Uma arquitetura para detecção online de transientes em sinais de eletrocardiograma sobre o protocolo IEEE 802.3 com PM-AH

The occurrence of transients in electrocardiogram (ECG) signals indicates an electrical phenomenon outside the heart. Thus, the identification of transients has been the most-used methodology in medical analysis since the invention of the electrocardiograph (device responsible for benchmarking of electrocardiogram signals). There are few papers related to this subject, which compels the creation of an architecture to do the pre-processing of this signal in order to identify transients. This paper proposes a method based on the signal energy of the Hilbert transform of electrocardiogram, being an alternative to methods based on morphology of the signal. This information will determine the creation of frames of the MP-HA protocol responsible for transmitting the ECG signals through an IEEE 802.3 network to a computing device. That, in turn, may perform a process to automatically sort the signal, or to present it to a doctor so that he can do the sorting manually

Desenvolvimento de métodos de processamento e inteligência computacional no ECG ambulatorial

A monitorização ambulatorial do eletrocardiograma (ECG) permite seguir as atividades cotidianas do paciente durante períodos de 24 horas (ou ainda maiores) possibilitando o estudo de casos que pudessem ter episódios arrítmicos fatais. Entretanto, o maior desafio tecnológico que este tipo de monitorização enfrenta é a perda de informação pela presença de ruídos e artefatos quando o paciente se move. A análise do intervalo QT de despolarização e repolarização ventricular do eletrocardiograma superficial é uma técnica não invasiva com um grande valor para a diagnose e prognósticos de cardiopatias e neuropatias, assim como para a predição da morte cardíaca súbita. A análise do desvio padrão do intervalo QT proporciona informação sobre a dispersão (temporal ou espacial) da repolarização ventricular, entretanto a influencia do ruído provoca erros na detecção do final da onda T que são apreciáveis devido ao fato dos valores pequenos do desvio padrão do QT tanto para sujeitos patológicos e quanto para os sãos. O objetivo geral desta tese é melhorar os métodos de processamento do sinal de ECG ambulatorial usando inteligência computacional, especificamente os métodos relacionados com a detecção do final da onda T, e os de reconhecimento morfológico de batimentos que invalidam a análise da variabilidade do intervalo QT. É proposto e validado (em termos de exatidão e precisão) um novo método e algoritmo para estimar o final da onda T baseado no calculo de áreas de trapézios, empregando sinais da base de dados QT da Physionet. O desempenho do método proposto foi testado e comparado com um dos métodos mais usados para detectar o final da onda T: o método baseado no limiar na primeira derivada. O método de inteligência computacional sugerido combina a extração de características usando o método de análise de componentes principais não lineares e a rede neural de tipo perceptron multicamada. O método de áreas de trapézios teve um bom desempenho em condições ruidosas e não depende de nenhum limiar empírico, sendo adequado para situações com níveis de elevados de ruído de banda larga. O método de reconhecimento morfológico de batimentos foi avaliado com sinais ambulatoriais com e sem artefatos pertencentes a bases de dados de prestigio internacional, e mostrou um bom desempenho.

Transcatheter aortic valve implantation with a self-expanding nitinol bioprosthesis: Prediction of the Need for Permanent Pacemaker Using Simple Baseline and Procedural Characteristics

