Removal of cardiac beat artifact in oesophageal pressure measurement by frequency analysis

Oesophageal pressure (P_es) measurements are important in medical research and useful in clinical diagnosis. Measurements, however, are contaminated heavily by cardiac artifacts. The spectrum and waveform of the P_es signal is obtained from the oesophageal balloon. Adaptive finite impulse response (AFIR) filter and modified adaptive noise cancellation (MANC) methods are adopted to filter out cardiac beat interference. These results are compared. In the frequency domain, frequency variations and spectral overlap between the P_es components and cardiac beat signal components impact on the performance of the filter. From our experimental results on power strength, the fourth or higher harmonics did not have any significant effect on the filter performance. However, the second harmonics of these signals had a significant effect on the filtering result. Thus, in the design of AFIR filters, attention is needed to minimise these effects. In frequency analysis, these harmonics or overlapping frequencies do not affect MANC. MANC was the better method for eliminating cardiac beat artifact in P_es measurement. The dynamic compliance (C_dyn) was also used to evaluate the performance of MANC and AFIR. The standard deviation of C_dyn was less than 0.15 using MANC, compared with standard deviations as high as 0.57 for AFIR. We conclude that MANC performs better than AFIR.     

Removal of Cardiac Beat Artifact in Esophageal Pressure Measurement via a Modified Adaptive Noise Cancellation Scheme

Information on volume–pressure relationships of human lungs is usually based on indirect determination of intrapleural pressure (P_ip) obtained from the esophagus. Unfortunately, cardiac beat artifact frequently corrupts measurement of esophageal pressure (P_es). In this study, we presented a modified adaptive noise cancellation (MANC) scheme for removing the cardiac beat artifact in the P_es signal. The proposed methodology used an airflow signal as the reference signal with least-mean-square method as the adaptive algorithm. The results of six experiments on two Brown–Norway rats showed a significant reduction of the apparent cardiac pulsation with minimal distortion of the P_es signal. The MANC filter also showed evidence of peak suppression at integer multiples of heart rate in the fast Fourier transform of the P_es signal while leaving the remaining spectrum largely unperturbed. A t-test method and the ratio of standard deviation to mean (std/mean) statistics of airway resistance (R_aw) values were used to evaluate the performance of the MANC filter. In all six experiments, a reduction of std/mean of R_aw by 12.5%–68% was obtained, indicating the effectiveness of the proposed scheme. © 2001 Biomedical Engineering Society. PAC01: 8780-y, 8719Hh, 8719Uv     

Application of a wavelet adaptive filter to minimise distortion of the ST-segment

A wavelet adaptive filter (WAF) for the removal of baseline wandering in ECG signals is described. The WAF consists of two parts. The first part is a wavelet transform that decomposes the ECG signal into seven frequency bands using Vaidyanathan-Hoang wavelets. The second part is an adaptive filter that uses the signal of the seventh lowest-frequency band among the wavelet transformed signals as primary input and a constant as reference input. To evaluate the performance of the WAF, two baseline wandering elimination filters are used, a commercial standard filter with a cutoff frequency of 0.5 Hz and a general adaptive filter. The MIT/BIH database and the European ST-T database are used for the evaluation. The WAF performs better in the average power of eliminated noise than the standard filter and adaptive filter. Furthermore, it shows a lower ST-segment distortion than the standard filter and the adaptive filter.     

Adaptive filtering of canine electrogastrographic signals. Part 1: system design

The study of the relation between gastric myo-electrical activities recorded from serosal and cutaneous electrodes is hindered by the poor quality of the cutaneous signal. This hindrance could be minimised by suitable filtering of the signal. Since it is not yet clear which aspects of the cutaneous signal constitute valuable information, the filter process should not affect phase, amplitude, frequency and waveform of the gastric component, while noise components should be suppressed strongly. The system design of a modified adaptive filter that meets these requirements is described. The filter was implemented on a digital Nova 2 minicomputer. the filter performance is described and tested.     

Exploring fMRI data for periodic signal components

We use a Bayesian framework to detect periodic components in fMRI data. The resulting detector is sensitive to periodic components with a flexible number of harmonics and with arbitrary amplitude and phases of the harmonics. It is possible to detect the correct number of harmonics in periodic signals even if the fundamental frequency is beyond the Nyquist frequency. We apply the signal detector to locate regions that are highly affected by periodic physiological artifacts, such as cardiac pulsation.     

