Optimized VCG signal compression using sparse PSO

Various techniques are used in diagnosing cardiac diseases. One of these techniques is using electrocardiogram (ECG) tool. In special cardiac cases like atrial fibrillation and posterior myocardial infraction the cardiologist need some information from posterior side of the patient heart, that it can be achieved by using right-posterior ECG method (17 lead ECG). In right-posterior method, position of the patient must be changed in his/her side, so time is waste and patient would be more tired because of taking ECG signals two times. In this study vectorcardiogram (VCG) signals are used as a tool for providing posterior information of the heart. However because for cardiologists is much easier to work with ECG signals for detecting some cardiac diseases, in this study a new method using partial linear transformation is introduced to get posterior ECG leads (V7, V8, V9) from VCG signal. VCG and ECG signals that were used in this study obtained from 30 healthy persons. We presented a statistical approach to transform 3-lead Frank VCG to 15-lead ECG signals and vice versa, based on partial linear transformation (Least Square Method). Also our linear transformation function would be compared with affine transformation functions. The recorder device was Cardiax digital recorder system. The results show that for healthy subjects, the partial linear transformation (least square method) that is presented in this paper maps 3-lead VCG to15-lead ECG, is more accurate than affine transformation function. Regarding the obtained results in this study, ECG signals that derived from VCG signals by using our method was more similar to measured ECG signals than ones derived by using affine transformation. Therefore, by using this transformation function achieving to posterior information of the case heart would be more accurate and useful.

BACKGROUND:Various techniques are used in diagnosing cardiac diseases. The electrocardiogram is one of these tools in common use. In this study vectorcardiogram (VCG) signals are used as a tool for detection of cardiac ischemia.METHODS:VCG signals used in this study were obtained form 60 patients suspected to have ischemia disease and 10 normal candidates. Verification of the ischemia had done by the cardiologist during strain test by the evaluation of electrocardiogram (ECG) records and patient's clinical history. The recorder device was Cardiax digital recorder system. The VCG signals were recorded in Frank lead configuration system.RESULTS:Extracted ischemia VCG signals have been configured with 22 features. Feature dimensionalities were reduced by the use of Independent Components Analysis and Principal Component Analysis tools. Results obtained from strain test indicated that among 60 subjects, 50 had negative results and 10 had positive results. Ischemia detection of neural network using VCG parameters indicates 86% accuracy. Classification result on neural network using ECG ischemia detection parameters is 73% accurate. Accumulative evaluation including VCG analysis and strain test indicates 90% consistency.CONCLUSIONS:Regarding the obtained results in this study, VCG has higher accuracy than ECG, so that in cases which ECG signal cannot provide certain diagnosis of existence or non-existence of ischemia, VCG signal can help in a wider range. We suggest the use of VCG as an auxiliary low cost tool in ischemia detection.

A new VCG signal compression technique based on discrete Karhunen-Loeve expansion and tunable quality wavelet transform.

BackgroundVectorcardiogram (VCG) signals monitor both spatial and temporal cardiac electrical activities along three orthogonal planes of the body. However, the absence of spatiotemporal resolution in conventional VCG representations is a major impediment for medical interpretation and clinical usage of VCG. This is especially so because time-domain features of 12-lead ECG, instead of both spatial and temporal characteristics of VCG, are widely used for the automatic assessment of cardiac pathological patterns.Materials and methodsWe present a novel representation approach that captures critical spatiotemporal heart dynamics by displaying the real time motion of VCG cardiac vectors in a 3D space. Such a dynamic display can also be realized with only one lead ECG signal (e.g., ambulatory ECG) through an alternative lag-reconstructed ECG representation from nonlinear dynamics principles. Furthermore, the trajectories are color coded with additional dynamical properties of space-time VCG signals, e.g., the curvature, speed, octant and phase angles to enhance the information visibility.ResultsIn this investigation, spatiotemporal VCG signal representation is used to characterize various spatiotemporal pathological patterns for healthy control (HC), myocardial infarction (MI), atrial fibrillation (AF) and bundle branch block (BBB). The proposed color coding scheme revealed that the spatial locations of the peak of T waves are in the Octant 6 for the majority (i.e., 74 out of 80) of healthy recordings in the PhysioNet PTB database. In contrast, the peak of T waves from 31.79% (117/368) of MI subjects are found to remain in Octant 6 and the rest (68.21%) spread over all other octants. The spatiotemporal VCG signal representation is shown to capture the same important heart characteristics as the 12-lead ECG plots and more.ConclusionsSpatiotemporal VCG signal representation is shown to facilitate the characterization of space-time cardiac pathological patterns and enhance the automatic assessment of cardiovascular diseases.

