9,303 research outputs found
Convolutional neural network for breathing phase detection in lung sounds
We applied deep learning to create an algorithm for breathing phase detection
in lung sound recordings, and we compared the breathing phases detected by the
algorithm and manually annotated by two experienced lung sound researchers. Our
algorithm uses a convolutional neural network with spectrograms as the
features, removing the need to specify features explicitly. We trained and
evaluated the algorithm using three subsets that are larger than previously
seen in the literature. We evaluated the performance of the method using two
methods. First, discrete count of agreed breathing phases (using 50% overlap
between a pair of boxes), shows a mean agreement with lung sound experts of 97%
for inspiration and 87% for expiration. Second, the fraction of time of
agreement (in seconds) gives higher pseudo-kappa values for inspiration
(0.73-0.88) than expiration (0.63-0.84), showing an average sensitivity of 97%
and an average specificity of 84%. With both evaluation methods, the agreement
between the annotators and the algorithm shows human level performance for the
algorithm. The developed algorithm is valid for detecting breathing phases in
lung sound recordings
Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey
Cough acoustics contain multitudes of vital information about
pathomorphological alterations in the respiratory system. Reliable and accurate
detection of cough events by investigating the underlying cough latent features
and disease diagnosis can play an indispensable role in revitalizing the
healthcare practices. The recent application of Artificial Intelligence (AI)
and advances of ubiquitous computing for respiratory disease prediction has
created an auspicious trend and myriad of future possibilities in the medical
domain. In particular, there is an expeditiously emerging trend of Machine
learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting
cough signatures. The enormous body of literature on cough-based AI algorithms
demonstrate that these models can play a significant role for detecting the
onset of a specific respiratory disease. However, it is pertinent to collect
the information from all relevant studies in an exhaustive manner for the
medical experts and AI scientists to analyze the decisive role of AI/ML. This
survey offers a comprehensive overview of the cough data-driven ML/DL detection
and preliminary diagnosis frameworks, along with a detailed list of significant
features. We investigate the mechanism that causes cough and the latent cough
features of the respiratory modalities. We also analyze the customized cough
monitoring application, and their AI-powered recognition algorithms. Challenges
and prospective future research directions to develop practical, robust, and
ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table
Respiratory Sound Analysis for the Evidence of Lung Health
Significant changes have been made on audio-based technologies over years in several different fields along with healthcare industry. Analysis of Lung sounds is a potential source of noninvasive, quantitative information along with additional objective on the status of the pulmonary system. To do that medical professionals listen to sounds heard over the chest wall at different positions with a stethoscope which is known as auscultation and is important in diagnosing respiratory diseases. At times, possibility of inaccurate interpretation of respiratory sounds happens because of clinician’s lack of considerable expertise or sometimes trainees such as interns and residents misidentify respiratory sounds. We have built a tool to distinguish healthy respiratory sound from non-healthy ones that come from respiratory infection carrying patients. The audio clips were characterized using Linear Predictive Cepstral Coefficient (LPCC)-based features and the highest possible accuracy of 99.22% was obtained with a Multi-Layer Perceptron (MLP)- based classifier on the publicly available ICBHI17 respiratory sounds dataset [1] of size 6800+ clips. The system also outperformed established works in literature and other machine learning techniques. In future we will try to use larger dataset with other acoustic techniques along with deep learning-based approaches and try to identify the nature and severity of infection using respiratory sounds
2D respiratory sound analysis to detect lung abnormalities
In this paper, we analyze deep visual features from 2D data representation(s) of the respiratory sound to detect evidence of lung abnormalities. The primary motivation behind this is that visual cues are more important in decision-making than raw data (lung sound). Early detection and prompt treatments are essential for any future possible respiratory disorders, and respiratory sound is proven to be one of the biomarkers. In contrast to state-of-the-art approaches, we aim at understanding/analyzing visual features using our Convolutional Neural Networks (CNN) tailored Deep Learning Models, where we consider all possible 2D data such as Spectrogram, Mel-frequency Cepstral Coefficients (MFCC), spectral centroid, and spectral roll-off. In our experiments, using the publicly available respiratory sound database named ICBHI 2017 (5.5 hours of recordings containing 6898 respiratory cycles from 126 subjects), we received the highest performance with the area under the curve of 0.79 from Spectrogram as opposed to 0.48 AUC from the raw data from a pre-trained deep learning model: VGG16. We also used machine learning algorithms using reliable data to improve Our study proved that 2D data representation could help better understand/analyze lung abnormalities as compared to 1D data. Our findings are also contrasted with those of earlier studies. For purposes of generality, we used the MFCC of neutrinos to determine if picture data or raw data produced superior results
