Bottom-up design of artificial neural network for single-lead electrocardiogram beat and rhythm classification

Performance improvement in computerized Electrocardiogram (ECG) classification is vital to improve reliability in this life-saving technology. The non-linearly overlapping nature of the ECG classification task prevents the statistical and the syntactic procedures from reaching the maximum performance. A new approach, a neural network-based classification scheme, has been implemented in clinical ECG problems with much success. The focus, however, has been on narrow clinical problem domains and the implementations lacked engineering precision. An optimal utilization of frequency information was missing. This dissertation attempts to improve the accuracy of neural network-based single-lead (lead-II) ECG beat and rhythm classification. A bottom-up approach defined in terms of perfecting individual sub-systems to improve the over all system performance is used. Sub-systems include pre-processing, QRS detection and fiducial point estimations, feature calculations, and pattern classification. Inaccuracies in time-domain fiducial point estimations are overcome with the derivation of features in the frequency domain. Feature extraction in frequency domain is based on a spectral estimation technique (combination of simulation and subtraction of a normal beat). Auto-regressive spectral estimation methods yield a highly sensitive spectrum, providing several local features with information on beat classes like flutter, fibrillation, and noise. A total of 27 features, including 16 in time domain and 11 in frequency domain are calculated. The entire data and problem are divided into four major groups, each group with inter-related beat classes. Classification of each group into related sub-classes is performed using smaller feed-forward neural networks. Input feature sub-set and the structure of each network are optimized using an iterative process. Optimal implementations of feed-forward neural networks provide high accuracy in beat classification. Associated neural networks are used for the more deterministic rhythm-classification task. An accuracy of more than 85% is achieved for all 13 classes included in this study. The system shows a graceful degradation in performance with increasing noise, as a result of the noise consideration in the design of every sub-system. Results indicate a neural network-based bottom-up design of single-lead ECG classification is able to provide very high accuracy, even in the presence of noise, flutter, and fibrillation

ECG Signal Analysis: Enhancement and R-Peak Detection

The project has been inspired by the need to find an efficient method for ECG Signal Analysis which is simple and has good accuracy and less computation time. The initial task for efficient analysis is the removal of noise. It actually involves the extraction of the required cardiac components by rejecting the background noise. Enhancement of signal is achieved by the use of Empirical Mode Decomposition method. The use of EMD was inspired by its adaptive nature. The second task is that of R peak detection which is achieved by the use of Continuous Wavelet Transform. Efficiency of the method is measured in terms of detection error rate. Various other methods of R peak detection like Hilbert Transform and Difference Operation Method are implemented and the results when compared with the Continuous Wavelet Transform prove that CWT is a better method. The simulation is done in MATLAB environment. The experiments are carried out on MIT-BIH database. The results show that our proposed method is very effective and an efficient method for fast computation of R peak detection

Predictive Monitoring for Respiratory Decompensation Leading to Urgent Unplanned Intubation in the Neonatal Intensive Care Unit

Background: Infants admitted to the neonatal intensive care unit (NICU), and especially those born with very low birth weight (VLBW; \u3c 1,500 g), are at risk for respiratory decompensation requiring endotracheal intubation and mechanical ventilation. I ntubation and mechanical ventilation are associated with increased morbidity, particularly in urgent unplanned cases. Methods: We tested the hypothesis that the systemic response associated with respiratory decombensation can be detected from physiological monitoring and that statistical models of bedside monitoring data can identify infants at increased risk of urgent unplanned intubation. We studied 287 VLBW infants consecutively admitted to our NICU and found 96 events in 51 patients, excluding intUbations occurring within. 12h of a previous extubation. Results: In order of importance in a multivariable statistical model, we found that the characteristics of reduced O-2 satura, tion, especially as heart rate was falling; increased heart rate correlation with respiratory rate; and the amount of apnea were aIF significant independent pr,edictors.\u27 The predictive model, validated internally by bootStrap, had a receiver-operating characteristic area of 0.84 + / - 0.04. Conclusion: We propose that predictive monitoring in the NICU for urgent unplanned intubation may improve outcomes by allowing clinicians to intervene noninvasively before intubation is required

Smoothening and Segmentation of ECG Signals Using Total Variation Denoising –Minimization-Majorization and Bottom-Up Approach

AbstractAn ECG Signal records electrical activity of heart. It includes information on heart's rhythm and is useful for diagnosis of heart related diseases. It encounters with various artifacts during acquisition and transmission. The unwanted signals/noises present in ECG signals disturb the clinical information present in it. This paper tries to reduce unwanted signals through Majorization-Minorization approach to optimize total variation in the signals. The denoised signal is then segmented using bottom up approach. The results show significant improvement in signal to noise ratio and successful segmentation of sections of ECG signals

