Electrical Activity Of Heart Research Articles

Intelligent control of cardiac rhythms using artificial neural networks

Cardiac rhythms are related to heart electrical activity, being an essential aspect of the cardiovascular physiology. Usually, these rhythms are represented by electrocardiograms (ECGs) that are useful to detect cardiac pathologies. This paper investigates the control of cardiac rhythms in order to induce normal rhythms from pathological responses. The strategy is based on the electrocardiograms and considers different pathologies. An intelligent controller is proposed considering the ECG as the observable variable. In order to allow the assessment of the control performance, synthetic ECGs are produced from a reduced-order mathematical model that presents close agreement with experimental measurements. The adopted model comprises a network of oscillators formed by sinoatrial node, atrioventricular node and His-Purkinje complex. Three nonlinear oscillators are employed to represent each one of these nodes that are connected by delayed couplings. The controller considers the control variable at the His-Purkinje complex. To evaluate the ability of the control law to deal with both intra- and interpatient variability, the heart model is assumed to be not available to the controller designer, being used only in the simulator to assess the control performance. The incorporation of artificial neural networks into a Lyapunov-based control scheme, however, allows the presented intelligent approach to compensate for unknown cardiac dynamics. Results show that abnormal rhythms can be avoided by applying the proposed control scheme, turning the electrocardiogram closer to the expected normal behavior and preventing critical cardiac responses.

A Double- electrodes Amplifier use for Portable ECG Equipment

Wearable electrocardiogram (ECG) sensors is a simple test that can be used to check people’s heart's rhythm and electrical activity. With individuals’ increasing attention to their own health, developing a timely and portable bio-sensing recorder has become an urgent task. This paper presents the working principle of a common ECG signal recorder. A double-electrode voltage amplifier that can be used for portable ECG sensors has been set out to design. The amplifier is mainly composed of a differential amplifier circuit, which can lower power supply and power consumption. CoolSpice environment was utilized to simulate the circuit and analyze the result. The simulation presented that the common-mode gain is under 30dB up to 5KHz and the differential-mode gain is larger than 40 dB up to 5KHz. And the total current consumption has been under 20uA. The result demonstrated that the amplifier can perform well. The noise from human body has been eliminated by the amplifier. The amplifier has the ability to extract and amplify clean ECG signals and its structure has the characteristics of low cost and portability. It may be used in portable wearable ECG sensor technology.

A Balanced OTA ECG Amplifier and Its Development Directions

Electrocardiograph (ECG) is a technology that uses an electrocardiograph to record the electrical activity change pattern generated by each cardiac cycle of the heart from the body surface. It is a simple test that can be used to check heart's rhythm and electrical activity of human. The amplifying and filtering of the ECG signal is the most important part of ECG signal procedures and this paper shows a balanced OTA that can fully remove the 50hz noise from the power line. Due to cost performance for the real situation in the hospital and industrial cost, the balanced OTA filter in this paper is simple and cannot guarantee a long stable use when it is implemented on an ECG machine with other models. This paper will show two different ways to increase gain and power efficiency which are using fully differential OTA and using higher bandwidth.

Influence of the Tikhonov Regularization Parameter on the Accuracy of the Inverse Problem in Electrocardiography.

