Detection of different recumbent body positions from the electrocardiogram

Changes in body position alter the relative angle between ECG electrodes and the mean electric axis of the heart. These changes influence the time interval during which the projection of the electric dipole, on any ECG lead, is positive (R-wave). In this study, measurements of R-wave duration (RWD) were used to identify changes in body position, and two of its uncorrelated features were used to classify each heartbeat into four basic groups relating to four body positions (supine, prone, left-side, right-side). Data were acquired from healthy volunteers during controlled condition experiments that included well-defined sequences of body positions and simultaneous recordings of ECG leads I, II and III. Results showed over 90% correct identifications ofbody position changes when using any of the three leads. Lead II had the best performance for theclassification of body position and correctly classified 80% of heartbeats. Classification did not improve for a combination of two leads. The technique can be used to reveal additional important clinical information and can be easily implemented, in a variety of applications where ECG is recorded, such as sleep studies, Holter recordings and ischaemia detection.     

Artificial neural network-based classification of body movements in ambulatory ECG signal

Abstract

Ambulatory ECG monitoring provides electrical activity of the heart when a person is involved in doing normal routine activities. Thus, the recorded ECG signal consists of cardiac signal along with motion artifacts introduced due to a person’s body movements during routine activities. Detection of motion artifacts due to different physical activities might help in further cardiac diagnosis. Ambulatory ECG signal analysis for detection of various motion artifacts using adaptive filtering approach is addressed in this paper. We have used BIOPAC MP 36 system for acquiring ECG signal. The ECG signals of five healthy subjects (aged between 22–30 years) were recorded while the person performed various body movements like up and down movement of the left hand, up and down movement of the right hand, waist twisting movement while standing and change from sitting down on a chair to standing up movement in lead I configuration. An adaptive filter-based approach has been used to extract the motion artifact component from the ambulatory ECG signal. The features of motion artifact signal, extracted using Gabor transform, have been used to train the artificial neural network (ANN) for classifying body movements.     

基于神经网络的波型检测方法

介绍了一种基于神经网络白化匹配滤波器的ＱＲＳ波检测方法。我们用神经网络白化匹配滤波器来处理ＥＣＧ信号的低频成分,模拟其非线性及非稳态的特性。处理后的信号中含有ＥＣＧ中大部分高频成分,让其通过一线性匹配滤波器来检测ＱＲＳ波及其位置。对于大噪声的ＥＣＧ信号,在匹配滤波器后加差分滤波,取平方及滑动平均等处理,提高检测正确率。使用这种方法我们对ＭＩＴ／ＢＩＨ心电信号数据库中噪声比较大的１０５号数据进行的处     

Detection of QRS complexes in electrocardiogram using support vector machine

This paper presents the application of a support vector machine (SVM) for the detection of QRS complexes in the electrocardiogram (ECG). The ECG signal is filtered using digital filtering techniques to remove noise and baseline wander. The support vector machine is used as a classifier to delineate QRS and non-QRS regions. Two different algorithms are presented for the detection of QRS complexes. The first uses a single-lead ECG at a time for the detection of QRS complexes, while the second uses 12-lead simultaneously recorded ECG. Both algorithms have been tested on the standard CSE ECG database. A detection rate of 99.3% is achieved when tested using a single-lead ECG. This improves to 99.75% for the simultaneously recorded 12-lead ECG signal. The percentage of false negative detection is 0.7% and the percentage of false positive detection is 12.4% in the single-lead QRS detection and it reduces to 0.26% and 1.61% respectively for QRS detection in simultaneously recorded 12-lead ECG signals. The performance of the algorithms depends strongly on the selection and the variety of the ECGs included in the training set, data representation and the mathematical basis of the classifier.     