Filters and Fourier analysis of gated blood pool studies: a search for the optimal combination

Fourier analysis of gated blood pool studies is performed after filtering the raw data by a spatial median 3 x 3, 9 x 9 or temporo-spatial 9 x 9 x 9 filter. 20 patients and a dynamic cardiac phantom were studied to determine the quantitative effects of these filters and of multiharmonic Fourier filtering (MHFF). The filtered MHFF data, with or without preprocessing, were compared with a 3 D or 2 D filter to the raw data using a chi 2 distribution. The MHFF (two or three harmonics) procedure applied to the raw data of patients without any preprocessing produced the smallest chi 2 value, thus demonstrating the very close relationship between filtered images and raw data. Preprocessing the raw data by the median filter also preserved the signal when two or three harmonics were applied, whereas the 3 D and 2 D (9 x 9) filters did not. The phantom study also demonstrated that MHFF preserved the signal better than any other preprocessing. The median filter introduced a smaller distortion than the 2 D (9 x 9) and 3 D filters. It is concluded that MHFF applied with two or three harmonics on the raw data or after preprocessing by a median (3 x 3) filter is the most successful way of preserving the real signal. It is believed that the other filters should be avoided. The clinical advantage of MHFF processing is to provide both very accurate filtering and parametric images.     

Frequency distribution,variance, and moving average of left ventricular rhythm and contractility during atrial fibrillation in dog

Mean levels of left ventricular rhythm and contractility averaged over arrhythmic beats would characterize the average cardiac performance during atrial fibrillation (AF). However, no consensus exists on the minimal number of beats for their reliable mean values. We analyzed their basic statistics to find out such a minimal beat number in canine hearts. We produced AF by electrically stimulating the atrium and measured left ventricular arrhythmic beat interval (RR) and peak isovolumic pressure (LVP). From these, we calculated instantaneous heart rate (HR = 60,000/RR), contractility (E(max) = LVP/isovolumic volume above unstressed volume), and beat interval ratio (RR1/RR2). We found that all their frequency distributions during AF were variably nonnormal with skewness and kurtosis. Their means +/- standard deviations alone cannot represent their nonnormal distributions. A 90% reduction of variances of E(max) and RR1/RR2 required a moving average of 15 and 24, respectively, arrhythmic beats on the average, whereas that of RR and HR required 60 beats on the average. These results indicate that a statistical characterization of arrhythmic cardiodynamic variables facilitates better understanding of cardiac performance during AF.     

心电信号高频分量的提取方法研究

研究了高频心电信号的提取方法,提出了基于空间向量环模板匹配的信号平均方法,改善了信号平均的基准估计,并确保参与信号叠加的心电信号的心源同一性,使得各导联至少都具有相同的等效低通滤波器效应,试验数据分析证实了上述结果。     

An ischemia detection method based on artificial neural networks

An automated technique was developed for the detection of ischemic episodes in long duration electrocardiographic (ECG) recordings that employs an artificial neural network. In order to train the network for beat classification, a cardiac beat dataset was constructed based on recordings from the European Society of Cardiology (ESC) ST-T database. The network was trained using a Bayesian regularisation method. The raw ECG signal containing the ST segment and the T wave of each beat were the inputs to the beat classification system and the output was the classification of the beat. The input to the network was produced through a principal component analysis (PCA) to achieve dimensionality reduction. The network performance in beat classification was tested on the cardiac beat database providing 90% sensitivity (Se) and 90% specificity (Sp). The neural beat classifier is integrated in a four-stage procedure for ischemic episode detection. The whole system was evaluated on the ESC ST-T database. When aggregate gross statistics was used the Se was 90% and the positive predictive accuracy (PPA) 89%. When aggregate average statistics was used the Se became 86% and the PPA 87%. These results are better than other reported.     

基于心电和脉搏波数据融合的呼吸率估计

本研究提出了一种通过心电和脉搏波提取呼吸信号并基于卡尔曼滤波的多路数据融合估计呼吸率的算法。算法分别从心电的RR间期、R波的绝对高度和脉搏波搏动周期中提取呼吸信号,利用AR模型估计呼吸率,根据信号波形、节律和频谱特征获得反映信号质量高低的质量指数,然后基于信号质量指数和卡尔曼滤波残差进行数据融合,获得融合呼吸率。14名志愿者参加了实验。结果表明,融合呼吸率比单独从心电或脉搏信号提取的呼吸率更好地反映了呼吸率的变化。与压阻式呼吸传感器提供的参考呼吸率相比,融合呼吸率误差为(-0.03±2.78)次/min,而从心电RR间期、R波的绝对高度和脉搏法提取的呼吸率的误差分别为(0.62±3.30)、(0.42±3.47)和(-0.17±2.69)次/min。总体认为,基于多路数据融合的方法可以有效避免干扰的影响,较准确地估计呼吸率。     

A nonlinear trimmed moving averaging-based system with its application to real-time QRS beat classification

In this paper, a real-time QRS beat classification system based on a nonlinear trimmed moving average filter is presented. This nonlinear system aims to identify abnormal beats of ventricular origin. The proposed beat classifier is designed to work in parallel with a real-time QRS detector, allowing the task of beat diagnosis to be performed immediately after a QRS complex is detected. Algorithm performance was evaluated against the ECG recordings drawn from the MIT-BIH arrhythmia database. Numerical results demonstrated that a beat classification rate of over 99.5% can be achieved by the algorithm.     