The electrocardiogram (ECG) is the standard method in clinical practice to non-invasively analyze the electrical activity of the heart, from electrodes placed on the body's surface. The ECG can provide a cardiologist with relevant information to assess the condition of the heart and the possible presence of cardiac pathology. Nonetheless, the global view of the heart's electrical activity given by the ECG cannot provide fully detailed and localized information about abnormal electrical propagation patterns and corresponding substrates on the surface of the heart. Electrocardiographic imaging, also known as the inverse problem in electrocardiography, tries to overcome these limitations by non-invasively reconstructing the heart surface potentials, starting from the corresponding body surface potentials, and the geometry of the torso and the heart. This problem is ill-posed, and regularization techniques are needed to achieve a stable and accurate solution. The standard approach is to use zero-order Tikhonov regularization and the L-curve approach to choose the optimal value for the regularization parameter. However, different methods have been proposed for computing the optimal value of the regularization parameter. Moreover, regardless of the estimation method used, this may still lead to over-regularization or under-regularization. In order to gain a better understanding of the effects of the choice of regularization parameter value, in this study, we first focused on the regularization parameter itself, and investigated its influence on the accuracy of the reconstruction of heart surface potentials, by assessing the reconstruction accuracy with high-precision simultaneous heart and torso recordings from four dogs. For this, we analyzed a sufficiently large range of parameter values. Secondly, we evaluated the performance of five different methods for the estimation of the regularization parameter, also in view of the results of the first analysis. Thirdly, we investigated the effect of using a fixed value of the regularization parameter across all reconstructed beats. Accuracy was measured in terms of the quality of reconstruction of the heart surface potentials and estimation of the activation and recovery times, when compared with ground truth recordings from the experimental dog data. Results show that values of the regularization parameter in the range (0.01-0.03) provide the best accuracy, and that the three best-performing estimation methods (L-Curve, Zero-Crossing, and CRESO) give values in this range. Moreover, a fixed value of the regularization parameter could achieve very similar performance to the beat-specific parameter values calculated by the different estimation methods. These findings are relevant as they suggest that regularization parameter estimation methods may provide the accurate reconstruction of heart surface potentials only for specific ranges of regularization parameter values, and that using a fixed value of the regularization parameter may represent a valid alternative, especially when computational efficiency or consistency across time is required.

The Cardiovascular Disease Prediction Using Machine Learning

Because of technology developments, the ECG yields improved outcomes in the realm of biomedical science and research. The Electrocardiogram reveals basic the heart's electrical activity. Early detection of aberrant heart disorders is crucial for diagnosing cardiac problems and averting sudden cardiac deaths. Measurements on an electrocardiogram (ECG) among people with comparable cardiac issues are essentially equal. Analyzing the Electrocardiogram characteristics can help predict abnormalities. Medical professionals presently base the preponderance of their Electrocardiogram diagnosis on their unique particular areas of expertise, which places a substantial load on their shoulders and reduces their performance. The use of technology that automatically analyses ECGs as hospital personnel performs their duties will be advantageous. A suitable algorithm must be able to categories Input signal with uncertain awesome feature on just how much they approximate Input signal having known characteristics in order to speed up the identification of heart illnesses. A possibility of identifying a tachycardia is raised if this predictor can reliably recognize connections, and this technique may be helpful in lab settings. To accurately diagnose myocardial illness, a powerful machine learning technique should be used. Through using recommended method, the effectiveness of cardiovascular disease identification using ECG dataset was evaluated. The reliability, sensitivities, and validity obtained using the Svm algorithm were 99.314%, 97.60%, and 97.60% respectively.

Effect of Muscle Fatigue on EMG Signal and Maximum Heart Rate for Pre and Post Physical Activity

Sport is a physical activity that can optimize body development through muscle movement. Physical activity without rest with strong and prolonged muscle contractions results in muscle fatigue. Muscle fatigue that occurs causes a decrease in the work efficiency of muscles. Electrocardiography (ECG) is a recording of the heart's electrical activity on the body's surface. EMG is a technique for measuring electrical activity in muscles. This study aims to detect the effect of muscle fatigue on cardiac signals by monitoring ECG and EMG signals. This research method uses the Maximum Heart Rate with a research design of one group pre-test-post-test. The independent variable is the ECG signal when doing plank activities, while the dependent variable is the result of monitoring the ECG signal. To get the Maximum Heart Rate results, respondents use the Karnoven formula and perform the T-test. Test results show a significant value (pValue <0.05) in pre-exercise and post-exercise. When the respondent experiences muscle fatigue, it shows the effect of changes in the shape of the ECG signal which is marked by the presence of movement artifact noise. It concluded that the tools in this study can be used properly. This study has limitations including noise in the AD8232 module circuit and the display on telemetry where the width of the box cannot be adjusted according to the ECG paper.is It recommended for further research to use components with better quality and replace the display using the Delphi interface.