Breath-by-Breath Analysis of Cardiorespiratory Interaction for Quantifying Developmental Maturity in Premature Infants

Breath-by-breath analysis of cardiorespiratory interaction for quantifying developmental maturity in premature infants. J Appl Physiol 112: 859-867, 2012. First published December 15, 2011; doi:10.1152/japplphysiol.01152.2011.-In healthy neonates, connections between the heart and lungs through brain stem chemosensory pathways and the autonomic nervous system result in cardiorespiratory synchronization. This interdependence between cardiac and respiratory dynamics can be difficult to measure because of intermittent signal quality in intensive care settings and variability of heart and breathing rates. We employed a phase-based measure suggested by Sch fer and coworkers (Sch fer C, Rosenblum MG, Kurths J, Abel HH. Nature 392: 239-240, 1998) to obtain a breath-by-breath analysis of cardiorespiratory interaction. This measure of cardiorespiratory interaction does not distinguish between cardiac control of respiration associated with cardioventilatory coupling and respiratory influences on the heart rate associated with respiratory sinus arrhythmia. We calculated, in sliding 4-min windows, the probability density of heartbeats as a function of the concurrent phase of the respiratory cycle. Probability density functions whose Shannon entropy had a \u3c 0.1% chance of occurring from random numbers were classified as exhibiting interaction. In this way, we analyzed 18 infant-years of data from 1,202 patients in the Neonatal Intensive Care Unit at University of Virginia. We found evidence of interaction in 3.3 patient-years of data (18%). Cardiorespiratory interaction increased several-fold with postnatal development, but, surprisingly, the rate of increase was not affected by gestational age at birth. We find evidence for moderate correspondence between this measure of cardiorespiratory interaction and cardioventilatory coupling and no evidence for respiratory sinus arrhythmia, leading to the need for further investigation of the underlying mechanism. Such continuous measures of physiological interaction may serve to gauge developmental maturity in neonatal intensive care patients and prove useful in decisions about incipient illness and about hospital discharge

Automated myocardial infarction diagnosis from ECG

In the present dissertation, an automated neural network-based ECG diagnosing system was designed to detect the presence of myocardial infarction based on the hypothesis that an artificial neural network-based ECG interpretation system may improve the clinical myocardial infarction. 137 patients were included. Among them 122 had myocardial infarction, but the remaining 15 were normal. The sensitivity and the specificity of present system were 92.2% and 50.7% respectively. The sensitivity was consistent with relevant research. The relatively low specificity results from the rippling of the low pass filtering. We can conclude that neural network-based system is a promising aid for the myocardial infarction diagnosis

Identification of cardiac signals in ambulatory ECG data

The Electrocardiogram (ECG) is the primary tool for monitoring heart function. ECG signals contain vital information about the heart which informs diagnosis and treatment of cardiac conditions. The diagnosis of many cardiac arrhythmias require long term and continuous ECG data, often while the participant engages in activity. Wearable ambulatory ECG (AECG) systems, such as the common Holter system, allow heart monitoring for hours or days. The technological trajectory of AECG systems aims towards continuous monitoring during a wide range of activities with data processed locally in real time and transmitted to a monitoring centre for further analysis. Furthermore, hierarchical decision systems will allow wearable systems to produce alerts or even interventions. These functions could be integrated into smartphones.A fundamental limitation of this technology is the ability to identify heart signal characteristics in ECG signals contaminated with high amplitude and non-stationary noise. Noise processing become more severe as activity levels increase, and this is also when many heart problems are present.This thesis focuses on the identification of heart signals in AECG data recorded during participant activity. In particular, it explored ECG filters to identify major heart conditions in noisy AECG data. Gold standard methods use Extended Kalman filters with extrapolation based on sum of Gaussian models. New methods are developed using linear Kalman filtering and extrapolation based on a sum of Principal Component basis signals. Unlike the gold standard methods, extrapolation is heartcycle by heartcycle. Several variants are explored where basic signals span one or two heartcycles, and applied to single or multi-channel ECG data.The proposed methods are extensively tested against standard databases or normal and abnormal ECG data and the performance is compared to gold standard methods. Two performance metrics are used: improvement in signal to noise ratio and the observability of clinically important features in the heart signal. In all tests the proposed method performs better, and often significantly better, than the gold standard methods. It is demonstrated that abnormal ECG signals can be identified in noisy AECG data

