ECG signal conditioning by morphological filtering

Clinically obtained electrocardiographic (ECG) signals are often contaminated with different types of noise and baseline drifting commonly occurs. In order to facilitate automated ECG analysis, signal conditioning is undoubtedly a necessity. In this paper, a modified morphological filtering (MMF) technique is used for signal conditioning in order to accomplish baseline correction and noise suppression with minimum signal distortion. Compared with existing methods for ECG signal conditioning, MMF performs well in terms of the filtering characteristics, low signal distortion ratio, low computational burden as well as good noise suppression ratio and baseline correction ratio.     

ECG signal denoising and baseline wander correction based on the empirical mode decomposition

The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Good quality ECG are utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts. Two dominant artifacts present in ECG recordings are: (1) high-frequency noise caused by electromyogram induced noise, power line interferences, or mechanical forces acting on the electrodes; (2) baseline wander (BW) that may be due to respiration or the motion of the patients or the instruments. These artifacts severely limit the utility of recorded ECGs and thus need to be removed for better clinical evaluation. Several methods have been developed for ECG enhancement. In this paper, we propose a new ECG enhancement method based on the recently developed empirical mode decomposition (EMD). The proposed EMD-based method is able to remove both high-frequency noise and BW with minimum signal distortion. The method is validated through experiments on the MIT-BIH databases. Both quantitative and qualitative results are given. The simulations show that the proposed EMD-based method provides very good results for denoising and BW removal.     

Simultaneous powerline interference and baseline wander removal from ECG and EMG signals by sinusoidal modeling

We present a compact approach to joint modeling of powerline interference (PLI) and baseline wonder (BW) for denoising of biopotential signals. Both PLI and BW are modeled by a set of harmonically related sinusoids modulated by low-order time polynomials. The sinusoids account on the harmonicity and mean instantaneous frequency of the PLI in the analysis window, while the polynomials capture the frequency and amplitude deviations from their nominal values and characterize the BW at the same time. The resulting model is linear-in-parameters and the solution to the corresponding linear system is estimated in a simple and efficient way through linear least-squares. The proposed modeling method was evaluated on real electrocardiographic (ECG) and electromyographic (EMG) signals against three reference methods for different analysis scenarios. The comparative study suggests that the proposed method outperforms the reference methods in terms of residual interference energy in the denoised biopotential signals.     

Fractal and EMD based removal of baseline wander and powerline interference from ECG signals

This paper presents novel methods for baseline wander removal and powerline interference removal from electrocardiogram (ECG) signals. Baseline wander and clean ECG have been modeled as 1st and 2nd-order fractional Brownian motion (fBm) processes, respectively. This fractal modeling is utilized to propose projection operator based approach for baseline wander removal. Powerline interference is removed by using a hybrid approach of empirical mode decomposition method (EMD) and wavelet analysis. Simulation results are presented to show the efficacy of both the methods. The proposed methods have been shown to preserve ECG shapes characteristic of heart abnormalities.     

Time-varying digital filtering of ECG baseline wander

Time-varying filtering techniques are applied to the problem of baseline correction by letting the cut-off frequency of a linear filter be controlled by the low-frequency properties of the ECG signal. The time-varying filter is implemented as a bank of linear low-pass filters, in which each filter has a slightly differing cut-off frequency. Sampling rate decimation and interpolation are employed because the design of a filter for baseline reduction can be treated as a narrowband filtering problem. All filters have a linear phase response to reduce, for example, ST-segment distortion. The performance of the technique presented is studied on ECG signals with different types of simulated baseline wander. The results are compared with the performance of time-invariant linear filtering and cubic spline interpolation. The results show that an improvement in performance can be achieved when using time-varying filtering, especially at low heart rates or during episodes with excessive baseline wander.     

Online Digital Filter and QRS Detector Applicable in Low Resource ECG Monitoring Systems

