Reduction of CPR artifacts in the ventricular fibrillation ECG by coherent line removal

<p>Abstract</p> <p>Background</p> <p>Interruption of cardiopulmonary resuscitation (CPR) impairs the perfusion of the fibrillating heart, worsening the chance for successful defibrillation. Therefore ECG-analysis <it>during ongoing chest compression </it>could provide a considerable progress in comparison with standard analysis techniques working only during "hands-off" intervals.</p> <p>Methods</p> <p>For the reduction of CPR-related artifacts in ventricular fibrillation ECG we use a localized version of the <it>coherent line removal </it>algorithm developed by Sintes and Schutz. This method can be used for removal of periodic signals with sufficiently coupled harmonics, and can be adapted to specific situations by optimal choice of its parameters (e.g., the number of harmonics considered for analysis and reconstruction). Our testing was done with 14 different human ventricular fibrillation (VF) ECGs, whose fibrillation band lies in a frequency range of [1 Hz, 5 Hz]. The VF-ECGs were mixed with 12 different ECG-CPR-artifacts recorded in an animal experiment during asystole. The length of each of the ECG-data was chosen to be 20 sec, and testing was done for all 168 = 14 × 12 pairs of data. VF-to-CPR ratio was chosen as -20 dB, -15 dB, -10 dB, -5 dB, 0 dB, 5 dB and 10 dB. Here -20 dB corresponds to the highest level of CPR-artifacts.</p> <p>Results</p> <p>For non-optimized <it>coherent line removal </it>based on signals with a VF-to-CPR ratio of -20 dB, -15 dB, -10 dB, -5 dB and 0 dB, the signal-to-noise gains (SNR-gains) were 9.3 ± 2.4 dB, 9.4 ± 2.4 dB, 9.5 ± 2.5 dB, 9.3 ± 2.5 dB and 8.0 ± 2.7 (mean ± std, <it>n </it>= 168), respectively. Characteristically, an original VF-to-CPR ratio of -10 dB, corresponds to a variance ratio <it>var</it>(VF):<it>var</it>(CPR) = 1:10. An improvement by 9.5 dB results in a restored VF-to-CPR ratio of -0.5 dB, corresponding to a variance ratio <it>var</it>(VF):<it>var</it>(CPR) = 1:1.1, the variance of the CPR in the signal being reduced by a factor of 8.9.</p> <p>Discussion</p> <p>The <it>localized coherent line removal </it>algorithm uses the information of a single ECG channel. In contrast to multi-channel algorithms, no additional information such as thorax impedance, blood pressure, or pressure exerted on the sternum during CPR is required. Predictors of defibrillation success such as mean and median frequency of VF-ECGs containing CPR-artifacts are prone to being governed by the harmonics of the artifacts. Reduction of CPR-artifacts is therefore necessary for determining reliable values for estimators of defibrillation success.</p> <p>Conclusions</p> <p>The <it>localized coherent line removal </it>algorithm reduces CPR-artifacts in VF-ECG, but does not eliminate them. Our SNR-improvements are in the same range as offered by multichannel methods of Rheinberger et al., Husoy et al. and Aase et al. The latter two authors dealt with different ventricular rhythms (VF and VT), whereas here we dealt with VF, only. Additional developments are necessary before the algorithm can be tested in real CPR situations.</p

Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition

A novel noise filtering algorithm based on ensemble empirical mode decomposition (EEMD) is proposed to remove artifacts in electrocardiogram (ECG) traces. Three noise patterns with different power—50 Hz, EMG, and base line wander – were embedded into simulated and real ECG signals. Traditional IIR filter, Wiener filter, empirical mode decomposition (EMD) and EEMD were used to compare filtering performance. Mean square error between clean and filtered ECGs was used as filtering performance indexes. Results showed that high noise reduction is the major advantage of the EEMD based filter, especially on arrhythmia ECGs

Seinale prozesaketan eta ikasketa automatikoan oinarritutako ekarpenak bihotz-erritmoen analisirako bihotz-biriketako berpiztean

