A New Algorithm for Detection of S1 and S2 Heart Sounds

Kumar, Dinesh; Carvalho, Raphael Torres Santos; Antunes, M.; Gil, Ricardo; Henriques, J.; Eugénio, Luís

doi:10.1109/icassp.2006.1660559

Cited by 41 publications

(21 citation statements)

References 4 publications

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“…Several segmentation techniques have been previously developed. While some techniques use information from an additional synchronized signal, such as electrocardiogram (ECG) or carotid pulse, to enhance the segmentation [6], others try to perform segmentation by merely analyzing the PCG signal [7]. Most of these techniques extract a smooth envelope of the signal using wavelet transform [4] or Hilbert transform [3]to detect the peak candidates, and then identify S1 and S2 components within each heart cycle by employing basic physiological assumptions (e.g.…”

Section: Introductionmentioning

confidence: 99%

Pediatric heart sound segmentation using Hidden Markov Model

Sedighian

Subudhi

Scalzo

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Recent advances in technology have enabled automatic cardiac auscultation using digital stethoscopes. This in turn creates the need for development of algorithms capable of automatic segmentation of heart sounds. Pediatric heart sound segmentation is a challenging task due to various confounding factors including the significant influence of respiration on children's heart sounds. The current work investigates the application of homomorphic filtering and Hidden Markov Model for the purpose of segmenting pediatric heart sounds. The efficacy of the proposed method is evaluated on the publicly available Pascal Challenge dataset and its performance is compared with those of three other existing methods. The results show that our proposed method achieves an accuracy of 92.4%±1.1% and 93.5%±1.1% in identifying the first and second heart sound components, respectively, and is superior to three other existing methods in terms of accuracy or computational complexity.

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Section: Introductionmentioning

confidence: 99%

Pediatric heart sound segmentation using Hidden Markov Model

Sedighian

Subudhi

Scalzo

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…The heart sound and ECG must be recorded simultaneously and because ECG analysis methods are well established, finding the location of ECG features is straightforward. The second approach, referred as independent segmentation [4,5,6,7], identifies the s1 and s2 heart sounds without any supplementary information or signals except from the heart sound itself. The work presented here tackles the second situation.…”

Section: Introductionmentioning

confidence: 99%

“…Several methods can be found in the literature where independent segmentation techniques are applied, based either on heart sound envelogram (envelope calculated using the normalized average Shannon energy) [3], or on frequency domain analysis [4,5,6,7]. These methods perform well (92 to 94% correct detections), however they either have been developed for a particular situation (only on non pathological situations, or a specific pathology), or suffer from a degradation in performance in the presence of heart murmurs (pathological noises produced by turbulent flow of blood), or noisy environments.…”

Section: Introductionmentioning

confidence: 99%

A robust method for S1/S2 heart sounds detection without ecg reference based on music beat tracking

Barabasa

Jafari

Plumbley

2012

2012 10th International Symposium on Electronics and Telecommunications

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Abstract-We present a robust method for the detection of the first and second heart sounds (s1 and s2), without ECG reference, based on a music beat tracking algorithm. An intermediate representation of the input signal is first calculated by using an onset detection function based on complex spectral difference. A music beat tracking algorithm is then used to determine the location of the first heart sound. The beat tracker works in two steps, it first calculates the beat period and then finds the temporal beat alignment. Once the first sound is detected, inverse Gaussian weights are applied to the onset function on the detected positions and the algorithm is run again to find the second heart sound. At the last step s1 and s2 labels are attributed to the detected sounds. The algorithm was evaluated in terms of location accuracy as well as sensitivity and specificity and the results showed good results even in the presence of murmurs or noisy signals.

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“…Therefore, for different heart murmurs, we should analyze HSs collected from different auscultation areas, while for VSD cases, it is reported that the HSs collected from tricuspid area can supply more important information [8]. In this study, analyzed HSs were collected from tricuspid area by the HSs acquisition system, meanwhile, sampling frequency Fs were set as 44.1 kHz.…”

Section: Hss Acquisition and Preprocessingmentioning

confidence: 99%

“…Since many experiments show that the duration of S1 or S2 is over than 0.06 second [4,8], we set δ=0.03×Fs=1323. As an example, Fig.1 plots x(t) daubed with gray and it's W t .…”

Section: Characteristic Waveform(w T ) Extractionmentioning

confidence: 99%

Heart Sound Analysis for Discrimination of VSD

Sun

江

Wang

et al. 2011

AEF

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Abstract.A ventricular septal defect (VSD) is the most common congenital heart disease, which can be cured with a high probability if it is detected in an early stage. In our previous researches on heart sounds (HSs) analysis, the detection methods of heart disease using the cardiac sound characteristic waveforms in time domain or in frequency domain were proposed, and have been succeed in discriminating several heart murmurs. In this paper, we are going to apply these methods to detect VSD. Based on analysis results, a new approach by using the feature parameters both in time domain and in frequency domain is proposed to achieve higher discrimination rates.

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A New Algorithm for Detection of S1 and S2 Heart Sounds

Cited by 41 publications

References 4 publications

Pediatric heart sound segmentation using Hidden Markov Model

Pediatric heart sound segmentation using Hidden Markov Model

A robust method for S1/S2 heart sounds detection without ecg reference based on music beat tracking

Heart Sound Analysis for Discrimination of VSD

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