Crackle Sounds Analysis By Empirical Mode Decomposition

Charleston-Villalobos, S.; González-Camarena, R.; Chi-Lem, G.; Aljama-Corrales, T.

doi:10.1109/memb.2007.289120

Cited by 76 publications

(34 citation statements)

References 15 publications

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“…Heart sounds were recorded from a healthy male subject (23 years old) by acoustic sensors built of electret sub-miniature microphones coupled in a plastic bell and described in [35]; sensors were placed over the aortic and pulmonary auscultation sites. Phonocardiography recordings were done during a period of inspiratory apnea using a frequency sampling of 5 kHz.…”

Section: Recorded Thoracic Soundsmentioning

confidence: 99%

Assessment of time–frequency representation techniques for thoracic sounds analysis

Reyes

Charleston-Villalobos

González-Camarena

2014

Computer Methods and Programs in Biomedicine

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Section: Recorded Thoracic Soundsmentioning

confidence: 99%

Assessment of time–frequency representation techniques for thoracic sounds analysis

Reyes

Charleston-Villalobos

González-Camarena

2014

Computer Methods and Programs in Biomedicine

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“…The sensor array of 5-by-5, attached to the subject's posterior thoracic surface, consisted of electret microphones inserted in plastic bells with a flat frequency response from 50 Hz to 3 kHz. Nomenclature of the sensor array is described in detail elsewhere [2,3]. During the acquisition, the subjects were seated, breathing through a calibrated type Fleisch pneumotachometer at airflow of 1.5 l s -1 , and wearing a nose clip; the acquisition session lasted 15 s with initial and final apnea phases of 5 s. In order to digitize the multichannel LS and airflow signals, a 12-bit A/D converter was used with a sampling frequency of 10 kHz.…”

Section: Normal Breathing (Basic) Ls and Preprocessing Stagementioning

confidence: 99%

“…Among such techniques are nonlinear digital filters [27], the spectral stationarity of LS [15], high-order statistics AR modeling [10], wavelet transform [11], neurofuzzy modeling [32], fractal dimension [12], the empirical mode decomposition [3,8], and recently a texturebased classification [9]. Nevertheless, the solution for automated crackle detection remains as a challenging research area due to factors such as: (a) non-stationarity behavior of crackle and breathing sounds (BS), (b) the signal-to-noise ratio (SNR) between crackle and BS, (c) temporal crackles overlapping, (d) crackle waveform distortion by BS, and (e) the task complexity in a multichannel scenario due to temporal and spatial changes of crackle and BS characteristics.…”

Section: Introductionmentioning

confidence: 99%

Acoustic thoracic image of crackle sounds using linear and nonlinear processing techniques

Charleston-Villalobos

Dorantes-Méndez

González-Camarena

et al. 2010

Med Biol Eng Comput

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In this study, a novel approach is proposed, the imaging of crackle sounds distribution on the thorax based on processing techniques that could contend with the detection and count of crackles; hence, the normalized fractal dimension (NFD), the univariate AR modeling combined with a supervised neural network (UAR-SNN), and the time-variant autoregressive (TVAR) model were assessed. The proposed processing schemes were tested inserting simulated crackles in normal lung sounds acquired by a multichannel system on the posterior thoracic surface. In order to evaluate the robustness of the processing schemes, different scenarios were created by manipulating the number of crackles, the type of crackles, the spatial distribution, and the signal to noise ratio (SNR) at different pulmonary regions. The results indicate that TVAR scheme showed the best performance, compared with NFD and UAR-SNN schemes, for detecting and counting simulated crackles with an average specificity very close to 100%, and average sensitivity of 98 ± 7.5% even with overlapped crackles and with SNR corresponding to a scaling factor as low as 1.5. Finally, the performance of the TVAR scheme was tested against a human expert using simulated and real acoustic information. We conclude that a confident image of crackle sounds distribution by crackles counting using TVAR on the thoracic surface is thoroughly possible. The crackles imaging might represent an aid to the clinical evaluation of pulmonary diseases that produce this sort of adventitious discontinuous lung sounds.

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“…Due to this MM, we found that EMD, when applied to some RS signals, resulted in poor separation of RS signal components [42]. Nevertheless, the original EMD has been used in other RS analysis approaches [45][46][47][48]. Among the proposed solutions for MM, the ensemble EMD (EEMD) [44,49] and the noise-assisted multivariate EMD (NA-MEMD) [50] are some of the most wellestablished and widely used methods, but they have rarely been applied to RS analysis [43,51].…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of the Hilbert–Huang transform for respiratory sound analysis and its application to continuous adventitious sound characterization

2016

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A B S TR A C TThe use of the Hilbert-Huang transform in the analysis of biomedical signals has increased during the past few years, but its use for respiratory sound (RS) analysis is still limited. The technique includes two steps: empirical mode decomposition (EMD) and instantaneous frequency (IF) estimation. Although the mode mixing (MM) problem of EMD has been widely discussed, this technique continues to be used in many RS analysis algorithms. In this study, we analyzed the MM effect in RS signals recorded from 30 asthmatic patients, and studied the performance of ensemble EMD (EEMD) and noise-assisted multivariate EMD (NA-MEMD) as means for preventing this effect. We propose quantitative parameters for measuring the size, reduction of MM, and residual noise level of each method. These parameters showed that EEMD is a good solution for MM, thus outperforming NA-MEMD. After testing different IF estimators, we propose Kay's method to calculate an EEMD-Kay-based Hilbert spectrum that offers high energy concentrations and high time and high frequency resolutions. We also propose an algorithm for the automatic characterization of continuous adventitious sounds (CAS).The tests performed showed that the proposed EEMD-Kay-based Hilbert spectrum makes it possible to determine CAS more precisely than other conventional time-frequency techniques.

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Crackle Sounds Analysis By Empirical Mode Decomposition

Cited by 76 publications

References 15 publications

Assessment of time–frequency representation techniques for thoracic sounds analysis

Assessment of time–frequency representation techniques for thoracic sounds analysis

Acoustic thoracic image of crackle sounds using linear and nonlinear processing techniques

Performance evaluation of the Hilbert–Huang transform for respiratory sound analysis and its application to continuous adventitious sound characterization

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