by Keyword: Sound

By year:[ 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 ]

Aviles, A. I., Widlak, T., Casals, A., Nillesen, M. M., Ammari, H., (2017). Robust cardiac motion estimation using ultrafast ultrasound data: A low-rank topology-preserving approach Physics in Medicine and Biology , 62, (12), 4831-4851

Cardiac motion estimation is an important diagnostic tool for detecting heart diseases and it has been explored with modalities such as MRI and conventional ultrasound (US) sequences. US cardiac motion estimation still presents challenges because of complex motion patterns and the presence of noise. In this work, we propose a novel approach to estimate cardiac motion using ultrafast ultrasound data. Our solution is based on a variational formulation characterized by the L 2-regularized class. Displacement is represented by a lattice of b-splines and we ensure robustness, in the sense of eliminating outliers, by applying a maximum likelihood type estimator. While this is an important part of our solution, the main object of this work is to combine low-rank data representation with topology preservation. Low-rank data representation (achieved by finding the k-dominant singular values of a Casorati matrix arranged from the data sequence) speeds up the global solution and achieves noise reduction. On the other hand, topology preservation (achieved by monitoring the Jacobian determinant) allows one to radically rule out distortions while carefully controlling the size of allowed expansions and contractions. Our variational approach is carried out on a realistic dataset as well as on a simulated one. We demonstrate how our proposed variational solution deals with complex deformations through careful numerical experiments. The low-rank constraint speeds up the convergence of the optimization problem while topology preservation ensures a more accurate displacement. Beyond cardiac motion estimation, our approach is promising for the analysis of other organs that exhibit motion.

Keywords: Cardiac analysis, Low-rank representation, Motion estimation, Topology preservation, Ultrafast ultrasound

Lozano-Garcia, M., Fiz, J. A., Jané, R., (2016). Performance evaluation of the Hilbert–Huang transform for respiratory sound analysis and its application to continuous adventitious sound characterization Signal Processing , 120, 99-116

Abstract The 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.

Keywords: Hilbert–Huang transform, Ensemble empirical mode decomposition, Instantaneous frequency, Respiratory sounds, Continuous adventitious sounds

Lozano-Garcia, M., Fiz, J. A., Jané, R., (2014). Analysis of normal and continuous adventitious sounds for the assessment of asthma IFMBE Proceedings XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013 (ed. Roa Romero, Laura M.), Springer International Publishing (London, UK) 41, 981-984

Assessment of asthma is a difficult procedure which is based on the correlation of multiple factors. A major component in the diagnosis of asthma is the assessment of BD response, which is performed by traditional spirometry. In this context, the analysis of respiratory sounds (RS) provides relevant and complementary information about the function of the respiratory system. In particular, continuous adventitious sounds (CAS), such as wheezes, contribute to assess the severity of patients with obstructive diseases. On the other hand, the intensity of normal RS is dependent on airflow level and, therefore, it changes depending on the level of obstruction. This study proposes a new approach to RS analysis for the assessment of asthmatic patients, by combining the quantification of CAS and the analysis of the changes in the normal sound intensity-airflow relationship. According to results obtained from three patients with different characteristics, the proposed technique seems more sensitive and promising for the assessment of asthma.

Keywords: Asthma, Bronchodilator response, Continuous adventitious sound, Respiratory sound intensity, Wheezes

Mesquita, J., Solà, J., Fiz, J. A., Morera, J., Jané, R., (2012). All night analysis of time interval between snores in subjects with sleep apnea hypopnea syndrome Medical and Biological Engineering and Computing , 50, (4), 373-381

Sleep apnea-hypopnea syndrome (SAHS) is a serious sleep disorder, and snoring is one of its earliest and most consistent symptoms. We propose a new methodology for identifying two distinct types of snores: the so-called non-regular and regular snores. Respiratory sound signals from 34 subjects with different ranges of Apnea-Hypopnea Index (AHI = 3.7-109.9 h -1) were acquired. A total number of 74,439 snores were examined. The time interval between regular snores in short segments of the all night recordings was analyzed. Severe SAHS subjects show a shorter time interval between regular snores (p = 0.0036, AHI cp: 30 h -1) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p = 0.006, AHI cp: 30 h -1) is seen for less severe SAHS subjects. Features derived from the analysis of time interval between regular snores achieved classification accuracies of 88.2 % (with 90 % sensitivity, 75 % specificity) and 94.1 % (with 94.4 % sensitivity, 93.8 % specificity) for AHI cut-points of severity of 5 and 30 h -1, respectively. The features proved to be reliable predictors of the subjects' SAHS severity. Our proposed method, the analysis of time interval between snores, provides promising results and puts forward a valuable aid for the early screening of subjects suspected of having SAHS.