ECG Monitoring Using Deep Learning – A Review

Electrocardiogram (ECG) is used to detect heart's rhythm and electrical activity. Heart rhythm problems (heart arrhythmias) occur when the electrical signals that coordinate the heart's beats don't work properly and faulty signaling causes the heart to beat too fast, too slow or irregularly. Patients facing arrhythmia have no indications of having an arrhythmia and only a doctor will be able to diagnose arrhythmias in a routine test. Therefore, low cost portable monitoring system plays a important role. The main objective of this project is to design and develop a model for predicting arrhythmia (atrial fibrillation) along with monitoring the ECG signals. To create an arrhythmia prediction model and an ECG monitoring system, Deep Learning algorithms, TensorFlow and Keras library are applied here. The system is being designed with Raspberry pi 3, Arduino UNO, AD8232 single lead ECG sensor, biomedical sensor pad and battery. This system will be helpful for remote ECG monitoring and make easier for doctors to monitor the ECG of their patients outside the hospital. Key Words: ECG, Deep Learning, arrhythmia.

Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

Arrhythmia has been classified using a variety of methods. Because of the dynamic nature of electrocardiogram (ECG) data, traditional handcrafted approaches are difficult to execute, making the machine learning (ML) solutions more appealing. Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives. Cardiac arrhythmia classification and prediction have greatly improved in recent years. Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish. Every year, it is one of the main reasons of mortality for both men and women, worldwide. For the classification of arrhythmias, this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors (KNN) classifier. The proposed method makes advantage of the UCI repository, which has a 279-attribute high-dimensional cardiac arrhythmia dataset. The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features. The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients. This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature. The achieved classification accuracy using the proposed approach is 99.8%.

The electrocardiographic changes generated by centrally applied arachidonic acid in rats

Arachidonic acid (AA) and its metabolites have multifunctional regulatory effects on the central nervous system. Our previous reports disclosed that centrally injected AA organized the cardiovascular system in normal or hypotensive conditions by regulating the central and peripheral mechanism. In the light of the knowledge of the potential cardiovascular effects of AA, the current study aimed to investigate the effects of intracerebroventricular (ICV) injected AA on the electrocardiography (ECG) of the anesthetized rats. The adult Sprague Dawley rats were anesthetized with ketamine and xylazine mixture (50 mg/kg and 20 mg/kg; i.m., respectively). Under the anesthesia, the guide cannula was inserted into the left lateral ventricle of the rats. The ECG traces obtained from the lead II were written by placing electrodes on the limbs of the rats. Centrally injected AA (150 μg; ICV) statistically significantly (p<0.05) caused to the lengthening of the ECG waves and intervals, resulting in a decrease in the heart rate of the rats without changing the ECG waveforms, the amplitude, and also the isoelectric line. The obtained results clearly show that centrally injection of AA caused the deceleration in the heart electrical activity. The deceleration in the electrical activity of the heart caused to show bradycardia in the rats by extending the duration of the ECG waves and intervals.

The Role of Transcription Factors in Coronary Artery Disease and Myocardial Infarction.

Coronary artery disease (CAD) and its main complication, myocardial infarction (MI), is a complex disease caused by environmental and genetic factors and their interaction. Family-based linkage analysis and genome-wide association studies have indicated many of genetic variations related to CAD and MI in recent years. Some are in the coding sequence, which mediates the coding protein, while others are in the non-coding region, which affects the expression of adjacent genes and forms differential gene expression. These variants and differential expressions will have varying degrees of impact on the development of the cardiovascular system and normal heart electrical activity function, subsequently leading to CAD and MI. Among these affected genes, some Transcription Factors (TFs), as important means of transcriptional regulation, have a key role in the pathogenesis of coronary artery disease and myocardial infarction. The GATAs binding protein 2 (GATA2) enhances monocyte adhesion and promoted vessel wall permeabilization through vascular EC adhesion molecule 1 (VCAM-1) upregulation, further revealing its atherosclerosis-promoting role. Myocyte enhancer factor 2 (MEF2) has a role in fostering many functions of the atherosclerotic endothelium and is a potential therapeutic target for atherosclerosis, thrombosis, and inflammation. Nuclear factor-kappa B (NF-κB) is an important promoter of vascular endothelial growth factor (VEGF)-driven angiogenesis, and its pathway has a key role in atherosclerosis-related complications such as angiogenesis, inflammation, apoptosis, and immune effects. Activating transcription factor 3 (ATF3) may be a novel prognostic biomarker and therapeutic target for atherosclerosis. The important role of signal transducer and activator of transcription 3 (STAT3) (especially in mitochondria) in endothelial cells (EC) dysfunction, inflammation, macrophage polarization and immunity in atherosclerosis.

