Electrocardiographic Diagnosis of Atrial Tachycardia: Classification, P-Wave Morphology, and Differential Diagnosis with Other Supraventricular Tachycardias

Atrial tachycardia is defined as a regular atrial activation from atrial areas with centrifugal spread, caused by enhanced automaticity, triggered activity or microreentry. New ECG classification differentiates between focal andmacroreentrant atrial tachycardia. Macroreentrant atrial tachycardias include typical atrial flutter and other well characterized macroreentrant circuits in right and left atrium. Typical atrial flutter has been described as counterclockwise reentry within right atrial and it presents a characteristic ECG “sawtooth” pattern on the inferior leads. The foci responsible for focal atrial tachycardia do not occur randomly throughout the atria but tend to cluster at characteristic anatomical locations. The surface ECG is a very helpful tool in directing mapping to particular areas of interest. Atrial tachycardia should be differentiated from other supraventricular tachycardias. We propose a diagnostic algorithm in order to help the physician to discriminate among those. Holter analysis could offer further details to differentiate between atrial tachycardia and another supraventricular tachycardia. However, if the diagnosis is uncertain, it is possible to utilize vagal maneuvers or adenosine administration. In conclusion, in spite of well–known limits, a good interpretation of ECG is very importan

Ventricular Tachycardia in the Absence of Structural Heart Disease

In up to 10% of patients who present with ventricular tachycardia (VT), obvious structural heart disease is not identified. In such patients, causes of ventricular arrhythmia include right ventricular outflow tract (RVOT) VT, extrasystoles, idiopathic left ventricular tachycardia (ILVT), idiopathic propranolol-sensitive VT (IPVT), catecholaminergic polymorphic VT (CPVT), Brugada syndrome, and long QT syndrome (LQTS). RVOT VT, ILVT, and IPVT are referred to as idiopathic VT and generally do not have a familial basis. RVOT VT and ILVT are monomorphic, whereas IPVT may be monomorphic or polymorphic. The idiopathic VTs are classified by the ventricle of origin, the response to pharmacologic agents, catecholamine dependence, and the specific morphologic features of the arrhythmia. CPVT, Brugada syndrome, and LQTS are inherited ion channelopathies. CPVT may present as bidirectional VT, polymorphic VT, or catecholaminergic ventricular fibrillation. Syncope and sudden death in Brugada syndrome are usually due to polymorphic VT. The characteristic arrhythmia of LQTS is torsades de pointes. Overall, patients with idiopathic VT have a better prognosis than do patients with ventricular arrhythmias and structural heart disease. Initial treatment approach is pharmacologic and radiofrequency ablation is curative in most patients. However, radiofrequency ablation is not useful in the management of inherited ion channelopathies. Prognosis for patients with VT secondary to ion channelopathies is variable. High-risk patients (recurrent syncope and sudden cardiac death survivors) with inherited ion channelopathies benefit from implantable cardioverter-defibrillator placement. This paper reviews the mechanism, clinical presentation, and management of VT in the absence of structural heart disease

May Fever Trigger Ventricular Fibrillation?

The clinical precipitants of ventricular fibrillation (VF) remain poorly understood. Clinical factors such as hypoxemia, acidosis or electrolyte imbalance, drug-related toxicity, autonomic nervous system disorders as well as viral myocarditis have been proposed to be associated with sudden cardiac death particularly in patients with structural heart disease. However, In the Brugada syndrome, concurrent febrile illness has been reported to unmask the electrocardiographic features of the Brugada syndrome and be associated with an increased propensity for VF. More recently, a febrile illnesses of infectious etiology was associated to polymorphic ventricular tachycardia or VF in patients with normal hearts and without known repolarization abnormality. In this review we detail this phenomenon and its putative mechanisms

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Exercise Testing Score for Myocardial Ischemia Gradation

Scores aimed at contributing to the optimization of exercise testing (ET) have been developed and the experience with their application in coronary artery disease (CAD) has proven to be favorable1. Although there is debate on the use of scores in clinical practice, those that stand for it argue that they may decrease the rate of undiagnosed CAD, besides reducing the number of patients without disease that undergo highly expensive tests2. Additionally, scores may be helpful, in a more consistent and organized fashion, in prognosis evaluation and in the adoption of an appropriate plan of action for the triage of this disease in the general population

An evaluation of planarity of the spatial QRS loop by three dimensional vectorcardiography: its emergence and loss

Aims: To objectively characterize and mathematically justify the observation that vectorcardiographic QRS loops in normal individuals are more planar than those from patients with ST elevation myocardial infarction (STEMI). Methods: Vectorcardiograms (VCGs) were constructed from three simultaneously recorded quasi-orthogonal leads, I, aVF and V2 (sampled at 1000 samples/s). The planarity of these QRS loops was determined by fitting a surface to each loop. Goodness of fit was expressed in numerical terms. Results: 15 healthy individuals aged 35–65 years (73% male) and 15 patients aged 45–70 years (80% male) with diagnosed acute STEMI were recruited. The spatial-QRS loop was found to lie in a plane in normal controls. In STEMI patients, this planarity was lost. Calculation of goodness of fit supported these visual observations. Conclusions: The degree of planarity of the VCG loop can differentiate healthy individuals from patients with STEMI. This observation is compatible with our basic understanding of the electrophysiology of the human heart