Objective: To ascertain incidence and predictors of new permanent pacemaker (PPM) following transcatheter aortic valve implantation (TAVI) with the self-expanding aortic bioprosthesis. Background: TAVI with the Medtronic Corevalve (MCV) Revalving System (Medtronic, Minneapolis, MN) has been associated with important post-procedural conduction abnormalities and frequent need for PPM. Methods: Overall, 73 consecutive patients with severe symptomatic AS underwent TAVI with the MCV at two institutions; 10 patients with previous pacemaker and 3 patients with previous aortic valve replacement were excluded for this analysis. Clinical, echocardiographic, and procedural data were collected prospectively in a dedicated database. A standard 12-lead ECG was recorded in all patients at baseline, after the procedure and predischarge. Decision to implant PPM was taken according to current guidelines. Logistic multivariable modeling was applied to identify independent predictors of PPM at discharge. Results: Patients exhibited high-risk features as evidenced by advanced age (mean = 82.1 +/- 6.2 years) and high surgical scores (logistic EuroSCORE 23.0 +/- 12.8%, STS score 9.4 +/- 6.9%). The incidence of new PPM was 28.3%. Interventricular septum thickness and logistic Euroscore were the baseline independent predictors of PPM. When procedural variables were included, the independent predictors of PPM were interventricular septum thickness (OR 0.52; 95% CI 0.320.85) and the distance between noncoronary cusp and the distal edge of the prosthesis (OR 1.37; 95% CI 1.031.83). Conclusions: Conduction abnormalities are frequently observed after TAVI with self-expandable bioprosthesis and definitive pacing is required in about a third of the patients, with a clear association with depth of implant and small interventricular septum thickness. (c) 2011 Wiley Periodicals, Inc.

Seizure prediction and control in epilepsy

The first part of my thesis presents an overview of the different approaches used in the past two decades in the attempt to forecast epileptic seizure on the basis of intracranial and scalp EEG. Past research could reveal some value of linear and nonlinear algorithms to detect EEG features changing over different phases of the epileptic cycle. However, their exact value for seizure prediction, in terms of sensitivity and specificity, is still discussed and has to be evaluated. In particular, the monitored EEG features may fluctuate with the vigilance state and lead to false alarms. Recently, such a dependency on vigilance states has been reported for some seizure prediction methods, suggesting a reduced reliability. An additional factor limiting application and validation of most seizure-prediction techniques is their computational load. For the first time, the reliability of permutation entropy [PE] was verified in seizure prediction on scalp EEG data, contemporarily controlling for its dependency on different vigilance states. PE was recently introduced as an extremely fast and robust complexity measure for chaotic time series and thus suitable for online application even in portable systems. The capability of PE to distinguish between preictal and interictal state has been demonstrated using Receiver Operating Characteristics (ROC) analysis. Correlation analysis was used to assess dependency of PE on vigilance states. Scalp EEG-Data from two right temporal epileptic lobe (RTLE) patients and from one patient with right frontal lobe epilepsy were analysed. The last patient was included only in the correlation analysis, since no datasets including seizures have been available for him. The ROC analysis showed a good separability of interictal and preictal phases for both RTLE patients, suggesting that PE could be sensitive to EEG modifications, not visible on visual inspection, that might occur well in advance respect to the EEG and clinical onset of seizures. However, the simultaneous assessment of the changes in vigilance showed that: a) all seizures occurred in association with the transition of vigilance states; b) PE was sensitive in detecting different vigilance states, independently of seizure occurrences. Due to the limitations of the datasets, these results cannot rule out the capability of PE to detect preictal states. However, the good separability between pre- and interictal phases might depend exclusively on the coincidence of epileptic seizure onset with a transition from a state of low vigilance to a state of increased vigilance. The finding of a dependency of PE on vigilance state is an original finding, not reported in literature, and suggesting the possibility to classify vigilance states by means of PE in an authomatic and objectic way. The second part of my thesis provides the description of a novel behavioral task based on motor imagery skills, firstly introduced (Bruzzo et al. 2007), in order to study mental simulation of biological and non-biological movement in paranoid schizophrenics (PS). Immediately after the presentation of a real movement, participants had to imagine or re-enact the very same movement. By key release and key press respectively, participants had to indicate when they started and ended the mental simulation or the re-enactment, making it feasible to measure the duration of the simulated or re-enacted movements. The proportional error between duration of the re-enacted/simulated movement and the template movement were compared between different conditions, as well as between PS and healthy subjects. Results revealed a double dissociation between the mechanisms of mental simulation involved in biological and non-biologial movement simulation. While for PS were found large errors for simulation of biological movements, while being more acurate than healthy subjects during simulation of non-biological movements. Healthy subjects showed the opposite relationship, making errors during simulation of non-biological movements, but being most accurate during simulation of non-biological movements. However, the good timing precision during re-enactment of the movements in all conditions and in both groups of participants suggests that perception, memory and attention, as well as motor control processes were not affected. Based upon a long history of literature reporting the existence of psychotic episodes in epileptic patients, a longitudinal study, using a slightly modified behavioral paradigm, was carried out with two RTLE patients, one patient with idiopathic generalized epilepsy and one patient with extratemporal lobe epilepsy. Results provide strong evidence for a possibility to predict upcoming seizures in RTLE patients behaviorally. In the last part of the thesis it has been validated a behavioural strategy based on neurobiofeedback training, to voluntarily control seizures and to reduce there frequency. Three epileptic patients were included in this study. The biofeedback was based on monitoring of slow cortical potentials (SCPs) extracted online from scalp EEG. Patients were trained to produce positive shifts of SCPs. After a training phase patients were monitored for 6 months in order to validate the ability of the learned strategy to reduce seizure frequency. Two of the three refractory epileptic patients recruited for this study showed improvements in self-management and reduction of ictal episodes, even six months after the last training session.