Automated Detection of Abnormalities in ECG signals using Deep Neural Network

The electrocardiogram (ECG) is a diagnostic procedure that uses a skin electrode to record the heart's electrical activity. Heart diseases are the leading cause of mortality globally, and they have significant monetary cost. In the clinic, automated detection and classification technology of abnormalities in heart beats can assist physicians in making prompt and accurate medical diagnoses. This paper concentrates on two proposed models: Model A: 1-D 8-Layer CNN Model and Model B: 1- D CNN and the LSTM model to characterise ECG data into five categories: normal beat, right bundle branch block beat, left bundle branch block beat, premature ventricular contraction beat, and atrial premature beat. The data used to create and validate the models is obtained from the MIT-BIH Dataset, which comprises of 48 half-hour samples of two-lead continuous ECG signals obtained from 48 individuals. The dataset is divided into two files-.csv and.txt. For each sample, the.csv files include the readings collected from both leads. The experimental results show that Model A has an Accuracy = 99.68%, Precision = 99.23%, and a F1 score = 99.22%. And Model B has an Accuracy= 99.51%, Precision = 98.76%, and a F1 score = 98.76%. Our study aims to assist the medical sector by reducing the diagnoses time by automating the process of detecting arrythmias.

Wavelet Transform Based Heart Rate Variability Analysis of ECG

Electrocardiography (ECG) is recording of heart electrical activity. For analyzing and diagnosis of heart diseases ECG is very important. In graphical ECG which used for clinical diagnosis all features are not visible. Different types of signal processing methods are present which can be used for extracting ECG signal features. Wavelet transforms is one kind of signal processing tool which is used for analyzing ECG signal. For features extraction multi-resolution wavelet transform can be used. During recording of ECG different kind of noise are added with ECG. So noise should be removed from ECG, than R peaks were detected which amplitude is higher than the other peaks. Referring to R peaks the others peak as P, Q, S and T were detected. Then different feature of the ECG signal were detected. Time differences between R peaks were calculated and then heart rate calculated from mean RR interval. In ECG RR interval indicate the change between consecutive heart rate (HR). Heart rate variability (HRV) explored how RR interval varies over time. HRV is calculated from RR interval series obtained from ECG signal analysis. From the RR intervals time domain indices of HRV were determined by using MATLAB programming and MIT-BIH database signal were used as input. In the time domain method SDNN, RMSSD, and pNN50 etc were determined here.

Design of Computer Based 12 Lead ECG Using STM32F401 Microcontroller

The research of designing computer-based 12 lead ECG using STM32F401 microcontroller has been done. ECG is a healthcare device recording heart electrical activity and assesses heart condition. ECG was chosen for health condition check-ups because it is low-cost and can monitor heart conditions in real-time. To get more details about a heart condition, an ECG with more than one lead is required, of which 12 leads are used for diagnostic application. The designed ECG uses a computer to display and record the ECG signal from the human body using a GUI program created with Visual Studio 2019, capable of recording 12 leads simultaneously and using USB to connect to the computer. STM32F401 is used as a digital signal processor to filter the ECG signal to make a good quality and noise-free ECG recording. Specifying the designed 12 lead ECG is a signal gain of 384 times with error less than 5%, 115.05 dB CMRR, and 0.05-200 Hz frequency response suitable for diagnostic application. The ECG was also capable of recording ECG signals from the human body.

Coupling and heterogeneity modulate pacemaking capability in healthy and diseased two-dimensional sinoatrial node tissue models.

Both experimental and modeling studies have attempted to determine mechanisms by which a small anatomical region, such as the sinoatrial node (SAN), can robustly drive electrical activity in the human heart. However, despite many advances from prior research, important questions remain unanswered. This study aimed to investigate, through mathematical modeling, the roles of intercellular coupling and cellular heterogeneity in synchronization and pacemaking within the healthy and diseased SAN. In a multicellular computational model of a monolayer of either human or rabbit SAN cells, simulations revealed that heterogenous cells synchronize their discharge frequency into a unique beating rhythm across a wide range of heterogeneity and intercellular coupling values. However, an unanticipated behavior appeared under pathological conditions where perturbation of ionic currents led to reduced excitability. Under these conditions, an intermediate range of intercellular coupling (900-4000 MΩ) was beneficial to SAN automaticity, enabling a very small portion of tissue (3.4%) to drive propagation, with propagation failure occurring at both lower and higher resistances. This protective effect of intercellular coupling and heterogeneity, seen in both human and rabbit tissues, highlights the remarkable resilience of the SAN. Overall, the model presented in this work allowed insight into how spontaneous beating of the SAN tissue may be preserved in the face of perturbations that can cause individual cells to lose automaticity. The simulations suggest that certain degrees of gap junctional coupling protect the SAN from ionic perturbations that can be caused by drugs or mutations.

