Journal ArticleDOI
Accessing heart dynamics to estimate durations of heart sounds
Vivek Nigam,Roland Priemer +1 more
TLDR
The objective of this work is to provide an efficient phonocardiogram segmentation technique, under difficult recording situations, by utilizing the underlying complexity of the dynamical system (heart) giving rise to the heart sound.Abstract:
Segmentation of the phonocardiogram into its major sound components is the first step in the automated diagnosis of cardiac abnormalities. Almost all of the existing phonocardiogram segmentation algorithms utilize absolute amplitude or frequency characteristics of heart sounds, which vary from one cardiac cycle to the other and across different patients. The objective of this work is to provide an efficient phonocardiogram segmentation technique, under difficult recording situations, by utilizing the underlying complexity of the dynamical system (heart) giving rise to the heart sound. Complexity-based segmentation is invariant to amplitude and frequency variations of the heart sound and yields better time gates for heart sounds.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.
Segmentation of heart sound recordings by a duration dependent Hidden Markov Model
TL;DR: The results indicate that the DHMM is an appropriate model of the heart cycle and suitable for segmentation of clinically recorded heart sounds.
Journal ArticleDOI
Segmentation of heart sound recordings by a duration-dependent hidden Markov model
TL;DR: In this article, a duration-dependent hidden Markov model (DHMM) was proposed for robust segmentation of heart sounds, based on duration of the events, amplitude of the signal envelope and a predefined model structure.
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
Efficient Heart Sound Segmentation and Extraction Using Ensemble Empirical Mode Decomposition and Kurtosis Features
TL;DR: An efficient heart sound segmentation method that automatically detects the location of first ( S1) and second ( S2) heart sound and extracts them from heart auscultatory raw data is presented here and paves the way for further exploitation of the diagnostic value of heart sounds in everyday clinical practice.
References
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Journal ArticleDOI
Extracting qualitative dynamics from experimental data
TL;DR: In this paper, the notion of qualitative information and the practicalities of extracting it from experimental data were considered, based on ideas from the generalized theory of information known as singular system analysis due to Bertero, Pike and co-workers.
Journal Article
"Detecting Strange Attractors in Turbulence, " in "Dynamical Systems and Turbulence"
Proceedings ArticleDOI
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.
Journal ArticleDOI
Stochastic complexity measures for physiological signal analysis
Iead Rezek,Stephen J. Roberts +1 more
TL;DR: This paper takes a paradigm shift and investigates four stochastic-complexity features and their advantages are demonstrated on synthetic and physiological signals; the latter recorded during periods of Cheyne-Stokes respiration, anesthesia, sleep, and motor-cortex investigation.
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