Electrocardiographic Signal Processing Research Articles

Automated Heart Rate Detection in Seismocardiograms Using Electrocardiogram-Based Algorithms-A Feasibility Study.

In recent decades, much work has been implemented in heart rate (HR) analysis using electrocardiographic (ECG) signals. We propose that algorithms developed to calculate HR based on detected R-peaks using ECG can be applied to seismocardiographic (SCG) signals, as they utilize common knowledge regarding heart rhythm and its underlying physiology. We implemented the experimental framework with methods developed for ECG signal processing and peak detection to be applied and evaluated on SCGs. Furthermore, we assessed and chose the best from all combinations of 15 peak detection and 6 preprocessing methods from the literature on the CEBS dataset available on Physionet. We then collected experimental data in the lab experiment to measure the applicability of the best-selected technique to the real-world data; the abovementioned method showed high precision for signals recorded during sitting rest (HR difference between SCG and ECG: 0.12 ± 0.35 bpm) and a moderate precision for signals recorded with interfering physical activity-reading out a book loud (HR difference between SCG and ECG: 6.45 ± 3.01 bpm) when compared to the results derived from the state-of-the-art photoplethysmographic (PPG) methods described in the literature. The study shows that computationally simple preprocessing and peak detection techniques initially developed for ECG could be utilized as the basis for HR detection on SCG, although they can be further improved.

12-lead ECG signal processing and atrial fibrillation prediction in clinical practice.

Because clinically used 12-lead electrocardiography (ECG) devices have high falsepositive errors in automatic interpretations of atrial fibrillation (AF), they require substantial improvements before use. A clinical 12-lead ECG pre-processing method with a parallel convolutional neural network (CNN) model for 12-lead ECG automatic AF recognition is introduced. Raw AF diagnosis data from a 12-lead ECG device were collected and analyzed by two cardiologists to differentiate between true- and false-positives. Using a stationary wavelet transform (SWT) and independent component analysis (ICA) noise reduction was conducted and baseline wandering was corrected for the raw signals. AF patterns were learned and predicted using a parallel CNN deep learning (DL) model. (1) The proposed method alleviates the decreased ECG QRS amplitude enhances the signal-to-noise ratio and clearly shows atrial and ventricular activities. (2) After training, the CNNbased AF detector significantly reduced false-positive errors. The precision of AF diagnosis increased from 77.3% to 94.0 ± 1.5% as compared to ECG device interpretation. For AF screening, the model showed an average sensitivity of 96.8 ± 2.2%, specificity of 79.0 ± 5.8%, precision of 94.0 ± 1.5%, F1-measure of 95.2 ± 1.0%, and overall accuracy of 92.7 ± 1.5%. The method can bridge the gap between the research and clinical practice The ECG signal pre-processing and DL-based AF interpretation can be rapidly implemented clinically.

Horizons in Single-Lead ECG Analysis From Devices to Data

Single-lead wearable electrocardiographic (ECG) devices for remote monitoring are emerging as critical components of the viability of long-term continuous health and wellness monitoring applications. These sensors make it simple to monitor chronically ill patients and the elderly in long-term care homes, as well as empower users focused on fitness and wellbeing with timely health and lifestyle information and metrics. This article addresses the future developments in single-lead electrocardiogram (ECG) wearables, their design concepts, signal processing, machine learning (ML), and emerging healthcare applications. A literature review of multiple wearable ECG remote monitoring devices is first performed; Apple Watch, Kardia, Zio, BioHarness, Bittium Faros and Carnation Ambulatory Monitor. Zio showed the longest wear time with patients wearing the patch for 14 days maximum but required users to mail the device to a processing center for analysis. While the Apple Watch and Kardia showed good quality acquisition of raw ECG but are not continuous monitoring devices. The design considerations for single-lead ECG wearable devices could be classified as follows: power needs, computational complexity, signal quality, and human factors. These dimensions shadow hardware and software characteristics of ECG wearables and can act as a checklist for future single-lead ECG wearable designs. Trends in ECG de-noising, signal processing, feature extraction, compressive sensing (CS), and remote monitoring applications are later followed to show the emerging opportunities and recent innovations in single-lead ECG wearables.