Keywords: Sleep apnea, Snore sounds, Snore time interval

Fiz, J. A., Jané, R., Solà, J., Abad, J., Garcia, M. A., Morera, J., (2010). Continuous analysis and monitoring of snores and their relationship to the apnea-hypopnea index Laryngoscope , 120, (4), 854-862

Objectives/Hypothesis: We used a new automatic snoring detection and analysis system to monitor snoring during full-night polysomnography to assess whether the acoustic characteristics of snores differ in relation to the apnea-hypopnea index (AHI) and to classify subjects according to their AHI Study Design: Individual Case-Control Study. Methods: Thirty-seven snorers (12 females and 25 males, ages 40-65 years; body mass index (BMI), 29.65 +/- 4.7 kg/m(2)) participated Subjects were divided into three groups: G1 (AHI <5), G2 (AHI >= 5, <15) and G3 (AHI >= 15) Snore and breathing sounds were : recorded with a tracheal microphone throughout 6 hours of nighttime polysomnography The snoring episodes identified were automatically and continuously analyzed with a previously trained 2-layer feed-forward neural network. Snore number, average intensity, and power spectral density parameters were computed for every subject and compared among AHI groups. Subjects were classified using different AHI thresholds by means of a logistic regression model. Results: There were significant differences in supine position between G1 and G3 in sound intensity, number of snores; standard deviation of the spectrum, power ratio in bands 0-500, 100-500, and 0-800 Hz, and the symmetry coefficient (P < .03); Patients were classified with thresholds AHI = 5 and AHI = 15 with a sensitivity (specificity) of 87% (71%) and 80% (90%), respectively. Conclusions: A new system for automatic monitoring and analysis of snores during the night is presented. Sound intensity and several snore frequency parameters allow differentiation of snorers according to obstructive sleep apnea syndrome severity (OSAS). Automatic snore intensity and frequency monitoring and analysis could be a promising tool for screening OSAS patients, significantly improving the managing of this pathology.

Keywords: Breathing sounds, Signal interpretation, Sleep apnea syndromes, Snoring

Torres, A., Fiz, J. A., Jané, R., Laciar, E., Galdiz, J. B., Gea, J., Morera, J., (2008). Renyi entropy and Lempel-Ziv complexity of mechanomyographic recordings of diaphragm muscle as indexes of respiratory effort IEEE Engineering in Medicine and Biology Society Conference Proceedings 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (ed. IEEE), IEEE (Vancouver, Canada) 1-8, 2112-2115

The study of the mechanomyographic (MMG) signals of respiratory muscles is a promising technique in order to evaluate the respiratory muscles effort. A new approach for quantifying the relationship between respiratory MMG signals and respiratory effort is presented by analyzing the spatiotemporal patterns in the MMG signal using two non-linear methods: Renyi entropy and Lempel-Ziv (LZ) complexity analysis. Both methods are well suited to the analysis of non-stationary biomedical signals of short length. In this study, MMG signals of the diaphragm muscle acquired by means of a capacitive accelerometer applied on the costal wall were analyzed. The method was tested on an animal model (dogs), and the diaphragmatic MMG signal was recorded continuously while two non anesthetized mongrel dogs performed a spontaneous ventilation protocol with an incremental inspiratory load. The performance in discriminating high and low respiratory effort levels with these two methods was analyzed with the evaluation of the Pearson correlation coefficient between the MMG parameters and respiratory effort parameters extracted from the inspiratory pressure signal. The results obtained show an increase of the MMG signal Renyi entropy and LZ complexity values with the increase of the respiratory effort. Compared with other parameters analyzed in previous works, both Renyi entropy and LZ complexity indexes demonstrates better performance in all the signals analyzed. Our results suggest that these non-linear techniques are useful to detect and quantify changes in the respiratory effort by analyzing MMG respiratory signals.

Keywords: Sound, Force