Interference cancellation in respiratory sounds via a multiresolution joint time-delay and signal-estimation scheme

Includes bibliographical references.This paper is concerned with the problem of cancellation of heart sounds from the acquired respiratory sounds using a new joint time-delay and signal-estimation (JTDSE) procedure. Multiresolution discrete wavelet transform (DWT) is first applied to decompose the signals into several subbands. To accurately separate the heart sounds from the acquired respiratory sounds, time-delay estimation (TDE) is performed iteratively in each subband using two adaptation mechanisms that minimize the sum of squared errors between these signals. The time delay is updated using a nonlinear adaptation, namely the Levenberg-Marquardt (LM) algorithm, while the function of the other adaptive system-which uses the block fast transversal filter (BFTF)—is to minimize the mean squared error between the outputs of the delay estimator and the adaptive filter. The proposed methodology possesses a number of key benefits such as the incorporation of multiple complementary information at different subbands, robustness in presence of noise, and accuracy in TDE. The scheme is applied to several cases of simulated and actual respiratory sounds under different conditions and the results are compared with those of the standard adaptive filtering. The results showed the promise of the scheme for the TDE and subsequent interference cancellation

Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed

Aerospace Medicine and Biology: A continuing bibliography (supplement 160)

This bibliography lists 166 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1976

Automatic analysis and classification of cardiac acoustic signals for long term monitoring

Objective: Cardiovascular diseases are the leading cause of death worldwide resulting in over 17.9 million deaths each year. Most of these diseases are preventable and treatable, but their progression and outcomes are significantly more positive with early-stage diagnosis and proper disease management. Among the approaches available to assist with the task of early-stage diagnosis and management of cardiac conditions, automatic analysis of auscultatory recordings is one of the most promising ones, since it could be particularly suitable for ambulatory/wearable monitoring. Thus, proper investigation of abnormalities present in cardiac acoustic signals can provide vital clinical information to assist long term monitoring. Cardiac acoustic signals, however, are very susceptible to noise and artifacts, and their characteristics vary largely with the recording conditions which makes the analysis challenging. Additionally, there are challenges in the steps used for automatic analysis and classification of cardiac acoustic signals. Broadly, these steps are the segmentation, feature extraction and subsequent classification of recorded signals using selected features. This thesis presents approaches using novel features with the aim to assist the automatic early-stage detection of cardiovascular diseases with improved performance, using cardiac acoustic signals collected in real-world conditions. Methods: Cardiac auscultatory recordings were studied to identify potential features to help in the classification of recordings from subjects with and without cardiac diseases. The diseases considered in this study for the identification of the symptoms and characteristics are the valvular heart diseases due to stenosis and regurgitation, atrial fibrillation, and splitting of fundamental heart sounds leading to additional lub/dub sounds in the systole or diastole interval of a cardiac cycle. The localisation of cardiac sounds of interest was performed using an adaptive wavelet-based filtering in combination with the Shannon energy envelope and prior information of fundamental heart sounds. This is a prerequisite step for the feature extraction and subsequent classification of recordings, leading to a more precise diagnosis. Localised segments of S1 and S2 sounds, and artifacts, were used to extract a set of perceptual and statistical features using wavelet transform, homomorphic filtering, Hilbert transform and mel-scale filtering, which were then fed to train an ensemble classifier to interpret S1 and S2 sounds. Once sound peaks of interest were identified, features extracted from these peaks, together with the features used for the identification of S1 and S2 sounds, were used to develop an algorithm to classify recorded signals. Overall, 99 features were extracted and statistically analysed using neighborhood component analysis (NCA) to identify the features which showed the greatest ability in classifying recordings. Selected features were then fed to train an ensemble classifier to classify abnormal recordings, and hyperparameters were optimized to evaluate the performance of the trained classifier. Thus, a machine learning-based approach for the automatic identification and classification of S1 and S2, and normal and abnormal recordings, in real-world noisy recordings using a novel feature set is presented. The validity of the proposed algorithm was tested using acoustic signals recorded in real-world, non-controlled environments at four auscultation sites (aortic valve, tricuspid valve, mitral valve, and pulmonary valve), from the subjects with and without cardiac diseases; together with recordings from the three large public databases. The performance metrics of the methodology in relation to classification accuracy (CA), sensitivity (SE), precision (P+), and F1 score, were evaluated. Results: This thesis proposes four different algorithms to automatically classify fundamental heart sounds – S1 and S2; normal fundamental sounds and abnormal additional lub/dub sounds recordings; normal and abnormal recordings; and recordings with heart valve disorders, namely the mitral stenosis (MS), mitral regurgitation (MR), mitral valve prolapse (MVP), aortic stenosis (AS) and murmurs, using cardiac acoustic signals. The results obtained from these algorithms were as follows: • The algorithm to classify S1 and S2 sounds achieved an average SE of 91.59% and 89.78%, and F1 score of 90.65% and 89.42%, in classifying S1 and S2, respectively. 87 features were extracted and statistically studied to identify the top 14 features which showed the best capabilities in classifying S1 and S2, and artifacts. The analysis showed that the most relevant features were those extracted using Maximum Overlap Discrete Wavelet Transform (MODWT) and Hilbert transform. • The algorithm to classify normal fundamental heart sounds and abnormal additional lub/dub sounds in the systole or diastole intervals of a cardiac cycle, achieved an average SE of 89.15%, P+ of 89.71%, F1 of 89.41%, and CA of 95.11% using the test dataset from the PASCAL database. The top 10 features that achieved the highest weights in classifying these recordings were also identified. • Normal and abnormal classification of recordings using the proposed algorithm achieved a mean CA of 94.172%, and SE of 92.38%, in classifying recordings from the different databases. Among the top 10 acoustic features identified, the deterministic energy of the sound peaks of interest and the instantaneous frequency extracted using the Hilbert Huang-transform, achieved the highest weights. • The machine learning-based approach proposed to classify recordings of heart valve disorders (AS, MS, MR, and MVP) achieved an average CA of 98.26% and SE of 95.83%. 99 acoustic features were extracted and their abilities to differentiate these abnormalities were examined using weights obtained from the neighborhood component analysis (NCA). The top 10 features which showed the greatest abilities in classifying these abnormalities using recordings from the different databases were also identified. The achieved results demonstrate the ability of the algorithms to automatically identify and classify cardiac sounds. This work provides the basis for measurements of many useful clinical attributes of cardiac acoustic signals and can potentially help in monitoring the overall cardiac health for longer duration. The work presented in this thesis is the first-of-its-kind to validate the results using both, normal and pathological cardiac acoustic signals, recorded for a long continuous duration of 5 minutes at four different auscultation sites in non-controlled real-world conditions.Open Acces

