Classification of the mechanisms by which cardiotoxic plant poisons exert their effects

2021 ◽  
pp. postgradmedj-2021-140406
Author(s):  
George Huntington
Keyword(s):  
Ion Channels ◽  
Cardiac Myocyte ◽  
Antiarrhythmic Agents ◽  
Ecg Changes ◽  
Psychotropic Effect ◽  
Source Plant ◽  
Receptor Targets ◽  
Electrocardiogram Ecg ◽  
Wild Garlic

Episodes of poisoning due to plant-based toxins are an unusual presentation to the emergency department. Plant poisons may be ingested if the source plant is misidentified as benign (eg, Lily of the Valley being mistaken for wild garlic and water hemlock being mistaken for wild celery), or taken as part of a complementary medicine regime or otherwise for psychotropic effect. Numerous plant poisons demonstrate cardiotoxic effects resulting from action against cardiac myocyte ion channels, or other cardiac receptor targets. These mechanisms will produce stereotyped symptoms and including electrocardiogram (ECG) changes dependent on which ion channels or receptors are targeted. These mechanisms are stereotyped and may be grouped by toxidromic effect. This article proposes a novel classification of cardiotoxic plant poisons based on these actions. Given that these mechanisms mirror the Vaughan Williams classification used to categorise therapeutic antiarrhythmic agents, it is felt that this will serve as a mnemonic and diagnostic aid in clinical situations of cardiotoxic plant ingestion.

scholarly journals Time Course of ECG Depolarization and Repolarization Changes during Ischemia in PTCA Recordings

2004 ◽  
Vol 43 (01) ◽  
pp. 43-46 ◽  
Author(s):  
J. García ◽  
G. Wagner ◽  
R. Bailón ◽  
L. Sörnmo ◽  
P. Laguna ◽  
...  
Keyword(s):  
Time Course ◽  
Wave Amplitude ◽  
Temporal Evolution ◽  
Delayed Response ◽  
S Wave ◽  
Ecg Changes ◽  
T Complex ◽  
Karhunen Loeve Transform ◽  
Low Amplitude ◽  
Electrocardiogram Ecg

Summary Objectives: In this work we studied the temporal evolution of changes in the electrocardiogram (ECG) as a consequence of the induced ischemia during prolonged coronary angioplasty, comparing the time course of indexes reflecting depolarization and those reflecting repolarization. Methods: We considered both local (measured at specific points of the ECG) and global (obtained from the Karhunen-Loève transform) indexes. In particular, the evolution of Q, R and S wave amplitudes during ischemia was analyzed with respect to classical indexes such as ST level. As a measurement of sensitivity we used an Ischemic Changes Sensor (ICS), which reflects the capacity of an index to detect changes in the ECG. Results: The results showed that, in leads with low-amplitude ST-T complexes, the S wave amplitude was more sensitive in detecting ischemia than was the commonly used index ST60. It was found that in such leads the S wave amplitude initially exhibited a delayed response to ischemia when compared to ST60, but its performance was better from the second minute of occlusion. The global indexes describing the ST-T complex were, in terms of the ICS, superior to the S wave amplitude for ischemia detection. Conclusions: Ischemic ECG changes occur both at repolarization and depolarization, with alterations in the depolarization period appearing later in time. Local indexes are less sensitive to ischemia than global ones.

scholarly journals Midventricular takotsubo syndrome: different dyskinesia location leading to a different electrocardiogram?

2021 ◽  
Vol 10 (Supplement_1) ◽  
Author(s):  
M Padilla Lopez ◽  
A Duran Cambra ◽  
M Vidal Burdeus ◽  
L Rodriguez Sotelo ◽  
J Sanchez Vega ◽  
...  
Keyword(s):  
Acute Phase ◽  
Clinical Symptoms ◽  
Takotsubo Syndrome ◽  
St Segment Elevation ◽  
Funding Sources ◽  
Wave Inversion ◽  
St Segment ◽  
Electrocardiogram Ecg ◽  
Study Inclusion

