Multichannel ECG Signals Research Articles

Tensor decomposition-based compression and noise reduction of multichannel ECG signals

Tensor decomposition-based compression and noise reduction of multichannel ECG signals

Multi-lead ECG signal analysis using RBFNN-MSO algorithm

In this paper, we present a method for electrocardiogram beat based on multi swarm optimization and radial basis function neural network. ECG is a non-surgical method for measuring and recording the electrical activity of the heart and on many occasions, an experienced cardiologist may not be available on the patient’s site. Therefore, a type of automated ECG analysis is required for the patient to take the electrocardiogram by a general practitioner or paramedical team attending the patient’s location. There is a need for automated ECG analysis. Finally, this paper gives the best analysis methodology for the automated analysis of multi-channel ECG signals. Diagnosis may be affected by the presence of artifacts and noise in multi-channel ECG signals. Some researchers calculated dynamic cutoff frequency parameter from noisy ECG signals to remove noise using the neural network method of Radial Basis function with particle swarm improvement method (RBFNN-PSO). But PSO only has Swarm and it takes a lot of time to give a response. To overcome these limitations, an improved version of the RBFNN-PSO algorithm called Radial Basis Function Neural Network with Multi Swarm Optimization (RBFNN-MSO) has been proposed. Finally, the cutoff frequency parameter is determined by the RBFNN-MSO methodology that is applied to digital low-frequency filters for impulse response (FIR). The next step after removing noise from multi-channel ECG signals is the feature extraction and reduction process. 24 features of the patient’s multi-channel ECG signals are extracted. The next part of the research is divided into two steps. The first step is whether or not the patient’s ECG signals are affected. The vector machine is supported with particle swarm improvement (SVM-PSO) and another way is to support the vector machine with multi swarm improvement (SVM-MSO) to detect ECG signals of the affected patient or not. Finally, SVM-MSO offers greater accuracy compared to SVM-PSO. When compared to all other existing architecture results, they used the rating with 86% of all the test accuracy. But in this paper, our proposed work has 90% overall in different situations. In another point of view also,our proposed work has proven that average accuracy is over 85% even then train data set is small.

Exploiting multi-scale signal information in joint compressed sensing recovery of multi-channel ECG signals

Abstract Computational complexity and power consumption are prominent issues in wireless telemonitoring applications involving physiological signals. Compressed sensing (CS) has emerged as a promising framework to address these challenges because of its energy-efficient data reduction procedure. In this work, a CS-based approach is studied for joint compression/reconstruction of multi-channel electrocardiogram (MECG) signals. The MECG signals share spatially correlated cardiac information across the channels, which is exploited in a joint CS framework for improved signal recovery. Weighted mixed-norm minimization (WMNM)-based joint sparse recovery algorithms are proposed, which can successfully recover the signals from all the channels simultaneously by utilizing the joint sparsity of MECG signals in wavelet domain. The proposed algorithms exploit multi-scale signal information through a multi-scale weighting approach. Under this strategy, weights are designed based on the diagnostic information contents of each wavelet subband/scale. In particular, clinically relevant information is captured in the form of subband energy, entropy, and amplitude decay, and based on this weighting rules are defined at each wavelet scale. Such a weighting approach emphasizes nonzero wavelet coefficients having high diagnostic importance during joint CS reconstruction. Insignificant coefficients are deemphasized simultaneously, resulting in a sparser solution. Simulation results using Physikalisch-Technische Bundesanstalt (PTB), Common Standards for Electrocardiography (CSE) and Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) MECG databases show that the proposed methods can achieve superior reconstruction quality with a lower number of measurements compared to their non-weighted counterpart and other existing CS-based methods. Reduction in the required number of measurements reduces computational burden and directly translates into higher compression efficiency, resulting in low energy consumption in CS-based telemonitoring systems. The diagnostic reconstruction quality of the algorithm is validated using diagnostic distortion measures like wavelet energy-based diagnostic distortion (WEDD) and with a post-reconstruction classification task. It achieves a classification accuracy of 73.2% even when MECG signals are jointly reconstructed using only about 10% of compressed measurements, validating the diagnosability of the reconstructed signals even at very low data rates.