Monitoring and analysis of lung sounds remotely
Visual and auditory analysis of respiratory sound signals promises improved detection of certain types of lung diseases. LabVIEW software was used to design a system that monitors the respiratory activity of the patient. The program developed calculates the respiratory rate, displays the time expanded waveform of the lung sound, and computes the fast Fourier transform and short-time Fourier transform to present the power spectrum and spectrogram respectively. These parameters are transmitted synchronously to the remote station using the Internet for online monitoring of the patient
Technology applications
A summary of NASA Technology Utilization programs for the period of 1 December 1971 through 31 May 1972 is presented. An abbreviated description of the overall Technology Utilization Applications Program is provided as a background for the specific applications examples. Subjects discussed are in the broad headings of: (1) cancer, (2) cardiovascular disease, (2) medical instrumentation, (4) urinary system disorders, (5) rehabilitation medicine, (6) air and water pollution, (7) housing and urban construction, (8) fire safety, (9) law enforcement and criminalistics, (10) transportation, and (11) mine safety
Multichannel analysis of normal and continuous adventitious respiratory sounds for the assessment of pulmonary function in respiratory diseases
Premi extraordinari doctorat UPC curs 2015-2016, à mbit d’Enginyeria IndustrialRespiratory sounds (RS) are produced by turbulent airflows through the airways and are
inhomogeneously transmitted through different media to the chest surface, where they can be recorded
in a non-invasive way. Due to their mechanical nature and airflow dependence, RS are affected by
respiratory diseases that alter the mechanical properties of the respiratory system. Therefore, RS provide
useful clinical information about the respiratory system structure and functioning.
Recent advances in sensors and signal processing techniques have made RS analysis a more objective
and sensitive tool for measuring pulmonary function. However, RS analysis is still rarely used in clinical
practice. Lack of a standard methodology for recording and processing RS has led to several different
approaches to RS analysis, with some methodological issues that could limit the potential of RS analysis
in clinical practice (i.e., measurements with a low number of sensors, no controlled airflows, constant
airflows, or forced expiratory manoeuvres, the lack of a co-analysis of different types of RS, or the use
of inaccurate techniques for processing RS signals).
In this thesis, we propose a novel integrated approach to RS analysis that includes a multichannel
recording of RS using a maximum of five microphones placed over the trachea and the chest surface,
which allows RS to be analysed at the most commonly reported lung regions, without requiring a large
number of sensors. Our approach also includes a progressive respiratory manoeuvres with variable
airflow, which allows RS to be analysed depending on airflow. Dual RS analyses of both normal RS
and continuous adventitious sounds (CAS) are also proposed. Normal RS are analysed through the RS
intensity–airflow curves, whereas CAS are analysed through a customised Hilbert spectrum (HS),
adapted to RS signal characteristics.
The proposed HS represents a step forward in the analysis of CAS. Using HS allows CAS to be fully
characterised with regard to duration, mean frequency, and intensity. Further, the high temporal and
frequency resolutions, and the high concentrations of energy of this improved version of HS, allow CAS
to be more accurately characterised with our HS than by using spectrogram, which has been the most
widely used technique for CAS analysis.
Our approach to RS analysis was put into clinical practice by launching two studies in the Pulmonary
Function Testing Laboratory of the Germans Trias i Pujol University Hospital for assessing pulmonary
function in patients with unilateral phrenic paralysis (UPP), and bronchodilator response (BDR) in
patients with asthma. RS and airflow signals were recorded in 10 patients with UPP, 50 patients with
asthma, and 20 healthy participants.
The analysis of RS intensity–airflow curves proved to be a successful method to detect UPP, since we
found significant differences between these curves at the posterior base of the lungs in all patients whereas no differences were found in the healthy participants. To the best of our knowledge, this is the
first study that uses a quantitative analysis of RS for assessing UPP.
