Automatic diagnosis of cardiovascular disorders by sub images of the ECG signal using multi-feature extraction methods and randomized neural network.

• ECG data have been exploited for the detection of cardiovascular morbidity in two-dimensional space. • The sub-images have been constructed by projecting the 12-ECG signal vector as a row of the image. • Cardiovascular defects have been detected with the classical image texture methods. • Two-dimensional features in the images contain determinative indicators of various diseases. • The proposed system has provided fast and largely accurate results. Electrocardiography has been employed successfully in medicine for many years to provide vital knowledge about the cardiovascular system. Although processing and evaluation of electrocardiogram (ECG) signals provide helpful information in the detection of anomalies in the vessel, diagnosis of heart defect, and treatment of diseases, multi-channel ECG signals have been started to be employed in order to achieve higher success. Utilizing a multi-channel ECG signal instead of a one-channel ECG signal yields more adequate achievements but require higher complexity in analysis and higher computational cost. To achieve faster and accurate results in multi-channel ECG signals, an artificial intelligence-based automatic diagnosis system employing the texture features of two-dimensional images, which are constructed by projecting the ECG signal vector as a row of the image, is proposed. The hypothesis proposed in this study conjectures that these texture features in the images contain determinative indicators of various diseases (cardiovascular abnormalities/disturbance) even for the short-time intervals. Accordingly, the main contribution of this study is to expose that detection of cardiovascular defects can be done with the classical image texture methods by utilizing multi-channel biomedical signals in a sufficiently short-time-interval. The methodology has been implemented in different time intervals of a large dataset constructed from a diverse population that is labeled as one normal sinus rhythm type and eight abnormal types of ECG signals. The accuracy of this hypothesis has been proven by achieving high detection rates of identifying cardiac abnormalities and reduced the computational load of the processing system without any sacrificing accuracy. [ABSTRACT FROM AUTHOR]