Effect of adaptive motion-artifact reduction on QRS detection

Motion artifact resulting from electrode and patient movement is a significant source of noise in ECG, EEG, EMG, and impedance pneumography recording. Noise resulting from motion is particularly troublesome in ambulatory ECG recordings, such as those made during Holter monitoring or stress tests, because the bandwidth of the motion artifact overlaps with the ECG signal bandwidth. The authors investigated the effect of an adaptive motion-artifact removal algorithm on the performance of a standard QRS detector. They made four ECG recordings on each of the three subjects while manually generating artifact. Adaptive noise removal was applied to the ECG signal using a skin-stretch signal as the noise reference. Adaptive noise removal reduced the number of false QRS detections in the records from 380 to 104, for an average reduction in false detections of 72.6%. False-detection reductions for individual records ranged from 12% to 93%.     

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.     

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.     

Wavelet energy based diagnostic distortion measure for ECG

In this paper, a novel Wavelet Energy based diagnostic distortion (WEDD) measure is proposed to assess the reconstructed signal quality for ECG compression algorithms. WEDD is evaluated from the Wavelet coefficients of the original and the reconstructed ECG signals. For each ECG segment, a Wavelet energy weight vector is computed via five-level biorthogonal discrete Wavelet transform (DWT). WEDD is defined as the sum of Wavelet energy weighted percentage root mean square difference of each subband. The effectiveness of this measure is validated by linear (linear polynomial and cubic polynomial) and nonlinear (logistic) regression analysis between the computed WEDD values and the mean opinion score (MOS) given by cardiologists. WEDD provides a better prediction accuracy and exhibits a statistically better monotonic relationship with the MOS ratings than Wavelet based weighted percentage root mean square difference (PRD) measure (WWPRD), PRD and other objective measures. Standard correlation coefficient and Spearman rank-order correlation coefficient (SROCC) between the WEDD/MOS ratings is 0.969 and 0.9624, respectively.     

An approach to removing spatially uncorrelated artifacts from EEG recordings

This work deals with elimination artifacts from electroencephalograms (EEGs). Though many methods for solving the problem have been proposed, they either require direct intervention of the researcher, are based on additional measurements (electrooculogram, electrocardiogram, etc.), or cannot remove artifact activity to a sufficient extent. We proposed an automated method that uses of the fact that the electromyogram (EMG) is not correlated and the artifacts of electrode movement at adjacent derivations and is based on the representation of electrical activity in one derivation through activity in other derivations. The analysis of the proposed method using model signals and examples of real EEG recordings has demonstrated its practical effectiveness.     

Epilepsy in kcnj10 Morphant Zebrafish Assessed with a Novel Method for Long-Term EEG Recordings

We aimed to develop and validate a reliable method for stable long-term recordings of EEG activity in zebrafish, which is less prone to artifacts than current invasive techniques. EEG activity was recorded with a blunt electrolyte-filled glass pipette placed on the zebrafish head mimicking surface EEG technology in man. In addition, paralysis of agarose-embedded fish using D-tubocurarine excluded movement artifacts associated with epileptic activity. This non-invasive recording technique allowed recordings for up to one hour and produced less artifacts than impaling the zebrafish optic tectum with a patch pipette. Paralyzed fish survived, and normal heartbeat could be monitored for over 1h. Our technique allowed the demonstration of specific epileptic activity in kcnj10a morphant fish (a model for EAST syndrome) closely resembling epileptic activity induced by pentylenetetrazol. This new method documented that seizures in the zebrafish EAST model were ameliorated by pentobarbitone, but not diazepam, validating its usefulness. In conclusion, non-invasive recordings in paralyzed EAST syndrome zebrafish proved stable, reliable and robust, showing qualitatively similar frequency spectra to those obtained from pentylenetetrazol-treated fish. This technique may prove particularly useful in zebrafish epilepsy models that show infrequent or conditional seizure activity.     

SVM-based algorithm for recognition of QRS complexes in electrocardiogram

Among all electrocardiogram (ECG) components, the QRS complex is the most significant feature. This paper presents a new algorithm for recognition of QRS complexes in the electrocardiogram (ECG) based on support vector machine (SVM). Digital filtering techniques are used to remove power line interference and baseline wander in the ECG signal. SVM is used as a classifier to delineate QRS and non-QRS regions. Algorithm performance was evaluated against the standard CSE ECG database. The results indicated that the algorithm achieved 99.3% of the detection rate. The percentage of false positive and false negative was 12.4 and 0.7% respectively. It could function reliably even under the condition of poor signal quality of the ECG signal.     

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%.     

