Spectral analysis of cardiovascular time series by the S-transform

Varanini, M.; Paolis, G. De; Emdin, Michele; Macerata, A.; Pola, S.; Cipriani, M.; Marchesi, Carlo

doi:10.1109/cic.1997.647913

Cited by 30 publications

(20 citation statements)

References 6 publications

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“…The ST has previously been used for visual analysis of HS signals. 18,39 There is a tendency in several methods to classify MI and PM as AS. The total number of MI + PM patients is 13, and out of these patients, 9 are classified incorrectly as AS using VFD, GMMx cycle, GMMx murmur and Eigentime/freq, 8 are classified incorrectly as AS using Shannon energy, WT entropy and HOS while 7 are classified incorrectly as AS using ST map, WT detail, and Eigenvalue.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

Feature Extraction for Systolic Heart Murmur Classification

et al. 2006

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“…This is also observed in the time-frequency plot (Figure 2(c)) of the WT based artifact free signal. The variation of multiplying factor for ST does not affect the PSD in the non-artifact zone within the specified band (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). The signal to noise ratio in the reconstructed signals for all the three methods is also compared and observed that, it is more in the case of ST based reconstructed signal (2.33 dB) as compared to the WT (1.81 dB) and ICA (0.98 dB) based methods.…”

Section: Resultsmentioning

confidence: 99%

“…There is, in addition, an easy and direct relation between the S-transform and the natural Fourier transform which cannot be achieved by wavelets. Since its development, the S-transform has been widely used in applications ranging from geophysics [10], oceanography [22], atmospheric physics [9,[23][24][25], medicine [26,27], hydrogeology [28] and mechanical engineering [29]. …”

Section: S-transformmentioning

confidence: 99%

Comparison of ICA and WT with S-transform based method for removal of ocular artifact from EEG signals

Senapati¹,

Routray²

2011

JBiSE

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“…As for the analysis of HS, many approaches have been proposed in feature exaction in the literatures such as wavelet decomposition and reconstruction method [1]- [3], short time Fourier transform (STFT) method [4], [5], and S-transform method [6], [7]. Most solely analyze the time frequency domain for feature extractions.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Feature Extraction Based on Timbre Model for Heart Sound Analysis

Wang¹,

Li²,

Fu³

et al. 2014

IJBBB

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Abstract-Cardiovascular diseases (CVDs) are currently the leading cause of deaths worldwide. The traditional auscultation is cost-effective and time-saving for the public to diagnose CVDs early. While many approaches for analysis of the heart sound (HS) signal from auscultation have been utilized successfully, few studies are focused on acoustic perspective to interpret the HS signal. This paper creatively proposes a multidimensional feature extraction technique based on timbre model to interpret HS, which stems from clinical diagnostic basis and medical knowledge. The extracted features have three dimensions, including spectral centroid (SC), log attack time (LT) and temporal centroid (TC). The simulation experiments indicate that the proposed method is promising in HS feature extraction and the later CVD diagnosis.Index Terms-Acoustics, feature extraction, Hilbert transform, spectral centroid, temporal centroid. I. INTRODUCTIONCardiovascular diseases (CVDs) have become a great threat to human' lives aggressively. A good way to prevent the death caused by CVDs is early discovery and interposition. Over the years, in spite of the advent of echocardiogram (ECHO), electrocardiogram (ECG), photoplethysmogram (PPG), sphygmogram (SPG), etc., the heart auscultation signal is still one of the most primary physiological signals. Physicians primarily analyze it thus to find signs of pathologic conditions, as it can provide clues to the diagnosis of many cardiac abnormalities including valvular heart disease, congestive heart failure and congenital heart lesions before requesting for ECHO, ECG, PPG, SPG, etc. Notably, in remote areas or less developed regions, auscultation may be the only means available. This leads the research on heart sound (HS) signal analysis and interpretation in order to provide a cost-effective and time-saving prognostic approach for the victims of CVDs.As for the analysis of HS, many approaches have been proposed in feature exaction in the literatures such as wavelet decomposition and reconstruction method . MFCC is based on the theory that human audition spaces are linearly at low frequency band and logarithmically at high frequency. As existing two inverse-transforms in MFCC, it encounters computation complexity.There exists another approach named timbre analysis from acoustic perspective, which hasn't been utilized and can avoid computation complexity. In acoustics, timbre is a significant attribute of three acoustic attributes, which embodies the texture of acoustic source. CVDs, as the pathological changes in the heart and the blood vessels, provide different timbre information. As a result, the timbre is well suitable for HS feature extractions. However, current timbre model analysis all aim at different music instrument recognition and little literature ever reported its exploration on HS feature extraction. In this paper, the timbre model based multidimensional feature extraction technique for HS analysis is creatively proposed. It is observed from simulations that the proposed method is ...

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Spectral analysis of cardiovascular time series by the S-transform

Cited by 30 publications

References 6 publications

Feature Extraction for Systolic Heart Murmur Classification

Feature Extraction for Systolic Heart Murmur Classification

Comparison of ICA and WT with S-transform based method for removal of ocular artifact from EEG signals

Multidimensional Feature Extraction Based on Timbre Model for Heart Sound Analysis

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