Multi parametric classification of cardiovascular sounds

Abstract

The present application relates to a method for classifying a cardiovascular sound recorded from a living subject. The method comprises the step of extracting at least two signal parameters (309) from said cardiovascular sound, said at least two signal parameters characterizes at least two different properties of at least a part of said cardiovascular sound. The method further comprises the step of classifying said cardiovascular sound using said at least two signal parameters in a multivariate classification method (310). Furthermore, the application relates to a system, stethoscope and server for classifying a cardiovascular sound recorded from a living subject, where the above-described method has been implemented.

Description

FIELD OF THE INVENTION[0001]The present invention relates to methods and systems for classification of heart sounds recorded from a living subject into classes describing whether or not murmurs due to coronary artery stenosis is present in the heart sound.BACKGROUND OF THE INVENTION[0002]Coronary artery disease is the single most common cause of death from cardiovascular disease in the western world. The heart muscle receives its blood supply through the coronary arteries, and atherosclerosis is the most common pathophysiologic process occurring in the coronary arteries giving rise to coronary artery disease (CAD). Atherosclerosis is a process that builds up plaques within the artery, and the blood flow can therefore be is reduced or even blocked by the plaque. The constantly working heart requires a continuous and efficient blood supply in order to work properly. Defects in the blood supply may be very severe and even fatal. Increasing degrees of luminal diameter reduction or steno...

AI Technical Summary

Benefits of technology

The present invention provides a method for classifying cardiovascular sounds using a simple method that takes into account different properties of the sound. The method uses at least two signal parameters that characterize the sound in a way that is uncorrelated with each other. The signal parameters can be extracted from the sound using a multivariate classification method. The method is robust and can be easily integrated into a software program or a digital stethoscope for clinicians and doctors to use in diagnosing cardiovascular diseases. The method takes into account different properties of the sound, such as the time duration of the diastolic segment, the most dominant frequency component, the energy in different frequency bands, and the mobility of the sound. The method can be easily taught and used by clinicians and doctors.

Problems solved by technology

Defects in the blood supply may be very severe and even fatal.

However, the above-mentioned techniques are all rather complicated and expensive to use and therefore only patients with specific symptoms are offered such examinations.

Using these conventional techniques, it is difficult to provide an automated device for diagnosis of coronary stenosis using auscultatory sounds.

Moreover, it is often difficult to implement the conventional techniques in a manner that may be applied in real-time or pseudo real-time to aid the clinician.

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