Combined Evaluation of ST-segment Elevation in lead AVR and ST-segment Depression in other Leads Enhances Prediction of in-hospital Cardiovascular Death in Patients with Non ST-segment Elevation Acute Coronary Syndrome

Objective: To investigate the prognostic significance of ST-segment elevation (STE) in aVR associated with ST-segment depression (STD) in other leads in patients with non-STE acute coronary syndrome (NSTE-ACS). Background: In NSTE-ACS patients, STD has been extensively associated with severe coronary lesions and poor outcomes. The prognostic role of STE in aVR is uncertain. Methods: We enrolled 888 consecutive patients with NSTE-ACS. They were divided into two groups according to the presence or not on admission ECG of aVR STE≥ 1mm and STD (defined as high risk ECG pattern). The primary and secondary endpoints were: in-hospital cardiovascular (CV) death and the rate of culprit left main disease (LMD). Results: Patients with high risk ECG pattern (n=121) disclosed a worse clinical profile compared to patients (n=575) without [median GRACE (Global-Registry-of-Acute-Coronary-Events) risk score =142 vs. 182, respectively]. A total of 75% of patients underwent coronary angiography. The rate of in-hospital CV death was 3.9%. On multivariable analysis patients who had the high risk ECG pattern showed an increased risk of CV death (OR=2.88, 95%CI 1.05-7.88) and culprit LMD (OR=4.67,95%CI 1.86-11.74) compared to patients who had not. The prognostic significance of the high risk ECG pattern was maintained even after adjustment for the GRACE risk score (OR = 2.28, 95%CI:1.06-4.93 and OR = 4.13, 95%CI:2.13-8.01, for primary and secondary endpoint, respectively). Conclusions: STE in aVR associated with STD in other leads predicts in-hospital CV death and culprit LMD. This pattern may add prognostic information in patients with NSTE-ACS on top of recommended scoring system.

A Phase Space Box-counting based Method for Arrhythmia Prediction from Electrocardiogram Time Series

Arrhythmia is one kind of cardiovascular diseases that give rise to the number of deaths and potentially yields immedicable danger. Arrhythmia is a life threatening condition originating from disorganized propagation of electrical signals in heart resulting in desynchronization among different chambers of the heart. Fundamentally, the synchronization process means that the phase relationship of electrical activities between the chambers remains coherent, maintaining a constant phase difference over time. If desynchronization occurs due to arrhythmia, the coherent phase relationship breaks down resulting in chaotic rhythm affecting the regular pumping mechanism of heart. This phenomenon was explored by using the phase space reconstruction technique which is a standard analysis technique of time series data generated from nonlinear dynamical system. In this project a novel index is presented for predicting the onset of ventricular arrhythmias. Analysis of continuously captured long-term ECG data recordings was conducted up to the onset of arrhythmia by the phase space reconstruction method, obtaining 2-dimensional images, analysed by the box counting method. The method was tested using the ECG data set of three different kinds including normal (NR), Ventricular Tachycardia (VT), Ventricular Fibrillation (VF), extracted from the Physionet ECG database. Statistical measures like mean (μ), standard deviation (σ) and coefficient of variation (σ/μ) for the box-counting in phase space diagrams are derived for a sliding window of 10 beats of ECG signal. From the results of these statistical analyses, a threshold was derived as an upper bound of Coefficient of Variation (CV) for box-counting of ECG phase portraits which is capable of reliably predicting the impeding arrhythmia long before its actual occurrence. As future work of research, it was planned to validate this prediction tool over a wider population of patients affected by different kind of arrhythmia, like atrial fibrillation, bundle and brunch block, and set different thresholds for them, in order to confirm its clinical applicability.

