Transient loss of consciousness and syncope

Syncope describes a sudden and brief transient loss of consciousness (TLOC) with postural failure due to cerebral global hypoperfusion. The term TLOC is used when the cause is either unrelated to cerebral hypoperfusion or is unknown. The most common causes of syncopal TLOC include: (1) cardiogenic syncope (cardiac arrhythmias, structural cardiac diseases, others); (2) orthostatic hypotension (due to drugs, hypovolemia, primary or secondary autonomic failure, others); (3) neurally mediated syncope (cardioinhibitory, vasodepressor, and mixed forms). Rarely neurologic disorders (such as epilepsy, transient ischemic attacks, and the subclavian steal syndrome) can lead to cerebal hypoperfusion and syncope. Nonsyncopal TLOC may be due to neurologic (epilepsy, sleep attacks, and other states with fluctuating vigilance), medical (hypoglycemia, drugs), psychiatric, or post-traumatic disorders. Basic diagnostic workup of TLOC includes a thorough history and physical examination, and a 12-lead electrocardiogram (ECG). Blood testing, electroencephalogram (EEG), magnetic resonance imaging (MRI) of the brain, echocardiography, head-up tilt test, carotid sinus massage, Holter monitoring, and loop recorders should be obtained only in specific contexts. Management strategies involve pharmacologic and nonpharmacologic interventions, and cardiac pacing.

Brain damage and addictive behavior: a neuropsychological and electroencephalogram investigation with pathologic gamblers.

BACKGROUND Gambling is a form of nonsubstance addiction classified as an impulse control disorder. Pathologic gamblers are considered healthy with respect to their cognitive status. Lesions of the frontolimbic systems, mostly of the right hemisphere, are associated with addictive behavior. Because gamblers are not regarded as "brain-lesioned" and gambling is nontoxic, gambling is a model to test whether addicted "healthy" people are relatively impaired in frontolimbic neuropsychological functions. METHODS Twenty-one nonsubstance dependent gamblers and nineteen healthy subjects underwent a behavioral neurologic interview centered on incidence, origin, and symptoms of possible brain damage, a neuropsychological examination, and an electroencephalogram. RESULTS Seventeen gamblers (81%) had a positive medical history for brain damage (mainly traumatic head injury, pre- or perinatal complications). The gamblers, compared with the controls, were significantly more impaired in concentration, memory, and executive functions, and evidenced a higher prevalence of non-right-handedness (43%) and, non-left-hemisphere language dominance (52%). Electroencephalogram (EEG) revealed dysfunctional activity in 65% of the gamblers, compared with 26% of controls. CONCLUSIONS This study shows that the "healthy" gamblers are indeed brain-damaged. Compared with a matched control population, pathologic gamblers evidenced more brain injuries, more fronto-temporo-limbic neuropsychological dysfunctions and more EEG abnormalities. The authors thus conjecture that addictive gambling may be a consequence of brain damage, especially of the frontolimbic systems, a finding that may well have medicolegal consequences.

Costs and yield of a 15-month preparticipation cardiovascular examination with ECG in 1070 young athletes in Switzerland: implications for routine ECG screening

Background The usefulness and modalities of cardiovascular screening in young athletes remain controversial, particularly concerning the role of 12-lead ECG. One of the reasons refers to the presumed false-positive ECGs requiring additional examinations and higher costs. Our study aimed to assess the total costs and yield of a preparticipation cardiovascular examination with ECG in young athletes in Switzerland. Methods Athletes aged 14–35 years were examined according to the 2005 European Society of Cardiology (ESC) protocol. ECGs were interpreted based on the 2010 ESC-adapted recommendations. The costs of the overall screening programme until diagnosis were calculated according to Swiss medical rates. Results A total of 1070 athletes were examined (75% men, 19.7±6.3 years) over a 15-month period. Among them, 67 (6.3%) required further examinations: 14 (1.3%) due to medical history, 15 (1.4%) due to physical examination and 42 (3.9%) because of abnormal ECG findings. A previously unknown cardiac abnormality was established in 11 athletes (1.0%). In four athletes (0.4%), the abnormality may potentially lead to sudden cardiac death and all of them were identified by ECG alone. The cost was 157 464 Swiss francs (CHF) for the overall programme, CHF147 per athlete and CHF14 315  per finding. Conclusions Cardiovascular preparticipation examination in young athletes using modern and athlete-specific criteria for interpreting ECG is feasible in Switzerland at reasonable cost. ECG alone is used to detect all potentially lethal cardiac diseases. The results of our study support the inclusion of ECG in routine preparticipation screening.