Mathematical modeling of cardiac electrical signals facilitates the simulation of realistic cardiac electrical behaviors, the evaluation of algorithms, and the characterization of underlying space-time patterns. However, there are practical issues pertinent to model efficacy, robustness, and generality. This paper presents a multiscale adaptive basis function modeling approach to characterize not only temporal but also spatial behaviors of vectorcardiogram (VCG) signals. Model parameters are adaptively estimated by the "best matching" projections of VCG characteristic waves onto a dictionary of nonlinear basis functions. The model performance is experimentally evaluated with respect to the number of basis functions, different types of basis function (i.e., Gaussian, Mexican hat, customized wavelet, and Hermitian wavelets), and various cardiac conditions, including 80 healthy controls and different myocardial infarctions (i.e., 89 inferior, 77 anterior-septal, 56 inferior-lateral, 47 anterior, and 43 anterior-lateral). Multiway analysis of variance shows that the basis function and the model complexity have significant effects on model performances while cardiac conditions are not significant. The customized wavelet is found to be an optimal basis function for the modeling of spacetime VCG signals. The comparison of QT intervals shows small relative errors (<;5%) between model representations and realworld VCG signals when the model complexity is greater than 10. The proposed model shows great potentials to model space-time cardiac pathological behaviors and can lead to potential benefits in feature extraction, data compression, algorithm evaluation, and disease prognostics.

Myocardial infarction (MI), also known as a heart attack, is a leading cause of mortality in the world. Spatial vectorcardiogram (VCG) signals are recorded on the body surface to monitor the underlying cardiac electrical activities in three orthogonal directions of the body, namely, frontal, transverse, and sagittal planes. The 3-D VCG vector loops provide a new way to study the cardiac dynamical behaviors, as opposed to the conventional time-delay reconstructed phase space from a single ECG trace. However, few, if any, previous approaches studied the relationships between cardiac disorders and recurrence patterns in VCG signals. This paper presents the recurrence quantification analysis (RQA) of VCG signals in multiple wavelet scales for the identification of cardiac disorders. The linear classification models using multiscale RQA features were shown to detect MI with an average sensitivity of 96.5% and an average specificity of 75% in the randomized classification experiments of PhysioNet Physikalisch-Technische Bundesanstalt database, which is comparable to the performance of human experts. This study is strongly indicative of potential automated MI classification algorithms for diagnostic and therapeutic purposes.

Myocardial infarction (MI) occurs due to the decrease in the blood flow into one part of the heart, and it further causes damage to the heart muscle. The 12-channel electrocardiogram (ECG) has been widely used to detect and localize MI pathology in clinical studies. The vectorcardiogram (VCG) is a 3-channel recording system used to measure the heart’s electrical activity in sagittal, transverse, and frontal planes. The VCG signals have advantages over the 12-channel ECG to localize posterior MI pathology. Detection and localization of MI using VCG signals are vital in clinical practice. This paper proposes a multi-channel multi-scale two-stage deep-learning-based approach to detect and localize MI using VCG signals. In the first stage, the multivariate variational mode decomposition (MVMD) decomposes the three-channel-based VCG signal beat into five components along each channel. The multi-channel multi-scale VCG tensor is formulated using the modes of each channel of VCG data, and it is used as the input to the deep convolutional neural network (CNN) to classify MI and normal sinus rhythm (NSR) classes. In the second stage, the multi-class deep CNN is used for the categorization of anterior MI (AMI), anterior-lateral MI (ALMI), anterior-septal MI (ASMI), inferior MI (IMI), inferior-lateral MI (ILMI), inferior-posterior-lateral (IPLMI) classes using MI detected multi-channel multi-scale VCG instances from the first stage. The proposed approach is developed using the VCG data obtained from a public database. The results reveal that the approach has obtained the accuracy, sensitivity, and specificity values of 99.58%, 99.18%, and 99.87%, respectively, for MI detection. Moreover, for MI localization, we have obtained the overall accuracy value of 99.86% in the second stage for our proposed network. The proposed approach has demonstrated superior classification performance compared to the existing VCG signal-based MI detection and localization techniques.