K-means algorithm for the detection and delineation of QRS-complexes in Electrocardiogram

Electrocardiogram (ECG) is an important bioelectrical signal used to asses the cardiac state of a patient. It consists of a recurrent wave sequence of P-wave, QRS-complex and T-wave associated with each beat. The QRS-complex is the prominent feature of the ECG. This paper presents a simple method using K-means clustering algorithm for the detection of QRS-complexes in ECG signal. Digital filters are used to remove the power line interference and baseline wander present in the ECG signal. K-means algorithm is used to classify QRS and non-QRS-region in the ECG signal. The performance of the algorithm is validated using dataset-3 of the CSE multi-lead measurement library. Detection rate of 98.66% is obtained. The percentage of false positive and false negative is 1.14% and 1.34% respectively. The mean and standard deviation of the errors between automatic and manual annotations is calculated to validate the delineation performance of the algorithm. The onsets and offsets of the detected QRS-complexes are found well within the tolerance limits as specified by the CSE library.     

Catheter Ablation of Idiopathic Premature Ventricular Contractions and Ventricular Tachycardias Originating from the Vicinity of Endocardial and Epicardial Mitral Annulus

Background

Several previous reports have revealed that idiopathic ventricular arrhythmias (VAs), including premature ventricular contractions (PVCs) and ventricular tachycardias (IVTs), can originate from endocardial mitral annulus (ENDO MA). However, these data are limited to ENDO MA VAs, and little is known about the electrocardiographic (ECG) characteristics and the efficacy of radiofrequency catheter ablation (RFCA) via the coronary venous system for the VAs arising from the epicardial MA (EPI MA).

Methodology/Principal Findings

Characteristics of body surface electrocardiogram and electrophysiologic recordings were analyzed in 21 patients with symptomatic PVCs/IVTs originating from the vicinity of MA. Among 597 patients with PVCs/IVTs, the incidence of VAs originating from the ENDO and EPI MA was 3.52% (21 cases). Eleven (52%) from the ENDO MA, and 10 (48%) from the EPI MA. There were different characteristics of ECG of PVCs/VT originating from the ENDO and EPI MA. The prolonged pseudodelta wave time and intrinsicoid deflection time in lead V2 and the precordial maximum deflection index reliably differentiated EPI MA VAs from ENDO MA VAs with high sensitivity and specificity. Successful RFCA in 18 patients could be achieved (85.7% acute procedural success).

Conclusions/Significance

ECG characteristics of PVCs/VTs originating from the different portions of the MA are different, and can help regionalize the origin of these arrhythmias. RFCA within the coronary venous system was relatively effective and safe for the PVCs/IVTs and should be seen as an alternative approach, when the MA PVCs/IVTs could not be eliminated by RFCA from the endocardium.     

Filtering the surface EMG signal: Movement artifact and baseline noise contamination

The surface electromyographic (sEMG) signal that originates in the muscle is inevitably contaminated by various noise signals or artifacts that originate at the skin-electrode interface, in the electronics that amplifies the signals, and in external sources. Modern technology is substantially immune to some of these noises, but not to the baseline noise and the movement artifact noise. These noise sources have frequency spectra that contaminate the low-frequency part of the sEMG frequency spectrum. There are many factors which must be taken into consideration when determining the appropriate filter specifications to remove these artifacts; they include the muscle tested and type of contraction, the sensor configuration, and specific noise source. The band-pass determination is always a compromise between (a) reducing noise and artifact contamination, and (b) preserving the desired information from the sEMG signal. This study was designed to investigate the effects of mechanical perturbations and noise that are typically encountered during sEMG recordings in clinical and related applications. The analysis established the relationship between the attenuation rates of the movement artifact and the sEMG signal as a function of the filter band pass. When this relationship is combined with other considerations related to the informational content of the signal, the signal distortion of filters, and the kinds of artifacts evaluated in this study, a Butterworth filter with a corner frequency of 20 Hz and a slope of 12 dB/oct is recommended for general use. The results of this study are relevant to biomechanical and clinical applications where the measurements of body dynamics and kinematics may include artifact sources.     

ECG signal denoising using higher order statistics in Wavelet subbands

In this work, we propose a novel denoising method based on evaluation of higher-order statistics at different Wavelet bands for an electrocardiogram (ECG) signal. Higher-order statistics at different Wavelet bands provides significant information about the statistical nature of the data in time and frequency. The fourth order cumulant, Kurtosis, and the Energy Contribution Efficiency (ECE) of signal in a Wavelet subband are combined to assess the noise content in the signal. Accordingly, four denoising factors are proposed. Performance of the denoising factors is evaluated and compared with the soft thresholding method. The filtered signal quality is assessed using Percentage Root Mean Square Difference (PRD), Wavelet Weighted Percentage Root Mean Square Difference (WWPRD), and Wavelet Energy-based Diagnostic Distortion (WEDD) measures. It is observed that the proposed denoising scheme not only filters the signal effectively but also helps retain the diagnostic information.     