Abstract 11999: Molecular and Cellular Characterization of RyR2 Mutations Linked to Long-QT Syndrome

Long-QT syndrome (LQTS) is a cardiac disorder characterized by prolonged QT interval and abnormal T waves on the electrocardiogram. Electrical abnormality can lead to severe arrhythmias, syncope and sudden cardiac death. Mutations in the Na + and K + channels or associated proteins account for the majority of cases; however, the genetic causes of about 20% of LQTS cases remain poorly understood. Recent genetic studies revealed novel LQTS-associated variants on the cardiac ryanodine receptor (RyR2), a Ca 2+ channel responsible for Ca 2+ -induced Ca 2+ release and excitation-contraction coupling. Given the important roles of RyR2 in the electrical activity of heart and previous evidence of RyR2 mutations in other inherited arrhythmias, we hypothesized that RyR2 mutations lead to functional alterations of the channels and contribute to dysregulated Ca 2+ release and pathological electrical remodeling. Eleven novel RyR2 mutations were identified in genetic studies of LQTS patients, including S166C, H877P, R1760W, G2094S, R2824W, R2920Q, R3673W, Y4287N, V4298M, P4534S and K4594Q. [3H] ryanodine (RyR2 ligand) binding assays revealed variable effects of the mutations on the opening probability of the RyR2 channel. In cell lines with stable expression of RyR2, Ca 2+ overload activates RyR2 channels and leads to spontaneous Ca 2+ release into the cytoplasm. Compared to WT RyR2, LQTS-associated RyR2 mutations cause an increase in the proportion of cells that exhibit spontaneous Ca 2+ release at various Ca 2+ concentrations, suggesting an increase in luminal Ca 2+ sensitivity of the channels. A transgenic mouse model carrying the R2920Q mutation was used to investigate the cellular and molecular pathways leading to LQTS. Homozygous mice showed prolonged QT interval and QTc at 5 months. Electrophysiological studies of the isolated cardiomyocytes revealed a depolarized resting membrane potential in the homozygous R2920Q cardiomyocytes and prolonged action potential duration in both heterozygous and homozygous R2920Q cardiomyocytes, compared to the WT cells. The availability of a mouse model that recapitulates the cardinal signs of LQTS will allow for deeper mechanistic insight into the role of RyR2 dysfunction as a driver of this arrhythmogenic syndrome.

A Novel method of QRS Detection Using Adaptive Multilevel Thresholding with Statistical False Peak Elimination

Abstract: Heart is the vital organ of a Human Body, because of its involvement in various works and processes in the entire body such as blood pumping etc., so recording a heart function is also a great thing, it is done through ECG signals. ECG signal records the electrical signals and activity of a Human Heart based on the electrical signals released by the Heart. ECG signal consists of PQRST waves, which are the reference points on an ECG signal. But, recording them is much easy than extracting and analyzing them, so, as to extract them, we are applying an advanced adaptive multi-level thresholding (AAMT) along with a selective statistical false peak elimination for the detection of QRS peaks of an ECG signal. Initially, median and moving average filters are applied for removing noise as well as terms. After AAMT is implemented on the complete dataset of ECG signals. Then selective statistical false peak elimination (SSFPE) is implemented for removing noise terms that might be missed out during filtering. At last, a search back stage will be implemented to search for low amplitude useful peaks.