Heart diseases alter the rhythmic behaviors of cardiac electrical activity. Recent advances in sensing technology bring the ease to acquire space-time electrical activity of the heart such as vectorcardiogram (VCG) signals. Recurrence analysis of successive heartbeats is conducive to detect the disease-altered cardiac activities. However, conventional recurrence analysis is more concerned about homogeneous recurrences, and overlook heterogeneous types of recurrence variations in VCG signals (i.e., in terms of state properties and transition dynamics). This paper presents a new framework of heterogeneous recurrence analysis for the characterization and modeling of disease-altered spatiotemporal patterns in multi-channel cardiac signals. Experimental results show that the proposed approach yields an accuracy of 96.9%, a sensitivity of 95.0%, and a specificity of 98.7% for the identification of myocardial infarctions. The proposed method of heterogeneous recurrence analysis shows strong potential to be further extended for the analysis of other physiological signals such as electroencephalogram (EEG) and electromyography (EMG) signals towards medical decision making.

Cross-recurrence quantification analysis (CRQA), based on the cross-recurrence plot (CRP), is an effective method to characterize and quantify the nonlinear interrelationships between a pair of nonlinear time series. It allows the flexibility of reconstructing signals in the phase space and to identify different types of patterns at arbitrary positions between trajectories. These advantages make CRQA attractive for time series data mining tasks, which have been of recent interest in the literature. However, little has been done to exploit CRQA for pattern matching of multidimensional, especially spatiotemporal, physiological signals. In this paper, we present a novel methodology in which CRQA statistics serve as measures of dissimilarity between pairs of signals and are subsequently used to uncover clusters within the data. This methodology is evaluated on a real dataset consisting of 3D spatiotemporal vectorcardiogram (VCG) signals from healthy and diseased patients. Experimental results show that Lmax, the length of the longest diagonal line in the CRP, yields the best-performing clustering that almost exactly matches the ground truth diagnoses of patients. Results also show that our proposed measure, Rτ max, which characterizes the maximum similarity between signals over all pairwise time-delayed alignments, outperforms all other tested CRQA measures (in terms of matching the ground truth) when the VCG signals are rescaled to reduce the effects of signal amplitude.

Vectorcardiogram (VCG) signals contain a wealth of dynamic information pertinent to space-time cardiac electrical activities. However, few, if any, previous investigations have studied disease-altered nonlinear dynamics in the spatiotemporal VCG signals. Most previous nonlinear dynamic methods considered the time-delay reconstructed state space from a single ECG trace. This paper presents a novel multiscale recurrence approach to not only explore VCG recurrence dynamics but also resolve the issue of recurrence computation for the large-scale datasets. As opposed to the traditional single-scale recurrence analysis, we characterize and quantify the recurrence behaviours in multiple wavelet scales. In addition, wavelet dyadic subsampling enables the large-scale recurrence analysis, but it is used to be highly expensive for a long-term time series. The classification experiments show that multiscale recurrence analysis detects the myocardial infarctions from 3-lead VCG with an average sensitivity of 96.8% and specificity of 92.8%, which show superior performance (i.e., 5.6% improvements) to the single-scale recurrence analysis.

Computationally Efficient Cosine Modulated Filter Bank Design for ECG Signal Compression

