Modifications in Electrocardiographic and Vectordardiographic Morphological Parameters in Elderly Males as Result of Cardiovascular Diseases and Diabetes Mellitus

Abstract

Purpose. Morphological electrocardiographic and vectorcardiographic features have been used in the detection of cardiovascular diseases and prediction of the risk of cardiac death for a long time. The objective of the current study was to investigate the morphological electrocardiographic modifications in the presence of cardiovascular diseases and diabetes mellitus in an elderly male population, most of them with multiple comorbidities. Methods. A database of ECG recordings from the Italian Longitudinal Study on Aging (ILSA-CNR), created to evaluate physiological and pathological modifications related to aging, was considered. The study examined a group of 1109 males with full clinical documentation aged 65–84 years. A healthy control group (219 individuals) was compared to the groups of diabetes mellitus (130), angina pectoris (99), hypertension (607), myocardial infarction (160), arrhythmia (386), congestive heart failure (73), and peripheral artery disease (95). Twenty-one electrocardiographic features were explored, and the effects of cardiovascular diseases and diabetes on these parameters were analyzed. The three-years mortality index was derived and analyzed. Results and Conclusions. Myocardial infarction and arrhythmia were the diagnostic groups that showed a significant deviation of 11 electrocardiographic parameters compared to the healthy group, followed by hypertension and congestive heart failure (10), angina pectoris (9), and diabetes mellitus and peripheral artery disease (8). In particular, a set of three parameters (QRS and T roundness and principal component analysis of T wave) increased significantly, whereas four parameters (T amplitude, T maximal vector, T vector ratio, and T wave area dispersion) decreased significantly in all cardiovascular diseases and diabetes mellitus with respect to healthy group. The QRS parameters show a more specific discrimination with a single disease or a group of diseases, whereas the T-wave features seems to be influenced by all the pathological conditions. The present investigation of disease-related electrocardiographic parameters changes can be used in assessing the risk analysis of cardiac death, and gender medicine.

Desarrollo de nuevos marcadores y clasificadores de bajo coste computacional para identificar afecciones cardiacas en registros ECG

“…Por otra parte, [Bhattacharyya 2020] afirma que el VCG permite distinguir pacientes con disfunción ventricular de aquellos sanos. Otra técnica inexplorada en el diagnóstico de la MCA es el análisis de componentes principales (PCA) de la señal ECG, el cuál ha sido utilizado con éxito, por ejemplo, en la identificación de pacientes con bloqueo de rama izquierda [Feeny 2020], o en otras patologías de origen auricular [Castells2005a, Castells 2007, Bonizzi 2010, Bortolan 2022.…”

“…Una de las técnicas a explorar es el Vectocardiograma (VCG), el cual es un método para analizar la trayectoria descrita por la actividad eléctrica cardiaca, obtenido a partir de las 12 derivaciones ECG estándar mediante la Transformada Inversa de Dower [Dower 1984, Edenbrandt 1988]. Se ha utilizado previamente para caracterizar distintas patologías, como por ejemplo el flutter auricular [Castells 2011], la hipertrofia ventricular izquierda y derecha [Titomir 2007], o estudiar recientemente las alteraciones cardiacas en población anciana [Bortolan 2022].…”

“…Por otro lado, el Análisis de Componentes Principales (PCA) también se ha utilizado con éxito en el procesamiento de señales de ECG con diferentes aplicaciones [Bortolan 2022, Feeny 2020, Bonizzi 2010, Castells 2007, Castells 2005a.…”