Association of major and minor ECG abnormalities with coronary heart disease events

In populations of older adults, prediction of coronary heart disease (CHD) events through traditional risk factors is less accurate than in middle-aged adults. Electrocardiographic (ECG) abnormalities are common in older adults and might be of value for CHD prediction.

Online distribution management framework by incorporating wind generation

In distribution system operations, dispatchers at control center closely monitor system operating limits to ensure system reliability and adequacy. This reliability is partly due to the provision of remote controllable tie and sectionalizing switches. While the stochastic nature of wind generation can impact the level of wind energy penetration in the network, an estimate of the output from wind on hourly basis can be extremely useful. Under any operating conditions, the switching actions require human intervention and can be an extremely stressful task. Currently, handling a set of switching combinations with the uncertainty of distributed wind generation as part of the decision variables has been nonexistent. This thesis proposes a three-fold online management framework: (1) prediction of wind speed, (2) estimation of wind generation capacity, and (3) enumeration of feasible switching combinations. The proposed methodology is evaluated on 29-node test system with 8 remote controllable switches and two wind farms of 18MW and 9MW nameplate capacities respectively for generating the sequence of system reconfiguration states during normal and emergency conditions.

Finite element analysis for prediction of bone strength

Article preview View full access options BoneKEy Reports | Review Print Email Share/bookmark Finite element analysis for prediction of bone strength Philippe K Zysset, Enrico Dall'Ara, Peter Varga & Dieter H Pahr Affiliations Corresponding author BoneKEy Reports (2013) 2, Article number: 386 (2013) doi:10.1038/bonekey.2013.120 Received 03 January 2013 Accepted 25 June 2013 Published online 07 August 2013 Article tools Citation Reprints Rights & permissions Abstract Abstract• References• Author information Finite element (FE) analysis has been applied for the past 40 years to simulate the mechanical behavior of bone. Although several validation studies have been performed on specific anatomical sites and load cases, this study aims to review the predictability of human bone strength at the three major osteoporotic fracture sites quantified in recently completed in vitro studies at our former institute. Specifically, the performance of FE analysis based on clinical computer tomography (QCT) is compared with the ones of the current densitometric standards, bone mineral content, bone mineral density (BMD) and areal BMD (aBMD). Clinical fractures were produced in monotonic axial compression of the distal radii, vertebral sections and in side loading of the proximal femora. QCT-based FE models of the three bones were developed to simulate as closely as possible the boundary conditions of each experiment. For all sites, the FE methodology exhibited the lowest errors and the highest correlations in predicting the experimental bone strength. Likely due to the improved CT image resolution, the quality of the FE prediction in the peripheral skeleton using high-resolution peripheral CT was superior to that in the axial skeleton with whole-body QCT. Because of its projective and scalar nature, the performance of aBMD in predicting bone strength depended on loading mode and was significantly inferior to FE in axial compression of radial or vertebral sections but not significantly inferior to FE in side loading of the femur. Considering the cumulated evidence from the published validation studies, it is concluded that FE models provide the most reliable surrogates of bone strength at any of the three fracture sites.

Asynchronous ECG time sampling: Saving bits with Golomb-Rice encoding

We present a technique for online compression of ECG signals using the Golomb-Rice encoding algorithm. This is facilitated by a novel time encoding asynchronous analog-to-digital converter targeted for low-power, implantable, long-term bio-medical sensing applications. In contrast to capturing the actual signal (voltage) values the asynchronous time encoder captures and encodes the time information at which predefined changes occur in the signal thereby minimizing the sensor's energy use and the number of bits we store to represent the information by not capturing unnecessary samples. The time encoder transforms the ECG signal data to pure time information that has a geometric distribution such that the Golomb-Rice encoding algorithm can be used to further compress the data. An overall online compression rate of about 6 times is achievable without the usual computations associated with most compression methods.

