Nonlinear Analysis of an Electroencephalogram (ECG) During Epileptic Seizure

A mathematical analysis of an electroencephalogram of a human Brain during an epileptic seizure shows that the K2 entropy decreases as compared to a clinically normal brain while the dimension of the attractor does not show significant deviation.

ECG scar quantification correlates with cardiac magnetic resonance scar size and prognostic factors in Chagas' disease

Objective To test the hypothesis that 12-lead ECG QRS scoring quantifies myocardial scar and correlates with disease severity in Chagas' heart disease. Design Patients underwent 12-lead ECG for QRS scoring and cardiac magnetic resonance with late gadolinium enhancement (CMR-LGE) to assess myocardial scar. Setting University of Sao Paulo Medical School, Sao Paulo, Brazil. Patients 44 Seropositive patients with Chagas' disease without a history of myocardial infarction and at low risk for coronary artery disease. Main outcome measures Correlation between QRS score, CMR-LGE scar size and left ventricular ejection fraction. Relation between QRS score, heart failure (HF) class and history of ventricular tachycardia (VT). Results QRS score correlated directly with CMR-LGE scar size (R=0.69, p<0.0001) and inversely with left ventricular ejection fraction (R=-0.54, p=0.0002), which remained significant in the subgroup with conduction defects. Patients with class II or III HF had significantly higher QRS scores than those with class I HF (5.1 +/- 3.4 vs 2.1 +/- 3.1 QRS points (p=0.002)) and patients with a history of VT had significantly higher QRS scores than those without a history of VT (5.3 +/- 3.2% vs 2.6 +/- 3.4 QRS points (p=0.02)). A QRS score >= 2 points had particularly good sensitivity and specificity (95% and 83%, respectively) for prediction of large CMR-LGE, and a QRS score >= 7 points had particularly high specificity (92% and 89%, respectively) for predicting significant left ventricular dysfunction and history of VT. Conclusions The wide availability of 12-lead ECG makes it an attractive screening tool and may enhance clinical risk stratification of patients at risk for more severe, symptomatic Chagas' heart disease.

Cardiac MRI for Detection of Unrecognized Myocardial Infarction in Patients With End-Stage Renal Disease: Comparison With ECG and Scintigraphy

OBJECTIVE. The purposes of this study were to use the myocardial delayed enhancement technique of cardiac MRI to investigate the frequency of unrecognized myocardial infarction (MI) in patients with end-stage renal disease, to compare the findings with those of ECG and SPECT, and to examine factors that may influence the utility of these methods in the detection of MI. SUBJECTS AND METHODS. We prospectively performed cardiac MRI, ECG, and SPECT to detect unrecognized MI in 72 patients with end-stage renal disease at high risk of coronary artery disease but without a clinical history of MI. RESULTS. Fifty-six patients (78%) were men ( mean age, 56.2 +/- 9.4 years) and 16 (22%) were women ( mean age, 55.8 +/- 11.4). The mean left ventricular mass index was 103.4 +/- 27.3 g/m(2), and the mean ejection fraction was 60.6% +/- 15.5%. Myocardial delayed enhancement imaging depicted unrecognized MI in 18 patients (25%). ECG findings were abnormal in five patients (7%), and SPECT findings were abnormal in 19 patients (26%). ECG findings were false-negative in 14 cases and false-positive in one case. The accuracy, sensitivity, and specificity of ECG were 79.2%, 22.2%, and 98.1% (p = 0.002). SPECT findings were false-negative in six cases and false-positive in seven cases. The accuracy, sensitivity, and specificity of SPECT were 81.9%, 66.7%, and 87.0% ( not significant). During a period of 4.9-77.9 months, 19 cardiac deaths were documented, but no statistical significance was found in survival analysis. CONCLUSION. Cardiac MRI with myocardial delayed enhancement can depict unrecognized MI in patients with end-stage renal disease. ECG and SPECT had low sensitivity in detection of MI. Infarct size and left ventricular mass can influence the utility of these methods in the detection of MI.

Can ECG changes predict the long-term outcome in patients admitted to hospital for suspected acute myocardial infarction?

7,028 patients with suspected acute myocardial infarction and discharged alive from hospital were followed in a 10-year community-based study. The long-term prognosis was relatively good if the electrocardiograms (ECGs) were normal (5-year all-cause death rate 5%), poor with uncodable ECGs showing rhythm or conduction disturbances (37%), and intermediate with new Q wave, new ST elevation, new T wave inversion or ischemic ECG (17-21%), and with new ST depression (27%). Similar patterns were found for ischemic cardiac death and reinfarction. The long-term prognosis of patients with suspected acute myocardial infarction is relatively good if the ECGs are normal and poor if ECGs are uncodable. ST depression may be a marker for a worse long-term outcome.

Optimizing electrode positioning in 3-lead ECG chest devices

Although the 12-lead electrocardiogram has become an essential medical and research tool, many current and envisaged applications would benefit from simpler devices, using 3-lead ECG configuration. This is particularly true for Ambient Assisted Living (in a broad perspective). However, the chest anatomy of female patients, namely during pregnancy, can hamper the adequate placement of a 3-lead ECG device and, very often, electrodes are placed below the chest rather than at the precise thoracic landmarks. Thus, the aim of this study was to compare the effect of electrode positioning on the ECG signal of pregnant women and provide guidelines for device development. The effect of breast tissue on the ECG signal was investigated by relating breast size with the signal-to-noise ratio, root mean square and R-wave amplitude. Results show that the 3-lead ECG should be placed on the breast rather than under the breast and indicate positive correlation between breast size and signal-to-noise ratio.

