A heart sound feature extraction algorithm based on wavelet decomposition and reconstruction

Liang, Hui; Hartimo, I.

doi:10.1109/iembs.1998.747181

Cited by 36 publications

(19 citation statements)

References 6 publications

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“…For example, the splitting parameter of the second HS used in Liang et al 15 could not be automatically extracted using the described wavelet method. A considerably more complex transient chirp model has been used to perform the same task 27 (extracting the aortic and pulmonary components of S2), but despite the more advanced methodology, the authors needed multiple sensors to obtain robust results.…”

Section: Discussionmentioning

confidence: 99%

“…37 Suitable features for classification of systolic murmurs should hence be able to describe information in these domains. The feature sets used in previous works often assumes linearity and ranges from time domain characteristics 2,26,32 via spectral characteristics 3,33,41 and frequency representations with time resolution 8,14,15,24,38,41 to parametric modeling. 22,35 The assumption of linearity basically requires all significant information to be contained in the frequency spectrum.…”

Section: Introductionmentioning

confidence: 99%

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Feature Extraction for Systolic Heart Murmur Classification

et al. 2006

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Abstract-Heart murmurs are often the first signs of pathological changes of the heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological murmur from a physiological murmur is however difficult, why an ''intelligent stethoscope'' with decision support abilities would be of great value. Phonocardiographic signals were acquired from 36 patients with aortic valve stenosis, mitral insufficiency or physiological murmurs, and the data were analyzed with the aim to find a suitable feature subset for automatic classification of heart murmurs. Techniques such as Shannon energy, wavelets, fractal dimensions and recurrence quantification analysis were used to extract 207 features. 157 of these features have not previously been used in heart murmur classification. A multi-domain subset consisting of 14, both old and new, features was derived using PudilÕs sequential floating forward selection (SFFS) method. This subset was compared with several single domain feature sets. Using neural network classification, the selected multi-domain subset gave the best results; 86% correct classifications compared to 68% for the first runner-up. In conclusion, the derived feature set was superior to the comparative sets, and seems rather robust to noisy data.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature Extraction for Systolic Heart Murmur Classification

et al. 2006

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“…In the last decade, many research activities were conducted concerning automated and semi-automated heart sound diagnosis, regarding it as a challenging and promising subject. Many researchers have conducted research on the segmentation of the heart sound into heart cycles [5-7], the discrimination of the first from the second heart sound [8], the analysis of the first, the second heart sound and the heart murmurs [9-12], and also on features extraction and classification of heart sounds and murmurs [14,15]. These activities mainly focused on the morphological characteristics of the heart sound waveforms.…”

Section: Introductionmentioning

confidence: 99%

A decision tree – based method for the differential diagnosis of Aortic Stenosis from Mitral Regurgitation using heart sounds

2004

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Background: New technologies like echocardiography, color Doppler, CT, and MRI provide more direct and accurate evidence of heart disease than heart auscultation. However, these modalities are costly, large in size and operationally complex and therefore are not suitable for use in rural areas, in homecare and generally in primary healthcare set-ups. Furthermore the majority of internal medicine and cardiology training programs underestimate the value of cardiac auscultation and junior clinicians are not adequately trained in this field. Therefore efficient decision support systems would be very useful for supporting clinicians to make better heart sound diagnosis. In this study a rule-based method, based on decision trees, has been developed for differential diagnosis between "clear" Aortic Stenosis (AS) and "clear" Mitral Regurgitation (MR) using heart sounds.

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“…The performance of each features class were evaluated by performing classification on the testing set. Each features set were measured by classification accuracy and sensitivity which defined as [10]:…”

Section: Resultsmentioning

confidence: 99%

Development of young oil palm tree recognition using Haar- based rectangular windows

Daliman

Abu-Bakar

Azam

2016

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. This paper presents development of Haar-based rectangular windows for recognition of young oil palm tree based on WorldView-2 imagery data. Haar-based rectangular windows or also known as Haar-like rectangular features have been popular in face recognition as used in Viola-Jones object detection framework. Similar to face recognition, the oil palm tree recognition would also need a suitable Haar-based rectangular windows that best suit to the characteristics of oil palm tree. A set of seven Haar-based rectangular windows have been designed to better match specifically the young oil palm tree as the crown size is much smaller compared to the matured ones. Determination of features for oil palm tree is an essential task to ensure a high successful rate of correct oil palm tree detection. Furthermore, features that reflects the identification of oil palm tree indicate distinctiveness between an oil palm tree and other objects in the image such as buildings, roads and drainage. These features will be trained using support vector machine (SVM) to model the oil palm tree for classifying the testing set and subimages of WorldView-2 imagery data. The resulting classification of young oil palm tree with sensitivity of 98.58% and accuracy of 92.73% shows a promising result that it can be used for intention of developing automatic young oil palm tree counting.

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A heart sound feature extraction algorithm based on wavelet decomposition and reconstruction

Cited by 36 publications

References 6 publications

Feature Extraction for Systolic Heart Murmur Classification

Feature Extraction for Systolic Heart Murmur Classification

A decision tree – based method for the differential diagnosis of Aortic Stenosis from Mitral Regurgitation using heart sounds

Development of young oil palm tree recognition using Haar- based rectangular windows

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