Open AccessJournal Article
An automatic tool for pediatric heart sounds segmentation
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In this article, a wavelet transform technique is used to determine the beginning and end points of each cardiac cycle by using wavelet transforms and then, first and second heart sounds within the cycles are identified over the PCG signal by paying attention to the spectral properties of the sounds.Abstract:
In this paper, we present a novel algorithm for pediatric heart sound segmentation, incorporated into a graphical user interface. The algorithm employs both the Electrocardiogram (ECG) and Phonocardiogram (PCG) signals for an efficient segmentation under pathological circumstances. First, the ECG signal is invoked in order to determine the beginning and end points of each cardiac cycle by using wavelet transform technique. Then, first and second heart sounds within the cycles are identified over the PCG signal by paying attention to the spectral properties of the sounds. The algorithm is applied on 120 recordings of normal and pathological children, totally containing 1976 cardiac cycles. The accuracy of the segmentation algorithm is 97% for S 1 and 94% for S 2 identification while all the cardiac cycles are correctly determined.read more
Citations
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Journal ArticleDOI
An open access database for the evaluation of heart sound algorithms.
Chengyu Liu,David Springer,Qiao Li,Benjamin Moody,Ricardo Abad Juan,Ricardo Abad Juan,Francisco J. Chorro,Francisco Castells,José Millet Roig,Ikaro Silva,Alistair E. W. Johnson,Zeeshan Syed,Samuel Emil Schmidt,Chrysa D. Papadaniil,Leontios J. Hadjileontiadis,H. Naseri,Ali Moukadem,Alain Dieterlen,Christian Brandt,Hong Tang,Maryam Samieinasab,Mohammad Reza Samieinasab,Reza Sameni,Roger G. Mark,Gari D. Clifford,Gari D. Clifford +25 more
TL;DR: A public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016, which comprises nine different heart sound databases sourced from multiple research groups around the world is described.
Journal ArticleDOI
The electronic stethoscope
Shuang Leng,Ru San Tan,Ru San Tan,Kevin T. C. Chai,Chao Wang,Dhanjoo N. Ghista,Liang Zhong,Liang Zhong +7 more
TL;DR: The paper provides the technological and medical basis for the development and commercialization of a real-time integrated heart sound detection, acquisition and quantification system.
Journal ArticleDOI
Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review
TL;DR: It is clear that, although a lot of research has been done in the field of automated analysis, there is still some work to be done to develop robust methods for identification and classification of various events in the cardiac cycle so that this could be effectively used to improve the diagnosis and management of cardiovascular diseases in combination with the wearable mobile technologies.
Journal ArticleDOI
Heart Sound Segmentation—An Event Detection Approach Using Deep Recurrent Neural Networks
TL;DR: A new methodology for the segmentation of heart sounds is introduced, suggesting an event detection approach with DRNNs using spectral or envelope features and the performance of different deep recurrent neural network (DRNN) architectures to detect the state sequence is investigated.
Journal ArticleDOI
A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network
Arash Gharehbaghi,Maria Lindén +1 more
TL;DR: A systematic procedure for finding the design parameter of the classification method for a one-versus-multiple class application and a novel validation method for evaluating the structural risk are suggested.
References
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Heart sound segmentation algorithm based on heart sound envelogram
TL;DR: A segmentation algorithm which separates the heart sound signal into four parts: the first heart sound, the systole, the second heart sound and the diastole is described, based on the normalized average Shannon energy of a PCG signal.
Proceedings ArticleDOI
A heart sound segmentation algorithm using wavelet decomposition and reconstruction
Liang Huiying,L. Sakari,H. Iiro +2 more
TL;DR: A heart sound segmentation algorithm, which separates the heart sound signal into four parts (the first heart sound, the systolic period, the second heart sound and the diastolic period), has been developed and shown to perform correctly in over 93% of cases.
Journal ArticleDOI
Artificial Neural Network–Based Method of Screening Heart Murmurs in Children
Curt G. DeGroff,Sanjay. R. Bhatikar,Jean Hertzberg,Robin Shandas,Lilliam M. Valdes-Cruz,Roop L. Mahajan +5 more
TL;DR: It is demonstrated that ANNs show significant potential in their use as an accurate diagnostic tool for the classification of heart sound data into innocent and pathological classes and offers great promise for the development of a device for high-volume screening of children for heart disease.