Denoising of ECG Signal using Soft Thresholding and Empirical Mode Decomposition

Electrocardiogram (ECG) is used to record the electrical activity of the heart. Electrocardiogram (ECG), a noninvasive technique which is used generally as a primary diagnostic tool for cardiovascular diseases. A cleaned ECG signal provides necessary information about the electrophysiology of the heart diseases and ischemic changes that may occur. The electrocardiographic signals are often contaminated by noise from diverse sources. Different noises of high frequencies and low frequencies are contaminated with ECG signal that may lead wrong interpretations. The noises that commonly disturb the basic electrocardiogram are power line interference, electrode contact noise, motion artifacts, electromyography (EMG) noise, and instrumentation noise. These noises can be classified according to their frequency content. It becomes very important to minimise these disturbances in ECG signal so that accuracy and the reliability can be improve. In this paper, denoising of the ECG signal is the major objective and technique used for this purpose is based on the Empirical Mode Decomposition (EMD) followed by wavelet based soft thresholding (Rigrsure). The experiments are carried out on MIT-BIH (Massachusetts Institute of Technology Beth Israel Hospital) database

A Review Of R Peak Detection Techniques Of Electrocardiogram (ECG)

Heart disease is one of the trivial issues regarding health problem over the last few decades in India. Numerous methods have been developed with still-ongoing modifications and ideas to observe and evaluate ECG signals based on each heart beat. Majority of research revolves around arrhythmia classification, heart rate monitoring and blood pressure measurements that require highly accurate assessments of rhythm disorders which can be possible by measuring QRS complex of ECG signal, so accurate QRS detection methods are very important to be utilized. There have been proposed many approaches to find out the R peak detection to analyze the ECG signals in past few years. Most recent and efficient techniques of R peak detection have been reviewed in this paper. Techniques which have been reviewed in this paper are Pan and Tompkins, Wavelet Transform, Empirical Mode Decomposition, Hilbert-Huang Transform, Fuzzy logic systems, Artificial neural networks

Cardiac activity impacts cortical motor excitability

Human cognition and action can be influenced by internal bodily processes such as heartbeats. For instance, somatosensory perception is impaired both during the systolic phase of the cardiac cycle and when heartbeats evoke stronger cortical responses. Here, we test whether these cardiac effects originate from overall changes in cortical excitability. Cortical and corticospinal excitability were assessed using electroencephalographic and electromyographic responses to transcranial magnetic stimulation while concurrently monitoring cardiac activity with electrocardiography. Cortical and corticospinal excitability were found to be highest during systole and following stronger cortical responses to heartbeats. Furthermore, in a motor task, hand-muscle activity and the associated desynchronization of sensorimotor oscillations were stronger during systole. These results suggest that systolic cardiac signals have a facilitatory effect on motor excitability – in contrast to sensory attenuation that was previously reported for somatosensory perception. Thus, distinct time windows may exist across the cardiac cycle that either optimize perception or action

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

Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging

Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET). Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations. Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes. Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien käyttö sydänkardiografiassa sekä lääketieteellisessä 4D-kuvantamisessa Tausta: Sydän- ja verisuonitaudit ovat yleisin kuolinsyy. Näistä kuolemantapauksista lähes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron häiriöistä. Moniulotteiset mikroelektromekaaniset järjestelmät (MEMS) mahdollistavat sydänlihaksen mekaanisen liikkeen mittaamisen, mikä puolestaan tarjoaa täysin uudenlaisen ja innovatiivisen ratkaisun sydämen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjärjestelmien käyttämisen sydämen toiminnan tutkimuksessa sekä lääketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa. Menetelmät: Tämä väitöskirjatyö esittelee uuden sydämen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien käyttöön. Uudet laskennalliset lähestymistavat, jotka perustuvat signaalinkäsittelyyn ja koneoppimiseen, mahdollistavat sydämen patologisten häiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. Tässä tutkimuksessa keskitytään erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). Näiden tekniikoiden avulla voidaan mitata kardiorespiratorisen järjestelmän mekaanisia ominaisuuksia. Tulokset: Kokeelliset analyysit osoittivat, että integroimalla usean sensorin dataa voidaan mitata syketiheyttä 99% (terveillä n=29) tarkkuudella, havaita sydämen rytmihäiriöt (n=435) 95-97%, tarkkuudella, sekä havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). Lisäksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydämen 4D PET-kuvan laatua, kun liikeepätarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillä, n=9) osoitti lupaavia tuloksia sydänsykkeen ajoituksen ja intervallien sekä sydänlihasmuutosten mittaamisessa. Päätelmä: Tämän tutkimuksen tulokset osoittavat, että kardiologisilla MEMS-liikeantureilla on kliinistä potentiaalia sydämen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistää eteisvärinän (AFib), sydäninfarktin (MI) ja CAD:n havaitsemista. Lisäksi MEMS-liiketunnistus parantaa sydämen PET-kuvantamisen luotettavuutta ja laatua

