Selection of Dynamic Features Based on Time–Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals

Quiceno-Manrique, A. F.; Godino-Llorente, Juan Ignacio; Blanco–Velasco, Manuel; Castellanos-Domínguez, G.

doi:10.1007/s10439-009-9838-3

Cited by 80 publications

(62 citation statements)

References 59 publications

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“…From frequency domain analysis, FFT statistics, Autoregressive Moving-Average (ARMA) features [1], Melfrequency Ceptral Coefficients (MFCC) features [2], sample entropy [6], music features [1], octave band features [1] etc. were extracted and it comprised a total of 102 features.…”

Section: Feature Extractionmentioning

confidence: 99%

A Novel Approach for Classification of Normal/Abnormal Phonocardiogram Recordings using Temporal Signal Analysis and Machine Learning

Vernekar

Nair²,

Vijaysenan

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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This paper discusses a novel approach used for classification of phonocardiogram (PCG) excerpts into normal and abnormal classes as a part of Physionet 2016 challenge [10]. The dataset used for the competition comprises of cardiac abnormalities such as mitral valve prolapse (MVP), benign murmurs, aortic diseases, coronary artery disease, miscellaneous pathological conditions etc.[3], We present the approach used for classification from a general machine learning application standpoint, giving details on feature extraction, type of classifiers used comparing their performances individually and in combination. We propose a technique which leverages previous research on feature extraction with a novel approach to modeling temporal dynamics of the signal using Markov chain analysis [7, 9]. These newly introduced Markov features along with other statistical and frequency domain features, trained over an ensemble of artificial neural networks and gradient boosting trees, with bagging, gave us an accuracy of 82% on the validation dataset provided in the competition and was consistent with the test data with the best result of 78%. IntroductionThis work describes a novel approach designed for Physionet 2016 Challenge Classification of Normal/Abnormal Heart Sound Recordings. The objective here is to classify Phonocardiogram (PCG) recordings into normal and abnormal categories. A comprehensive detail on the database, explaining how the PCG signals were collected and the type of abnormalities found are discussed in the paper mentioned in the reference section [3].Usually, statistical features such as means, standard deviations of systole, diastole intervals, and signal complexity features are used as features for classification and these are enough to give decent results. But these features fail to completely capture the temporal information of the signal. This could be very important since it represents how each heart beat changes over time.To capture the temporal dynamics, we take PCG signal beat by beat and assign each beat a symbol/category based on different thresholds set on features (ratios of systole intervals to RR interval, diastole intervals to RR interval, beat energy, the power of frequency component above 200 Hz). Thus, a sequence of symbols for the entire signal is obtained. We then extract features out of this sequence for classification. One of the ways we employed is to create a Markov chain with symbols being the states of the matrix and the resulting transition probabilities are used as features. These features along with marginal probabilities of states and rest of the acoustic features like sample entropy, instantaneous frequency analysis etc. are used to train an ensemble/bag of 4 class boosted tree classifiers [8, 13] and 4 artificial neural networks [12]. PreprocessingThe training data consists of PCG signals of varying length, anywhere between 5s to just over 120s all sampled at 2000 Hz. For training, all the signals were re-sampled to 1000 Hz and features were extracted. Since PCG record...

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Section: Feature Extractionmentioning

confidence: 99%

A Novel Approach for Classification of Normal/Abnormal Phonocardiogram Recordings using Temporal Signal Analysis and Machine Learning

Vernekar

Nair²,

Vijaysenan

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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“…In order to estimate such t-f representations, both parametric and nonparametric estimations are generally employed. Among the most popular nonparametric approaches are: short time fourier transform (STFT), wavelet transform (WT); matching pursuit (MP); Choi-Williams distribution (CWD), Wigner-Ville distribution (WVD) [2,6]; and among the parametric models: time-variant autoregressive models, and adaptive filtering [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, a signal decomposition grounded on matching pursuit does not necessarily provide the same number of t-f atoms for each recording, hence the multidimensional reduction arises as an additional issue to handle dynamic features of different lengths. Additionally, two-dimensional timefrequency/scale approaches, such as the t-f distributions (linear or quadratic) or even the Wavelet analysis, have also been widely used in biosignal processing, in particular for EEG [5,10] and PCG [6,11]. In this sense, an approach to create optimized quadratic t-f representations is proposed in [12] by designing kernels that lead to the maximum separability among classes.…”

Section: Introductionmentioning

confidence: 99%

Time–frequency based feature selection for discrimination of non-stationary biosignals

Martínez-Vargas

Godino-Llorente

Castellanos-Domínguez

2012

EURASIP J. Adv. Signal Process.

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This research proposes a generic methodology for dimensionality reduction upon time-frequency representations applied to the classification of different types of biosignals. The methodology directly deals with the highly redundant and irrelevant data contained in these representations, combining a first stage of irrelevant data removal by variable selection, with a second stage of redundancy reduction using methods based on linear transformations. The study addresses two techniques that provided a similar performance: the first one is based on the selection of a set of the most relevant time-frequency points, whereas the second one selects the most relevant frequency bands. The first methodology needs a lower quantity of components, leading to a lower feature space; but the second improves the capture of the time-varying dynamics of the signal, and therefore provides a more stable performance. In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals. Even when these two databases contain samples with different degrees of complexity and a wide variety of characterizing patterns, the results demonstrate a good accuracy for the detection of pathologies, over 98%. The results open the possibility to extrapolate the methodology to the study of other biosignals.

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“…Specially, changes in physiological conditions and pathologies may produce significant variations. It has been found that non-stationary conditions give rise to changes in the spectral content of the biosignal (Hassanpour et al, 2004;Quiceno-Manrique et al, 2010;Sepúlveda-Cano et al, 2011;Subasi, 2007;Tarvainen et al, 2009;Tzallas et al, 2008). Therefore, time-frequency (t-f) features have been previously proposed for examining the dynamic properties of the spectral parameters during transient physiological or pathological episodes.…”

Section: Introductionmentioning

confidence: 99%

“…Time-frequency dynamic features (TFDF) based on spectral sub-band methods, summarize t-f information in a compact fashion. TFDF set has also demonstrated its capability for discriminating between normal and pathological patterns (Quiceno-Manrique et al, 2010;Sepúlveda-Cano et al, 2011). In this sense, the TFDF set can be suitably estimated by filter bank methods, such as sub-band spectral centroid, sub-band spectral centroid energy, linear cepstral coefficients, or discrete wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency Based Feature Extraction for Non-Stationary Signal Classification

Avendaño-Valencia¹,

Acosta-Medina²,

Castellanos-Domínguez³

2011

Applied Biomedical Engineering

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Selection of Dynamic Features Based on Time–Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals

Cited by 80 publications

References 59 publications

A Novel Approach for Classification of Normal/Abnormal Phonocardiogram Recordings using Temporal Signal Analysis and Machine Learning

A Novel Approach for Classification of Normal/Abnormal Phonocardiogram Recordings using Temporal Signal Analysis and Machine Learning

Time–frequency based feature selection for discrimination of non-stationary biosignals

Time-Frequency Based Feature Extraction for Non-Stationary Signal Classification

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