110 research outputs found
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
Acoustic HMMs to Detect Abnormal Respiration with Limited Training Data
In many situations, abnormal sounds, called adventitious sounds, are included with the lung sounds of a subject suffering from pulmonary diseases. Thus, a method to automatically detect abnormal sounds in auscultation was proposed. The acoustic features of normal lung sounds for control subjects and abnormal lung sounds for patients are expressed using hidden markov models (HMMs) to distinguish between normal and abnormal lung sounds. Furthermore, abnormal sounds were detected in a noisy environment, including heart sounds, using a heart-sound model. However, the F1-score obtained in detecting abnormal respiration was low (0.8493). Moreover, the duration and acoustic properties of segments of respiratory, heart, and adventitious sounds varied. In our previous method, the appropriate HMMs for the heart and adventitious sound segments were constructed. Although the properties of the types of adventitious sounds varied, an appropriate topology for each type was not considered. In this study, appropriate HMMs for the segments of each type of adventitious sound and other segments were constructed. The F1-score was increased (0.8726) by selecting a suitable topology for each segment. The results demonstrate the effectiveness of the proposed method
Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1
A reliable, remote, and continuous real-time respiratory sound monitor with
automated respiratory sound analysis ability is urgently required in many
clinical scenarios-such as in monitoring disease progression of coronavirus
disease 2019-to replace conventional auscultation with a handheld stethoscope.
However, a robust computerized respiratory sound analysis algorithm has not yet
been validated in practical applications. In this study, we developed a lung
sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds
(duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels,
13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze
labels, 686 stridor labels, and 4,740 rhonchi labels), and 15,606 discontinuous
adventitious sound labels (all crackles). We conducted benchmark tests for long
short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM
(BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM,
CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and
adventitious sound detection. We also conducted a performance comparison
between the LSTM-based and GRU-based models, between unidirectional and
bidirectional models, and between models with and without a CNN. The results
revealed that these models exhibited adequate performance in lung sound
analysis. The GRU-based models outperformed, in terms of F1 scores and areas
under the receiver operating characteristic curves, the LSTM-based models in
most of the defined tasks. Furthermore, all bidirectional models outperformed
their unidirectional counterparts. Finally, the addition of a CNN improved the
accuracy of lung sound analysis, especially in the CAS detection tasks.Comment: 48 pages, 8 figures. To be submitte
Analysis of Respiratory Sounds: State of the Art
Objective This paper describes state of the art, scientific publications and ongoing research related to the methods of analysis of respiratory sounds. Methods and material Review of the current medical and technological literature using Pubmed and personal experience. Results The study includes a description of the various techniques that are being used to collect auscultation sounds, a physical description of known pathologic sounds for which automatic detection tools were developed. Modern tools are based on artificial intelligence and on technics such as artificial neural networks, fuzzy systems, and genetic algorithms… Conclusion The next step will consist in finding new markers so as to increase the efficiency of decision aid algorithms and tools
Robust and Interpretable Temporal Convolution Network for Event Detection in Lung Sound Recordings
This paper proposes a novel framework for lung sound event detection,
segmenting continuous lung sound recordings into discrete events and performing
recognition on each event. Exploiting the lightweight nature of Temporal
Convolution Networks (TCNs) and their superior results compared to their
recurrent counterparts, we propose a lightweight, yet robust, and completely
interpretable framework for lung sound event detection. We propose the use of a
multi-branch TCN architecture and exploit a novel fusion strategy to combine
the resultant features from these branches. This not only allows the network to
retain the most salient information across different temporal granularities and
disregards irrelevant information, but also allows our network to process
recordings of arbitrary length. Results: The proposed method is evaluated on
multiple public and in-house benchmarks of irregular and noisy recordings of
the respiratory auscultation process for the identification of numerous
auscultation events including inhalation, exhalation, crackles, wheeze,
stridor, and rhonchi. We exceed the state-of-the-art results in all
evaluations. Furthermore, we empirically analyse the effect of the proposed
multi-branch TCN architecture and the feature fusion strategy and provide
quantitative and qualitative evaluations to illustrate their efficiency.
Moreover, we provide an end-to-end model interpretation pipeline that
interprets the operations of all the components of the proposed framework. Our
analysis of different feature fusion strategies shows that the proposed feature
concatenation method leads to better suppression of non-informative features,
which drastically reduces the classifier overhead resulting in a robust
lightweight network.The lightweight nature of our model allows it to be
deployed in end-user devices such as smartphones, and it has the ability to
generate predictions in real-time.Comment: preprint submitted to JBH
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, àmbit 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 vías 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 clínica relevante sobre la estructura y el funcionamiento del sistema respiratorio. La falta de una metodología 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 podrían haber restringido el potencial y el uso de esta técnica en la práctica clínica (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 características 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 energía 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 clínica 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 características de los SAC tras la broncodilatación en pacientes con RBD negativa en la espirometría. Por lo tanto, sugerimos que el análisis combinado de las curvas intensidad-flujo y las características 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 espirometría. 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 espirometría, este método puede tener una aplicación clínica directa en la mejora de la evaluación de la función pulmonar en pacientes con enfermedades respiratoriasAward-winningPostprint (published version
IMPROVING THE QUALITY, ANALYSIS AND INTERPRETATION OF BODY SOUNDS ACQUIRED IN CHALLENGING CLINICAL SETTINGS
Despite advances in medicine and technology, Acute Lower Respiratory Diseases are a leading cause of sickness and mortality worldwide, highly affecting countries where access to appropriate medical technology and expertise is scarce. Chest auscultation provides a low-cost, non-invasive, widely available tool for the examination of pulmonary health. Despite universal adoption, its use is riddled by a number of issues including subjectivity in interpretation and vulnerability to ambient noise, limiting its diagnostic capability. Digital auscultation and computerized methods come as a natural aid towards overcoming such imposed limitations.
Focused on the challenges, we address the demanding real-life scenario of pediatric lung auscultation in busy clinical settings. Two major objectives lead to our contributions: 1) Can we improve the quality of the delicate auscultated sounds and reduce unwanted noise contamination; 2) Can we augment the screening capabilities of current stethoscopes using computerized lung sound analysis to capture the presence of abnormal breaths, and can we standardize findings. To address the first objective, we developed an adaptive noise suppression scheme that tackles contamination coming from a variety of sources, including subject-centric and electronic artifacts, and environmental noise. The proposed method was validated using objective and subjective measures including an expert reviewer panel and objective signal quality metrics. Results revealed the ability and superiority of the proposed method to i) suppress unwanted noise when compared to state-of-the-art technology, and ii) faithfully maintain the signature of the delicate body sounds.
The second objective was addressed by exploring appropriate feature representations that capture distinct characteristics of body sounds. A biomimetic approach was employed, and the acoustic signal was projected onto high-dimensional spaces spanning time, frequency, temporal dynamics and spectral modulations. Trained classifiers produced localized decisions on these breath content features, indicating lung diseases. Unlike existing literature, our proposed scheme is further able to combine and integrate the localized decisions into individual, patient-level evaluation. A large corpus of annotated patient data was used to validate our approach, demonstrating the superiority of the proposed features and patient evaluation scheme.
Overall findings indicate that improved accessible auscultation care is possible, towards creating affordable health care solutions with worldwide impact
- …