A robust heart sounds segmentation module based on S-transform

Moukadem, Ali; Dieterlen, Alain; Hueber, Nicolas; Brandt, Christian

doi:10.1016/j.bspc.2012.11.008

Cited by 138 publications

(75 citation statements)

References 19 publications

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“…The localization of the first and the second heart sounds (S1 and S2), the number of their internal components, their frequential content, etc. can be considered as pertinent information very useful for patricians and for classification systems [6]. The application proposed in this paper consists to detect splits in heart sounds.…”

Section: Application On Real Non-stationary Signals: Heart Soundsmentioning

confidence: 99%

“…• First, the heart sound is segmented by using the proposed algorithm in [6] to detect the first and the second heart sounds.…”

Section: Application On Real Non-stationary Signals: Heart Soundsmentioning

confidence: 99%

“…It has gained popularity in the signal processing community because of its easy interpretation and fast computation [3]. The S-transform has been shown high performance in classification and feature extraction problems applied on non-stationary signals, such as heart sounds [4][5][6], power quality signals [7], EEG signals [8] etc. Generally the S-transform uses a Gaussian window, whose standard deviation varies over frequency.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A new optimized Stockwell transform applied on synthetic and real non-stationary signals

Moukadem

Bouguila

Abdeslam

et al. 2015

Digital Signal Processing

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Section: Application On Real Non-stationary Signals: Heart Soundsmentioning

confidence: 99%

“…• First, the heart sound is segmented by using the proposed algorithm in [6] to detect the first and the second heart sounds.…”

Section: Application On Real Non-stationary Signals: Heart Soundsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A new optimized Stockwell transform applied on synthetic and real non-stationary signals

Moukadem

Bouguila

Abdeslam

et al. 2015

Digital Signal Processing

Self Cite

View full text Add to dashboard Cite

“…It is performed by calculating the sensitivity (SE) and positive predictive value (PPV) as A sound is true positive (TP) or correctly located if the detected sound corresponds to a S1 or S2 sound predefined manually by the cardiologist, all other detected sounds are defined as false positive (FP) and all missed sounds are considered as false negative (FN) [24]. By implementing our proposed algorithms to the MARS500 database, a high overall sensitivity (SE) (96 %) and a positive predictive value (PPV) (93 %) are obtained based on the physician assessments (see Table 8.1 for detail results).…”

Section: Performance Analysismentioning

confidence: 99%

Time-Scale-Based Segmentation for Degraded PCG Signals Using NMF

Sattar

Jin

Moukadem

et al. 2015

Signals and Communication Technology

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This article deals with the challenging problem of segmenting narrowly spaced cardiac events (S1 and S2) in noisy phonocardiogram (PCG) signals by using a novel application of NMF based on time-scale approach. A novel energy-based method is proposed for the segmentation of noisy PCG signals in order to detect cardiac events, which could be closely spaced and separated by noisy gaps. The method is based on time-scale transform as well as nonnegative matrix factorization (NMF) and the segmentation problem is formulated using the paradigm of binary statistical hypothesis testing. The energy of the Morlet wavelet transform and NMF output is employed as a test statistics for segmentation where the number of scales are selected based on the preferences calculated along the time-scales. The simulation results using real recorded noisy PCG data that provide promising performance with high overall accuracy on the segmentation of narrowly separated, high noisy signals by our proposed method.

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“…Accurate segregation of the fundamental Heart sounds (FHSs) is considered as the major prerequisite of any kind of analysis job dealing with PCG. There is a vast literature available ( [5], [6]) for automatic segregation of hearts sounds. All recordings in the dataset is sampled at 2000 Hz.…”

Section: Segmentation and Feature Extractionmentioning

confidence: 99%

Time:Frequency Analysis of Phonocardiogram for Classifying Heart Disease

Banerjee¹,

Biswas²,

Banerjee³

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Analysis of heart sounds is a popular research area for non invasive identification of several heart diseases. This paper proposes a set of 88 time-frequency features along with five different methodologies for classifying normal and abnormal heart sounds. State of the art approach was applied for segregating the fundamental heart sounds. Apart from a baseline two class classifier, separate classifiers for long and short heart sounds were also explored in order to get rid of the dependency of features on the duration of the recordings. Finally, a three class classifier was explored to deal with the noisy data present in the dataset. Both balanced and unbalanced sets were considered for crating of the training models. A comparative analysis showed that, out of all the methodologies, the three class classifier based approach produces the most optimum performance by simultaneously yielding high values of both sensitivity and specificity.

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A robust heart sounds segmentation module based on S-transform

Cited by 138 publications

References 19 publications

A new optimized Stockwell transform applied on synthetic and real non-stationary signals

A new optimized Stockwell transform applied on synthetic and real non-stationary signals

Time-Scale-Based Segmentation for Degraded PCG Signals Using NMF

Time:Frequency Analysis of Phonocardiogram for Classifying Heart Disease

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