A heart sound segmentation and feature extraction algorithm using wavelets

Omran, Safaa S.; Tayel, Mazhar B.

doi:10.1109/isccsp.2004.1296268

Cited by 24 publications

(8 citation statements)

References 3 publications

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“…Heart sound signals recorded by electronic stethoscopes are often encompassed with high-frequency noise; hence, preprocessing is essential. 18–20 The signals were filtered to eliminate the noise followed by normalization and segmentation. These steps were illustrated in the following.…”

Section: Methodsmentioning

confidence: 99%

Detection and identification of first and second heart sounds using empirical mode decomposition

Bajelani

Navidbakhsh

Behnam

et al. 2013

Proc Inst Mech Eng H

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We present a novel, low complexity method for the detection of the first and second of heart sounds (S1 and S2, respectively) and the periods of systole and diastole without using an electrocardiogram reference. The algorithm uses a technique called empirical mode decomposition to produce intensity envelopes of the main heart sounds in the time domain. The performance of the algorithm was evaluated using 14,000 cardiac periods from 100 normal and abnormal digital phonocardiographic recordings. The sensitivity of the detection method was 88.3% for both S1 and S2, and the precision (positive predictive value) was 95.8% for both S1 and S2.

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Section: Methodsmentioning

confidence: 99%

Detection and identification of first and second heart sounds using empirical mode decomposition

Bajelani

Navidbakhsh

Behnam

et al. 2013

Proc Inst Mech Eng H

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“…With a basic normalization and thresholding on PCG, S1 and S2 are detected, extracted and counted to derive the HR. PCG segmentation techniques that analyze heart sound features are also introduced to make the detection more robust [5] [6]. For example, Wavelet Transform is commonly adopted for PCG time-series processing.…”

Section: Introductionmentioning

confidence: 99%

Robust heart rate measurement with phonocardiogram by on-line template extraction and matching

Chen

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-As health care becomes popular, daily monitoring of health-status related parameters, including the heart rate (HR), is getting more and more valued. An easy, comfortable and robust solution is therefore an important issue. Phonocardiogram (PCG) is a physiological signal reflecting the cardiovascular status. It could be recorded by microphoneequipped on-hand devices, like the smartphone, even without direct skin contact. However, high inter-and intra-variance of PCG make its processing challenging. For PCG-based HR measurement, a robust method is still strongly required. In this paper, we propose a HR measurement algorithm on the processing of PCG that uses on-line template extraction and matching. Through several experiments where traditional methods cannot effectively handle, the robustness of our method is verified by its accurate HR measurement results.

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“…The mother wavelet used was Daubechies. Past research have indicated that level 4 and 5 approximations are able to capture important aspects of the murmurs under study, namely aortic and mitral stenosis and aortic and mitral insufficiency [11], [12]. Note also in Figure 4, that the noise level within the murmurs, S1 (systolic) and S2 (diastolic) components is reduced significantly due to filtering.…”

Section: Discrete Wavelet Decompositionmentioning

confidence: 82%

“…The reasons partly have been the lack of further pursuit, unproven or low accuracy rates and primarily unrealizability in the hardware level because of the size of the algorithm. Some design methods use segmentation, wavelets, neural networks, S-transform, Hilbert Transform, decision tree, Hidden Markov models, etc [5][6][7][8][9][10][11][12][13][14][15][16][17]. Despite having designed a variety of algorithms, researchers faced a challenging situation in terms of classification of the heart sounds because of the large number of possible cases as well as the characteristics of the sound from a signal processing perspective.…”

Section: Descriptionmentioning

confidence: 99%

Classification of Four Types of Common Murmurs using Wavelets and a Learning Vector Quantization Network

Rios-Gutiérrez¹,

Alba-Flores²,

Ejaz³

et al.

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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In this work we present the development of a system that can be used for the study, detection and classification of human heart sounds using digital signal processing and artificial intelligence techniques. The design and implementation of such system is broken down into two processes: digital signal processing part and artificial intelligence part. The ultimate goal of the project is to develop an intelligent system that can be used for the detection and classification of various types of human heart murmurs. *

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A heart sound segmentation and feature extraction algorithm using wavelets

Cited by 24 publications

References 3 publications

Detection and identification of first and second heart sounds using empirical mode decomposition

Detection and identification of first and second heart sounds using empirical mode decomposition

Robust heart rate measurement with phonocardiogram by on-line template extraction and matching

Classification of Four Types of Common Murmurs using Wavelets and a Learning Vector Quantization Network

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