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.

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.

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.

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.

Multiscale PCA based Quality Controlled Denoising ofMultichannel ECG Signals

In this article, it is suggested to apply Multiscale PCA to multichannel ECG signals, for quality controlled denoising. PCA is applied at wavelet scales after forming multivariate data matrices. The quality controlled denoising is a two steps process, (a) selection of multivariate matrices at wavelet scale for PCA based dimension reduction and (b) the choice of number of Principal Component (PC) at each wavelet matrices. The selection of effective PCs is normally based on the cumulative percentage of total variation shown by eigenvalues. For conventional PCA, it is taken 70% to 99% [3]. The diagnostic quality of denoised signal is qualitatively compared with the original signal. The quantitative distortion measures like PRD and WEDD are evaluated [10], [12], [13]. The enhanced multichannel signals contain all the clinical components with required 'PQRST' morphologies of ECG signals while reducing the noise.

Detection of Fetal Heart Rate Through 3-D Phase Space Analysis From Multivariate Abdominal Recordings

A novel three-stage methodology for the detection of fetal heart rate (fHR) from multivariate abdominal ECG recordings is introduced. In the first stage, the maternal R-peaks and fiducial points (maternal QRS onset and offset) are detected, using band-pass filtering and phase space analysis. The maternal fiducial points are used to eliminate the maternal QRS complexes from the abdominal ECG recordings. In the second stage, two denoising procedures are applied to enhance the fetal QRS complexes. The phase space characteristics are employed to identify fetal heart beats not overlapping with the maternal QRSs, which are eliminated in the first stage. The extraction of the fHR is accomplished in the third stage, using a histogram-based technique in order to identify the location of the fetal heart beats that overlap with the maternal QRSs. The methodology is evaluated on simulated multichannel ECG signals, generated by a recently proposed model with various SNRs, and on real signals, recorded from pregnant women in various weeks during gestation. In both cases, the obtained results indicate high performance; in the simulated ECGs, the accuracy ranges from 72.78% to 98.61%, depending on the employed SNR, while in the real recordings, the average accuracy is 95.45%. The proposed methodology is advantageous since it copes with the existence of noise from various sources while it is applicable in multichannel abdominal recordings.

Predicting the QRS complex and detecting small changes using principal component analysis

In this paper, a new method for QRS complex analysis and estimation based on principal component analysis (PCA) and polynomial fitting techniques is presented. Multi-channel ECG signals were recorded and QRS complexes were obtained from every channel and aligned perfectly in matrices. For every channel, the covariance matrix was calculated from the QRS complex data matrix of many heartbeats. Then the corresponding eigenvectors and eigenvalues were calculated and reconstruction parameter vectors were computed by expansion of every beat in terms of the principal eigenvectors. These parameter vectors show short-term fluctuations that have to be discriminated from abrupt changes or long-term trends that might indicate diseases. For this purpose, first-order poly-fit methods were applied to the elements of the reconstruction parameter vectors. In healthy volunteers, subsequent QRS complexes were estimated by calculating the corresponding reconstruction parameter vectors derived from these functions. The similarity, absolute error and RMS error between the original and predicted QRS complexes were measured. Based on this work, thresholds can be defined for changes in the parameter vectors that indicate diseases.

Classifying multichannel ECG patterns with an adaptive neural network

In this article the authors describe the application of a new artificial neural network model aimed at the morphological classification of heartbeats detected on a multichannel ECG signal. They emphasize the special characteristics of the algorithm as an adaptive classifier with the capacity to dynamically self-organize its response to the characteristics of the ECG input signal. They also present evaluation results based on traces from the MIT-BIH arrhythmia database.