Peculiarities of changes in the cardiovascular system in the course of sepsis in newborns

The issues of early diagnostics of cardiovascular disorders in neonates with sepsis using up-to-date methods of examination are relevant today and will remain so in the future. They can be used as screening methods with the purpose to verify the possible development of cardiovascular dysfunction. Aim of the study: The study aimed to investigate the peculiarities of possible changes in the cardiovascular system and physiological electrical heart activity in neonates with signs of generalised infectious-inflammatory process, and to assess possible diagnostic methods and use of electrocardiography to verify cardiovascular disorders in newborns with neonatal sepsis. Materials and methods: To achieve the study objective, we followed up a total of 69 neonates with signs of generalised infectious-inflammatory process. Group I (32 patients – 46.4%) included neonates with a term of gestation of 37–42 weeks, and group II included 37 preterm neonates (53.6%) with a term of gestation under 36 weeks inclusive. Results: Generalised infectious-inflammatory process during the neonatal period of term infants is accompanied by electrocardiographic signs of left ventricular overload associated with the female sex (r = 0.30), delivery by cesarean section (r = 0.27), and assessment of neonatal condition by a 5-minute Apgar score (r = −0.33). The study demonstrates that increased values of lactate dehydrogenase activity in the blood plasma both in the term and preterm neonates are associated with left ventricular overload among the former, and the right ventricular overload among the latter, that is, preterm neonates. Conclusions: Increased values of lactate dehydrogenase activity in the blood serum of both term and preterm neonates are associated with left ventricular overload in term neonates, while right ventricular overload in preterm infants thus can be considered as a marker of cardiovascular dysfunction caused by neonatal sepsis, especially in the subpopulation of term patients considering its diagnostic value. Changes found in electrophysiological heart activity promote the necessity of routine use of electrocardiography in neonates with signs of the septic process.

Heart Electrical Activity during Ventricular Repolarization in Rats after Acute Exhaustive Treadmill Running

Heart Electrical Activity during Ventricular Repolarization in Rats after Acute Exhaustive Treadmill Running

Heart Response to Harmonic Music Interval Stimuli Via Deep Learning Structures

The effect of music on the heart is reflected in variables such as heart rate and electrocardiographic (ECG) signals. ECG is a record of heart electrical activity and is a useful tool in diagnosing various cardiopathies. Artificial intelligence techniques have recently been implemented to analyze ECG and RR-interval data and are used thus in the present study to examine the influence on the heart of harmonic musical intervals and colored noise. Harmonic intervals were chosen because of their emotional response, while noise has been linked to positive responses such as improved sleep quality. A deep learning system was implemented, employing the ResNet-18 and GoogLeNet pre-trained networks to discriminate 31 different classes of ECG and RR-interval responses to the sound stimuli. Following an exploratory approach, deep learning was selected as an alternative to traditional analysis with the expectation that it could be incorporated into future music perception research. Classification revealed the ability of the implemented system to demonstrate heart response to the stimuli. ECG signals performed best, with 97% accuracy and Matthew’s coefficient of 0.97, while RR-interval achieved a 93% accuracy and Matthews coefficient of 0.93, suggesting that the considered stimuli of harmonic musical intervals and noise produced different responses in the heart. Moreover, the Matthews coefficient values above 0.7 and close to 1 imply a correlation between the two types of stimuli and the heart response, as measured by ECG and RR-interval signals.

Evaluation of the Cardiac Electrophysiological and Haemodynamic Effects of Elsholtzia ciliata Essential Oil on Swine.

The demand for the development of novel medicines with few side effects and no proarrhythmic properties is increasing. Extensive research on herbal extracts has been conducted with the expectation that the compounds will exert precise effects without harmful side effects. Elsholtzia ciliata (Thunb.) Hyl. essential oil (EO) possesses antiarrhythmic properties similar to those of class 1B antiarrhythmics, such as prolonging myocardial activation of the QRS complex and shortening the QT interval. In this study, we determined the kinetic profile of EO phytocompounds and the effects of EO on heart electrical activity and arterial blood pressure. For this study, we chose to use local breed pigs that were anaesthetized. The effects of an intravenous bolus of EO on ECG parameters, arterial blood pressure, heart rate variability, and blood levels of haematological and biochemical parameters were registered and evaluated. Following an intravenous injection of a bolus, EO exerted a vasodilatory effect, resulting in significant reductions in arterial blood pressure. EO also increased the heart rate and altered ECG parameters. The bolus of EO prolonged the QRS complex, shortened the QT interval, and nonmonotonically altered the PQ interval. After the administration of a bolus of EO, the activity of the autonomic nervous system was altered. This study confirms that EO possesses similar properties to class 1B antiarrhythmics and exerts a hypotensive effect; it reduces arterial blood pressure possibly by modulating peripheral vascular resistance.