An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.

Usefulness of electrocardiographic parameters for risk prediction in arrhythmogenic right ventricular dysplasia

The value of electrocardiographic findings predicting adverse outcome in patients with arrhythmogenic right ventricular dysplasia (ARVD) is not well known. We hypothesized that ventricular depolarization and repolarization abnormalities on the 12-lead surface electrocardiogram (ECG) predict adverse outcome in patients with ARVD. ECGs of 111 patients screened for the 2010 ARVD Task Force Criteria from 3 Swiss tertiary care centers were digitized and analyzed with a digital caliper by 2 independent observers blinded to the outcome. ECGs were compared in 2 patient groups: (1) patients with major adverse cardiovascular events (MACE: a composite of cardiac death, heart transplantation, survived sudden cardiac death, ventricular fibrillation, sustained ventricular tachycardia, or arrhythmic syncope) and (2) all remaining patients. A total of 51 patients (46%) experienced MACE during a follow-up period with median of 4.6 years (interquartile range 1.8 to 10.0). Kaplan-Meier analysis revealed reduced times to MACE for patients with repolarization abnormalities according to Task Force Criteria (p = 0.009), a precordial QRS amplitude ratio (∑QRS mV V1 to V3/∑QRS mV V1 to V6) of ≤ 0.48 (p = 0.019), and QRS fragmentation (p = 0.045). In multivariable Cox regression, a precordial QRS amplitude ratio of ≤ 0.48 (hazard ratio [HR] 2.92, 95% confidence interval [CI] 1.39 to 6.15, p = 0.005), inferior leads T-wave inversions (HR 2.44, 95% CI 1.15 to 5.18, p = 0.020), and QRS fragmentation (HR 2.65, 95% CI 1.1 to 6.34, p = 0.029) remained as independent predictors of MACE. In conclusion, in this multicenter, observational, long-term study, electrocardiographic findings were useful for risk stratification in patients with ARVD, with repolarization criteria, inferior leads TWI, a precordial QRS amplitude ratio of ≤ 0.48, and QRS fragmentation constituting valuable variables to predict adverse outcome.

Bufferless Compression of Asynchronously Sampled ECG Signals in Cubic Hermitian Vector Space.

Asynchronous level crossing sampling analog-to-digital converters (ADCs) are known to be more energy efficient and produce fewer samples than their equidistantly sampling counterparts. However, as the required threshold voltage is lowered, the number of samples and, in turn, the data rate and the energy consumed by the overall system increases. In this paper, we present a cubic Hermitian vector-based technique for online compression of asynchronously sampled electrocardiogram signals. The proposed method is computationally efficient data compression. The algorithm has complexity O(n), thus well suited for asynchronous ADCs. Our algorithm requires no data buffering, maintaining the energy advantage of asynchronous ADCs. The proposed method of compression has a compression ratio of up to 90% with achievable percentage root-mean-square difference ratios as a low as 0.97. The algorithm preserves the superior feature-to-feature timing accuracy of asynchronously sampled signals. These advantages are achieved in a computationally efficient manner since algorithm boundary parameters for the signals are extracted a priori.

Prediction of hidden multiferroic order in graphene zigzag ribbons

An electronic phase with coexisting magnetic and ferroelectric order is predicted for graphene ribbons with zigzag edges. The electronic structure of the system is described with a mean-field Hubbard model that yields results very similar to those of density functional calculations. Without further approximations, the mean-field theory is recasted in terms of a BCS wave function for electron-hole pairs in the edge bands. The BCS coherence present in each spin channel is related to spin-resolved electric polarization. Although the total electric polarization vanishes, due to an internal phase locking of the BCS state, strong magnetoelectric effects are expected in this system. The formulation naturally accounts for the two gaps in the quasiparticle spectrun, Δ0 and Δ1, and relates them to the intraband and interband self-energies.