Publications

Differentiating normal from adventitious respiratory sounds (RS) is a major challenge in the diagnosis of pulmonary diseases. Particularly, continuous adventitious sounds (CAS) are of clinical interest because they reflect the severity of certain diseases. This study presents a new classifier that automatically distinguishes normal sounds from CAS. It is based on the multi-scale analysis of instantaneous frequency (IF) and envelope (IE) calculated after ensemble empirical mode decomposition (EEMD). These techniques have two major advantages over previous techniques: high temporal resolution is achieved by calculating IF-IE and a priori knowledge of signal characteristics is not required for EEMD. The classifier is based on the fact that the IF dispersion of RS signals markedly decreases when CAS appear in respiratory cycles. Therefore, CAS were detected by using a moving window to calculate the dispersion of IF sequences. The study dataset contained 1494 RS segments extracted from 870 inspiratory cycles recorded from 30 patients with asthma. All cycles and their RS segments were previously classified as containing normal sounds or CAS by a highly experienced physician to obtain a gold standard classification. A support vector machine classifier was trained and tested using an iterative procedure in which the dataset was randomly divided into training (65%) and testing (35%) sets inside a loop. The SVM classifier was also tested on 4592 simulated CAS cycles. High total accuracy was obtained with both recorded (94.6% ± 0.3%) and simulated (92.8% ± 3.6%) signals. We conclude that the proposed method is promising for RS analysis and classification.

JTD Keywords: Diseases, Dispersion, Empirical mode decomposition, Feature extraction, Informatics, Support vector machines

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.

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

The scope of our work focuses on investigating the potential use of the built-in accelerometer of the smartphones for the recording of the respiratory activity and deriving the respiratory rate. Five healthy subjects performed an inspiratory load protocol. The excursion of the right chest was recorded using the built-in triaxial accelerometer of a smartphone along the x, y and z axes and with an external uniaxial accelerometer. Simultaneously, the respiratory airflow and the inspiratory mouth pressure were recorded, as reference respiratory signals. The chest acceleration signal recorded in the z axis with the smartphone was denoised using a scheme based on the ensemble empirical mode decomposition, a noise data assisted method which decomposes nonstationary and nonlinear signals into intrinsic mode functions. To distinguish noisy oscillatory modes from the relevant modes we use the detrended fluctuation analysis. We reported a very strong correlation between the acceleration of the z axis of the smartphone and the reference accelerometer across the inspiratory load protocol (from 0.80 to 0.97). Furthermore, the evaluation of the respiratory rate showed a very strong correlation (0.98). A good agreement was observed between the respiratory rate estimated with the chest acceleration signal from the z axis of the smartphone and with the respiratory airflow signal: Bland-Altman limits of agreement between -1.44 and 1.46 breaths per minute with a mean bias of -0.01 breaths per minute. This preliminary study provides a valuable insight into the use of the smartphone and its built-in accelerometer for respiratory monitoring.

JTD Keywords: Acceleration, Accelerometers, Correlation, Empirical mode decomposition, Fluctuations, Protocols, Time series analysis

Non-invasive evaluation of respiratory activity is an area of increasing research interest, resulting in the appearance of new monitoring techniques, ones of these being based on the analysis of the diaphragm mechanomyographic (MMGdi) signal. The MMGdi signal can be decomposed into two parts: (1) a high frequency activity corresponding to lateral vibration of respiratory muscles, and (2) a low frequency activity related to excursion of the thoracic cage. The purpose of this study was to apply the empirical mode decomposition (EMD) method to obtain the low frequency of MMGdi signal and selecting the intrinsic mode functions related to the respiratory movement. With this intention, MMGdi signals were acquired from a healthy subject, during an incremental load respiratory test, by means of two capacitive accelerometers located at left and right sides of rib cage. Subsequently, both signals were combined to obtain a new signal which contains the contribution of both sides of thoracic cage. Respiratory rate (RR) measured from the mechanical activity (RR_Mmg) was compared with that measured from inspiratory pressure signal (RR_P). Results showed a Pearson's correlation coefficient (r = 0.87) and a good agreement (mean bias = -0.21 with lower and upper limits of -2.33 and 1.89 breaths per minute, respectively) between RR_mmg and RR_P measurements. In conclusion, this study suggests that RR can be estimated using EMD for extracting respiratory movement from low mechanical activity, during an inspiratory test protocol.

JTD Keywords: Accelerometers, Band-pass filters, Biomedical measurement, Empirical mode decomposition, Estimation, IP networks, Muscles