Multichannel ECG Research Articles

Recognition and eradication of prime artefact from the abdominal electrocardiogram

This paper proffers an algorithm that recognises and eradicates Maternal Electrocardiogram (MECG), the supreme dominant artefact from the multichannel abdominal Electrocardiogram to diagnose the presence of foetal heart deformities. The proffered technique has three stages; initial pre-processing stage computes averaged Abdominal Electrocardiogram (AECG) from multichannel AECG realisations. At the second stage Smoothed Pseudo Wigner-Ville Distribution (SPWVD) time-frequency analysis has been performed for energy calculation which is intrinsic in the identification of maternal R peaks, fiducial points and maternal P, T waves. Last stage eradicates the entire maternal influence by subtracting the mean of subsequent identified maternal PQRST repetitions. This work is validated using multichannel abdominal Electrocardiogram acquired from the physionet non-invasive foetal ECG databank.

Tensor-based detection of T wave alternans using ECG.

T wave alternans is defined as changes in the T wave amplitude in an ABABAB-pattern. It can be found in ECG signals of patients with heart diseases and is a possible indicator to predict the risk on sudden cardiac death. Due to its low amplitude, robust automatic T wave alternans detection is a difficult task. We present a new method to detect T wave alternans in multichannel ECG signals. The use of tensors (multidimensional matrices) permits the combination of the information present in different channels, making detection more reliable. The possibility of decomposition of incomplete tensors is exploited to deal with noisy ECG segments. Using a sliding window of 128 heartbeats, a tensor is constructed of the T waves of all channels. Canonical Polyadic Decomposition is applied to this tensor and the resulting loading vectors are examined for information about the T wave behavior in three dimensions. T wave alternans is detected using a sign change counting method that is able to extract both the T wave alternans length and magnitude. When applying this novel method to a database of patients with multiple positive T wave alternans tests using the clinically available spectral method tests, both the length and the magnitude of the detected T wave alternans is larger for these subjects than for subjects in a control group.

PO-0461 Heart Rate Variability In Full-term Neonates With Hypoxic Ischaemic Encephalopathy

<h3>Background</h3> Hypoxic Ischaemic Encephalopathy (HIE) remains a significant cause of neonatal death and long term disability. Heart rate variability (HRV) may help identify the presence and severity of encephalopathy. Our aim was to analyse HRV features in full-term neonates with HIE and assess its ability to grade severity of HIE and predict neurodevelopmental outcome at 2-years of age. <h3>Methods</h3> This was a retrospective study of healthy full-term neonates and full-term neonates with HIE. All neonates had multichannel EEG and ECG monitoring from as soon as possible after birth. EEGs were graded at 12, 24, and 48 h (mild, moderate, severe) and 1 h epochs of EEG and ECG data were extracted. Features of HRV were calculated from ECG recordings in each epoch. A comparison of HRV features between HIE and healthy groups and within HIE groups (mild/moderate/severe) was performed. The ability of HRV features to predict neurodevelopmental outcome at 2-years of age was also assessed. <h3>Results</h3> 44 neonates with HIE and 17 healthy controls were included. Measures of HRV were significantly negatively correlated with EEG grade of HIE severity. HRV was significantly reduced between mild and moderate HIE groups. EEG grade of HIE measured at 12, 24, and 24 h after birth has a strong positive predictive value and reduced HRV at 24 and 48 h has a strong negative predictive value for 2 year neurodevelopmental outcome. <h3>Conclusion</h3> HRV features significantly correlate with the grade of HIE severity and may be useful for the prediction of long term outcome.

Simultaneous Greedy Analysis Pursuit for compressive sensing of multi-channel ECG signals.