Analysis of Methods for Suppressing Power-line Noise in the Tasks of Digital Processing of Electrocardiographic Signals

Analysis of Methods for Suppressing Power-line Noise in the Tasks of Digital Processing of Electrocardiographic Signals

Sensitivity and Positive Prediction of Secured Electrocardiograph (ECG) Transmission using Fully Homomorphic Encryption Technique (FHE)

Health connected is a technology that links medical devices, telecommunications and security techniques. It empowers patients to be observed and treated remotely from their homes. Patient’s healthcare records with a connected healthcare system should be stored securely before transmitted for further investigation and interpretation. Electrocardiogram (ECG) is the clinical method utilized to screen heart execution and utilized for the detection of various arrhythmias. For diagnostic purposes, individuals with a background of heart diseases have long records of ECGs, which results in the requirement of a large amount of storage space and labor. Hence, there is a requirement for a system that involves digital signal processing and signal security so that the spared information is made sure about at one spot and an only authentic individual can see and utilize this ECG signal for additional findings. This study presents a set of security solutions that can be deployed in a connected healthcare territory, which includes the fully homomorphic encryption (FHE) techniques used to secure the ECG signals. The study helps the medical provider to record ECG signals confidentially and to prevent mistreatment. The study focuses on Pan and Tompkins algorithm methods for the detection of the ECG Signal. As a result, the output of the Pan and Tompkins algorithm for ECG signal processing with the FHE technique shows a sensitivity of 92.59% and a positive prediction of 90.00%.

Digital ECG Phantom Design to Represent the Human Heart Signal for Early Test on ECG Machine in Hospital

Electrocardiograph (ECG) is a diagnostic tool that can record the electrical activity of the human heart. By analyzing the resulting waveforms of the recorded electrical activity of the heart, it is possible to record and diagnose disease. Given the importance of the ECG recording device, it is necessary to check the function of the ECG recording device, namely by performing a device calibration procedure using the Phantom ECG which aims to simulate the ECG signal. The purpose of this research is to check the ECG device during repairs, besides that the Electrocardiograph (EKG) tool functions for research purposes on ECG signals or for educational purposes. Electrocardiograph (EKG) simulator or often called Phantom ECG is in principle a signal generator in the form of an ECG like signal or a recorded ECG signal. This device can be realized based on microcontroller and analog circuit. The advantage of this simulator research is that the ECG signal displayed is the original ECG recording and has an adequate ECG signal database. ECG This simulator also has the advantage of providing convenience for research on digital signal processing applications for ECG signal processing. In its application this simulator can be used as a tool to study various forms of ECG signals. Based on the measurement results, the error value at BPM 30 and 60 is 0.00% at the sensitivity of 0.5mV, 1.0mV, and 2.0mV, then the measurement results for the error value at BPM 120 are 0.33% and at the BPM 180 value, the error value is 0.22%. From these results, it can be concluded that the highest error value is at BPM 120 with sensitivities of 0.5mV, 1.0mV, and 2.0mV.

Robust nonlinear aggregation operator for ECG powerline interference reduction

ObjectiveNowadays, the most effective methods for ElectroCardioGraphic (ECG) PowerLine (PL) interference suppression, based on the subtraction operation, require the determination of signal fragments with a low electrical activity of the heart. Any errors in searching for these fragments result in a loss of effectiveness of interference suppression and an increase in signal distortion. This article proposes a method that does not need to determine such signal fragments. MethodsThe robust nonlinear aggregation operator is used for the PL disturbance estimation. This results in an automatic reduction (or elimination) of the influence of the ECG signal fragments with an increased electrical activity of the heart. In other words, the samples with the lowest electrical activity of the heart will be used for the PL interference estimation. ResultsThe method leads to a significant reduction of the ECG signal distortions, unattainable for other methods, leaving the residual noise of the order of single micro-volts, regardless of the PL interference level tackled. The method can be applied using an algorithm that is computationally very efficient, which can help to reduce the total cost of ECG signal processing. The new method proposed is experimentally compared to the traditional ones using signals from the Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG database. ConclusionThe method proposed is an accurate, robust and computationally efficient algorithm for reduction of PL interference disturbing ECG signals. SignificanceNew prospects emerge for systems processing the ECG signal, it is possible to analyze traces with a more unfavorable signal-to-noise ratio. This applies to monitoring and exercise systems as well as to a special advanced analysis of e.g. cardiac micro-potentials.

