Diagnostic value of electrocardiogram during routine electroencephalogram

The Epileptic Heart Syndrome: Epidemiology, pathophysiology and clinical detection

Population studies report elevated incidence of cardiovascular events in patients with chronic epilepsy. Multiple pathophysiologic processes have been implicated, including accelerated atherosclerosis, myocardial infarction, altered autonomic tone, heart failure, atrial and ventricular arrhythmias, and hyperlipidemia. These deleterious influences on the cardiovascular system have been attributed to seizure-induced surges in catecholamines and hypoxemic damage to the heart and coronary vasculature. Certain antiseizure medications can accelerate heart disease through enzyme-inducing increases in plasma lipids and/or increasing risk for life-threatening ventricular arrhythmias as a result of sodium channel blockade. In this review, we propose that this suite of pathophysiologic processes constitutes "The Epileptic Heart Syndrome." We further propose that this condition can be diagnosed using standard electrocardiography, echocardiography, and lipid panels. The ultimate goal of this syndromic approach is to evaluate cardiac risk in patients with chronic epilepsy and to promote improved diagnostic strategies to reduce premature cardiac death.

The Value of Concurrent Electrocardiography When Performing an Electroencephalograph

Introduction. The use of concurrent, single lead electrocardiograph (ECG) recording, when performing a routine electroencephalograph (EEG), has been standard practice for many years. Previous studies have reported on the usefulness of concurrent EEG in assessing syncope and the detection of newly identified cardiac dysrhythmia but have relied on specialist cardiologist interpretation of the ECG trace. This study expands the understanding of concurrent ECG and provides demographic information regarding the incidence, nature of ECG changes and diagnostic utility of ECG interpretation, during routine EEGs, as evaluated by neurologists. Methods. A single center, retrospective study of routine concurrent EEG and ECG recordings was performed. All routine EEGs, performed within a 12 month period were analysed. Demographic data, underlying comorbidities, reasons for referral and ECG changes were assessed. Results. ECG abnormalities were identified in 147 (13.5%) of concurrent ECG/EEG routine recordings. The presence of ECG abnormalities was significantly associated with the reason for referral, namely being assessed for the evaluation of seizure activity and with increasing patient age. Thirty-eight patients (3.5%) had newly identified ECG abnormalities, of which atrial fibrillation (AF) (12 patients) and sinus bradycardia (9 patients) were the most common. Five patients (0.5%) had a change in their management consequent to the identified ECG changes. Conclusions. These findings support the value of neurologists' interpretation and need for ongoing concurrent ECGs, during routine EEG recordings. The study raises concern about the requesting clinician's response to the identification of newly diagnosed cardiac dysrhythmias.

Routine Interictal EEG Recording Should be Performed Together with Simultaneous Two-Lead ECG Recording

We aimed to evaluate the contribution of simultaneous electrocardiography (ECG) recording during routine interictal electroencephalography (EEG) recording in patients with seizures or epilepsy and therefore to provide evidence-based data on this subject. Patients with interictal cardiac arrhythmia on routine EEG-ECG recordings were determined and evaluated based on cardiologic and neurologic findings. Out of 1,078 patients aged between 5 and 16 years (mean: 10.2 ± 3.2), 9 (0.08%) patients were found to have an arrhythmia. Six patients had both epilepsy and cardiac arrhythmia (premature ventricular contractions [PVCs] in 5; Wolff-Parkinson-White [WPW] in 1 patient) and the remaining three patients had nonepileptic paroxysmal events (NPEs) and arrhythmia (PVC in 2; WPW in 1). Three patients had other diseases (neurofibromatosis type 1, tuberous sclerosis, and congenital heart disease status postsurgery). Cardiac arrhythmia required radiofrequency ablation or antiarrhythmic drug treatment in two patients with epilepsy and also two patients with NPE; however, it improved with no specific treatment in the remaining five patients. NPE was not related to arrhythmia in one of three patients with NPE. Our study suggests that routine interictal EEG-ECG recording provides a valuable and feasible opportunity to reveal unnoticed or new-onset cardiac arrhythmias. Therefore, ECG should be recorded simultaneously during routine interictal EEG recordings. Cardiac arrhythmias detected by routine interictal EEG-ECG recordings would require arrhythmia treatment in nearly half of the patients.

EEG Analysis with Wavelet Transform under Music Perception Stimulation

In order to improve the classification accuracy and reliability of emotional state assessment and provide support and help for music therapy, this paper proposes an EEG analysis method based on wavelet transform under the stimulation of music perception. Using the data from the multichannel standard emotion database (DEAP), α, ß, and θ rhythms are extracted in frontal (F3 and F4), temporal (T7 and T8), and central (C3 and C4) channels with wavelet transform. EMD is performed on the extracted EEG rhythm to obtain intrinsic mode function (IMF) components, and then, the average energy and amplitude difference eigenvalues of IMF components of EEG rhythm waves are further extracted, that is, each rhythm wave contains three average energy characteristics and two amplitude difference eigenvalues so as to fully extract EEG feature information. Finally, emotional state evaluation is realized based on a support vector machine classifier. The results show that the correct rate between no emotion, positive emotion, and negative emotion can reach more than 90%. Among the pairwise classification problems among the four emotions selected, the classification accuracy obtained by this EEG feature extraction method is higher than that obtained by general feature extraction methods, which can reach about 70%. Changes in EEG α wave power were closely correlated with the polarity and intensity of emotion; α wave power varied significantly between "happiness and fear," "pleasure and fear," and "fear and sadness." It has a good application prospect in both psychological and physiological research of emotional perception and practical application.