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Distribution of the inspiratory (top) and expiratory (bottom) wheeze rates determined by computerized lung sound analysis using the PulmoTrack®.
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Background
Several respiratory diseases are associated with specific respiratory sounds. In contrast to auscultation, computerized lung sound analysis is objective and can be performed continuously over an extended period. Moreover, audio recordings can be stored. Computerized lung sounds have rarely been assessed in neonates during the first year...
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We propose a sound analysis system for the detection of audio events in surveillance applications. The method that we propose combines short-and long-time analysis in order to increase the reliability of the detection. The basic idea is that a sound is composed of small, atomic audio units and some of them are distinctive of a particular class of s...
Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnosis relies on subjective lung auscultation by physicians. Enabling this capability via a low-profile, objective wearable device for remote patient monitoring (RPM) could offer pre-emptive, accurate res...
Background:
Since 2020, peoples' lifestyles have been largely changed due to the coronavirus pandemic worldwide. In the medical field, although many patients prefer remote medical care, the physician cannot check the patient directly, so it is important for the patients themselves to accurately convey their condition to the physician. Thus, remote...
![Fig.1 Adaptive Filter d[n] = Primary input (Lung sound contaminated...](profile/Ravindra-Potdar/publication/352440328/figure/fig1/AS:1035253812568065@1623835151799/Adaptive-Filter-dn-Primary-input-Lung-sound-contaminated-with-heart-sound-dn_Q320.jpg)
Auscultation is the most important and effective clinical technique for evaluating a patient's respiratory function. Auscultation of the chest is a diagnostic method used by physicians, owing to its simplicity and noninvasiveness. Hence, there is interest in lung sound analysis using time and frequency domain techniques to increase its usefulness i...
Lung auscultation is an important part of a physical examination. However, its biggest drawback is its subjectivity. The results depend on the experience and ability of the doctor to perceive and distinguish pathologies in sounds heard via a stethoscope. This paper investigates a new method of automatic sound analysis based on neural networks (NNs)...
Citations
... Several digital technologies have been or are currently being developed to support parents and health professionals, especially at the primary care level, in the management of wheezing disorders. These include symptom diaries [9,10] and asthma action plans accessible within mobile health applications for smartphones [11], adherence support via gamification [12], digital therapeutics such as smarthalers [13], or digitally connected diagnostic tools like wirelessly connected peak flow meters [14], digital stethoscopes for health care professionals [15] or mobile wheeze [16,17], and cough detectors [18]. The market for mobile phone applications is growing remarkably every year, but no quality control system is in place to distinguish guideline-based medical support from arbitrarily compiled or simply wrong content [19]. ...
Background
Viral airway infections are a major reason for doctor’s visits at pre-school age, especially when associated with wheezing. While proper treatment requires adequate recognition of airway obstruction, caretakers are often struggling with this judgment, consequently leading to insufficient or late treatment and an unnecessary discomfort of the patient. Digital technologies may serve to support parental decision taking. The aim of the present pilot study is to acquire data on the feasibility of recruitment and observation procedures for a randomized controlled trial on the impact of a digital wheeze detector in a home management setting of pre-school wheezing.
Methods
This single-armed pilot study enrolled patients with a doctor’s diagnosis of wheezing aged 9 to 72 months. Participants were asked to use a digital wheeze detector (WheezeScan, Omron Healthcare, Japan) 2×/day for 30 days and record the child’s respiratory symptoms, detection of wheezing, and medication intake via an electronic diary (eDiary) app. Demographic and clinical data were collected at the recruitment visit. The asthma control test and the Parent Asthma Management Self-Efficacy Scale (PAMSES) were assessed both, at recruitment and follow-up.
Results
Twenty families were recruited and completed the monitoring. All but one completed the follow-up after 30 days. The recruitment procedures were feasible, and adherence to daily monitoring reached an average of 81%. The use of the wheeze detector was rated as uncomplicated. Parents detected wheezing without digital support in only 22/708 (3.1%) of the recorded events. By contrast, the wheeze detector indicated an airway obstruction in 140/708 (19.8%) of the recordings.