This paper addresses compressive sensing for multi-channel ECG. Compared to the traditional sparse signal recovery approach which decomposes the signal into the product of a dictionary and a sparse vector, the recently developed cosparse approach exploits sparsity of the product of an analysis matrix and the original signal. We apply the cosparse Greedy Analysis Pursuit (GAP) algorithm for compressive sensing of ECG signals. Moreover, to reduce processing time, classical signal-channel GAP is generalized to the multi-channel GAP algorithm, which simultaneously reconstructs multiple signals with similar support. Numerical experiments show that the proposed method outperforms the classical sparse multi-channel greedy algorithms in terms of accuracy and the single-channel cosparse approach in terms of processing speed.

Multichannel Data Compression using Wavelet Subbands Arranging Technique

To reduce the amount of data and preserve necessary signal quality for multichannel data transmission in many applications such as meteorology, or telemedicine, a new technique called WSAT is presented. The proposed technique is designed to deal with the large amount of multichannel data for transmission, and real-time analysis. The proposed approach has exact control on the bit rate in order to achieve the required quality. For different applications, the proposed method is tested. For telemedicine, the method is employed on selected records from the MIT-BIH arrhythmia database. For meteorology, climate data from Nevada climate change database is utilized. From the obtained results, it is concluded that the proposed technique is an appropriate approach to simultaneously compress multichannel data with significant low compressed data rate at low error. As an example, APRD values for multichannel ECG compression is mostly less than 5% which is recommended by the American Heart Association for routine visual readings of compressed and reconstructed ECG signals.

NOVEL METHOD TO FIND THE PARAMETER FOR NOISE REMOVAL FROM MULTI-CHANNEL ECG WAVEFORMS

In general, electrocardiogram (ECG) waveforms are aby noise and artifacts and it is essential to remove the noise in order to support any decision making for specialist. It is v ery difficult to remove the noise from 12 channel E CG waveforms using standard noise removal methodologies. Removal of the noise f rom ECG waveforms is majorly classified into two ty pes in signal processing namely Digital filters and Analog filters. Digital filters are more accurate than analog filters becau se analog filters introduce nonlinear phase shift. Most advanced research digit al filters are FIR and IIR.FIR filters are stable a s they have non-recursive structure. They give the exact linear phase and eff iciently realizable in hardware. The filter respons e is finite duration. Thus noise removal using FIR digital filter is better option iwith IIR digital filter. But it is very difficult to find the cut-off frequency parameter for dynamic multi-channel ECG waveforms u sing existing traditional methods. So, in this research, newly introduced Multi-Swarm Optimization (MSO) methodology for automatically id entifying the cut-off frequency parameter of multichannel ECG waveforms f or low-pass filtering is inspecting. Generally, the spectrums of the ECG waveforms are extracted from four classes: normal srhythm, atria fibrillation, arrhythmia and sup raventricular. Baseline wander is removed using the Moving Median Filter. A datase t of the extracted features of the ECG spectrums is used to train the MSO. The performance of the MSO with various parameters is i nvestigated. Finally, the MSO-identified cut-off fr equency parameter, it’s applied to a Finite Impulse Response (FIR) filter. The resu lting signal is evaluated against the original clea n and conventional filtered ECG signal.

Localization of premature ventricular contraction foci in normal individuals based on multichannel electrocardiogram signals processing

A premature ventricular contraction (PVC) is relatively a common event where the heartbeat is initiated by the other pathway rather than by the Sinoatrial node, the normal heartbeat initiator. Determining PVC foci is important for ablation procedure and it can help in pre-procedural planning and potentially may improve ablation outcome.In this study, 12-lead Electrocardiogram (ECG) of 87 patients without structural cardiac diseases, who had experienced PVC, were obtained. Initially, PVC foci were labeled based on Electrophysiology study (EPS) reports. PVC beats were detected by wavelet method and their foci were classified using Mahalanobis distance and One-way ANOVA. Using morphological, frequency and spectrogram features, these foci in the heart were classified into five groups: Left Ventricular Outflow Tract (LVOT), Right Ventricular Outflow Tract (RVOT) septum, basal Right Ventricular (RV), RVOT free-wall, and Aortic Cusp (AC).The results showed that 88.4% of patients are classified correctly.Electronic supplementary materialThe online version of this article (doi:10.1186/2193-1801-2-486) contains supplementary material, which is available to authorized users.