Tesis inglés 218 p. -- Tesis euskera 220 p.Out-of-hospital cardiac arrest (OHCA ) is characterized by the sudden loss of the cardiac function, andcauses around 10% of the total mortality in developed countries. Survival from OHCA depends largelyon two factors: early defibrillation and early cardiopulmonary resuscitation (CPR). The electrical shock isdelivered using a shock advice algorithm (SAA) implemented in defibrillators. Unfortunately, CPR mustbe stopped for a reliable SAA analysis because chest compressions introduce artefacts in the ECG. Theseinterruptions in CPR have an adverse effect on OHCA survival. Since the early 1990s, many efforts havebeen made to reliably analyze the rhythm during CPR. Strategies have mainly focused on adaptive filtersto suppress the CPR artefact followed by SAAs of commercial defibrillators. However, these solutionsdid not meet the American Heart Association¿s (AHA) accuracy requirements for shock/no-shockdecisions. A recent approach, which replaces the commercial SAA by machine learning classifiers, hasdemonstrated that a reliable rhythm analysis during CPR is possible. However, defibrillation is not theonly treatment needed during OHCA, and depending on the clinical context a finer rhythm classificationis needed. Indeed, an optimal OHCA scenario would allow the classification of the five cardiac arrestrhythm types that may be present during resuscitation. Unfortunately, multiclass classifiers that allow areliable rhythm analysis during CPR have not yet been demonstrated. On all of these studies artefactsoriginate from manual compressions delivered by rescuers. Mechanical compression devices, such as theLUCAS or the AutoPulse, are increasingly used in resuscitation. Thus, a reliable rhythm analysis duringmechanical CPR is becoming critical. Unfortunately, no AHA compliant algorithms have yet beendemonstrated during mechanical CPR. The focus of this thesis work is to provide new or improvedsolutions for rhythm analysis during CPR, including shock/no-shock decision during manual andmechanical CPR and multiclass classification during manual CPR

Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition

[[abstract]]A novel noise filtering algorithm based on ensemble empirical mode decomposition (EEMD) is proposed to remove artifacts in electrocardiogram (ECG) traces. Three noise patterns with different power—50 Hz, EMG, and base line wander – were embedded into simulated and real ECG signals. Traditional IIR filter, Wiener filter, empirical mode decomposition (EMD) and EEMD were used to compare filtering performance. Mean square error between clean and filtered ECGs was used as filtering performance indexes. Results showed that high noise reduction is the major advantage of the EEMD based filter, especially on arrhythmia ECGs

Diagnóstico del ritmo cardiaco durante la realización de compresiones torácicas en paradas cardiorrespiratorias atendidas con desfibrilador externo automático (DEA).

[ES]La parada cardiorrespiratoria extra hospitalaria es una de las principales causas de mortalidad en los países desarrollados. La única manera de combatir su fatal desenlace es efectuar una intervención rápida y eficaz. En este país, ambos factores se ven condicionados por los servicios que cada comunidad ofrece en relación a la cardioprotección de espacios públicos. Estos servicios van desde el despliegue de flotas de ambulancias garantizando la llegada del equipo de salvamento en tiempos de en torno a diez minutos hasta la colocación estratégica de desfibriladores externos automáticos en lugares públicos concurridos. Este trabajo pretende mejorar la eficiencia de los desfibriladores externos automáticos mediante el desarrollo de un algoritmo de diagnostico que posibilite una desfibrilación más temprana y eficiente, aumentando así las probabilidades de supervivencia de los pacientes de paradas cardiorrespiratorias extrahospitalarias.[EU]Ospitale kanpoko bihotz-biriken gelditzea garatutako herrialdeen heriotza-kausa nagusietako bat da. Bere bukaera larriari aurre egiteko bide bakarra esku-hartze arina eta eraginkorra da. Herrialde honetan, bi faktore hauek, komunitate bakoitzak eskaintzen dituzten espazio publikoen kardio-babeserako zerbitzuen aurrean baldintzatuak ikusten dira. Zerbitzu hauek anbulantzien hedapenetik, hamar minutuko denboran erreskate taldearen etorrera bermatuz kanpoko desfibriladore automatikoen kokapen estrategikora doaz. Lan honek kanpoko desfibriladore automatikoen eraginkortasuna hobetu nahi du, desfibrilazioa lehenago eta eraginkorrago ahalbidetzen duen diagnostiko algoritmo bat garatuz. Horrela, kanpoko bihotz-biriken gelditzearen jasaileen biziraupen aukerak areagotuko dira.[EN]Out of hospital cardiac arrest is one of the main causes of mortality in developed countries. The only way to fight its fatal outcome is to make a quick and effective intervention. In this country, both factors are conditioned by the services that each community offers in relation to the heart protecction of public spaces. These services range from the deployment of ambulance fleets guaranteeing the arrival of the rescue team in times of ten minutes to the strategic placement of automatic external defibrillators in crowded public places. This work aims to improve the efficiency of automatic external defibrillators by developing a diagnostic algorithm that enables earlier and more efficient defibrillation, thus increasing the chances of survival of patients with out-of-hospital cardiopulmonary arrest