Characterizing cardiac autonomic dynamics of fear learning in humans

Understanding transient dynamics of the autonomic nervous system during fear learning remains a critical step to translate basic research into treatment of fear-related disorders. In humans, it has been demonstrated that fear learning typically elicits transient heart rate deceleration. However, classical analyses of heart rate variability (HRV) fail to disentangle the contribution of parasympathetic and sympathetic systems, and crucially, they are not able to capture phasic changes during fear learning. Here, to gain deeper insight into the physiological underpinnings of fear learning, a novel frequency-domain analysis of heart rate was performed using a short-time Fourier transform, and instantaneous spectral estimates extracted from a point-process modeling algorithm. We tested whether spectral transient components of HRV, used as a noninvasive probe of sympathetic and parasympathetic mechanisms, can dissociate between fear conditioned and neutral stimuli. We found that learned fear elicited a transient heart rate deceleration in anticipation of noxious stimuli. Crucially, results revealed a significant increase in spectral power in the high frequency band when facing the conditioned stimulus, indicating increased parasympathetic (vagal) activity, which distinguished conditioned and neutral stimuli during fear learning. Our findings provide a proximal measure of the involvement of cardiac vagal dynamics into the psychophysiology of fear learning and extinction, thus offering new insights for the characterization of fear in mental health and illness

An approach to diagnose cardiac conditions from electrocardiogram signals.

Lu, Yan."October 2010."Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (leaves 65-68).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1. --- Introduction --- p.1Chapter 1.1 --- Electrocardiogram --- p.1Chapter 1.1.1 --- ECG Measurement --- p.2Chapter 1.1.2 --- Cardiac Conduction Pathway and ECG Morphology --- p.4Chapter 1.1.3 --- A Basic Clinical Approach to ECG Analysis --- p.6Chapter 1.2 --- Cardiovascular Disease --- p.7Chapter 1.3 --- Motivation --- p.9Chapter 1.4 --- Related Work --- p.10Chapter 1.5 --- Overview of Proposed Approach --- p.11Chapter 1.6 --- Thesis Outline --- p.13Chapter 2. --- ECG Signal Preprocessing --- p.14Chapter 2.1 --- ECG Model and Its Generalization --- p.14Chapter 2.1.1 --- ECG Dynamic Model --- p.14Chapter 2.1.2 --- Generalization of ECG Model --- p.15Chapter 2.2 --- Empirical Mode Decomposition --- p.17Chapter 2.3 --- Baseline Wander Removal --- p.20Chapter 2.3.1 --- Sources of Baseline Wander --- p.20Chapter 2.3.2 --- Baseline Wander Removal by EMD --- p.20Chapter 2.3.3 --- Experiments on Baseline Wander Removal --- p.21Chapter 2.4 --- ECG Denoising --- p.24Chapter 2.4.1 --- Introduction --- p.24Chapter 2.4.2 --- Instantaneous Frequency --- p.26Chapter 2.4.3 --- Problem of Direct ECG Denoising by EMD : --- p.28Chapter 2.4.4 --- Model-based Pre-filtering --- p.30Chapter 2.4.5 --- EMD Denoising Using Significance Test --- p.33Chapter 2.4.6 --- EMD Denoising using Instantaneous Frequency --- p.35Chapter 2.4.7 --- Experiments --- p.39Chapter 2.5 --- Chapter Summary --- p.44Chapter 3. --- ECG Classification --- p.45Chapter 3.1 --- Database --- p.45Chapter 3.2 --- Feature Extraction --- p.46Chapter 3.2.1 --- Feature Selection --- p.46Chapter 3.2.2 --- Feature Dimension Reduction by GDA --- p.48Chapter 3.3 --- Classification by Support Vector Machine --- p.50Chapter 3.4 --- Experiments --- p.53Chapter 3.4.1 --- Performance of Feature Reduction --- p.54Chapter 3.4.2 --- Performance of Classification --- p.57Chapter 3.4.3 --- Performance Comparison with Other Works --- p.60Chapter 3.5 --- Chapter Summary --- p.61Chapter 4. --- Conclusions --- p.63Reference --- p.6

Stochastic Modeling of Central Apnea Events in Preterm Infants

A near-ubiquitous pathology in very low birth weight infants is neonatal apnea, breathing pauses with slowing of the heart and falling blood oxygen. Events of substantial duration occasionally occur after an infant is discharged from the neonatal intensive care unit (NICU). It is not known whether apneas result from a predictable process or from a stochastic process, but the observation that they occur in seemingly random clusters justifies the use of stochastic models. We use a hidden-Markov model to analyze the distribution of durations of apneas and the distribution of times between apneas. The model suggests the presence of four breathing states, ranging from very stable (with an average lifetime of 12 h) to very unstable (with an average lifetime of 10 s). Although the states themselves are not visible, the mathematical analysis gives estimates of the transition rates among these states. We have obtained these transition rates, and shown how they change with post-menstrual age; as expected, the residence time in the more stable breathing states increases with age. We also extrapolated the model to predict the frequency of very prolonged apnea during the first year of life. This paradigm-stochastic modeling of cardiorespiratory control in neonatal infants to estimate risk for severe clinical events-may be a first step toward personalized risk assessment for life threatening apnea events after NICU discharge