Estimating the Universal Positions of Wireless Body Electrodes for Measuring Cardiac Electrical Activity

A methodology is presented for estimating the wireless body electrode (WE) positions and for calculating the linear transformations that enable the synthesis of a 12-lead ECG or a multichannel ECG from three WEs, which in turn simplifies and improves the acquisition of ECGs. We present, compare, and evaluate three approaches to the synthesis: fully personalized, fully universal, and combined with universal leads and personalized transformations. The evaluation results show that WEs are an acceptable alternative to the standard 12-lead ECG device for patients with chronic myocardial ischemia, if either the fully personalized or combined approach is used. The median correlation coefficients are all higher than 0.94 and 0.92 for the fully personalized and combined approaches, respectively. The corresponding kappa and percentual diagnostic agreements between the synthesized and target 12-lead ECGs are 0.88 (95%) and 0.83 (92%), respectively. The evaluation additionally shows that the personalization of the transformations has more impact on the quality of the synthesized ECGs than the personalization of the WEs' positions.

Automated Cardiac Beat Classification Using RBF Neural Networks

This paper proposes a four stage, denoising, feature extraction, optimization and classification method for detection of premature ventricular contractions. In the first stage, we investigate the application of wavelet denoising in noise reduction of multi-channel high resolution ECG signals. In this stage, the Stationary Wavelet Transform is used. Feature extraction module extracts ten ECG morphological features and one timing interval feature. Then a number of radial basis function (RBF) neural networks with different value of spread parameter are designed and compared their ability for classification of three different classes of ECG signals. Genetic Algorithm is used to find best value of RBF parameters. A classification accuracy of 100% for training dataset and 95.66% for testing dataset and an overall accuracy of detection of 95.83% were achieved over seven files from the MIT/BIH arrhythmia database.

Variation of Human Ventricular Iks Heterogeneities to Reconstruct Measured Multi-Channel ECG Data

Article Variation of Human Ventricular Iks Heterogeneities to Reconstruct Measured Multi-Channel ECG Data was published on September 7, 2013 in the journal Biomedical Engineering / Biomedizinische Technik (volume 58, issue SI-1-Track-N).

Probabilistic Source Separation for Robust Fetal Electrocardiography

Blind source separation (BSS) techniques are widely used to extract signals of interest from a mixture with other signals, such as extracting fetal electrocardiogram (ECG) signals from noninvasive recordings on the maternal abdomen. These BSS techniques, however, typically lack possibilities to incorporate any prior knowledge on the mixing of the source signals. Particularly for fetal ECG signals, knowledge on the mixing is available based on the origin and propagation properties of these signals. In this paper, a novel source separation method is developed that combines the strengths and accuracy of BSS techniques with the robustness of an underlying physiological model of the fetal ECG. The method is developed within a probabilistic framework and yields an iterative convergence of the separation matrix towards a maximum a posteriori estimation, where in each iteration the latest estimate of the separation matrix is corrected towards a tradeoff between the BSS technique and the physiological model. The method is evaluated by comparing its performance with that of FastICA on both simulated and real multichannel fetal ECG recordings, demonstrating that the developed method outperforms FastICA in extracting the fetal ECG source signals.

Improved excitation fidelity in cardiac imaging with 2-spoke parallel excitation at 7 Tesla

Background Cardiac MRI may greatly benefit from ultra high field (UHF) providing higher SNR and intrinsic contrast. But shorter RF wavelength yields transmit B1 (B1+) heterogeneity and contrast variations through the heart. These can be addressed by parallel transmission (pTX) using multispoke RF pulses as previously shown in other organs. However, applying pTX in cardiac MRI at 7T is challenging and requires rapid, multi-channel ECG triggered B1+ calibration and ECG synchronized, motion-insensitive pTX acquisitions. In this initial work we investigate the impact of 2-spoke RF pulse design on cardiac imaging at 7T using a 16-channel pTX system