Evaluation of Data Processing and Artifact Removal Approaches Used for Physiological Signals Captured Using Wearable Sensing Devices during Construction Tasks

Wearable sensing devices (WSDs) have enormous promise for monitoring construction worker safety. They can track workers and send safety-related information in real time, allowing for more effective and preventative decision making. WSDs are particularly useful on construction sites since they can track workers’ health, safety, and activity levels, among other metrics that could help optimize their daily tasks. WSDs may also assist workers in recognizing health-related safety risks (such as physical fatigue) and taking appropriate action to mitigate them. The data produced by these WSDs, however, is highly noisy and contaminated with artifacts that could have been introduced by the surroundings, the experimental apparatus, or the subject’s physiological state. These artifacts are very strong and frequently found during field experiments. So, when there is a lot of artifacts, the signal quality drops. Recently, artifacts removal has been greatly enhanced by developments in signal processing, which has vastly enhanced the performance. Thus, the proposed review aimed to provide an in-depth analysis of the approaches currently used to analyze data and remove artifacts from physiological signals obtained via WSDs during construction-related tasks. First, this study provides an overview of the physiological signals that are likely to be recorded from construction workers to monitor their health and safety. Second, this review identifies the most prevalent artifacts that have the most detrimental effect on the utility of the signals. Third, a comprehensive review of existing artifact-removal approaches were presented. Fourth, each identified artifact detection and removal approach was analyzed for its strengths and weaknesses. Finally, in conclusion, this review provides a few suggestions for future research for improving the quality of captured physiological signals for monitoring the health and safety of construction workers using artifact removal approaches

Motion Artifact Processing Techniques for Physiological Signals

The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an ageing population and this is placing an ever-increasing burden on our healthcare systems. The urgent need to address this so called healthcare \time bomb" has led to a rapid growth in research into ubiquitous, pervasive and distributed healthcare technologies where recent advances in signal acquisition, data storage and communication are helping such systems become a reality. However, similar to recordings performed in the hospital environment, artifacts continue to be a major issue for these systems. The magnitude and frequency of artifacts can vary signicantly depending on the recording environment with one of the major contributions due to the motion of the subject or the recording transducer. As such, this thesis addresses the challenges of the removal of this motion artifact removal from various physiological signals. The preliminary investigations focus on artifact identication and the tagging of physiological signals streams with measures of signal quality. A new method for quantifying signal quality is developed based on the use of inexpensive accelerometers which facilitates the appropriate use of artifact processing methods as needed. These artifact processing methods are thoroughly examined as part of a comprehensive review of the most commonly applicable methods. This review forms the basis for the comparative studies subsequently presented. Then, a simple but novel experimental methodology for the comparison of artifact processing techniques is proposed, designed and tested for algorithm evaluation. The method is demonstrated to be highly eective for the type of artifact challenges common in a connected health setting, particularly those concerned with brain activity monitoring. This research primarily focuses on applying the techniques to functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) data due to their high susceptibility to contamination by subject motion related artifact. Using the novel experimental methodology, complemented with simulated data, a comprehensive comparison of a range of artifact processing methods is conducted, allowing the identication of the set of the best performing methods. A novel artifact removal technique is also developed, namely ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA), which provides the best results when applied on fNIRS data under particular conditions. Four of the best performing techniques were then tested on real ambulatory EEG data contaminated with movement artifacts comparable to those observed during in-home monitoring. It was determined that when analysing EEG data, the Wiener lter is consistently the best performing artifact removal technique. However, when employing the fNIRS data, the best technique depends on a number of factors including: 1) the availability of a reference signal and 2) whether or not the form of the artifact is known. It is envisaged that the use of physiological signal monitoring for patient healthcare will grow signicantly over the next number of decades and it is hoped that this thesis will aid in the progression and development of artifact removal techniques capable of supporting this growth