A Novel Method for Segmentation of QRS Complex on ECG Signals and Classify Cardiovascular Diseases via a Hybrid Model Based on Machine Learning

Automated-detecting intelligent programs and methods are developing to find out diseases in medicine in recent years. Developing new methods and improving existing ones are currently ongoing research. One of the most important health problems is heart diseases for all people in the world. Electrocardiography (ECG) is a diagnosis tool that gives substantially functional information about heart and cardiac system. In this work, it is primarily aimed at developing an intelligent system based on ECG signal processing, analysis, and classification via a hybrid machine learning model. This work uses 837 ECG signal fragments that includes 7 different classes shared in MIT-BIH Arrhythmia database for one lead. The ECG signals are applied on a preprocessing to smooth signals and correct baselines. Q, R and S waves (QRS) complex on ECG signals are segmented based on k-means clustering and tracking local extrema points. Feature extraction and selection are then performed, and a dataset is created by calculating measurement parameters for each QRS points separately. Training sets and test sets based on 8-fold cross validation are generated. A hybrid model based on machine learning models including decision tree (DT), k-nearest neighbor (KNN), random forest (RF), naive bayes (NB), linear discriminant analysis (LDA), support vector machines (SVM) and quadratic discriminant analysis (QDA) is developed to classify cardiovascular diseases (CVD) into 7 different classes such as normal sinus rhythm (NSR), atrial premature beat (APB), atrial fibrillation (AFIB), premature ventricular contraction (PVC), ventricular bigeminy (VB), left bundle branch block beat (LBBBB) and right bundle branch block beat (RBBBB). Sensitivity, specificity, accuracy, and Matthews correlation coefficient (MCC) of detection of QRS complex are obtained respectively as 94.75%, 95.96%, 95.57% and 0.90. Sensitivity, specificity, accuracy and MCC of classification of CVD classes are obtained respectively as 92.33%, 92.50%, 92.41%, 0.85.

Secured Electrocardiograph (ECG) Signal Using Partially Homomorphic Encryption Technique–RSA Algorithm

ECG signal differs from individual to individual, making it hard to be emulated and copied. In recent times ECG is being used for identifying the person. Hence, there is a requirement for a system that involves digital signal processing and signal security so that the saved data are secured at one place and an authentic person can see and use the ECG signal for further diagnosis. The study presents a set of security solutions that can be deployed in a connected healthcare territory, which includes the partially homomorphic encryption (PHE) techniques used to secure the electrocardiogram (ECG) signals. This is to record confidentially and prevent the information from meddling, imitating and replicating. First, Pan and Tompkins’s algorithm was applied to perform the ECG signal processing. Then, partially homomorphic encryption (PHE) technique - Rivest-Shamir-Adleman (RSA) algorithm was used to encrypt the ECG signal by using the public key. The PHE constitutes a gathering of semantically secure encryption works that permits certain arithmetical tasks on the plaintext to be performed straightforwardly on the ciphertext. The study shows a faster and 90% accurate result before and after encryption that indicates the lightweight and accuracy of the RSA algorithm. Secure ECG signal provides innovation in multiple healthcare sectors such as medical research, patient care and hospital database.

Conversion from electrocardiosignals to equivalent electrical sources on heart surface

BackgroundThe actual task of electrocardiographic examinations is to increase the reliability of diagnosing the condition of the heart. Within the framework of this task, an important direction is the solution of the inverse problem of electrocardiography, based on the processing of electrocardiographic signals of multichannel cardio leads at known electrode coordinates in these leads (Titomir et al. Noninvasiv electrocardiotopography, 2003), (Macfarlane et al. Comprehensive Electrocardiology, 2nd ed. (Chapter 9), 2011).ResultsIn order to obtain more detailed information about the electrical activity of the heart, we carry out a reconstruction of the distribution of equivalent electrical sources on the heart surface. In this area, we hold reconstruction of the equivalent sources during the cardiac cycle at relatively low hardware cost. ECG maps of electrical potentials on the surface of the torso (TSPM) and electrical sources on the surface of the heart (HSSM) were studied for different times of the cardiac cycle. We carried out a visual and quantitative comparison of these maps in the presence of pathological regions of different localization. For this purpose we used the model of the heart electrical activity, based on cellular automata.ConclusionsThe model of cellular automata allows us to consider the processes of heart excitation in the presence of pathological regions of various sizes and localization. It is shown, that changes in the distribution of electrical sources on the surface of the epicardium in the presence of pathological areas with disturbances in the conduction of heart excitation are much more noticeable than changes in ECG maps on the torso surface.

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

The analysis of cardiac signals is still regarded as attractive by both the academic community and industry because it helps physicians in detecting abnormalities and improving the diagnosis and therapy of diseases. Electrocardiographic signal processing for detecting irregularities related to the occurrence of low-amplitude waveforms inside the cardiac signal has a considerable workload as cardiac signals are heavily contaminated by noise and other artifacts. This paper presents an effective approach for the detection of ventricular late potential occurrences which are considered as markers of sudden cardiac death risk. Three stages characterize the implemented method which performs a beat-to-beat processing of high-resolution electrocardiograms (HR-ECG). Fifteen lead HR-ECG signals are filtered and denoised for the improvement of signal-to-noise ratio. Five features were then extracted and used as inputs of a classifier based on a machine learning approach. For the performance evaluation of the proposed method, a HR-ECG database consisting of real ventricular late potential (VLP)-negative and semi-simulated VLP-positive patterns was used. Experimental results show that the implemented system reaches satisfactory performance in terms of sensitivity, specificity accuracy, and positive predictivity; in fact, the respective values equal to 98.33%, 98.36%, 98.35%, and 98.52% were achieved.