Two Proximal Skin Electrodes — A Respiration Rate Body Sensor

We propose a new body sensor for extracting the respiration rate based on the amplitude changes in the body surface potential differences between two proximal body electrodes. The sensor could be designed as a plaster-like reusable unit that can be easily fixed onto the surface of the body. It could be equipped either with a sufficiently large memory for storing the measured data or with a low-power radio system that can transmit the measured data to a gateway for further processing. We explore the influence of the sensor's position on the quality of the extracted results using multi-channel ECG measurements and considering all the pairs of two neighboring electrodes as potential respiration-rate sensors. The analysis of the clinical measurements, which also include reference thermistor-based respiration signals, shows that the proposed approach is a viable option for monitoring the respiration frequency and for a rough classification of breathing types. The obtained results were evaluated on a wireless prototype of a respiration body sensor. We indicate the best positions for the respiration body sensor and prove that a single sensor for body surface potential difference on proximal skin electrodes can be used for combined measurements of respiratory and cardiac activities.

Multichannel ECG Data Compression Based on Multiscale Principal Component Analysis

In this paper, multiscale principal component analysis (MSPCA) is proposed for multichannel electrocardiogram (MECG) data compression. In wavelet domain, principal components analysis (PCA) of multiscale multivariate matrices of multichannel signals helps reduce dimension and remove redundant information present in signals. The selection of principal components (PCs) is based on average fractional energy contribution of eigenvalue in a data matrix. Multichannel compression is implemented using uniform quantizer and entropy coding of PCA coefficients. The compressed signal quality is evaluated quantitatively using percentage root mean square difference (PRD), and wavelet energy-based diagnostic distortion (WEDD) measures. Using dataset from CSE multilead measurement library, multichannel compression ratio of 5.98:1 is found with PRD value 2.09% and the lowest WEDD value of 4.19%. Based on, gold standard subjective quality measure, the lowest mean opinion score error value of 5.56% is found.

Artifact Reduction in Multichannel ECG Recordings Acquired with Textile Electrodes

Article Artifact Reduction in Multichannel ECG Recordings Acquired with Textile Electrodes was published on August 30, 2012 in the journal Biomedical Engineering / Biomedizinische Technik (volume 57, issue SI-1-Track-F).

Multi-Channel ECG Signals Compression Algorithm Using Simultaneous Orthogonal Matching Pursuit

Sparse decompression is a new theory for signal processing, having the advantage in that the base (dictionary) used in this theory is over-complete, and can reflect the nature of signa1. So the sparse decompression of signal can get sparse representation, which is very important in data compression. In this paper, a novel ECG compression method for multi-channel ECG signals was introduced based on the Simultaneous Orthogonal Matching Pursuit (S-OMP). The proposed method decomposes multi-channel ECG signals simultaneously into different linear expansions of the same atoms that are selected from a redundant dictionary, which is constructed by Hermite fuctions and Gobar functions in order to the best match the characteristic of the ECG waveform. Compression performance has been tested using a subset of multi-channel ECG records from the St.-Petersburg Institute of Cardiological Technics database, the results demonstrate that much less atoms are selected to present signals and the compression ratio of Multi-channel ECG can achieve better performance in comparison to Simultaneous Matching Pursuit (SMP).

Multi-Channel ECG Signals Compression Algorithm Using Simultaneous Orthogonal Matching Pursuit

Sparse decompression is a new theory for signal processing, having the advantage in that the base (dictionary) used in this theory is over-complete, and can reflect the nature of signa1. So the sparse decompression of signal can get sparse representation, which is very important in data compression. In this paper, a novel ECG compression method for multi-channel ECG signals was introduced based on the Simultaneous Orthogonal Matching Pursuit (S-OMP). The proposed method decomposes multi-channel ECG signals simultaneously into different linear expansions of the same atoms that are selected from a redundant dictionary, which is constructed by Hermite fuctions and Gobar functions in order to the best match the characteristic of the ECG waveform. Compression performance has been tested using a subset of multi-channel ECG records from the St.-Petersburg Institute of Cardiological Technics database, the results demonstrate that much less atoms are selected to present signals and the compression ratio of Multi-channel ECG can achieve better performance in comparison to Simultaneous Matching Pursuit (SMP).