High Resolution Multi-parametric Diagnostics and Therapy of Atrial Fibrillation: Chasing Arrhythmia Vulnerabilities in the Spatial Domain

After a century of research, atrial fibrillation (AF) remains a challenging disease to study and exceptionally resilient to treatment. Unfortunately, AF is becoming a massive burden on the health care system with an increasing population of susceptible elderly patients and expensive unreliable treatment options. Pharmacological therapies continue to be disappointingly ineffective or are hampered by side effects due to the ubiquitous nature of ion channel targets throughout the body. Ablative therapy for atrial tachyarrhythmias is growing in acceptance. However, ablation procedures can be complex, leading to varying levels of recurrence, and have a number of serious risks. The high recurrence rate could be due to the difficulty of accurately predicting where to draw the ablation lines in order to target the pathophysiology that initiates and maintains the arrhythmia or an inability to distinguish sub-populations of patients who would respond well to such treatments. There are electrical cardioversion options but there is not a practical implanted deployment of this strategy. Under the current bioelectric therapy paradigm there is a trade-off between efficacy and the pain and risk of myocardial damage, all of which are positively correlated with shock strength. Contrary to ventricular fibrillation, pain becomes a significant concern for electrical defibrillation of AF due to the fact that a patient is conscious when experiencing the arrhythmia. Limiting the risk of myocardial injury is key for both forms of fibrillation. In this project we aim to address the limitations of current electrotherapy by diverging from traditional single shock protocols. We seek to further clarify the dynamics of arrhythmia drivers in space and to target therapy in both the temporal and spatial domain; ultimately culminating in the design of physiologically guided applied energy protocols. In an effort to provide further characterization of the organization of AF, we used transillumination optical mapping to evaluate the presence of three-dimensional electrical substrate variations within the transmural wall during acutely induced episodes of AF. The results of this study suggest that transmural propagation may play a role in AF maintenance mechanisms, with a demonstrated range of discordance between the epicardial and endocardial dynamic propagation patterns. After confirming the presence of epi-endo dyssynchrony in multiple animal models, we further investigated the anatomical structure to look for regional trends in transmural fiber orientation that could help explain the spectrum of observed patterns. Simultaneously, we designed and optimized a multi-stage, multi-path defibrillation paradigm that can be tailored to individual AF frequency content in the spatial and temporal domain. These studies continue to drive down the defibrillation threshold of electrotherapies in an attempt to achieve a pain-free AF defibrillation solution. Finally, we designed and characterized a novel platform of stretchable electronics that provide instrumented membranes across the epicardial surface or implanted within the transmural wall to provide physiological feedback during electrotherapy beyond just the electrical state of the tissue. By combining a spatial analysis of the arrhythmia drivers, the energy delivered and the resulting damage, we hope to enhance the biophysical understanding of AF electrical cardioversion and xiii design an ideal targeted energy delivery protocol to improve upon all limitations of current electrotherapy