Myocardial infarction is a coronary artery ailment, and it is characterized by the changes in the morphological features such as the shape of T-wave, Q-wave and ST-segment of ECG signal. In clinical standard, it is a challenging problem to diagnose MI pathology using 12-lead ECG and vectorcardiogram (VCG). VCG has the advantage to record the heart electrical activities in three orthogonal planes (frontal, sagittal and transverse). This paper proposes a new method for automated detection or grading of MI pathology from vectorcardiogram (VCG) signals. The method uses relevance vector machine (RVM) classifier and the multiscale features of VCG signal for MI detection. The multiscale analysis of VCG signal is performed using dual-tree complex wavelet transform. The diagnostic features such as the complex wavelet sub-band (CWS) \(L_{1}\)-Norm (CWS \(L_{1}\)-norm) and the complex wavelet entropy (CWE) are evaluated from the sub-band complex wavelet coefficients of each orthogonal lead of VCG. The RVM classifier is considered to evaluate the performance of the combination of the CWS \(L_{1}\)-norm and the CWE features of VCG. Three different kernel functions such as Gaussian, bubble and Cauchy are used for RVM. The results show that the RVM classifier with Gaussian kernel function has an average accuracy, an average sensitivity and an average specificity values of 99.80, 99.67 and 99.90%, respectively. The performance of RVM classifier is compared with the existing methods for detection of MI from VCG and 12-lead ECG signals.

As a result of RLE and DWT, an effective technique for compressing and transmitting EEG signals was developed in this study. With low percent root-mean-square difference (PRD) values, this algorithm's compression ratio (CR) is high. The life database had 50 EEG patient records. In clinical and research contexts, EEG signals are often recorded at sample rates between 250 and 2000 Hz. New EEG data-collection devices, on the other hand, may record at sampling rates exceeding 20,000 Hz. Time domain (TD) and frequency domain (FD) analysis of EEG data utilizing DWT retains the essential and major features of EEG signals. The thresholding and quantization of EEG signal coefficients are the next steps in implementing this suggested technique, followed by encoding the signals utilizing RLE, which improves CR substantially. A stable method for compressing EEG signals and transmission based on DWT (discrete wavelet transform) and RLE (run length encoding) is presented in this paper in order to improve and increase the compression of the EEG signals. According to the proposed model, CR, PRD, PRDN (normalized percentage root mean square difference), QS (quality score), and SNR (signal to noise ratio) are averaged over 50 records of EEG data and range from 44.0% to 0.36 percent to 5.87 percent to 143 percent to 3.53 percent to 59 percent, respectively.

Heart disorders are one of the most problematic issues of human health. There are currently many efforts to reduce the time for first assistance based on electronic systems that continuously records the electric heart activity for further inspection and anomalies detection. The most popular are portable monitoring systems based on the Electrocardiogram (ECG) signal. However, an efficient detection of heart problems still being a big challenge mainly due to the difficulty of accessing some specific cardiac problems and signal variations between different patients. A different technique for heart diagnosing is based on spatial recording of electrical heart activity, commonly known as Vectorcardiogram (VCG). VCG is pointed by several authors as a more efficient tool than ECG for heart inspection and problem detection. This research explores the possibility of using VCG signals in a portable device for constant heart monitoring and injuries detection. It is presented a portable solution with VCG recording and digital signal processing for automatic diagnosis. This paper covers the aspects of the VCG signal and its ability to be converted into a 12-lead ECG signal. It is also presented a system for automatic diagnosis based on VCG which is based on a multichannel portable hardware platform to support noise cancellation, parameter extraction and decision making algorithms.

Cardiac resynchronisation therapy (CRT) is a successful treatment for patients with chronic heart failure. However, 30–50 % of patients do not respond to the therapy, and novel predictive tools are needed to improve patient selection. In this pilot study, we proposed novel features of vectorcardiogram (VCG) signals derived from the principal component analysis (PCA) and evaluated their ability to predict response to CRT. Retrospective data from 47 patients who underwent CRT device implantation were used. For each patient, the 12-lead ECG was recorded and the VCG was reconstructed using the Kors' regression. In addition to conventional indices characterising the area of QRS complex, we performed PCA of VCG signals to characterise the VCG loop shape and complexity. Linear supervised machine learning models were fed with VCG features to predict CRT response defined as more than 5% increase in left ventricular ejection fraction in a year after implantation. The best performing Support Vector Machine model (ROC AUC 0.75, F1-score 0.75, precision 0.78, recall 0.72) utilized two novel VCG features: Shape Planarity index as a measure of the VCG loop deviation from the main PCA plane and PCA1 index characterising the VCG loop elongation along the first PCA axis. The Shape Planarity index showed the highest significance in predicting CRT response among the tested features. Our results suggest a great potential of PCA based VCG features in predicting CRT response.