Systematic Approach to Processing and Analysis Diagnostic Indicators of Electrocardiograms Based on Labview

Introduction. Cardiovascular disease occupies an important place throughout the world, which necessitates the development of more effective modern means of diagnosis and treatment. The primary diagnosis of heart disease is based on analysis and processing of an electrocardiogram (ECG). Despite the fact that there are many methods and algorithms for ECG analysis and processing, one of the urgent problems of cardiology remains to obtain the most complete information about heart electric potential, respectively, the behavior of the waves P, Q, R, S and T.Aim. Development of algorithms and software for processing and analysis of electrocardiograms (ECGs), as well as calculation of heart rate and detection of arrhythmias based on Labview.Materials and methods. The methods for removing noise using the wavelet transform method to eliminate baseline deviation ,to extract ECG signs ,to calculate heart rate and to detect arrhythmias based on Labview have been adopted as a mathematical apparatus for processing and analyzing ECGs.Results. Organizing of the ECG database, developing algorithms for converting the ECG file of the database into a useful format for Labview, processing of the ECG signal with removing noise from the original ECG signal, extracting signs for obtaining ECG diagnostic indicators, calculating heart rate and detecting arrhythmias.Conclusion. An analysis of the results demonstrates that systematic approaches to evaluating ECG signals allow to avoid one-way decisions and to integrate different methods into an integrated system of ideas of the state. The implementation of the proposed algorithms using Labview programming system ensures the removal of noise and artifacts, the extraction of the necessary ECG signs, the calculation of heart contractions and the detection of arrhythmias.Introduction. Cardiovascular disease occupies an important place throughout the world, which necessitates the development of more effective modern means of diagnosis and treatment. The primary diagnosis of heart disease is based on analysis and processing of an electrocardiogram (ECG). Despite the fact that there are many methods and algorithms for ECG analysis and processing, one of the urgent problems of cardiology remains to obtain the most complete information about heart electric potential, respectively, the behavior of the waves P, Q, R, S and T.Aim. Development of algorithms and software for processing and analysis of electrocardiograms (ECGs), as well as calculation of heart rate and detection of arrhythmias based on Labview.Materials and methods. The methods for removing noise using the wavelet transform method to eliminate baseline deviation ,to extract ECG signs ,to calculate heart rate and to detect arrhythmias based on Labview have been adopted as a mathematical apparatus for processing and analyzing ECGs.Results. Organizing of the ECG database, developing algorithms for converting the ECG file of the database into a useful format for Labview, processing of the ECG signal with removing noise from the original ECG signal, extracting signs for obtaining ECG diagnostic indicators, calculating heart rate and detecting arrhythmias.Conclusion. An analysis of the results demonstrates that systematic approaches to evaluating ECG signals allow to avoid one-way decisions and to integrate different methods into an integrated system of ideas of the state. The implementation of the proposed algorithms using Labview programming system ensures the removal of noise and artifacts, the extraction of the necessary ECG signs, the calculation of heart contractions and the detection of arrhythmias

Real-Time Monitoring and Analysis of Zebrafish Electrocardiogram with Anomaly Detection.

Heart disease is the leading cause of mortality in the U.S. with approximately 610,000 people dying every year. Effective therapies for many cardiac diseases are lacking, largely due to an incomplete understanding of their genetic basis and underlying molecular mechanisms. Zebrafish (Danio rerio) are an excellent model system for studying heart disease as they enable a forward genetic approach to tackle this unmet medical need. In recent years, our team has been employing electrocardiogram (ECG) as an efficient tool to study the zebrafish heart along with conventional approaches, such as immunohistochemistry, DNA and protein analyses. We have overcome various challenges in the small size and aquatic environment of zebrafish in order to obtain ECG signals with favorable signal-to-noise ratio (SNR), and high spatial and temporal resolution. In this paper, we highlight our recent efforts in zebrafish ECG acquisition with a cost-effective simplified microelectrode array (MEA) membrane providing multi-channel recording, a novel multi-chamber apparatus for simultaneous screening, and a LabVIEW program to facilitate recording and processing. We also demonstrate the use of machine learning-based programs to recognize specific ECG patterns, yielding promising results with our current limited amount of zebrafish data. Our solutions hold promise to carry out numerous studies of heart diseases, drug screening, stem cell-based therapy validation, and regenerative medicine

