Automatic detection of sounds and murmurs in patients with lonescu-Shiley aortic bioprostheses

Baranek, Hayley; Lee, H. C.; Cloutier, Guy; Durand, Louis-Gilles

doi:10.1007/bf02441460

Cited by 19 publications

(7 citation statements)

References 20 publications

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“…Using only R-waves in the ECG signal as reference, an earlier algorithm has been developed to detect heart sounds. 3 In that algorithm difficulties in S 2 detection caused by changes in S 2 location in relation to the R-R interval, due to heart rate variation, were not highlighted. In the paediatric population heart rates are affected by age or incipient heart failure.…”

Section: Heart Sound Detectionmentioning

confidence: 94%

Computer-Based Detection and Analysis of Heart Sound and Murmur

El-Segaier

Lilja

Lukkarinen³

et al. 2005

Ann Biomed Eng

112

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To develop a digital algorithm that detects first and second heart sounds, defines the systole and diastole, and characterises the systolic murmur. Heart sounds were recorded in 300 children with a cardiac murmur, using an electronic stethoscope. A Digital algorithm was developed for detection of first and second heart sounds. R-waves and T-waves in the electrocardiography were used as references for detection. The sound signal analysis was carried out using the short-time Fourier transform. The first heart sound detection rate, with reference to the R-wave, was 100% within 0.05-0.2R-R interval. The second heart sound detection rate between the end of the T-wave and the 0.6R-R interval was 97%. The systolic and diastolic phases of the cardiac cycle could be identified. Because of the overlap between heart sounds and murmur a systolic segment between the first and second heart sounds (20-70%) was selected for murmur analysis. The maximum intensity of the systolic murmur, its average frequency, and the mean spectral power were quantified. The frequency at the point with the highest sound intensity in the spectrum and its time from the first heart sound, the highest frequency, and frequency range were also determined. This method will serve as the foundation for computer-based detection of heart sounds and the characterisation of cardiac murmurs.

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Section: Heart Sound Detectionmentioning

confidence: 94%

Computer-Based Detection and Analysis of Heart Sound and Murmur

El-Segaier

Lilja

Lukkarinen³

et al. 2005

Ann Biomed Eng

112

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show abstract

“…The Hilbert transform is proper for time-varying signal analysis, used frequently for envelope detection with heart sounds [31]. The analytic function of [ ] z n contains a realvalue function [ ] x n and a complex value function [ ] x n , which is the Hilbert transform pair of [ ] x n .…”

Section: Hilbert Transformmentioning

confidence: 99%

A Localization Method for First and Second Heart Sounds Based on Energy Detection and Interval Regulation

Min¹,

Shin²

2015

Journal of Electrical Engineering and Technology

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-The present study suggests a localization method for the first (S 1 ) and the second (S 2 ) feature of heart sounds, based on an algorithm involving frequency filtering, energy detection, and interval regulation. Localization accuracy was evaluated by comparing the algorithm with the traditional Hilbert transform-based localization method. Results show that the sensitivity and the positive predictivity value of proposed method, respectively, were 97.27 % and 99.94 % in S 1 detection and 94.99 % and 100 % in S 2 detection.

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“…These methods can be divided into four categories: envelope-based methods, feature-based methods, probabilistic model-based methods, and machine learning methods. Among these methods, the hidden semi-Markov model (HSMM)-based algorithms and the deep recurrent neural network (DRNN) algorithms, which are from the third and fourth categories, respectively, have demonstrated their promising performance [3,7,11,18,23,31,37,42]. Generally, both HSMM-based and DRNN algorithms consider the HSS as a sequence tagging task [23] by assigning a categorical label to each member of a sequence of the observed values.…”

Section: Introductionmentioning

confidence: 99%

End-to-end heart sound segmentation using deep convolutional recurrent network

Yao

Sun

et al. 2021

Complex Intell. Syst.

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Heart sound segmentation (HSS) aims to detect the four stages (first sound, systole, second heart sound and diastole) from a heart cycle in a phonocardiogram (PCG), which is an essential step in automatic auscultation analysis. Traditional HSS methods need to manually extract the features before dealing with HSS tasks. These artificial features highly rely on extraction algorithms, which often result in poor performance due to the different operating environments. In addition, the high-dimension and frequency characteristics of audio also challenge the traditional methods in effectively addressing HSS tasks. This paper presents a novel end-to-end method based on convolutional long short-term memory (CLSTM), which directly uses audio recording as input to address HSS tasks. Particularly, the convolutional layers are designed to extract the meaningful features and perform the downsampling, and the LSTM layers are developed to conduct the sequence recognition. Both components collectively improve the robustness and adaptability in processing the HSS tasks. Furthermore, the proposed CLSTM algorithm is easily extended to other complex heart sound annotation tasks, as it does not need to extract the characteristics of corresponding tasks in advance. In addition, the proposed algorithm can also be regarded as a powerful feature extraction tool, which can be integrated into the existing models for HSS. Experimental results on real-world PCG datasets, through comparisons to peer competitors, demonstrate the outstanding performance of the proposed algorithm.

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Automatic detection of sounds and murmurs in patients with lonescu-Shiley aortic bioprostheses

Cited by 19 publications

References 20 publications

Computer-Based Detection and Analysis of Heart Sound and Murmur

Computer-Based Detection and Analysis of Heart Sound and Murmur

A Localization Method for First and Second Heart Sounds Based on Energy Detection and Interval Regulation

End-to-end heart sound segmentation using deep convolutional recurrent network

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