基于小波变换的心电信号去噪综合算法

目的：研究去除心电信号中的基线漂移、工频干扰和肌电干扰等噪声,提高心电信号的自动识别和诊断精度。方法：利用Coif4小波对心电信号进行8尺度分解,采用小波分解重构法去除基线漂移,然后利用改进的小波闽值算法去除工频干扰和肌电干扰。结果：利用Matlab仿真工具,选择MIT-BIH心率失常数据库中信号进行验证,能有效去除这三种噪声,并且很好的保持R波的信息。结论：本算法在不丢失心电信号有用信息的前提下,可以较好的去除三种常见的噪声,可以用于心电信号自动分析之前的预处理。     

基于小波变换的心电信号去噪算法

目的:去除在心电信号采集过程中混入的肌电干扰、工频干扰、基线漂移等噪声信号,避免噪声对心电信号特征点的识别和提取造成误判和漏判。方法:首先利用coif4小波对心电信号按Mallat算法进行分解,然后采用软、硬阈值折衷与小波重构的算法进行去噪。结果:采用MIT/BIH Arrhythmia Database中的心电信号进行仿真、验证,有效去除了三种常见的噪声信号。结论:本方法实时性好,为临床分析与诊断奠定了基础。     

Array CGH data modeling and smoothing in Stationary Wavelet Packet Transform domain

Background

Array-based comparative genomic hybridization (array CGH) is a highly efficient technique, allowing the simultaneous measurement of genomic DNA copy number at hundreds or thousands of loci and the reliable detection of local one-copy-level variations. Characterization of these DNA copy number changes is important for both the basic understanding of cancer and its diagnosis. In order to develop effective methods to identify aberration regions from array CGH data, many recent research work focus on both smoothing-based and segmentation-based data processing. In this paper, we propose stationary packet wavelet transform based approach to smooth array CGH data. Our purpose is to remove CGH noise in whole frequency while keeping true signal by using bivariate model.

Results

In both synthetic and real CGH data, Stationary Wavelet Packet Transform (SWPT) is the best wavelet transform to analyze CGH signal in whole frequency. We also introduce a new bivariate shrinkage model which shows the relationship of CGH noisy coefficients of two scales in SWPT. Before smoothing, the symmetric extension is considered as a preprocessing step to save information at the border.

Conclusion

We have designed the SWTP and the SWPT-Bi which are using the stationary wavelet packet transform with the hard thresholding and the new bivariate shrinkage estimator respectively to smooth the array CGH data. We demonstrate the effectiveness of our approach through theoretical and experimental exploration of a set of array CGH data, including both synthetic data and real data. The comparison results show that our method outperforms the previous approaches.

     

Wavelet threshold based ECG compression using USZZQ and Huffman coding of DSM

In this paper, a new Wavelet threshold based ECG signal compression technique using uniform scalar zero zone quantizer (USZZQ) and Huffman coding on differencing significance map (DSM) is proposed. Wavelet coefficients are selected based on the energy packing efficiency of each sub-band. Significant Wavelet coefficients are quantized with uniform scalar zero zone quantizer. Significance map is created to store the indices of the significant coefficients. This map is encoded efficiently with less number of bits by applying Huffman coding on the differences between indices in the significance map. ECG records from the MIT-BIH arrhythmia database are selected as test data. For the record 117, the proposed technique achieves a compression ratio of 18.7:1 with lower percentage root mean square difference (PRD) compared to other threshold based methods. The proposed technique is tested for MIT-BIH arrhythmia record 119 and a compression ratio of 21.81:1 is achieved with a PRD value of 3.716% which is much lower compared to the reported PRD value of 5.0 and 5.5% of set partitioning in hierarchical tress (SPIHT) and analysis by synthesis ECG compressor (ASEC), respectively. The noise eliminating capability of the proposed technique is also demonstrated in this work. The proposed technique achieves the required compression ratio with less reconstruction error for GSM-based cellular telemedicine system.     