A Review of ECG Signal De-noising and Peaks Detection Techniques

The Modern era is marked by tension and, therefore, heart is in trouble. Whole of the world is busy making research in ECG techniques. on Electrocardiogram and its feature extraction is the area of interest.. Analysis and classification let the medical professional to detect the heart ailments and other diseases. In our research method for De-noising of ECG signal and Feature extraction Algorithm is proposed. We considered the baseline wander problem in ECG signal which is basically due to the measurement error. This work involves the IIR filter Savitzky-Golay filter and Wavelet Transform. ECG signal is de-noised without affecting any information from ECG. We have also designed a feature Extraction algorithm which automatically detects the RR interval and QRS interval along with the amplitude of Q, R, and S. The whole algorithm including the de-noising of signal and Feature extraction. This work has been simulated on the MATLab software

Artificial intelligence based ECG signal classification of sendetary, smokers and athletes

The current study deals with the design of a computer aided diagnosis procedure to classify 3 groups of people with different lifestyles, namely sedentary, smoker and athletes. The ECG Classification based on statistical analysis of HRV and ECG features. The heart rate variability (HRV) parameters and ECG statistical features were used for the pattern recognition in Artificial Intelligence classifiers. The ECG was recorded for a particular time duration using the EKG sensor. The HRV, time domain and wavelet parameters were calculated using NI BIOMEDICAL STARTUP KIT 3.0 and LABVIEW 2010. The important HRV features, time domain and wavelet features were calculated by the statistical non-linear classifiers (CART and BT).the important parameters were fed as input to artificial intelligence classifiers like ANN and SVM. The Artificial Intelligence classifiers like artificial neural network (ANN) and Support vector Machine (SVM) were used to classify 60 numbers of ECG signal. It was observed from result that the Multi layer perceptron (MLP) based ANN classifier gives an accuracy of 95%, which is highest among other the classifiers. The HRV study implies that the time domain parameters (RMSSD and PNN50), frequency domain parameters (HF power and LF/HF peak), Poincare parameter (SD1) and geometric parameters (RR triangular index and TINN) are higher in athlete class and lower in smoker class. The Higher values of HRV parameters indicate increase in parasympathetic activity and decrease in sympathetic activity of the ANS. This indicates that the athlete class has better heath and less chance of cardiovascular diseases where smoker class has high chances of cardiovascular diseases. These HRV parameters of sedentary class were higher than smoker class but lower than athlete class. This indicates less chances of cardiovascular disease in sedentary class as compared to smoker class

Time-frequency investigation of heart rate variability and cardiovascular system modeling of normal and chronic obstructive pulmonary disease (COPD) subjects