Signal Processing Methods for Heart Rate Detection Using the Seismocardiogram

Cardiac diseases are one of the major causes of death. Heart monitoring/diagnostic techniques have been developed over decades to address this concern. Monitoring a vital sign such as heart rate is a powerful technique for heart abnormalities detection (e.g., arrhythmia). The novelty of this work is that offers new heart rate detection methods which are both robust and adaptive compared to existing heart rate detec- tion methods. Utilized data sets in this research have been provided from two sources of PhysioNet and a research group. In this work, utilized methods for heart rate detection include Signal Energy Thresholding (SET), Empirical Mode Decomposition (EMD) and Empirical Wavelet Transform (EWT). To the best of the author’s knowledge, this work is the first to use EMD and EWT for heart rate detection from Seismocardiogram (SCG) signal. Obtained result from applying SET to ECG signal is selected as our ground truth. Then, all three methods are used for heart rate detection from the SCG signal. The average error of SET method, EWT and EMD respectively 13.9 ms, 13.8 ms and 16 ms. Based on the obtained results, EMD and EWT are promising techniques for heart rate detection and interpretation from the SCG signal. Another contribution of this work is arrhythmia detection using EWT. EWT provides us with the instantaneous frequency changes of the corresponding modes to ECG signal. Based on the estimated power spectral density of each mode, power spectral density of arrhythmia affected ECG is higher (≥ 50dB) compared to the power spectral density of a normal ECG (≤ 20dB). This provides the potential for arrhythmia detection using EWT

Signal Processing Methods for Heart Rate Detection Using the Seismocardiogram

Cardiac diseases are one of the major causes of death. Heart monitoring/diagnostic techniques have been developed over decades to address this concern. Monitoring a vital sign such as heart rate is a powerful technique for heart abnormalities detection (e.g., arrhythmia). The novelty of this work is that offers new heart rate detection methods which are both robust and adaptive compared to existing heart rate detec- tion methods. Utilized data sets in this research have been provided from two sources of PhysioNet and a research group. In this work, utilized methods for heart rate detection include Signal Energy Thresholding (SET), Empirical Mode Decomposition (EMD) and Empirical Wavelet Transform (EWT). To the best of the author’s knowledge, this work is the first to use EMD and EWT for heart rate detection from Seismocardiogram (SCG) signal. Obtained result from applying SET to ECG signal is selected as our ground truth. Then, all three methods are used for heart rate detection from the SCG signal. The average error of SET method, EWT and EMD respectively 13.9 ms, 13.8 ms and 16 ms. Based on the obtained results, EMD and EWT are promising techniques for heart rate detection and interpretation from the SCG signal. Another contribution of this work is arrhythmia detection using EWT. EWT provides us with the instantaneous frequency changes of the corresponding modes to ECG signal. Based on the estimated power spectral density of each mode, power spectral density of arrhythmia affected ECG is higher (≥ 50dB) compared to the power spectral density of a normal ECG (≤ 20dB). This provides the potential for arrhythmia detection using EWT