基于EMD和小波变换的运动分析

运动分析是视觉信息加工中的一个重要问题。本文利用Ｒｅｉｃｈａｒｄｔ的相关型初级运动检测器（ＥＭＤ）二维阵列可以有效地进行图象－背景相对运动分辨的特点,以及小波变换的频谱分析特性与人类视觉多频率通道特性相类似的性质,将ＥＭＤ模型、小波变换和图象的塔式结构处理有机地结合起来,提出了一种类似视觉信息加工方式的新的运动分析算法。计算机仿真结果表明,算法能够较好地模拟视觉运动检测的功能,与Ｈｏｒｎ＆Ｓｃｈｕｎｃｋ算法［１］相比,提高了运动估计的速度与精度。     

EMD-based EEG signal enhancement for auditory evoked potential recovery under high stimulus-rate paradigm

Short inter-stimulus interval (ISI) is one inherent characteristic of the high stimulus-rate (HSR) paradigms for studying auditory evoked potentials (AEPs). At short ISIs, the AEPs to adjacent stimuli overlap. To resolve the AEP to a specific stimulus requires an inverse process of overlapping. Inverse filtering (also called as deconvolution) has been commonly employed to achieve this goal. However, the resulted signal may be severely distorted as inverse filtering can substantially amplify such undesired components as noises and artifacts in the raw EEG recordings. In practice, even if care be taken to obtain quality EEGs, noises and artifacts are unavoidable. It is thus critical to remove or at least supress these undesired components for studies using HSR paradigms. In this paper, we propose a systematic approach to EEG signal enhancement based on empirical mode decomposition (EMD) and threshold filtering/rejection. Using synthetic and real data, we test the effectiveness of our approach. Results for both types of data consistently demonstrate that our methods can significantly improve the quality of recovered AEPs, according to visual inspection and SNRs estimated using two metrics.     

Wavelet threshold based TDL and TDR algorithms for real-time ECG signal compression

In this paper, two novel and simple, target distortion level (TDL) and target data rate (TDR), Wavelet threshold based ECG compression algorithms are proposed for real-time applications. The issues on the use of objective error measures, such as percentage root mean square difference (PRD) and root mean square error (RMSE) as a quality measures, in quality controlled/guranteed algorithm are investigated with different sets of experiments. For the proposed TDL and TDR algorithm, data rate variability and reconstructed signal quality is evaluated under different ECG signal test conditions. Experimental results show that the TDR algorithm achieves the required compression data rate to meet the demands of wire/wireless link while the TDL algorithm does not. The compression performance is assessed in terms of number of iterations required to achieve convergence and accuracy, reconstructed signal quality and coding delay. The reconstructed signal quality is evaluated by correct diagnosis (CD) test through visual inspection. Three sets of ECG data from three different databases, the MIT-BIH Arrhythmia (mita) (F_s=360 Hz, 11 b/sample), the Creighton University Ventricular Tachyarrhythmia (cuvt) (F_s=250 Hz, 12 b/sample) and the MIT-BIH Supraventricular Arrhythmia (mitsva) (F_s=128 Hz, 10 b/sample), are used for this work. For each set of ECG data, the compression ratio (CR) range is defined. The CD value of 100% is achieved for CR ≤12, CR ≤ 8 and CR ≤ 4 for data from mita, cuvt and mitsva databases, respectively. The experimental results demonstrate that the proposed TDR algorithm is suitable for real-time applications.     

Rectification of SEMG as a tool to demonstrate synchronous motor unit activity during vibration

The use of surface electromyography (SEMG) in vibration studies is problematic since motion artifacts occupy the same frequency band with the SEMG signal containing information on synchronous motor unit activity. We hypothesize that using a harsher, 80–500 Hz band-pass filter and using rectification can help eliminate motion artifacts and provide a way to observe synchronous motor unit activity that is phase locked to vibration using SEMG recordings only. Multi Motor Unit (MMU) action potentials using intramuscular electrodes along with SEMG were recorded from the gastrocnemius medialis (GM) of six healthy male volunteers. Data were collected during whole body vibration, using vibration frequencies of 30 Hz, 35 Hz, 40 Hz or 50 Hz. A computer simulation was used to investigate the efficacy of filtering under different scenarios: with or without artifacts and/or motor unit synchronization. Our findings indicate that motor unit synchronization took place during WBV as verified by MMU recordings. A harsh filtering regimen along with rectification proved successful in demonstrating motor unit synchronization in SEMG recordings. Our findings were further supported by the results from the computer simulation, which indicated that filtering and rectification was efficient in discriminating motion artifacts from motor unit synchronization. We suggest that the proposed signal processing technique may provide a new methodology to evaluate the effects of vibration treatments using only SEMG. This is a major advantage, as this non-intrusive method is able to overcome movement artifacts and also indicate the synchronization of underlying motor units.