Journal ArticleDOI
Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier
TLDR
A technique to improve the performance of the Least Square Support Vector Machine (LSSVM) is proposed for classification of normal and abnormal heart sounds using wavelet based feature set using Lagrange multiplier and weight vector.Abstract:
Auscultation, the technique of listening to heart sounds with a stethoscope can be used as a primary detection system for diagnosing heart valve disorders. Phonocardiogram, the digital recording of heart sounds is becoming increasingly popular as it is relatively inexpensive. In this paper, a technique to improve the performance of the Least Square Support Vector Machine (LSSVM) is proposed for classification of normal and abnormal heart sounds using wavelet based feature set. In the proposed technique, the Lagrange multiplier is modified based on Least Mean Square (LMS) algorithm, which in turn modifies the weight vector to reduce the classification error. The basic idea is to enlarge the separating boundary surface, such that the separability between the clusters is increased. The updated weight vector is used at the time of testing. The performance of the proposed systems is evaluated on 64 different recordings of heart sounds comprising of normal and five different pathological cases. It is found that the proposed technique classifies the heart sounds with higher recognition accuracy than competing techniques.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 classification based on scaled spectrogram and tensor decomposition
TL;DR: A scaled spectrogram and tensor decomposition based method to extract more discriminative features for heart sound classification is proposed and is evaluated on three public datasets offered by the PASCAL classifying heart sounds challenge and 2016 PhysioNet challenge.
Journal ArticleDOI
Heart sounds classification using a novel 1-D convolutional neural network with extremely low parameter consumption
TL;DR: A block-stacked style architecture with clique blocks is employed, and in each clique block a bidirectional connection structure is introduced in the proposed CNN, which achieves both spatial and channel attention leading a promising classification performance.
References
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Improving support vector machine classifiers by modifying kernal functions
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TL;DR: Simulation results for both artificial and real data show remarkable improvement of generalization errors, supporting the idea of modifying a kernel function to enlarge the spatial resolution around the separating boundary surface by a conformal mapping, such that the separability between classes is increased.
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An open access database for the evaluation of heart sound algorithms.
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