The Experts below are selected from a list of 17265 Experts worldwide ranked by ideXlab platform
Luis Eugenio - One of the best experts on this subject based on the ideXlab platform.
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a new algorithm for detection of s1 and s2 Heart Sounds
International Conference on Acoustics Speech and Signal Processing, 2006Co-Authors: Dinesh Kumar, Rita Ribeiro Carvalho, Manuel J Antunes, R Gil, J Henriques, Luis EugenioAbstract:This paper presents a new algorithm for segmentation and classification of S1 and S2 Heart Sounds without ECG reference. The proposed approach is composed of three main stages. In the first stage the fundamental Heart sound lobes are identified using a fast wavelet transform and the Shannon energy. Next, these lobes are validated and classified into S1 and S2 classes based on Mel-frequency coefficients and on a non supervised neural network. Finally, regular Heart cycles are identified in a post-processing stage by a Heart rhythm criterion. This approach was tested using sound samples collected from prosthetic valve implanted patients. Results are comparable with ECG based approaches.
Zeng Qing-ning - One of the best experts on this subject based on the ideXlab platform.
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An Algorithm for Heart Sounds Segmentation Based on Simplicity and Energy
Computer Simulation, 2011Co-Authors: Zeng Qing-ningAbstract:Heart Sounds segmentation is an important part of the phonocardiogram(PCG) analysis.The quality of Heart Sounds segmentation will directly affect the level of Heart disease diagnosis.In this paper,we have proposed a segmentation algorithm based on simplicity and normalized average Shannon energy(NASE) in order to improve the accuracy of Heart Sounds segmentation,by utilizing the dynamical complexity theory.Whereas the Heart sound signals were intricate,the algorithm not only has high accuracy,but also accurately calculates the first and second Heart sound time limit.Tested with 144 cases of normal and abnormal Heart Sounds,the results show that the proposed algorithm is an efficient and robust technique for Heart Sounds segmentation.
Tianshuang Qiu - One of the best experts on this subject based on the ideXlab platform.
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fetal Heart rate monitoring from phonocardiograph signal using repetition frequency of Heart Sounds
Journal of Electrical and Computer Engineering, 2016Co-Authors: Hong Tang, Tianshuang Qiu, Yongwan ParkAbstract:As a passive, harmless, and low-cost diagnosis tool, fetal Heart rate FHR monitoring based on fetal phonocardiography fPCG signal is alternative to ultrasonographic cardiotocography. Previous fPCG-based methods commonly relied on the time difference of detected Heart sound bursts. However, the performance is unavoidable to degrade due to missed Heart Sounds in very low signal-to-noise ratio environments. This paper proposes a FHR monitoring method using repetition frequency of Heart Sounds. The proposed method can track time-varying Heart rate without both Heart sound burst identification and denoising. The average accuracy rate comparison to benchmark is 88.3% as the SNR ranges from −4.4 dB to −26.7 dB.
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Segmentation of Heart Sounds based on dynamic clustering
Biomedical Signal Processing and Control, 2012Co-Authors: Hong Tang, Tianshuang Qiu, Yongwan ParkAbstract:Abstract The Heart sound signal is first separated into cycles, where the cycle detection is based on an instantaneous cycle frequency. The Heart sound data of one cardiac cycle can be decomposed into a number of atoms characterized by timing delay, frequency, amplitude, time width and phase. To segment Heart Sounds, we made a hypothesis that the atoms of a Heart sound congregate as a cluster in time–frequency domains. We propose an atom density function to indicate clusters. To suppress clusters of murmurs and noise, weighted density function by atom energy is further proposed to improve the segmentation of Heart Sounds. Therefore, Heart Sounds are indicated by the hybrid analysis of clustering and medical knowledge. The segmentation scheme is automatic and no reference signal is needed. Twenty-six subjects, including 3 normal and 23 abnormal subjects, were tested for Heart sound signals in various clinical cases. Our statistics show that the segmentation was successful for signals collected from normal subjects and patients with moderate murmurs.