Statistical Derivation of the Evolution Equation of Liquid Water Path Fluctuations in Clouds

How to distinguish and quantify deterministic and random influences on the statistics of turbulence data in meteorology cases is discussed from first principles. Liquid water path (LWP) changes in clouds, as retrieved from radio signals, upon different delay times, can be regarded as a stochastic Markov process. A detrended fluctuation analysis method indicates the existence of long range time correlations. The Fokker-Planck equation which models very precisely the LWP $fluctuation$ empirical probability distributions, in particular, their non-Gaussian heavy tails is explicitly derived and written in terms of a drift and a diffusion coefficient. Furthermore, Kramers-Moyal coefficients, as estimated from the empirical data, are found to be in good agreement with their first principle derivation. Finally, the equivalent Langevin equation is written for the LWP increments themselves. Thus rather than the existence of hierarchical structures, like an energy cascade process, {\it strong correlations} on different $time$ $scales$, from small to large ones, are considered to be proven as intrinsic ingredients of such cloud evolutions.Comment: 17 pages, 6 figures; to be published in Journal of Geophysical Research - Atmosphere

One-Dimensional Navier-Stokes Finite Element Flow Model

This technical report documents the theoretical, computational, and practical aspects of the one-dimensional Navier-Stokes finite element flow model. The document is particularly useful to those who are interested in implementing, validating and utilizing this relatively-simple and widely-used model.Comment: 46 pages, 1 tabl

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 130, July 1974

This special bibliography lists 291 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1974