Should we perform a screening ECG among asymptomatic adults?

The resting ECG is a safe, low cost and widely available in the clinical investigation of several cardiac symptoms. However, there is controversy regarding the use as a screening tool or routine cardiovascular (CV) risk assessment test among healthy asymptomatic adults. Two recent studies reported that ECG adds supplemental information in the estimation of coronary artery disease (CAD) risk in asymptomatic patients, especially in those with intermediate risk. However, we currently need more data on the impact of ECG on the prevention of clinical CV outcomes, especially in a randomized clinical trial, and on additional costs of testing and treatment. For the time being, routine ECG testing is not recommended for the prevention of CV events among healthy asymptomatic adults.

The spectrum of the non-rapid eye movement sleep electroencephalogram following total sleep deprivation is trait-like

The sleep electroencephalogram (EEG) spectrum is unique to an individual and stable across multiple baseline recordings. The aim of this study was to examine whether the sleep EEG spectrum exhibits the same stable characteristics after acute total sleep deprivation. Polysomnography (PSG) was recorded in 20 healthy adults across consecutive sleep periods. Three nights of baseline sleep [12 h time in bed (TIB)] following 12 h of wakefulness were interleaved with three nights of recovery sleep (12 h TIB) following 36 h of sustained wakefulness. Spectral analysis of the non-rapid eye movement (NREM) sleep EEG (C3LM derivation) was used to calculate power in 0.25 Hz frequency bins between 0.75 and 16.0 Hz. Intraclass correlation coefficients (ICCs) were calculated to assess stable individual differences for baseline and recovery night spectra separately and combined. ICCs were high across all frequencies for baseline and recovery and for baseline and recovery combined. These results show that the spectrum of the NREM sleep EEG is substantially different among individuals, highly stable within individuals and robust to an experimental challenge (i.e. sleep deprivation) known to have considerable impact on the NREM sleep EEG. These findings indicate that the NREM sleep EEG represents a trait.

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.

Desarrollo de herramienta software para el estudio del complejo QRS en las señales de ECG

Las enfermedades cardíacas son un problema grave y en aumento en la sociedad actual. El estudio automático del complejo QRS es fundamental en el diagnóstico de enfermedades cardíacas. Para ayudar al diagnóstico se propone una herramienta software que analiza el complejo QRS en señales de electrocardiograma. Los pasos a seguir para diseñar dicha herramienta serán: - Estudio de los diferentes métodos de detección del complejo QRS. - Propuesta de una herramienta de detección del complejo QRS en un electrocardiograma. - Diseño de un paquete software que realice diferentes estudios del complejo QRS con sus "interfaces" gráficas para facilitar la aplicación de la herramienta propuesta a nivel usuario.

Grouped sparsity algorithm for multichannel intracardiac ECG synchronization

In this paper, a new method is presented to ensure automatic synchronization of intracardiac ECG data, yielding a three-stage algorithm. We first compute a robust estimate of the derivative of the data to remove low-frequency perturbations. Then we provide a grouped-sparse representation of the data, by means of the Group LASSO, to ensure that all the electrical spikes are simultaneously detected. Finally, a post-processing step, based on a variance analysis, is performed to discard false alarms. Preliminary results on real data for sinus rhythm and atrial fibrillation show the potential of this approach.

Desarrollo de un instrumento para adquisición y procesado de EEG (Electro-encefalograma) y ECG (Electro-cardiograma)

Diseño y construcción de un aparato de bajo costo para adquisición y procesamiento de señales bioeléctricas, compuesto por un hardware capaz de amplificar y filtrar las señales, y por un instrumento virtual basado en labVIEW encargado de la adquisición de los distintas bioseñales y de su procesamiento en tiempo real. Este sistema permitirá dar soporte diagnóstico en modelos animales con desórdenes neurológicos sometidos a diferentes tipos de intervención terapéutica.

Sleep electroencephalogram delta-frequency amplitude, night plasma levels of tumor necrosis factor alpha, and human immunodeficiency virus infection.