The present work describes fast computation methods for real-time digital filtration and QRS detection, both applicable in autonomous personal ECG systems for long-term monitoring. Since such devices work under considerable artifacts of intensive body and electrode movements, the input filtering should provide high-quality ECG signals supporting the accurate ECG interpretation. In this respect, we propose a combined high-pass and power-line interference rejection filter, introducing the simple principle of averaging of samples with a predefined distance between them. In our implementation (sampling frequency of 250 Hz), we applied averaging over 17 samples distanced by 10 samples (Filter10x17), thus realizing a comb filter with a zero at 50 Hz and high-pass cut-off at 1.1 Hz. Filter10x17 affords very fast filtering procedure at the price of minimal computing resources. Another benefit concerns the small ECG distortions introduced by the filter, providing its powerful application in the preprocessing module of diagnostic systems analyzing the ECG morphology. Filter10x17 does not attenuate the QRS amplitude, or introduce significant ST-segment elevation/depression. The filter output produces a constant error, leading to uniform shifting of the entire P-QRS-T segment toward about 5% of the R-peak amplitude. Tests with standardized ECG signals proved that Filter10x17 is capable to remove very strong baseline wanderings, and to fully suppress 50 Hz interferences. By changing the number of the averaged samples and the distance between them, a filter design with different cut-off and zero frequency could be easily achieved. The real-time QRS detector is designed with simplified computations over single channel, low-resolution ECGs. It relies on simple evaluations of amplitudes and slopes, including history of their mean values estimated over the preceding beats, smart adjustable thresholds, as well as linear logical rules for identification of the R-peaks in real-time. The performance of the QRS detector was tested with internationally recognized ECG databases (AHA, MIT-BIH, European ST-T database), showing mean sensitivity of 99.65% and positive predictive value of 99.57%. The performance of the presented QRS detector can be highly rated, comparable and even better than other published real-time QRS detectors. Examples representing some typical unfavorable conditions in real ECGs, illustrate the common operation of Filter10x17 and the QRS detector.     

心电图波形基线漂移的数学形态滤波在线实时处理

研究数学形态学滤波器消除心电图波形的基线漂移。选择Maragos类型形态滤波器,得到平结构元为消除波形基线漂移最佳形态滤波形式,信号的衰减幅度由信号数字频率与结构元长度的比值唯一决定,由此归纳出平结构元长度大于或等于要滤波除去的分量的横向宽度的结论。借助Matlab软件进行算法模拟,并移植到DSP硬件平台上进行工程调试,实验表明,该方法运算简单,花费时间少于1 ms,能有效并实时地滤除基漂。     

基于统计加权滑动平均的去心电信号基线漂移

基线漂移(BW)是心电(ECG)信号一种常见的干扰。为了有效去除BW而且保留更多的潜在ECG成分,本文提出了一种简单的基于统计加权滑动平均滤波器(SMA)。首先,对滑动窗口中的ECG数据进行分段,统计并确定几个具有最大概率的数据段范围[ak,bk]。然后,将滑动窗口中数值属于[ak,bk]采样点的权值置为1,否则为0。最后,对窗口中权值为1的采样值进行平均以估计滑动窗口的基线。通过www.physionet.org提供的共享ECG数据对该算法性能进行实验,结果表明:与传统滑动平均滤波器(MA)和小波包(WP)变换滤波相比,该算法能更有效地去除BW,而且产生的ECG扭曲最小。     

RPCA-based detection and quantification of motion artifacts in ECG signals

In this paper, a recursive principal component analysis (RPCA)-based algorithm is applied for detecting and quantifying the motion artifact episodes encountered in an ECG signal. The motion artifact signal is synthesized by low-pass filtering a random noise signal with different spectral ranges of LPF (low pass filter): 0–5 Hz, 0–10 Hz, 0–15 Hz and 0–20 Hz. Further, the analysis of the algorithm is carried out for different values of SNR levels and forgetting factors (α) of an RPCA algorithm. The algorithm derives an error signal, wherever a motion artifact episode (noise) is present in the entire ECG signal with 100% accuracy. The RPCA error magnitude is almost zero for the clean signal portion and considerably high wherever the motion artifacts (noisy episodes) are encountered in the ECG signals. Further, the general trend of the algorithm is to produce a smaller magnitude of error for higher SNR (i.e. low level of noise) and vice versa. The quantification of the RPCA algorithm has been made by applying it over 25 ECG data-sets of different morphologies and genres with three different values of SNRs for each forgetting factor and for each of four spectral ranges.     

Component wave delineation of ECG by filtering in the Fourier domain

A complete solution to the fundamental problem of delineation of an ECG signal into its component waves by filtering the discrete Fourier transform of the signal is presented. The set of samples in a component wave is transformed into a complex sequence with a distinct frequency band. The filter characteristics are determined from the time signal itself. Multiplication of the transformed signal with a complex sinusoidal function allows the use of a bank of low-pass filters for the delineation of all component waves. Data from about 300 beats have been analysed and the results are highly satisfactory both qualitatively and quantitatively.     