Embedded platform for ECG biometric recognition

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

ECG denoising based on adaptive signal processing technique

An Electrocardiogram (ECG) monitoring system deals with several challenges related with noise sources. The main goal of this text was the study of Adaptive Signal Processing Algorithms for ECG noise reduction when applied to real signals. This document presents an adaptive ltering technique based on Least Mean Square (LMS) algorithm to remove the artefacts caused by electromyography (EMG) and power line noise into ECG signal. For this experiments it was used real noise signals, mainly to observe the di erence between real noise and simulated noise sources. It was obtained very good results due to the ability of noise removing that can be reached with this technique. A recolha de sinais electrocardiogr a cos (ECG) sofre de diversos problemas relacionados com ru dos. O objectivo deste trabalho foi o estudo de algoritmos adaptativos para processamento digital de sinal, para redu c~ao de ru do em sinais ECG reais. Este texto apresenta uma t ecnica de redu c~ao de ru do baseada no algoritmo Least Mean Square (LMS) para remo c~ao de ru dos causados quer pela actividade muscular (EMG) quer por ru dos causados pela rede de energia el ectrica. Para as experiencias foram utilizados ru dos reais, principalmente para aferir a diferen ca de performance do algoritmo entre os sinais reais e os simulados. Foram conseguidos bons resultados, essencialmente devido as excelentes caracter sticas que esta t ecnica tem para remover ru dos.

Unveiling the Biometric Potential of Finger-Based ECG Signals

The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications.

Embedded System for ECG Biometrics

Comunication in Internationa Conference with Peer Review First International Congress on Cardiovasular Technologies - CARDIOTECHNIX, Vilamoura, Portugal, 2013

Dominant set approach to ECG biometrics

Electrocardiographic (ECG) signals are emerging as a recent trend in the field of biometrics. In this paper, we propose a novel ECG biometric system that combines clustering and classification methodologies. Our approach is based on dominant-set clustering, and provides a framework for outlier removal and template selection. It enhances the typical workflows, by making them better suited to new ECG acquisition paradigms that use fingers or hand palms, which lead to signals with lower signal to noise ratio, and more prone to noise artifacts. Preliminary results show the potential of the approach, helping to further validate the highly usable setups and ECG signals as a complementary biometric modality.

Check your biosignals here: a new dataset for off-person ECG biometrics

The Check Your Biosignals Here initiative (CYBHi) was developed as a way of creating a dataset and consistently repeatable acquisition framework, to further extend research in electrocardiographic (ECG) biometrics. In particular, our work targets the novel trend towards off-the-person data acquisition, which opens a broad new set of challenges and opportunities both for research and industry. While datasets with ECG signals collected using medical grade equipment at the chest can be easily found, for off-the-person ECG data the solution is generally for each team to collect their own corpus at considerable expense of resources. In this paper we describe the context, experimental considerations, methods, and preliminary findings of two public datasets created by our team, one for short-term and another for long-term assessment, with ECG data collected at the hand palms and fingers. (C) 2013 Elsevier Ireland Ltd. All rights reserved.

ECG Analysis Using Consensus Clustering

Biosignals analysis has become widespread, upstaging their typical use in clinical settings. Electrocardiography (ECG) plays a central role in patient monitoring as a diagnosis tool in today's medicine and as an emerging biometric trait. In this paper we adopt a consensus clustering approach for the unsupervised analysis of an ECG-based biometric records. This type of analysis highlights natural groups within the population under investigation, which can be correlated with ground truth information in order to gain more insights about the data. Preliminary results are promising, for meaningful clusters are extracted from the population under analysis. © 2014 EURASIP.

ECG Biometrics: A Template Selection Approach

Electrocardiography (ECG) biometrics is emerging as a viable biometric trait. Recent developments at the sensor level have shown the feasibility of performing signal acquisition at the fingers and hand palms, using one-lead sensor technology and dry electrodes. These new locations lead to ECG signals with lower signal to noise ratio and more prone to noise artifacts; the heart rate variability is another of the major challenges of this biometric trait. In this paper we propose a novel approach to ECG biometrics, with the purpose of reducing the computational complexity and increasing the robustness of the recognition process enabling the fusion of information across sessions. Our approach is based on clustering, grouping individual heartbeats based on their morphology. We study several methods to perform automatic template selection and account for variations observed in a person's biometric data. This approach allows the identification of different template groupings, taking into account the heart rate variability, and the removal of outliers due to noise artifacts. Experimental evaluation on real world data demonstrates the advantages of our approach.

ECG signals for biometric applications: are we there yet?

The potential of the electrocardiographic (ECG) signal as a biometric trait has been ascertained in the literature over the past decade. The inherent characteristics of the ECG make it an interesting biometric modality, given its universality, intrinsic aliveness detection, continuous availability, and inbuilt hidden nature. These properties enable the development of novel applications, where non-intrusive and continuous authentication are critical factors. Examples include, among others, electronic trading platforms, the gaming industry, and the auto industry, in particular for car sharing programs and fleet management solutions. However, there are still some challenges to overcome in order to make the ECG a widely accepted biometric. In particular, the questions of uniqueness (inter-subject variability) and permanence over time (intra-subject variability) are still largely unanswered. In this paper we focus on the uniqueness question, presenting a preliminary study of our biometric recognition system, testing it on a database encompassing 618 subjects. We also performed tests with subsets of this population. The results reinforce that the ECG is a viable trait for biometrics, having obtained an Equal Error Rate of 9.01% and an Error of Identification of 15.64% for the entire test population.