A machine learning method for screening children with patent ductus arteriosus using intelligent phonocardiography

• This paper presents a sophisticated machine learning method for screening children with patent ductus arteriosus (PDA) using the phonocardiogram recording as the input signal. The method is based on our original algorithm for finding the discriminative contents of the signal for the healthy children and the diseased ones, which we called Short Time Arash-Band (STAB) method. The STAB employs our specific discriminant analysis method for finding the joint temporal and spectral characteristics of the signal which provide the optimal segregation. Fifty pediatric referrals to a children university hospital, composed of 30 healthy and 20 with PDA, were participated in this study after obtaining the informed consent, according to the guidelines of the hospital which is in compliance with the declaration of Helsinki. The accuracy/sensitivity of the method was evaluated to be 86%/85%, using the leave-one-out validation method. Results show a potential for the approach in pediatric cardiac assessments that is considered as a demanding clinical application. Such an approach, which we called the intelligent phonocardiography, can be easily employed by the nurses or practitioners to improve the screening accuracy in the primary healthcare centers. 

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)

Nyckelord

Intelligent phonocardiography

Patent ductus arteriosus

Time-growing neural network

Publikations- och innehållstyp

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