ECG characterization of paroxysmal atrial fibrillation: parameter extraction and automatic diagnosis algorithm

Paroxysmal atrial fibrillation (PAF) is one of the most common heart arrhythmias. It is very difficult to detect unless an explicit Atrial Fibrillation episode occurs during the exploration. The present paper describes a number of low level parameters extracted from ECG traces where no Atrial Fibrillation process is present. The ability of this parameter set to characterize PAF patients is studied and discussed. Based on these parameters a modular automatic classification algorithm for PAF diagnosis is developed and evaluated.     

Statistical morphological skull stripping of adult and infant MRI data

This paper describes a novel automatic statistical morphology skull stripper (SMSS) that uniquely exploits a statistical self-similarity measure and a 2-D brain mask to delineate the brain. The result of applying SMSS to 20 MRI data set volumes, including scans of both adult and infant subjects is also described. Quantitative performance assessment was undertaken with the use of brain masks provided by a brain segmentation expert. The performance is compared with an alternative technique known as brain extraction tool. The results suggest that SMSS is capable of skull-stripping neurological data with small amounts of over- and under-segmentation.     

Three-dimensional reconstruction of cardiac displacement patterns on the chest wall during the P,QRS and T-segments of the ECG by laser speckle inteferometry

A noninvasive and noncontact technique based on the principle of laser speckle interferometry has been developed to record the cardiac displacements observed on the chest wall. These displacements are then reconstructed in the form of three-dimensional plots, during the P, QRS and T-waves of the ECG. A comparison of these patterns shows that the mechanical activity of each region varies significantly during these phases of cardiac cycle. As these displacements depend on the clinical status of the heart, its use with a cardiac patient shows the functional changes of the affected regions in the form of alteration of these patterns.     

The morphological diversity of the quadrate bone in squamate reptiles as revealed by high-resolution computed tomography and geometric morphometrics

We examined the morphological diversity of the quadrate bone in squamate reptiles (i.e. lizards, snakes, amphisbaenians). The quadrate is the principal splanchnocranial element involved in suspending the lower jaw from the skull, and its shape is of particular interest because it is potentially affected by several factors, such as phylogenetic history, allometry, ecology, skull kinesis and hearing capabilities (e.g. presence or absence of a tympanic ear). Due to its complexity, the quadrate bone is also considered one of the most diagnostic elements in fragmentary fossil taxa. We describe quadrates from 38 species spread across all major squamate clades, using qualitative and quantitative (e.g. geometric morphometrics) methods. We test for possible correlations between shape variation and factors such as phylogeny, size, ecology and presence/absence of a tympanum. Our results show that the shape of the quadrate is highly evolutionarily plastic, with very little of the diversity explained by phylogenetic history. Size variation (allometric scaling) is similarly unable to explain much shape diversity in the squamate quadrate. Ecology (terrestrial/fossorial/aquatic) and presence of a tympanic ear are more significant, but together explain only about 20% of the diversity observed. Other unexplored and more analytically complex factors, such as skull biomechanics, likely play additional major roles in shaping the quadrates of lizards and snakes.     

Kaurene diterpene induces apoptosis in U87 human malignant glioblastoma cells by suppression of anti-apoptotic signals and activation of cysteine proteases

Gliomas are the most common and malignant primary brain tumors in humans. Studies have shown that classes of kaurene diterpene have anti-tumor activity related to their ability to induce apoptosis. We investigated the response of the human glioblastoma cell line U87 to treatment with ent-kaur-16-en-19-oic acid (kaurenoic acid, KA). We analyzed cell survival and the induction of apoptosis using flow cytometry and annexin V staining. Additionally, the expression of anti-apoptotic (c-FLIP and miR-21) and apoptotic (Fas, caspase-3 and caspase-8) genes was analyzed by relative quantification (real-time PCR) of mRNA levels in U87 cells that were either untreated or treated with KA (30, 50, or 70 µM) for 24, 48, and 72 h. U87 cells treated with KA demonstrated reduced viability, and an increase in annexin V- and annexin V/PI-positive cells was observed. The percentage of apoptotic cells was 9% for control cells, 26% for cells submitted to 48 h of treatment with 50 µM KA, and 31% for cells submitted to 48 h of treatment with 70 µM KA. Similarly, in U87 cells treated with KA for 48 h, we observed an increase in the expression of apoptotic genes (caspase-8, -3) and a decrease in the expression of anti-apoptotic genes (miR-21 and c-FLIP). KA possesses several interesting properties and induces apoptosis through a unique mechanism. Further experiments will be necessary to determine if KA may be used as a lead compound for the development of new chemotherapeutic drugs for the treatment of primary brain tumors.