Techniques of Acquisition and Processing of Electrocardiographic Signals in the Detection of Cardiac Arrhythmias

The development of ambulatory monitoring systems and its electrocardiographic (ECG) signal processing techniques has become an important field of investigation, due to its relevance in the early detection of cardiovascular diseases such as the arrhythmias. The current trend of this technology is oriented to the use of portable equipment and mobile devices such as Smartphones, which have been widely accepted due to the technical characteristics and common integration in daily life. A fundamental characteristic of these systems is their ability to reduce the most common types of noise by means of digital signal processing techniques. Among the most used techniques are the adaptive filters and the Discrete Wavelet Transform (DWT) which have been successfully implemented in several studies. There are systems that integrate classification stages based on artificial intelligence, which increases the performance in the process of arrhythmias detection. These techniques are not only evaluated for their functionality but for their computational cost, since they will be used in real-time applications, and implemented in embedded systems. This paper shows a review of each of the stages in the construction of a standard ambulatory monitoring system, for the contextualization of the reader in this type of technology.

ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing.

Methods of the electrocardiography (ECG) signal features extraction are required to detect heart abnormalities and different kinds of diseases. However, different artefacts and measurement noise often hinder providing accurate features extraction. One of the standard techniques developed for ECG signals employs linear prediction. Referring to the fact that prediction is not required for ECG signal processing, smoothing can be more efficient. In this paper, we employ the p-shift unbiased finite impulse response (UFIR) filter, which becomes smooth by p < 0. We develop this filter to have an adaptive averaging horizon: optimal for slow ECG behaviours and minimal for fast excursions. It is shown that the adaptive UFIR algorithm developed in such a way provides better denoising and suboptimal features extraction in terms of the output signal-noise ratio (SNR). The algorithm is developed to detect durations and amplitudes of the P-wave, QRS-complex, and T-wave in the standard ECG signal map. Better performance of the algorithm designed is demonstrated in a comparison with the standard linear predictor, UFIR filter, and UFIR predictive filter based on real ECG data associated with normal heartbeats.

Analysis of ECG Signal Processing and Filtering Algorithms

Electrocardiography (ECG) is a common technique for recording the electrical activity of human heart. Accurate computer analysis of ECG signal is challenging as it is exceedingly prone to high frequency noise and various other artifacts due to its low amplitude. In remote heath care systems, computer based high level understanding of ECG signals is performed using advanced machine learning algorithms. The accuracy of these algorithms relies on the Signal-to-Noise-Ratio (SNR) of the input ECG signal. In this paper, we analyse various methods for removing the high frequency noise components from the ECG signal and evaluate the performance of several adaptive filtering algorithms. The result suggest that the Normalized Least Mean Square (NLMS) algorithm achieves high SNR and Sign LMS is computationally efficient.

Data Fusion of Multivariate Time Series: Application to Noisy 12-Lead ECG Signals

Twelve-lead Electrocardiograph (ECG) signals fusion is crucial for further ECG signal processing. In this paper, based on the idea of the local weighted linear prediction algorithm, a novel fusion data algorithm is proposed, which was applied in data fusion of the 12-lead ECG signals. In order to analyze the signal quality comprehensively, the quality characteristics should be adequately retained in the final fused result. In our algorithm, the values for the weighted coefficient of state points were closely related to the final fused result. Thus, two fuzzy inference systems were designed to calculate the weighted coefficients. For the sake of assessing the performance of our method, synthetic ECG signals and realistic ECG signals were applied in the experiments. Experimental results indicate that our method can fuse the 12-lead ECG signals effectively with inherit the quality characteristics of original ECG signals inherited properly.

A novel low-complexity digital filter design for wearable ECG devices.

Wearable and implantable Electrocardiograph (ECG) devices are becoming prevailing tools for continuous real-time personal health monitoring. The ECG signal can be contaminated by various types of noise and artifacts (e.g., powerline interference, baseline wandering) that must be removed or suppressed for accurate ECG signal processing. Limited device size, power consumption and cost are critical issues that need to be carefully considered when designing any portable health monitoring device, including a battery-powered ECG device. This work presents a novel low-complexity noise suppression reconfigurable finite impulse response (FIR) filter structure for wearable ECG and heart monitoring devices. The design relies on a recently introduced optimally-factored FIR filter method. The new filter structure and several of its useful features are presented in detail. We also studied the hardware complexity of the proposed structure and compared it with the state-of-the-art. The results showed that the new ECG filter has a lower hardware complexity relative to the state-of-the-art ECG filters.