A study has been designed to add insight to some questions that have not been fully investigated in the heart rate variability field and the cardiovascular regulation system in normal and Chronic Obstructive Pulmonary Disease (COPD) subjects. It explores the correlations between heart rate variability and cardiovascular regulation, which interact through complex multiple feedback and control loops. This work examines the coupling between heart rate (HR), respiration (RESP), and blood pressure (BP) via closed-loop system identification techniques in order to noninvasively assess the underlying physiology. In the first part of the study, the applications of five different bilinear time-frequency representations are evaluated on modeled HRV test signals, actual electrocardiograms (ECG), BP and RESP signals. Each distribution: the short time Fourier transform (STFT), the smoothed pseudo Wigner-Ville (SPWVD), the ChoiWilliams (CWD), the Bom-Jordan-Cohen (BJC) and wavelet distribution (WL), has unique characteristics which is shown to affect the amount of smoothing and the generation of cross-terms. The CWD and the WL are chosen for further application because of overcoming the drawbacks of other distributions by providing higher resolution in time and frequency while suppressing interferences between the signal components. In the second part of the study, the Morlet, Meyer, Daubechies 4, Mexican Hat and Haar wavelets are used to investigate the heart rate and blood pressure variability from both COPD and normal subjects. The results of wavelet analysis give much more useful information than the Cohen\u27s class representations. Here we are able to quantitatively assess the parasympathetic (HF) and sympatho-vagal balance (LF:HF) changes as a function of time. As a result, COPD subjects breathe faster, have higher blood pressure variability and lower HRV. In the third part of the study, a special class of the exogenous autoregressive (ARX) model is developed as an analytical tool for uncovering the hidden autonomic control processes. Non-parametric relationships between the input and outputs of the ARX model resulting in transfer function estimations of the noise filters and the input filter were used as mechanistic cardiovascular models that have shown to have predictive capabilities for the underlying autonomic nervous system activity of COPD patients. Transfer functions of COPD cardiovascular models have similar DC gains but show a larger lag in phase as compared to the models of normal subjects. Finally, a method of severity classification is presented. This method combines the techniques of principal component analysis (PCA) and cluster analysis (CA) and has been shown to separate the COPD from the normal population with 100% accuracy. It can also classify the COPD population into at risk , mild , moderate and severe stages with 100%, 90%, 88% and 100% accuracy respectively. As a result, cluster and principal component analysis can be used to separate COPD and normal subjects and can be used successfully in COPD severity classification

Investigation of heart rate variability during sleep apnea

Sleep apnea is a disorder, where there are repetitive pauses in respiratory flow of at least 10 seconds or longer duration, and which occur more than five times per hour. Apnea has strong modulating effects on the autonomic nervous system, with prominent heart rate variation. It can be assumed that during sleep, internal influences (sympathetic and parasympathetic nervous system activities) dominate the autonomic nervous system; in addition repetitive apneas are accompanied by a pronounced increase in average heart rate. The aim of this study was to investigate the heart rate variability using spectral analysis and time-frequency analysis during sleep apnea. A total of 22 subjects (18 males and 4 females, 49 ± 20 years) were studied who were experiencing both obstructive sleep apnea and central sleep apnea in whom sleep-disordered breathing was diagnosed. In addition 6 control subjects were studied where sleep apnea was not expected. Spectral and wavelet analysis were used to investigate the heart rate variability from the sleep apnea subjects and control subjects. The results of the wavelet analysis gave information about the parasympathetic (HF) and sympatho-vagal balance (LF: HF) changes as a function of time and frequency. The spectral parameters LF, HF and LF/HF confirmed reduced parasympathetic activity in patients with sleep apnea compared to normal subjects. In addition the repetitive apneas are accompanied by a pronounced increased cyclic variation of heart rate

Relationship between the autonomic nervous system and the recovering heart post exercise using heart rate variability

The goal of this thesis is to determine if a relationship exists between the activity of the autonomic nervous system (ANS) and the speed of heart rate recovery post maximal exercise. The parasympathetic branch of the ANS was assessed during rest by obtaining the high frequency (HF) component of the heart rate variability (HRV) frequency spectrum. The recovery time was estimated by fitting an exponential curve to the heart rate post exercise where the exponent was used to calculate the time constant to indicate recovery time. A regression was then performed on the HF and recovery time in order to find a relationship between the ANS activity and the recovery time. Changes in the ANS through all stages of the experiment were characterized using two different time-frequency methods; Continuous Wavelet Transform (CWT) and the Short Time Fourier Transform (STFT). A moderate relationship (R2=0.5164) exists between the HF and the rate of recovery. Changes in the ANS throughout the experiment indicated a drop in both the LF and HF (where LF represents a mixture of sympathetic and parasympathetic activity) values during exercise and may indicate sympathetic saturation. The normalized LF and HF values contained a large spike in the LF and a corresponding drop in the HF at the start of exercise and again at the start recovery indicating sympathetic activation and sympathetic desaturation, respectively