We tested the hypothesis that increases in tumor necrosis factor alpha (TNF-alpha) induced by human immunodeficiency virus (HIV) are associated with the increases in slow-wave sleep seen in early HIV infection and the decrease with sleep fragmentation seen in advanced HIV infection. Nocturnal sleep disturbances and associated fatigue contribute to the disability of HIV infection. TNF-alpha causes fatigue in clinical use and promotes slow-wave sleep in animal models. With slow progress toward a vaccine and weak effects from current therapies, efforts are directed toward extending productive life of HIV-infected individuals and shortening the duration of disability in terminal illness. We describe previously unrecognized nocturnal cyclic variations in plasma levels of TNF-alpha in all subjects. In 6 of 10 subjects (1 control subject, 3 HIV-seropositive patients with CD4+ cell number > 400 cells per microliters, and 2 HIV-positive patients with CD4+ cell number < 400 cells per microliters), these fluctuations in TNF-alpha were coupled to the known rhythm of electroencephalogram delta amplitude (square root of power) during sleep. This coupling was not present in 3 HIV-positive subjects with CD4+ cell number < 400 cells per microliters and 1 control subject. In 5 HIV subjects with abnormally low CD4+ cell counts ( < 400 cells per microliters), the number of days since seroconversion correlated significantly with low correlation between TNF-alpha and delta amplitude. We conclude that a previously unrecognized normal, physiological coupling exists between TNF-alpha and delta amplitude during sleep and that the lessened likelihood of this coupling in progressive HIV infection may be important in understanding fatigue-related symptoms and disabilities.

Electroencephalogram based Causality Graph Analysis in Behavior Tasks of Parkinson’s Disease Patients

Electroencephalographic (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this thesis is to investigate the interactions and causality of different parts of a brain using EEG signals recorded during a performance subjects of verbal fluency tasks. Subjects who have Parkinson's Disease (PD) have difficulties with mental tasks, such as switching between one behavior task and another. The behavior tasks include phonemic fluency, semantic fluency, category semantic fluency and reading fluency. This method uses verbal generation skills, activating different Broca's areas of the Brodmann's areas (BA44 and BA45). Advanced signal processing techniques are used in order to determine the activated frequency bands in the granger causality for verbal fluency tasks. The graph learning technique for channel strength is used to characterize the complex graph of Granger causality. Also, the support vector machine (SVM) method is used for training a classifier between two subjects with PD and two healthy controls. Neural data from the study was recorded at the Colorado Neurological Institute (CNI). The study reveals significant difference between PD subjects and healthy controls in terms of brain connectivities in the Broca's Area BA44 and BA45 corresponding to EEG electrodes. The results in this thesis also demonstrate the possibility to classify based on the flow of information and causality in the brain of verbal fluency tasks. These methods have the potential to be applied in the future to identify pathological information flow and causality of neurological diseases.

Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.

Taquicardia ventricular idiopática : estudo de correlação do ECG com o local de origem

Trabalho Final do Curso de Mestrado Integrado em Medicina, Faculdade de Medicina, Universidade de Lisboa, 2014

Electrodynamic heart model construction and ECG simulation

Objectives: In this paper, we present a unified electrodynamic heart model that permits simulations of the body surface potentials generated by the heart in motion. The inclusion of motion in the heart model significantly improves the accuracy of the simulated body surface potentials and therefore also the 12-lead ECG. Methods: The key step is to construct an electromechanical heart model. The cardiac excitation propagation is simulated by an electrical heart model, and the resulting cardiac active forces are used to calculate the ventricular wall motion based on a mechanical model. The source-field point relative position changes during heart systole and diastole. These can be obtained, and then used to calculate body surface ECG based on the electrical heart-torso model. Results: An electromechanical biventricular heart model is constructed and a standard 12-lead ECG is simulated. Compared with a simulated ECG based on the static electrical heart model, the simulated ECG based on the dynamic heart model is more accordant with a clinically recorded ECG, especially for the ST segment and T wave of a V1-V6 lead ECG. For slight-degree myocardial ischemia ECG simulation, the ST segment and T wave changes can be observed from the simulated ECG based on a dynamic heart model, while the ST segment and T wave of simulated ECG based on a static heart model is almost unchanged when compared with a normal ECG. Conclusions: This study confirms the importance of the mechanical factor in the ECG simulation. The dynamic heart model could provide more accurate ECG simulation, especially for myocardial ischemia or infarction simulation, since the main ECG changes occur at the ST segment and T wave, which correspond with cardiac systole and diastole phases.