Feature selection for ECG signal processing using improved genetic algorithm and empirical mode decomposition

Abstract This paper proposes a novel scheme of feature selection, which employs a modified genetic algorithm that uses a variable-range searching strategy and empirical mode decomposition (EMD). Combined with support vector machines (SVMs), a new pattern recognition method for electrocardiograph (ECG) is developed. First, the ECG signal is decomposed into intrinsic mode functions (IMFs) that represent signal characteristics with sample oscillatory modes. Then, the modified genetic algorithm with variable-range encoding and dynamic searching strategy is used to optimize statistical feature subsets. Next, a statistical model based on receiver operating characteristic (ROC) analysis is developed to select the dominant features. Finally, the SVM-based pattern recognition model is used to classify different ECG patterns. Comparative studies with peer-reviewed results and two other well-known feature selection methods demonstrate that the proposed method can select dominant features in processing ECG signal, and achieve better classification performance with lower feature dimensionality.

Abstract 74: Cardiopulmonary Resuscitation Quality Assessed by Electrocardiography Signal Processing Using Hilbert-Huang Transform Correlates Well with Accelerometer

Objectives High quality cardiopulmonary resuscitation (CPR) is paramount to patient outcomes in cardiac arrests (CA). Methods to assess CPR quality using widely available data should be explored. This study was aimed to compare CPR quality assessed by surface ECG signal processing vs. accelerometer. Methods Adult patients with CA receiving CPR in the emergency department of a tertiary university hospital, from Dec 2010 to March 2011 were included. CPR was monitored continuously by recording the surface ECG signals and sternal displacement via an accelerometer. CPR quality parameters including total compression numbers, no flow time (pause &gt; 1.5 sec), total flow time (time with pause &lt; 1.5 sec) and average compression rate for up to the first 10 min of CPR were obtained by 1) ECG signal processing algorithm using empirical mode decomposition via Hilbert-Huang Transform (ECG-HHT) and 2) sternal displacement recorded by an accelerometer (ACCEL). Results CPR sessions of 9 CA (6 males, mean age 79.9 years) were analyzed. Compared to ACCEL, ECG-HHT showed lower total compressions, higher no flow time, and lower total flow time. There are good correlations between ECG-HHT vs. ACCEL in total compression numbers (R=0.95) and total flow time (R=0.97). Conclusions ECG-HHT correlates well with ACCEL in certain CPR quality parameters and may be used to assess CPR quality through widely available ECG data.

Implementation of compressive sensing in ECG and EEG signal processing

The purpose of this paper is to exploit compressive sensing (CS) method in dealing with electrocardiography (ECG) and electroencephalography (EEG) signals at a high compression ratio. In order to get sparse data of ECG and EEG signals before being compressed, a combined scheme was presented by using wavelet transform and iterative threshold method; then, compressive sensing is executed to make the data compressed. After doing compressive sensing, Bayesian compressive sensing (BCS) is used to reconstruct the original signals. The simulation results show that compressive sensing is an effective method to make data compressed for ECG and EEG signals with high compression ratio and good quality of reconstruction. Furthermore, it shows that the proposed scheme has good denoising effects.

Quantifying the PR Interval Pattern During Dynamic Exercise and Recovery

We present a novel analysis tool for time delay estimation in electrocardiographic signal processing. This tool enhances PR interval estimation (index of the atrioventricular conduction time) by limiting the distortion effect of the T wave overlapping the P wave at high heart rates. Our approach consists of modeling the T wave, cancelling its influence, and finally estimating the PR intervals during exercise and recovery with the proposed generalized Woody method. Different models of the T wave are presented and compared in a statistical summary that quantitatively justifies the improvements introduced by this study. Among the different models tested, we found that a piecewise linear function significantly reduces the T wave-induced bias in the estimation process. Combining this modeling with the proposed time delay estimation method leads to accurate PR interval estimation. Using this method on real ECGs recorded during exercise and its recovery, we found: 1) that the slopes of PR interval series in the early recovery phase are dependent on the subjects' training status (average of the slopes for sedentary men = 0.11 ms/s, and for athlete men = 0.28 ms/s), and 2) an hysteresis phenomenon exists in the relation PR/RR intervals when data from exercise and recovery are compared.