Cardiomyopathy Detection from Electrocardiogram Features

Cardiomyopathy means heart (cardio) muscle (myo) disease (pathy) . Currently, cardiomyopathies are defined as myocardial disorders in which the heart muscle is structurally and/or functionally abnormal in the absence of a coronary artery disease, hypertension, valvular heart disease or congenital heart disease sufficient to cause the observed myocardial abnormalities. This book provides a comprehensive, state-of-the-art review of the current knowledge of cardiomyopathies. Instead of following the classic interdisciplinary division, the entire cardiovascular system is presented as a functional unity, and the contributors explore pathophysiological mechanisms from different perspectives, including genetics, molecular biology, electrophysiology, invasive and non-invasive cardiology, imaging methods and surgery. In order to provide a balanced medical view, this book was edited by a clinical cardiologist

Classificiation of Atrial Fibrillation Prone Patients Using Electrocardiographic Parameters in Neuro-Fuzzy Modeling,

Atrial Fibrillation (AF) is a significant clinical problem and the complications of cardiovascular postoperative AF often lead to longer hospital stays and higher heath care costs. The literature showed that AF may be preceded by changes in electrocardiogram (ECG) characteristics such as premature atrial activity, heart rate variability (HRV), and P-wave morphology. We hypothesize that the limitations of statistics-based attempts to predict AF occurrence may be overcome using a hybrid neuro-fuzzy prediction model that is better capable of uncovering complex, non-linear interactions between ECG parameters. We created a neuro-fuzzy network that was able to classify the patients into the control and AF groups with the performances: 99.42% sensitivity, 99.89% specificity, and 99.74% accuracy for 30 minutes just before AF onset

Biogeography-based optimization of neuro-fuzzy system parameters for diagnosis of cardiac disease

Cardiomyopathy refers to diseases of the heart muscle that becomes enlarged, thick, or rigid. These changes affect the electrical stability of the myocardial cells, which in turn predisposes the heart to failure or arrhythmias. Cardiomyopathy in its two common forms, dilated and hypertrophic, implies enlargement of the atria; therefore, we investigate its diagnosis through P wave features. In particular, we design a neuro-fuzzy network trained with a new evolutionary algorithm called biogeography-based optimization (BBO). The neuro-fuzzy network recognizes and classifies P wave features for the diagnosis of cardiomyopathy. In addition, we incorporate opposition-based learning in the BBO algorithm for improved training. First we develop a neuro-fuzzy model structure to diagnose cardiomyopathy using P wave features. Next we train the network using BBO and a clinical database of ECG signals. Preliminary results indicate that cardiomyopathy can be reliably diagnosed with these techniques

Prediction of Paroxysmal Atrial Fibrillation Onset in Postoperative Patients Using Neuro-Fuzzy Modeling

ATRIAL FIBRILLATION (AF) is the most common cardiac arrhythmia. In the United States alone, it affects more than 2.5 million people annually. The onset of AF is frequently associated with thoracic surgery and it is estimated to occur in 25% of patients that undergo cardiac surgery. The AF may be preceded by changes in electrocardiogram (ECG) characteristics such as premature atrial activity, heart rate variability (HRV), and P-wave morphology [3]. A valid question regarding the availability of a time lag that could be used to provide adequate treatment against AF onset was raised by Dr. Lombardi in his editorial [1]. We are using a hybrid neuro-fuzzy prediction model that exploits non-linear interactions between ECG parameters. The techniques are non-invasive and analyze 5-lead ECG waveforms. This will allow the model to be easily applied in a Cardio-Vascular Intensive Care Unit setting with very few modifications. http://ama-ieee.embs.org/2011conf/wp-content/uploads/2011/10/AMA_IEEE_2011_Ovreiu_AF_prediction.pd

Prediction of Paroxysmal Atrial Fibrillation Onset in Postoperative Patients Using Neuro-Fuzzy Modeling

ATRIAL FIBRILLATION (AF) is the most common cardiac arrhythmia. In the United States alone, it affects more than 2.5 million people annually. The onset of AF is frequently associated with thoracic surgery and it is estimated to occur in 25% of patients that undergo cardiac surgery. The AF may be preceded by changes in electrocardiogram (ECG) characteristics such as premature atrial activity, heart rate variability (HRV), and P-wave morphology [3]. A valid question regarding the availability of a time lag that could be used to provide adequate treatment against AF onset was raised by Dr. Lombardi in his editorial [1]. We are using a hybrid neuro-fuzzy prediction model that exploits non-linear interactions between ECG parameters. The techniques are non-invasive and analyze 5-lead ECG waveforms. This will allow the model to be easily applied in a Cardio-Vascular Intensive Care Unit setting with very few modifications. http://ama-ieee.embs.org/2011conf/wp-content/uploads/2011/10/AMA_IEEE_2011_Ovreiu_AF_prediction.pd