Anomaly Detection using Multi-channel FLAC for Supporting Diagnosis of ECG

In this paper, we propose an approach for abnormality detection in multi-channel ECG signals. This system serves as front end to detect the irregular sections in ECG signals, where symptoms may be observed. Thereby, the doctor can focus on only the detected suspected symptom sections, ignoring the disease-free parts. Hence the workload of the inspection by the doctors is significantly reduced and the diagnosis efficiency can be sharply improved. For extracting the predominant characteristics of multi-channel ECG signals, we propose multi-channel Fourier local auto-correlations (m-FLAC) features on multi-channel complex spectrograms. The method characterizes the amplitude and phase information as well as temporal dynamics of the multi-channel ECG signal. At the anomaly detection stage, we employ complex subspace method for statistically modeling the normal (healthy) ECG patterns as in one-class learning. Then, we investigate the input ECG signals by measuring its deviation distance to the trained subspace. The ECG sections with disordered spectral distributions can be effectively discerned based on such distance metric. To validate the proposed approach, we conducted experiments on ECG dataset. The experimental results demonstrated the effectiveness of the proposed approach including promising performance and high efficiency, compared to conventional methods.

Influence of &lt;formula formulatype="inline"&gt;&lt;tex Notation="TeX"&gt;${I_{Ks}}$&lt;/tex&gt;&lt;/formula&gt; Heterogeneities on the Genesis of the T-wave: A Computational Evaluation

Despite the commonly accepted notion that action potential duration (APD) is distributed heterogeneously throughout the ventricles and that the associated dispersion of repolarization is mainly responsible for the shape of the T-wave, its concordance and exact morphology are still not completely understood. This paper evaluated the T-waves for different previously measured heterogeneous ion channel distributions. To this end, cardiac activation and repolarization was simulated on a high resolution and anisotropic biventricular model of a volunteer. From the same volunteer, multichannel ECG data were obtained. Resulting transmembrane voltage distributions for the previously measured heterogeneous ion channel expressions were used to calculate the ECG and the simulated T-wave was compared to the measured ECG for quantitative evaluation. Both exclusively transmural (TM) and exclusively apico-basal (AB) setups produced concordant T-waves, whereas interventricular (IV) heterogeneities led to notched T-wave morphologies. The best match with the measured T-wave was achieved for a purely AB setup with shorter apical APD and a mix of AB and TM heterogeneity with M-cells in midmyocardial position and shorter apical APD. Finally, we probed two configurations in which the APD was negatively correlated with the activation time. In one case, this meant that the repolarization directly followed the sequence of activation. Still, the associated T-waves were concordant albeit of low amplitude.

Synthesis of the 12-Lead Electrocardiogram From Differential Leads

A new approach is proposed for synthesizing the standard 12-lead ECG from three differential leads formed by pairs of proximal electrodes on the body surface. The method is supported by a statistical analysis that gives the best personalized positions of electrodes. The measurements from multichannel ECGs were used to calculate the differential leads. Our algorithm searches for optimal differential leads and the corresponding personalized transformation matrix that is used to synthesize the standard 12-lead ECG. The algorithm has been evaluated on 99 multichannel ECGs measured on 30 healthy subjects and 35 patients scheduled for elective cardiac surgery. It is shown that the algorithm significantly outperforms the synthesis based on the EASI lead system with medians of correlation coefficients greater than 0.954 for all 12 standard leads. To determine the optimal number of differential leads, the syntheses for two, three, and four differential leads were calculated. The analysis shows that 3 is the optimal number of differential leads for practical applications. Because of the proximity of the differential electrodes, the proposed approach offers an opportunity for the synthesis of a standard 12-lead ECG with wireless electrodes.