Detection of QRS complexes in electrocardiogram using support vector machine

This paper presents the application of a support vector machine (SVM) for the detection of QRS complexes in the electrocardiogram (ECG). The ECG signal is filtered using digital filtering techniques to remove noise and baseline wander. The support vector machine is used as a classifier to delineate QRS and non-QRS regions. Two different algorithms are presented for the detection of QRS complexes. The first uses a single-lead ECG at a time for the detection of QRS complexes, while the second uses 12-lead simultaneously recorded ECG. Both algorithms have been tested on the standard CSE ECG database. A detection rate of 99.3% is achieved when tested using a single-lead ECG. This improves to 99.75% for the simultaneously recorded 12-lead ECG signal. The percentage of false negative detection is 0.7% and the percentage of false positive detection is 12.4% in the single-lead QRS detection and it reduces to 0.26% and 1.61% respectively for QRS detection in simultaneously recorded 12-lead ECG signals. The performance of the algorithms depends strongly on the selection and the variety of the ECGs included in the training set, data representation and the mathematical basis of the classifier.     

基于自适应学习的心律失常心拍分类方法

心律失常是因心脏疾病引起的心电活动中的异常症状,早期心室收缩（PVC）是由异位心跳引起的常见心律失常形式。通过心电图（ECG）信号检测PVC对于预测可能的心力衰竭具有重要意义。本文提出一种面向PVC心拍分类的心电信号分类算法,重点研究基于自适应学习的PVC异常心拍分类特征提取模型,通过计算心拍关联后验概率,结合领域专家标注信息训练分类器,提高整体分类效果。实验采用MIT-BIH心律失常数据库的ECG数据,研究结果表明所提方法针对非线性流形结构数据,能够有效提升小样本心拍自适应分类器的准确性。     

Elimination of electrocardiogram artifacts from electroencephalogram records by using the simultaneously recorded electrocardiograph data

We developed a simple method to eliminate electrocardiogram (ECG) artifacts from electroencephalogram (EEG) records by using simultaneously recorded ECG data. The raw EEG data, the real EEG data and the ECG data were regarded as multi-dimensional vectors Ea, Er and C, respectively. Also, the ECG data, with reduced amplitude whose coefficient was denoted as 'k', were assumed to be overlapped on the real EEG. These assumptions introduced the equations [Ea = Er + k.C], [Er.C = 0] and finally [k = Ea. C/C.C]. This calculation method was implemented by a Macintosh computer using data exported from digital EEG recordings (sampled at 200 Hz with 16-bit resolution). In several subjects, sampling intervals of 5 or 10 seconds for calculation succeeded in eliminating ECG artifacts. However, regardless of the sampling interval, this elimination condition was not always efficient in several other subjects, including a brain-dead patient. It was suggested that the ECG data used were insufficient for the calculation, because only one hand-to-hand reference was used for simultaneous recording, as usual. This one ECG reference was able to express only one ECG projection. Then two other hand-to-foot references of ECG were added to the recordings, and the elimination procedure was performed using all of the simultaneously recorded ECG data at the three references. Consequently, elimination was much improved in most subjects, including the brain-dead patient. Our method may be useful for eliminating ECG artifacts without changing reference electrodes.     

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.     

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.     

Quantitative measures of cluster quality for use in extracellular recordings

While the use of multi-channel electrodes (stereotrodes and tetrodes) has allowed for the simultaneous recording and identification of many neurons, quantitative measures of the quality of neurons in such recordings are lacking. In multi-channel recordings, each spike waveform is discriminated in a high-dimensional space, making traditional measures of unit quality inapplicable. We describe two measures of unit isolation quality, Lratio and Isolation Distance, and evaluate their performance using simulations and tetrode recordings. Both measures quantified how well separated the spikes of one cluster (putative neuron) were from other spikes recorded simultaneously on the same multi-channel electrode. In simulations and tetrode recordings, both Lratio and Isolation Distance discriminated well- and poorly-separated clusters. In data sets from the rodent hippocampus in which neurons were simultaneously recorded intracellularly and extracellularly, values of Isolation Distance and Lratio were related to the correct identification of spikes.     

