9,872 research outputs found

    Convolutional neural network for breathing phase detection in lung sounds

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    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

    Automatic wheeze detection based on auditory modelling

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    Automatic wheeze detection has several potential benefits compared with reliance on human auscultation: it is experience independent, an automated historical record can easily be kept, and it allows quantification of wheeze severity. Previous attempts to detect wheezes automatically have had partial success but have not been reliable enough to become widely accepted as a useful tool. In this paper an improved algorithm for automatic wheeze detection based on auditory modelling is developed, called the frequency- and duration-dependent threshold algorithm. The mean frequency and duration of each wheeze component are obtained automatically. The detected wheezes are marked on a spectrogram. In the new algorithm, the concept of a frequency- and duration-dependent threshold for wheeze detection is introduced. Another departure from previous work is that the threshold is based not on global power but on power corresponding to a particular frequency range. The algorithm has been tested on 36 subjects, 11 of whom exhibited characteristics of wheeze. The results show a marked improvement in the accuracy of wheeze detection when compared with previous algorithms

    Review of sensors for remote patient monitoring

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    Remote patient monitoring (RPM) of physiological measurements can provide an efficient method and high quality care to patients. The physiological signals measurement is the initial and the most important factor in RPM. This paper discusses the characteristics of the most popular sensors, which are used to obtain vital clinical signals in prevalent RPM systems. The sensors discussed in this paper are used to measure ECG, heart sound, pulse rate, oxygen saturation, blood pressure and respiration rate, which are treated as the most important vital data in patient monitoring and medical examination

    Performance evaluation of the Hilbert–Huang transform for respiratory sound analysis and its application to continuous adventitious sound characterization

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    © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/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.Postprint (author's final draft

    Fundamentals of Lung Auscultation

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    Chest auscultation has long been considered a useful part of the physical examination, going back to the time of Hippocrates. However, it did not become a widespread practice until the invention of the stethoscope by René Laënnec in 1816, which made the practice convenient and hygienic.1 During the second half of the 20th century, technological advances in ultrasonography, radiographic computed tomography (CT), and magnetic resonance imaging shifted interest from lung auscultation to imaging studies, which can detect lung disease with an accuracy never previously imagined. However, modern computer-assisted techniques have also allowed precise recording and analysis of lung sounds, prompting the correlation of acoustic indexes with measures of lung mechanics. This innovative, though still little used, approach has improved our knowledge of acoustic mechanisms and increased the clinical usefulness of auscultation. In this review, we present an overview of lung auscultation in the light of modern concepts of lung acoustics

    Assessment of pulmonary edema: principles and practice

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    Pulmonary edema increasingly is recognized as a perioperative complication affecting outcome. Several risk factors have been identified, including those of cardiogenic origin, such as heart failure or excessive fluid administration, and those related to increased pulmonary capillary permeability secondary to inflammatory mediators. Effective treatment requires prompt diagnosis and early intervention. Consequently, over the past 2 centuries a concentrated effort to develop clinical tools to rapidly diagnose pulmonary edema and track response to treatment has occurred. The ideal properties of such a tool would include high sensitivity and specificity, easy availability, and the ability to diagnose early accumulation of lung water before the development of the full clinical presentation. In addition, clinicians highly value the ability to precisely quantify extravascular lung water accumulation and differentiate hydrostatic from high permeability etiologies of pulmonary edema. In this review, advances in understanding the physiology of extravascular lung water accumulation in health and in disease and the various mechanisms that protect against the development of pulmonary edema under physiologic conditions are discussed. In addition, the various bedside modalities available to diagnose early accumulation of extravascular lung water and pulmonary edema, including chest auscultation, chest roentgenography, lung ultrasonography, and transpulmonary thermodilution, are examined. Furthermore, advantages and limitations of these methods for the operating room and intensive care unit that are critical for proper modality selection in each individual case are explored

    Mechanical Ventilation: Neonate (Respiratory Therapy)

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    Confirming the position of a nasogastric tube what does the literature say?

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    Nasogastric tubes are a medical device that can be used for a number of purposes. The process of inserting them however can be complicated. Nurses must therefore use an evidence based approach to confirm the correct position of nasogastric tubes and there are three main techniques described in the literature to do so. To date, one group of authors has published the majority of the studies on these techniques. This paper reviews their work. <br /

    Classification of Normal and Crackles Respiratory Sounds into Healthy and Lung Cancer Groups

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    Lung cancer is the most common cancer worldwide and the third most common cancer in Malaysia. Due to its high prevalence worldwide and in Malaysia, it is an utmost importance to have the disease detected at an early stage which would result in a higher chance of cure and possibly better survival. The current methods used for lung cancer screening might not be simple, inexpensive and safe and not readily accessible in outpatient clinics. In this paper, we present the classification of normal and crackles sounds acquired from 20 healthy and 23 lung cancer patients, respectively using Artificial Neural Network. Firstly, the sounds signals were decomposed into seven different frequency bands using Discrete Wavelet Transform (DWT) based on two different mother wavelets namely Daubechies 7 (db7) and Haar. Secondly, mean, standard deviation and maximum PSD of the detail coefficients for five frequency bands (D3, D4, D5, D6, and D7) were calculated as features. Fifteen features were used as input to the ANN classifier. The results of classification show that db7 based performed better than Haar with perfect 100% sensitivity, specificity and accuracy for testing and validation stages when using 15 nodes at the hidden layer. While for Haar, only testing stage shows the perfect 100% for sensitivity, specificity, and accuracy when using 10 nodes at the hidden layer
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