Abstract Funding Acknowledgements Type of funding sources: None. Background Takotsubo syndrome (TKS) is characterized by the appearance of apical reversible dyskinesia in its typical form. Electrocardiogram (ECG) in the acute phase (<12 from symptom onset) generally shows anterior ST segment elevation. Nonetheless, other atypical forms of TKS have been described depending on the location of the dyskinetic segments, such as, mid-ventricular, basal and focal forms. Considering the different segments involved in these atypical forms, it seems reasonable to consider that ST changes in acute phase ECG could be different. Purpose To compare ECG in the acute phase of typical TKS versus mid-ventricular TKS, as it was the more frequent form of atypical TKS in our registry. Methods Patients included in the prospective TKS registry of our center according to the Mayo Clinic diagnostic criteria, with the first ECG performed less than 12 hours from the symptoms onset were reviewed. All cardiac left ventriculographies were reviewed to ensure a correct classification of the different types of TKS. Results A total of 297 patients were included in our local registry. 80 patients met our study inclusion criteria. 56 ECGs of typical apical TKS were compared to 24 ECGs of atypical midventricular TKS. There were no differences between the baseline characteristics in both groups, except for mid-ventricular TKS, that was more frequently triggered by physical stressor. Regarding the ECG analysis, the main difference found in our serie was related to ST-segment deviation (Table 1). While ST-segment elevation was more common in typical TKS than in atypical TKS (73% vs 50%), ST-segment depression (generally in inferior leads) was observed in 54% of patients with atypical TKS and in no patient with typical TKS (figure 1). Conclusion The different location of dyskinesia between typical TKS and mid-ventricular TKS is associated to significant differences in the ECG obtained in the first hours after the onset of the clinical symptoms. The presence of ST-segment depression is highly suggestive of mid-ventricular TKS. ECG characteristicsTypical (n = 56)Midventricular (n = 24)pSTe > 1mm, no (%)41 (73)12 (50)0,044STd >0,5 mm, no (%)013 (54)< 0,001T wave inversion, no (%)12 (21)4 (17)0,626Q wave, no (%)22 ( 39)12 (50)0,374cQT, mean (SD)445 (54)438 (37)0,578QRS low voltages*, n (%)9 ( 16)1 (4)0,328STe ST-segment elevation, STd: ST-segment depression, cQT: corrected QT interval *Voltages <5mm in all limb leads or <10mm in all precordial leads Abstract Figure. 12-lead ECG and left ventriculography

scholarly journals ECG Beats Classification Using Mixture of Features

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Manab Kumar Das ◽  
Samit Ari
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Ectopic Beat ◽  
Extraction Techniques ◽  
Efficient System ◽  
Ecg Signals ◽  
Temporal Features ◽  
S Transform ◽  
Electrocardiogram Ecg

Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.

scholarly journals Postoperative ‘STEMI’ in Intracerebral Hemorrhage due to Arteriovenous Malformation: A Case Report and Review of Literature

2019 ◽  
Vol 2019 ◽  
pp. 1-5
Author(s):  
Ravinder Datt Bhanot ◽  
Jasleen Kaur ◽  
Shitiz Sriwastawa ◽  
Kendall Bell ◽  
Kushak Suchdev
Keyword(s):  
Myocardial Infarction ◽  
Intracerebral Hemorrhage ◽  
Arteriovenous Malformation ◽  
Cardiac Biomarkers ◽  
Review Of Literature ◽  
Fibrinolytic Agents ◽  
Intraparenchymal Hemorrhage ◽  
Ecg Changes ◽  
St Segment ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) changes suggestive of cardiac ischemia are frequently demonstrated in patients with ischemic stroke and subarachnoid hemorrhage. However, little is known of such changes particularly acute ST segment myocardial infarction (STEMI) in patients with intracerebral hemorrhage (ICH), especially after neurosurgery. We present a patient with intraparenchymal hemorrhage due to cerebral arteriovenous malformation (AVM) who exhibited acute STEMI after neurosurgery. Serial cardiac biomarkers and echocardiograms were performed which did not reveal any evidence of acute myocardial infarction. The patient was managed conservatively from cardiac stand point with no employment of anticoagulants, antiplatelet therapy, fibrinolytic agents, or angioplasty and recovered well with minimal neurological deficit. This case highlights that diffuse cardiac ischemic signs on the ECG can occur in the setting of an ICH after neurosurgery, potentially posing a difficult diagnostic and management conundrum.

scholarly journals Classification of Hypertensive and Normotensive Subjects Using Bilateral Differential Biopotential Signals

2021 ◽  
Author(s):  
Arindam Sarkar ◽  
Bhaswati Goswami ◽  
Ratna Ghosh
Keyword(s):  
Blood Pressure ◽  
Random Forest ◽  
High Blood Pressure ◽  
Pivotal Role ◽  
Modern World ◽  
Hypertensive Subject ◽  
Normotensive Subjects ◽  
Electrocardiogram Ecg ◽  
Day By Day