Regarding asthma, we found appreciable changes in the RS intensity–airflow curves and CAS features
after bronchodilation in patients with negative BDR in spirometry. Therefore, we suggest that the
combined analysis of RS intensity–airflow curves and CAS features—including number, duration, mean
frequency, and intensity—seems to be a promising technique for assessing BDR and improving the
stratification of BDR levels, particularly among patients with negative BDR in spirometry.
The novel approach to RS analysis developed in this thesis provides a sensitive tool to obtain objective
and complementary information about pulmonary function in a simple and non-invasive way. Together
with spirometry, this approach to RS analysis could have a direct clinical application for improving the
assessment of pulmonary function in patients with respiratory diseases.Los sonidos respiratorios (SR) se generan con el paso del flujo de aire a través de las vÃas respiratorias y se transmiten de forma no homogénea hasta la superficie torácica. Dada su naturaleza mecánica, los SR se ven afectados en gran medida por enfermedades que alteran las propiedades mecánicas del sistema respiratorio. Por lo tanto, los SR proporcionan información clÃnica relevante sobre la estructura y el funcionamiento del sistema respiratorio. La falta de una metodologÃa estándar para el registro y procesado de los SR ha dado lugar a la aparición de diferentes estrategias de análisis de SR con ciertas limitaciones metodológicas que podrÃan haber restringido el potencial y el uso de esta técnica en la práctica clÃnica (medidas con pocos sensores, flujos no controlados o constantes y/o maniobras forzadas, análisis no combinado de distintos tipos de SR o uso de técnicas poco precisas para el procesado de los SR). En esta tesis proponemos un método innovador e integrado de análisis de SR que incluye el registro multicanal de SR mediante un máximo de cinco micrófonos colocados sobre la tráquea yla superficie torácica, los cuales permiten analizar los SR en las principales regiones pulmonares sin utilizar un número elevado de sensores . Nuestro método también incluye una maniobra respiratoria progresiva con flujo variable que permite analizar los SR en función del flujo respiratorio. También proponemos el análisis combinado de los SR normales y los sonidos adventicios continuos (SAC), mediante las curvas intensidad-flujo y un espectro de Hilbert (EH) adaptado a las caracterÃsticas de los SR, respectivamente. El EH propuesto representa un avance importante en el análisis de los SAC, pues permite su completa caracterización en términos de duración, frecuencia media e intensidad. Además, la alta resolución temporal y frecuencial y la alta concentración de energÃa de esta versión mejorada del EH permiten caracterizar los SAC de forma más precisa que utilizando el espectrograma, el cual ha sido la técnica más utilizada para el análisis de SAC en estudios previos. Nuestro método de análisis de SR se trasladó a la práctica clÃnica a través de dos estudios que se iniciaron en el laboratorio de pruebas funcionales del hospital Germans Trias i Pujol, para la evaluación de la función pulmonar en pacientes con parálisis frénica unilateral (PFU) y la respuesta broncodilatadora (RBD) en pacientes con asma. Las señales de SR y flujo respiratorio se registraron en 10 pacientes con PFU, 50 pacientes con asma y 20 controles sanos. El análisis de las curvas intensidad-flujo resultó ser un método apropiado para detectar la PFU , pues encontramos diferencias significativas entre las curvas intensidad-flujo de las bases posteriores de los pulmones en todos los pacientes , mientras que en los controles sanos no encontramos diferencias significativas. Hasta donde sabemos, este es el primer estudio que utiliza el análisis cuantitativo de los SR para evaluar la PFU. En cuanto al asma, encontramos cambios relevantes en las curvas intensidad-flujo yen las caracterÃsticas de los SAC tras la broncodilatación en pacientes con RBD negativa en la espirometrÃa. Por lo tanto, sugerimos que el análisis combinado de las curvas intensidad-flujo y las caracterÃsticas de los SAC, incluyendo número, duración, frecuencia media e intensidad, es una técnica prometedora para la evaluación de la RBD y la mejora en la estratificación de los distintos niveles de RBD, especialmente en pacientes con RBD negativa en la espirometrÃa. El método innovador de análisis de SR que se propone en esta tesis proporciona una nueva herramienta con una alta sensibilidad para obtener información objetiva y complementaria sobre la función pulmonar de una forma sencilla y no invasiva. Junto con la espirometrÃa, este método puede tener una aplicación clÃnica directa en la mejora de la evaluación de la función pulmonar en pacientes con enfermedades respiratoriasAward-winningPostprint (published version
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