Forth heart sound detection using backward time-growing neural network
2020 (English)In: IFMBE Proceedings, Springer Verlag , 2020, p. 341-345Conference paper, Published paper (Refereed)
Abstract [en]
This paper presents a novel method for processing heart sound signal for screening forth heart sound (S4). The proposed method is based on time growing neural network with a new scheme, which we call the Backward Time-Growing Neural Network (BTGNN). The BTGNN is trained for detecting S4 in recordings of heart sound signal. In total, 83 children patients, referred to a children University hospital, participated in the study. The collected signals are composed of the subjects with and without S4 for training and testing the method. Performance of the method is evaluated using the Leave-One-Out and the repeated random sub sampling methods. The accuracy/sensitivity of the method is estimated to be 88.3%/82.4% and the structural risk is calculated to be 18.3% using repeated random sub sampling and the A-Test methods, respectively.
Place, publisher, year, edition, pages
Springer Verlag , 2020. p. 341-345
Keywords [en]
A-Test method, Backward time-growing neural network, Intelligent phonocardiography, Time-growing neural network, Biochemical engineering, Diagnosis, Heart, Phonocardiography, Risk perception, Testing, Backward time, Heart sound signal, Leave one out, Structural risks, Sub-sampling, Sub-sampling methods, Test method, Training and testing, Cardiology
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-43874DOI: 10.1007/978-3-030-17971-7_53ISI: 000491311000053Scopus ID: 2-s2.0-85066034972ISBN: 9783030179700 (print)OAI: oai:DiVA.org:mdh-43874DiVA, id: diva2:1323100
Conference
International Conference on Medical and Biological Engineering in Bosnia and Herzegovina, CMBEBIH 2019, 16 May 2019 through 18 May 2019
2019-06-112019-06-112019-12-12Bibliographically approved