Cardiomyopathy Detection from Electrocardiogram Features

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 predisposes the heart to failure or arrhythmias. Cardiomyopathy in its two common forms, dilated and hypertrophic, implies enlargement of the atria. Therefore, computer intelligence techniques are proposed for the recognition and classification of P wave features for cardiomyopathy diagnosis. The technique that we propose is a neuro-fuzzy network. The neuro-fuzzy classifier will be trained with innovative evolutionary algorithms, which have recently been shown to be efficient global optimizers. Cardiomyopathy is a significant clinical problem which is mainly generated by volume/diastolic overload. To accommodate the increased blood volume, the heart chambers may stretch or dilate. Valvular regurgitation and congestive heart failure are two conditions that contribute to chamber dilation. Cardiomyopathy is generally diagnosed by an electrocardiographic (ECG) investigation. In the current standards published by the American Heart Association, chamber hypertrophy or enlargement is a separate diagnostic category which can be detected with ECG analysis (Masson, Hancock, & Gettes, 2007). Although many algorithms have been implemented for ECG analysis, the proposed research is unique in several ways.  We propose the development of non-invasive and automatic cardiomyopathy diagnosis, which has not been reported in the literature.  We propose the development of algorithms for P wave analysis, which have not been reported in the literature.  We propose the use of 5-lead ECG data, which is more readily available than 12-lead data.  We propose the use of a powerful neuro-fuzzy architecture for ECG analysis, which has not been reported in the literature.  We propose neuro-fuzzy ECG classifier optimization using evolutionary algorithms, which has not been reported in the literature. Our preliminary studies of postoperative cardiovascular patients reveal our hypothesis: the ECG presents different electrical activity for patients with cardiomyopathy, compared with patients who do not have cardiomyopathy. This working hypothesis indicates that an automated method that selects the best ECG parameters to include in a cardiomyopathy