Application of the matching pursuit method for structural decomposition and averaging of phonocardiographic signals

The paper evaluates the performance of an automatic adaptive time-frequency method to detect each cardiac cycle of a phonocardiogram (PCG) and extract average heart sounds and PCG cycles. The proposed method combines a global search of the PCG, in terms of the energy distribution of the most important components, with a local search relating to the specific events found within a cardiac cycle. The method is applied to 100 PCG recordings from 50 patients with an aortic bioprosthetic valve. The performance of the proposed method is compared with a commonly used semi-automatic method that is based on the combined analysis of an electrocardiogram (ECG) and the PCG signal. Results show that the proposed method clearly outperforms the semiautomatic method, especially in the case of patients with malfunctioning bioprostheses. By eliminating the need to record an ECG as the time-reference signal, this method reduces hardware overheads when analysis of PCG signals is the primary aim. It is also independent of subjective human judgment for selection of reference templates and threshold levels. Furthermore, the method is robust to artefacts, background noise and other kinds of signal interferences. With minor modifications, the procedure described could be applied to other types of biomedical signal in order to extract coherent transient components and identify specific events.     

Reduction of heart sound interference from lung sound signals using empirical mode decomposition technique

During the recording time of lung sound (LS) signals from the chest wall of a subject, there is always heart sound (HS) signal interfering with it. This obscures the features of lung sound signals and creates confusion on pathological states, if any, of the lungs. A novel method based on empirical mode decomposition (EMD) technique is proposed in this paper for reducing the undesired heart sound interference from the desired lung sound signals. In this, the mixed signal is split into several components. Some of these components contain larger proportions of interfering signals like heart sound, environmental noise etc. and are filtered out. Experiments have been conducted on simulated and real-time recorded mixed signals of heart sound and lung sound. The proposed method is found to be superior in terms of time domain, frequency domain, and time–frequency domain representations and also in listening test performed by pulmonologist.     

Electrocardiogram beat detection enhancement using Independent Component Analysis

Beat detection is a basic and fundamental step in electrocardiogram (ECG) processing. In many ECG applications strong artifacts from biological or technical sources could appear and cause distortion of ECG signals. Beat detection algorithm desired property is to avoid these distortions and detect beats in any situation. Our developed method is an extension of Christov's beat detection algorithm, which detects beat using combined adaptive threshold on transformed ECG signal (complex lead). Our offline extension adds estimation of independent components of measured signal into the transformation of ECG creating a signal called complex component, which enhances ECG activity and enables beat detection in presence of strong noises. This makes the beat detection algorithm much more robust in cases of unpredictable noise appearances, typical for holter ECGs and telemedicine applications of ECG. We compared our algorithm with the performance of our implementation of the Christov's and Hamilton's beat detection algorithm.     

一种用于心室晚电位逐搏检测的时序自适应滤波器

本文介绍了一种用于心室晚电位逐搏检测的时序自适应滤波器,目的：用时序自适应滤波器取代心电信号的叠加平均来提高心室晚电位信号的信噪比,以便比进行心室晚晚位的逐搏检测,方法：先从理论上论证时序自适应滤波器的工作原理,然后通过实验来证实自适应滤波器能提高心室晚电位信号的信噪经,结果：时序自适应滤波器能有效地降低信号民中的随机噪声,滤波后心室晚电位信号得到了相对增强,结论：时序自应滤波器能有效提高心室晚电位信号的信噪比,可用于心室晚电位的逐搏检测。     

Rapid measurement of somatosensory evoked potential response to cerebral artery occlusion

The aim of the paper is to determine the speed of the neurological response to cerebral artery occlusion by monitoring transient changes in somatosensory evoked potentials (SEPs). SEPs, continuously monitored during temporary clipping of the middle cerebral artery (MCA) in anaesthetised cats, are analysed. The SEP signals are modelled by a quasi-periodic Fourier series, the coefficients of which are estimated with the aid of two adaptive least squares estimation algorithms. The energy levels at various harmonics throughout the protocol are obtained directly from the filter weights. Noise covariance is estimated from pre-stimulus recording, and the adaptation rate of the algorithm is adjusted sweep-by-sweep to accomodate transient changes in the pre-stimulus noise level. After the occlusion, a significant decrease (p<0·05) in SEP amplitude is observed. The change in latency is not statistically significant (p≅0·5). The spectral trends show a sudden decline in energy at all harmonics immediately following occlusion, although when the amplifier bandwidth is changed to 5–1500 Hz (from an initial setting of 30–1500 Hz), the fundamental frequency component of the SEP signal shows the greatest responsiveness to injury. The average time constant of the decline in amplitude resulting from MCA occlusion is only 10·6±4·0 s. It is concluded that rapid detection of cerebral artery occlusion and ischaemia may be feasible by continuously monitoring SEP signals and analysing transient changes in time and frequency domains.     

