Electrocardiogram (ECG), a noninvasive technique which is used generally as a primary diagnostic tool for cardiovascular diseases. A cleaned ECG signal provides necessary information about the electrophysiology of the heart diseases and ischemic changes that may occur. However in real situation, noise is often embedded with ECG signal during acquisition. In this paper, a novel ECG signal denoising technique is proposed using Stockwell transform (S-transform). This method is evaluated on several normal and abnormal ECG signals of MIT/BIH arrhythmia database, by artificially adding white Gaussian noises to visually inspected clean ECG recordings. The experimental results demonstrate that the proposed method shows the better signal to noise ratio (SNR), lower root mean square error (RMSE) and percent root mean square difference (PRD) compared to generally used ECG denoising method like wavelet transform.     

Cine 4DCT imaging artifacts: Quantification and correlations with scanning parameters and target kinetics

To study temporal resolved computed tomography imaging (4-Dimensional Computed Tomography: 4DCT) artifacts correlations with scanning parameters and target kinetics and to assess uncertainty introduced by 4DCT in radiotherapy treatment planning.In this work we classified 4DCT artifacts as finite gantry rotation speed related (FGS) and finite sampling frequency related (FSF). We studied FGS artifacts using a respiratory phantom and FSF artifacts using a Monte Carlo simulation of acquisition timing.From our analysis FGS localization error is comparable with image resolution determined by voxel dimensions. Remaining FGS artifacts are correlated with gantry rotation time (Trot), target velocity (v) and their interaction.FSF artifacts occurrence is correlated with sampling ratio (SR), i.e. the ratio of patient respiratory period (Tresp) and sampling time (Ts).In the studied velocity range (0–2 cm/s), using a Trot of 0,5s and a SR higher than 15, FGS and FSF artifacts became comparable with other sources of uncertainty.Our considerations are valid for “ideal” breathing pattern only. When variations from periodical breathing, high target velocity (more than 2 cm/s) or high peak to peak amplitude (more than 2 cm) are present, patient specific images artifacts analysis is recommended.