Abstract Hypertension or high blood pressure is a severe health issue in the modern world, especially in this pandemic scenario, that can cause many heart related diseases or even death, and it is increasing day by day. For this reason, a reliable, automatic and easy to use system for hypertensive subject detection is an important focus for the researchers. Biopotential signals can play a pivotal role in this regard. Though, few strategies were proposed based on electrocardiogram (ECG) or electrodermal (EDA) signals, but those require special circuitry, as well as trained persons. In this article, a method is proposed to classify hypertensive and normotensive subjects using differential biopotential signals. Neither special circuitry, nor much expertise is required for handling this system. It was assumed that progression of rest is dependent upon blood pressure. To serve the purpose, signals were acquired from both hypertensive and normotensive subjects bilaterally for 10 continuous minutes. Result of the random forest (RF) classification establishes that from the analysis of the progression of the bilaterally acquired differential biopotential signals, hypertensive subjects can be distinguished from normotensive subjects.

scholarly journals HYPERMNESIA EUPHORIA OR ANY OTHER DESIRABLE FROM THE POINT OF VIEW OF THE CONSUMER, A PSYCHOTROPIC EFFECT - FIRST AND LEADING ELEMENT IN THE OCCURRENCE AND PRESERVATION OF THE BODY’S DEPENDENCE ON PSYCHOACTIVE FACTORS

2017 ◽  
Vol 9 (3) ◽  
pp. 40-46
Author(s):  
D G Baitubayev ◽  
M D Baitubayeva
Keyword(s):  
Substance Dependence ◽  
Point Of View ◽  
The Body ◽  
Long Term Memory ◽  
Vegetative Nervous System ◽  
Term Memory ◽  
Psychotropic Effect

The work shows the role of the vegetative nervous system (VNS) in the functioning of long-term memory, identity mechanisms of long-term memory in the human evolutionary adaptation and substance dependence. It is shown that, depending on the substance of the body are states like pro- gressive adaptation, that the bodycondition, depending on the chemical and psychogenic psychoactive- factors state of the same circle. It proposed the creation of a branch of medicine that combines study of the dependence of the organism, both on the chemical and psychoactive psychogenic factors. Given the classification of psychoactive factors.Onomastics formulated definitions of terminology changes and additions to be used in a new branch of medicine. Proposed allocation of the International Classifica- tion of diseases separate chapter for the classification of states like progressive adaptation of the body depending on psychoactive factors.

scholarly journals Classification of cardiac arrhythmias using deep learning

2018 ◽  
Vol 7 (3.3) ◽  
pp. 401
Author(s):  
Jeong Hwan Kim ◽  
Jeong Whan Lee ◽  
Kyeong Seop Kim
Keyword(s):  
Deep Learning ◽  
Normal Sinus Rhythm ◽  
Premature Ventricular Contraction ◽  
Heart Rhythm ◽  
Future Research ◽  
Normal Sinus ◽  
Input Layer ◽  
Hidden Layer ◽  
Electrocardiogram Ecg

Background/Objectives: The main objective of this research is to design Deep Learning (DL) architecture to classify an electrocardiogram (ECG) signal into normal sinus rhythm (NSR), premature ventricular contraction (PVC), atrial premature contraction (APC) or right/left bundle branch block (RBBB/LBBB) arrhythmia by empirically optimizing the numbers of hidden layers, the number of neurons in each hidden layer and the number of neurons in input layer in DL model.Methods/Statistical analysis: For our experimental simulations, PhysioBank-MIT/BIH annotated ECG database was considered to classify heart beats into abnormal rhythms (PVC, APC, RBBB, LBBB) or normal sinus. The performance of classifying ECG beats by the proposed DL architecture was evaluated by computing the overall accuracy of classifying NSR or four different arrhythmias.Findings: Base on testing MIT/BIH arrhythmia database, the proposed DL model can classify the heart rhythm into one of NSR, PVC, APC, RBBB or LBBB beat with the mean accuracy of 95.5% by implementing DL architecture with 200 neurons in input layer, 100 neurons in the first and second hidden layer, respectively and 80 neurons in the 3rd hidden layer.Improvements/Applications: Our experimental results show that the proposed DL model might not be quite accurate for detecting APC beats due to its morphological resemblance of NSR. Therefore, we might need to design more sophisticated DL architecture by including more temporal characteristics of APC to increase the classification accuracy of APC arrhythmia in the future research efforts. 

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