The importance of assessing the fidelity of electrocardiographs by using a simple calibration signal

ECG distortion, mimicking changes seen in acute myocardial infarction, occurred in traces transmitted by the South East Coast NHS Ambulance Trust. This was due to low frequency phase nonlinearity produced by baseline filtering. The very narrow calibration pulses made the detection of this distortion difficult. A 1 mV square wave signal revealed the cause. Adequate calibration pulses 0.3 s wide should be recorded with all ECGs, to show that the signal is being reproduced reliably.     

The importance of assessing the fidelity of electrocardiographs by using a simple calibration signal

ECG distortion, mimicking changes seen in acute myocardial infarction, occurred in traces transmitted by the South East Coast NHS Ambulance Trust. This was due to low frequency phase nonlinearity produced by baseline filtering. The very narrow calibration pulses made the detection of this distortion difficult. A 1 mV square wave signal revealed the cause. Adequate calibration pulses 0.3 s wide should be recorded with all ECGs, to show that the signal is being reproduced reliably.     

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.     

Accuracy of heartbeat perception is reflected in the amplitude of the heartbeat-evoked brain potential

Neurotransmission from the heart to the brain results in a heartbeat-evoked potential (HEP). In this study, the influence of the ability to detect one's heartbeats based on the HEP was examined. According to their results in a heartbeat perception task, subjects were classified as good (n=18) or poor (n=26) heartbeat perceivers. EEG, EOG, and ECG were recorded while participants attended to their heartbeats. The R-wave of the ECG served as a trigger for EEG averaging. In the latency range of 250-350 ms after the ECG R-wave, the HEP amplitude at the right central location was significantly higher in good heartbeat perceivers. A significantly positive correlation was observed between the heartbeat perception score and the mean HEP amplitude. Our results confirm that the accuracy of heartbeat perception is reflected in the amplitude of the HEP. Thus, the HEP may be a suitable research tool for the study of brain processes related to visceral perception.     

Noise and baseline wandering suppression of ECG signals by morphological filter

Electrocardiogram (ECG) signals describe the electrical activity of the heart, and are universally by physicists in the diagnosis of cardiac pathologies. However, during the acquisition of ECGs they are often contaminated with different sources of noise, making interpretation difficult. Different techniques have been used to filter the ECG signal, in order to optimize the signal to noise ratio (S/N). In this paper, an approach based on morphological filtering is developed in order to filter the ECG. Morphological filtering is concerned with the detection of the ECG morphology, therefore allowing the suppression of noises and particularly baseline wandering. The implemented filter is evaluated using signals taken from the MIT-BIH ECG universal database. The results show that the performance of this filter is good compared with other filtering techniques.     

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.     

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.     

Effects of data limitations on heartbeat detection in the method of constant stimuli

In the method of constant stimuli applied to measuring heartbeat detection, subjects judge the simultaneity of heartbeats and exteroceptive comparison stimuli presented at various intervals after the R-wave (0, 100, 200, 300, 400, and 500 ms). Using versions of this procedure, investigators have found that between 20% (Yates, Jones, Marie, & Hogben, 1985) and 54% (Brener, Liu, & Ring, 1993) of subjects can detect heartbeat sensations. Whereas Yates et al. used a single comparison stimulus on each trial and Brener et al. used 10, the present study examined whether this disparity in heartbeat detection performance could be attributed to the number of comparison stimuli presented on each trial. In each of 360 trials, 30 subjects judged the simultaneity of heartbeat sensations and tones following 1, 5, or 10 comparison stimulus presentations. Significantly fewer subjects met the criterion for heartbeat detection with I tone presentation (13%) than with either 5 (43%) or 10 (47%) tone presentations. It is concluded that a single stimulus presentation imposes data limitations that result in underestimation of the accuracy of heartbeat detection. The presentation of at least 5 stimuli in each trial alleviates this limitation.