Accessing heart dynamics to estimate durations of heart sounds

Nigam, Vivek; Priemer, Roland

doi:10.1088/0967-3334/26/6/010

Cited by 55 publications

(45 citation statements)

References 14 publications

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“…12 Comparing HS, murmurs and background noise, HS have a certain structure while murmurs are more complex and noise has no structure at all. 23 This is all reflected in the fractal dimension. The variance fractal dimension (VFD) was used to estimate the fractal dimension.…”

Section: Fractal Dimensionmentioning

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: Fractal Dimensionmentioning

confidence: 99%

“…The quotient between the minima of S1 and S2 and the minima of the five systolic VFD values was also used as a feature, VFD 8. Fractal dimension trajectories have previously been used to locate S1 and S2, 7,23 but to our knowledge not to classify heart murmurs.…”

Section: Fractal Dimensionmentioning

confidence: 99%

Feature Extraction for Systolic Heart Murmur Classification

et al. 2006

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“…To this end many PCG heart sound segmentation methods have been developed, enabling the detection of fundamental heart sound markers such as the beginning/end of S1, systole, S2 and diastole. These segmentation techniques use different approaches such as signal envelopes [4][5], frequency and amplitude features [6] as well as phase [7] and complexity features [8], hidden markov models (HMM) [9][10], and machine learning approaches [11] [12].…”

Section: Introductionmentioning

confidence: 99%

Identification of Abnormal Heart Sounds

Yazdani

Schlatter

Atyabi

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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As a part of 2016 Physionet/CinC callenge, this work aims at the detection of abnormal phonocardiogram (PCG) recordings. Heart sound signal analysis has been an active research topic over the past decades with various studies such as heart sound segmentation and classification. We used the Physionet/CinC2016 challenge PCG database, which contains a large public collection of PCG recordings from a variety of clinical and nonclinical environments. The PCG classification in this work is performed in two steps. PCG heartbeats are first segmented and various heart sound markers are delineated. Then, a series of beat-specific features are extracted from the segmented heartbeats. Finally, PCG recordings are classified into normal and abnormal groups by performing classification based on tape-long features and by analyzing beatextracted features from the PCG. Our method achieved an overall score of 80 in the unofficial phase of the challenge. In the official phase, the overall score of the proposed method was 82, with a sensitivity of 89%. IntroductionMechanical activity of the heart, which is triggered by the propagation of electrical activity within the heart, is audible at different locations on the chest wall. Phonocardiogram (PCG) is an audio recording of these mechanical activities recorded at the chest surface. Heat sounds provide valuable information which can help in the diagnosis of heart valve disorders. PCG comprises fundamental heart sounds from which S1 and S2 are the most prominent in normal recordings, respectively representing the beginning of ventricular contraction and the beginning of diastole [1]. In abnormal cases, various markers maybe present beside the fundamental heart sounds such as murmurs. Murmurs are noise-like high frequency sounds and long systolic murmurs as well as diastolic and continuous murmurs are generally pathologic [2].Auscultation is a common cost-effective technique that provides valuable information about heart valve problems. However, by listening to mechanical activity of the heart with a stethoscope, physicians cannot examine all physical characteristics of heart sounds, as it has been shown that only limited activity of heart sounds and murmurs are within human audibility range [1] [3].Over the years, various heart sound classification methods have been proposed. Generally, the analysis of heart sounds is performed on the heart cycle. To this end many PCG heart sound segmentation methods have been developed, enabling the detection of fundamental heart sound markers such as the beginning/end of S1, systole, S2 and diastole. These segmentation techniques use different approaches such as signal envelopes With the segmented cardiac cycles, the classification of heart sound pathologies is made possible and several methods have been proposed over the last decades. Among these studies, artificial neural networks [13], support vector machines [14] and HMM based [15] approaches are common. Classification based on clustering has also been shown to be effective in heart sound...

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“…But they are mostly affected by amplitude and frequency variations of the heart sounds. Recently, simplicity that shows a large value in the regions where the major components of the PCG occur has been proposed as a new measure [4], [5]. The advantage of simplicity measure is that it is robust to amplitude and frequency variation of a heart sound signal.…”

Section: Introductionmentioning

confidence: 99%

Detection of S1/S2 Components with Extraction of Murmurs from Phonocardiogram

Quan

Seok

Bae

2015

IEICE Trans. Inf. & Syst.

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SUMMARYThe simplicity is a type of measurement that represents visual simplicity of a signal, regardless of its amplitude and frequency variation. We propose an algorithm that can detect major components of heart sound using Gaussian regression to the smoothed simplicity profile of a heart sound signal. The weight and spread of the Gaussians are used as features to discriminate cardiac murmurs from major components of a heart sound signal. Experimental results show that the proposed method is very promising for robust and accurate detection of major heart sound components as well as cardiac murmurs.

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Accessing heart dynamics to estimate durations of heart sounds

Cited by 55 publications

References 14 publications

Feature Extraction for Systolic Heart Murmur Classification

Feature Extraction for Systolic Heart Murmur Classification

Identification of Abnormal Heart Sounds

Detection of S1/S2 Components with Extraction of Murmurs from Phonocardiogram

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