Visión Electrónica

Analysis of Heart Rate Variability in Dilated Ventricular Cardiomyopathy

Resumen

A case study of dilated ventricular cardiomyopathy (DCM) is performed. Pathology is known as a disease of the heart muscle in which there is evidence of left, right or biventricular ventricular dilatation and systolic dysfunction in the absence of hypertension, coronary artery disease or valvular heart disease to justify it. In addition, ventricular dilatation and resulting systolic dysfunction can lead to congestive heart failure. This means that the heart cannot pump enough blood to meet the body's needs, causing symptoms such as fatigue, shortness of breath, fluid accumulation in the tissues and general weakness. MVD can increase the risk of developing cardiac arrhythmias, such as irregular or rapid heartbeats, due to the altered electrical structure and function of the heart. Furthermore, detection of the main causes is challenging due to geographic variability, incomplete penetrance of the disease, or late onset of presentation. However, it is possible to find a correlation of parameters extracted from an ECG (Electrocardiogram) in relation to heart rate variability (HRV) as a determinant in the identification of the pathology.  The main causes and risk factors that generate MVD are related to genetic predisposition, viral infections, excessive alcohol consumption, drug abuse and autoimmune diseases. In addition, some risk factors include family history of cardiomyopathy, arterial hypertension, coronary artery disease and obesity. Therefore, treatment of the pathology usually includes medication for symptom control, but in more severe cases, it may be necessary to consider a pacemaker or cardiac transplantation. For the study performed, a 47-year-old test subject was taken, who has a subdermal pacemaker, given that he has eccentric left ventricular hypertrophy with severely decreased ventricular systolic function, aortic and mitral sclerosis with moderate insufficiency, and even severe biauricular dilatation with dysfunction. Thus, to recognize and diagnose MVD in a shorter time, HRV analysis is performed, allowing the collection of features that enable the acquisition of a pattern given by the nature of the disease. Additionally, as a future work, it is expected to submit these features to artificial intelligence algorithms to evaluate the possibility of diagnosing MVD by extracting ECG features.

Palabras clave