Removing ECG noise from surface EMG signals using adaptive filtering

Surface electromyograms (EMGs) are valuable in the pathophysiological study and clinical treatment for dystonia. These recordings are critically often contaminated by cardiac artefact. Our objective of this study was to evaluate the performance of an adaptive noise cancellation filter in removing electrocardiogram (ECG) interference from surface EMGs recorded from the trapezius muscles of patients with cervical dystonia. Performance of the proposed recursive-least-square adaptive filter was first quantified by coherence and signal-to-noise ratio measures in simulated noisy EMG signals. The influence of parameters such as the signal-to-noise ratio, forgetting factor, filter order and regularisation factor were assessed. Fast convergence of the recursive-least-square algorithm enabled the filter to track complex dystonic EMGs and effectively remove ECG noise. This adaptive filter procedure proved a reliable and efficient tool to remove ECG artefact from surface EMGs with mixed and varied patterns of transient, short and long lasting dystonic contractions.     

Adaptive cancellation of the respiratory artifact in surface recording of small intestinal electrical activity

In the human small intestine there is omnipresent electrical activity with a frequency of 0.15–0.2 Hz. The electrical activity of the small intestine can be measured by surface electrodes placed on the abdominal skin. The most annoying problem in the surface electrical recording is the respiratory artifact which is not discernible from the small intestinal signal. The frequency of the respiration is about 0.2–0.4 Hz, which is very close to that of small intestinal activity, making the use of the conventional bandpass filtering impractical. In this paper a selective frequency domain adaptive filter was proposed for the cancellation of the respiratory artifact. The basic principle of the selective frequency domain adaptive filter is that only selected filter weights are adapted based on the frequency characteristics of the respiratory artifact. Therefore, a substantial reduction of computation is achieved. A series of computer simulations was conducted for the optimization of the system parameters and for the investigation of the system performace. It was demonstrated in this paper that the selective frequency domain adaptive filter is as effective as, but more efficient than, the conventional frequency domain adaptive filter. The adaptive system for the cancellation of the respiratory artifact based on the selective frequency domain adaptive filter is very efficient in computation, has a fast convergence (about 100 adaptations), substantial reduction of the respiratory artifact and little effect (or distortion) on the small intestinal electrical signal.     

On-line respiratory artefact removal via adaptive FIR filters in rheopneumographic measurement

Impedance rheopneumography is a simple non-invasive technique that can reflect the vascular condition in the human pulmonary circulatory system. However, the much larger and almost in-banding respiratory artefact present has greatly restricted its usefulness to only respiratory patients of a less severe type, in view of the existing practice of requiring the subject to stop breathing momentarily during measurement. Conventional fixed or adaptive filtering cannot satisfactorily remove the artefact in view of the non-time stationary characteristic of the latter. In the paper, a fast adaptive FIR filter design method, which is based on the filter coefficient look-up table (CLT) concept, is presented as a solution. The CLT is constructed with the coefficients indexed to the cut-off frequency to separate the two components of the plethysmogram. An on-line fast Fourier transform is calculated to track the cut-off frequency. This filter can adaptively change its coefficients, not only for different subjects, but also for the same subject during long-term monitoring. Results show that this filter design is capable of providing an almost respiratory artefact-free signal for a majority of patients. The high speed of implementation also renders it a possibility for real-time monitoring applications.     

Recursive running DCT algorithm and its application in adaptive filtering of surface electrical recording of small intestine

A rhythmic electrical signal is present in the human small intestine and can be recorded using surface electrodes. Surface electrical recordings of the small intestine contain severe interference that obscures the electrical signal of the small intestine. An adaptive system is proposed in the paper for the enhancement of the small-intestinal signal. To obtain better performance, adaptive signal enhancement is performed in the transform domain using a discrete cosine transform (DCT). A fast recursive algorithm is developed for the calculation of running DCT. The computational complexity of the proposed recursive algorithm is only 2/N (N=length of the adaptive filter) of the direct calculation of the running DCT. A series of simulations are conducted to investigate the performance of the proposed transform-domain adaptive filtering using DCT in comparison with time-domain adaptive filtering and with transform-domain adaptive filtering using a discrete Fourier transform (DFT). The parameters of the proposed adaptive system are optimised, and their effects on system performance are investigated. The application of the proposed method for the enhancement of the small-intestinal signal is presented and discussed.