Segmentation and classification of heart sounds

Gupta, Cota Navin; Palaniappan, Rajkumar; Rajan, Sreeraman; Swaminathan, Sridhar; Krishnan, Shankar

doi:10.1109/ccece.2005.1557305

Cited by 31 publications

(23 citation statements)

References 9 publications

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“…However, the results of this work showed better performance than the results obtained with other techniques based mainly in the application of neural networks to perform the classification with an accuracy between 85% and 95% [12], [13].…”

Section: Discussioncontrasting

confidence: 63%

Feature extraction for murmur detection based on support vector regression of time-frequency representations

Jaramillo-Garzón

Quiceno-Manrique

Godino-Llorente

et al. 2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-This paper presents a nonlinear approach for time-frequency representations (TFR) data analysis, based on a statistical learning methodology -support vector regression (SVR), that being a nonlinear framework, matches recent findings on the underlying dynamics of cardiac mechanic activity and phonocardiographic (PCG) recordings. The proposed methodology aims to model the estimated TFRs, and extract relevant features to perform classification between normal and pathologic PCG recordings (with murmur). Modeling of TFR is done by means of SVR, and the distance between regressions is calculated through dissimilarity measures based on dot product. Finally, a k-nn classifier is used for the classification stage, obtaining a validation performance of 97.85%.

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Section: Discussioncontrasting

confidence: 63%

Feature extraction for murmur detection based on support vector regression of time-frequency representations

Jaramillo-Garzón

Quiceno-Manrique

Godino-Llorente

et al. 2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…Next we determined the peaks with Gierałtowski et al method [5] based on the slope detection in the signal. In the next part of the method, we calculate Wavelet coefficients of the whole signal by using Daubechies-2 wavelet at second decomposition level (according to [6]) and use these coefficients as an input signal to later evaluations. We created three sets of criteria for assessing signal quality.…”

Section: Assessment Of Signal Qualitymentioning

confidence: 99%

PCG Classification Using a Neural Network Approach

Grzegorczyk

Soliński

Łepek

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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“…Overall, our method was able to increase the total precision of the classification using this dataset. The results shown in columns 2-3 of Table 4 are from [5] (winning entry) that used the segmentation in [9] and the peak conditioning technique in [6] which were also adopted in this study. An extensive feature set was extracted, including features other than those derived from distance between S1 and S2 primarily used by [5] and [4].…”

Section: Resultsmentioning

confidence: 99%

Classification of heart sounds using discrete and continuous wavelet transform and random forests

Balili

Sobrepena

Naval

2015

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)

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This study proposes an integrated approach to heart sound classification using wavelet analysis and random forests classifiers. The heart sounds were first segmented through detection of the S1 and S2 heart sounds using Shannon energy. Time-and frequency-based features derived from Discrete and Continuous Wavelet Transforms were used as feature vectors for the random forest classifier that categorized heart sounds into Normal, Murmur, Extrasystole, and Artifact. The segmentation process produced lower errors compared to related literature using the same dataset. In our study, the classification using features derived from DWT recorded the highest total precision for the noiser samples while CWT did the same for the less noisy samples. Overall, the proposed approach performed better than previous related studies.

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Segmentation and classification of heart sounds

Cited by 31 publications

References 9 publications

Feature extraction for murmur detection based on support vector regression of time-frequency representations

Feature extraction for murmur detection based on support vector regression of time-frequency representations

PCG Classification Using a Neural Network Approach

Classification of heart sounds using discrete and continuous wavelet transform and random forests

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