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noise and disturbance reduction for Heart Sounds in cycle frequency domain based on nonlinear time scaling
IEEE Transactions on Biomedical Engineering, 2010Co-Authors: Hong Tang, Tianshuang QiuAbstract:Through an investigation of various clinical cases, Heart Sounds are found to be quasi-cyclostationary. Nonlinear time scaling from cycle-to-cycle is proposed to enhance cyclic stationarity, where nonlinear time scaling is approximated by a piecewise linear function. The techniques of cyclostationary signal processing are employed in this paper to reduce noise and disturbance in the cycle-frequency domain. Heart Sounds can be theoretically recovered in the presence of additive, zero mean noise, and disturbance (perhaps non-Gaussian, nonstationary, or colored). The experimental tests in various conditions confirm the theoretical results.
Hong Tang - One of the best experts on this subject based on the ideXlab platform.
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fetal Heart rate monitoring from phonocardiograph signal using repetition frequency of Heart Sounds
Journal of Electrical and Computer Engineering, 2016Co-Authors: Hong Tang, Tianshuang Qiu, Yongwan ParkAbstract:As a passive, harmless, and low-cost diagnosis tool, fetal Heart rate FHR monitoring based on fetal phonocardiography fPCG signal is alternative to ultrasonographic cardiotocography. Previous fPCG-based methods commonly relied on the time difference of detected Heart sound bursts. However, the performance is unavoidable to degrade due to missed Heart Sounds in very low signal-to-noise ratio environments. This paper proposes a FHR monitoring method using repetition frequency of Heart Sounds. The proposed method can track time-varying Heart rate without both Heart sound burst identification and denoising. The average accuracy rate comparison to benchmark is 88.3% as the SNR ranges from −4.4 dB to −26.7 dB.
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Segmentation of Heart Sounds based on dynamic clustering
Biomedical Signal Processing and Control, 2012Co-Authors: Hong Tang, Tianshuang Qiu, Yongwan ParkAbstract:Abstract The Heart sound signal is first separated into cycles, where the cycle detection is based on an instantaneous cycle frequency. The Heart sound data of one cardiac cycle can be decomposed into a number of atoms characterized by timing delay, frequency, amplitude, time width and phase. To segment Heart Sounds, we made a hypothesis that the atoms of a Heart sound congregate as a cluster in time–frequency domains. We propose an atom density function to indicate clusters. To suppress clusters of murmurs and noise, weighted density function by atom energy is further proposed to improve the segmentation of Heart Sounds. Therefore, Heart Sounds are indicated by the hybrid analysis of clustering and medical knowledge. The segmentation scheme is automatic and no reference signal is needed. Twenty-six subjects, including 3 normal and 23 abnormal subjects, were tested for Heart sound signals in various clinical cases. Our statistics show that the segmentation was successful for signals collected from normal subjects and patients with moderate murmurs.
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noise and disturbance reduction for Heart Sounds in cycle frequency domain based on nonlinear time scaling
IEEE Transactions on Biomedical Engineering, 2010Co-Authors: Hong Tang, Tianshuang QiuAbstract:Through an investigation of various clinical cases, Heart Sounds are found to be quasi-cyclostationary. Nonlinear time scaling from cycle-to-cycle is proposed to enhance cyclic stationarity, where nonlinear time scaling is approximated by a piecewise linear function. The techniques of cyclostationary signal processing are employed in this paper to reduce noise and disturbance in the cycle-frequency domain. Heart Sounds can be theoretically recovered in the presence of additive, zero mean noise, and disturbance (perhaps non-Gaussian, nonstationary, or colored). The experimental tests in various conditions confirm the theoretical results.
A Jain - One of the best experts on this subject based on the ideXlab platform.
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segmentation of Heart Sounds using simplicity features and timing information
International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: J Vepa, P Tolay, A JainAbstract:Automatic analysis of Heart Sounds aid physicians in the diagnosis of abnormal Heart valve conditions. Segmentation, i.e. identification of first (S1) and second (S2) Heart Sounds, is the first step in the automatic analysis. In this work, we have proposed a segmentation method which uses energy-based and simplicity-based features computed from multi-level wavelet decomposition coefficients. This method utilizes timing information of S1 and S2 based on biomedical domain knowledge. Proposed method has been evaluated on several normal and abnormal Heart Sounds for identification of S1 and S2 and compared with windowed energy-based and simplicity-based approaches individually. The proposed method is an efficient and robust technique for identification and gating of S1 and S2, and yields better results than the above approaches.