Conclusion
In parallel to feasible recruitment procedures, we observed good usability of the wheeze detection device and high adherence to eDiary recording. The positive outcomes show that the WheezeScan may empower parents by increasing their capacity for wheeze detection. This deserves to be investigated in a larger randomized controlled trial.
... Judgments were agreed between two physicians. If the judgments of the two physicians did not match, the supervisor made the judgment [18]. ...
Background:
The detection of wheezes as an exacerbation sign is important in certain respiratory diseases. However, few highly accurate clinical methods are available for automatic detection of wheezes in children. This study aimed to develop a wheeze detection algorithm for practical implementation in children.
Methods:
A wheeze recognition algorithm was developed based on wheezes features following the Computerized Respiratory Sound Analysis guidelines. Wheezes can be detected by auscultation with a stethoscope and using an automatic computerized lung sound analysis. Lung sounds were recorded for 30 s in 214 children aged 2 months to 12 years and 11 months in a pediatric consultation room. Files containing recorded lung sounds were assessed by two specialist physicians and divided into two groups: 65 were designated as "wheeze" files, and 149 were designated as "no-wheeze" files. All lung sound judgments were agreed between two specialist physicians. We compared wheeze recognition between the specialist physicians and using the wheeze recognition algorithm and calculated the sensitivity, specificity, positive predictive value, and negative predictive value for all recorded sound files to evaluate the influence of age on the wheeze detection sensitivity.
Results:
The detection of wheezes was not influenced by age. In all files, wheezes were differentiated from noise using the wheeze recognition algorithm. The sensitivity, specificity, positive predictive value, and negative predictive value of the wheeze recognition algorithm were 100%, 95.7%, 90.3%, and 100%, respectively.
Conclusions:
The wheeze recognition algorithm could identify wheezes in sound files and therefore may be useful in the practical implementation of respiratory illness management at home using properly developed devices.
... El análisis computarizado de ruidos respiratorios podría aportar objetividad al momento de analizar ruidos respiratorios debido a la estandarización que presenta actualmente 10,11 . Esta técnica permite la descomposición de la señal en sus diferentes frecuencias para caracterizar con más precisión este tipo de señales 12,13 y analizar así de forma más objetiva los ruidos respiratorios (figuras 1, 2); sin embargo, la auscultación con equipos electrónicos todavía no han logrado filtrar los ruidos ambientales que interfieren con la señal digital y tampoco el análisis está disponible de forma inmediata para el diagnóstico clínico. ...
... Esta técnica la implementaron en la pesquisa de sibilancias en 120 niños menores de 1 año, reportando una sensibilidad de 85,7% y 84,6% para sibilancias inspiratorias y espiratorias respectivamente, junto con una especificidad de 80,7% y 82,5% 10 . En comparación, la evaluación por profesionales de la salud en dicho estudio mostró un nivel moderado de acuerdo inter-observador, demostrando que CORSA es una herramienta confiable que otorga información objetiva respecto a la presencia de sibilancias. ...
La auscultación pulmonar es parte fundamental del examen físico para el diagnóstico de las enfermedades respiratorias. La estandarización que ha alcanzado la nomenclatura de los ruidos respiratorios, sumado a los avances en el análisis computacional de los mismos, han permitido mejorar la utilidad de esta técnica. Sin embargo, el rendimiento de la auscultación pulmonar ha sido cuestionado por tener una concordancia variable entre profesionales de la salud. Aun cuando la incorporación de nuevas herramientas diagnósticas de imágenes y función pulmonar han revolucionado la precisión diagnóstica en enfermedades respiratorias, no existe tecnología que permita reemplazar la técnica de auscultación pulmonar para guiar el proceso diagnóstico. Por una parte, la auscultación pulmonar permite seleccionar a aquellos pacientes que se beneficiarán de una determinada técnica diagnóstica, se puede repetir cuantas veces sea necesario para tomar decisiones clínicas, y frecuentemente permite prescindir de exámenes adicionales que no siempre son fáciles de realizar o no se encuentran disponibles. En esta revisión se presenta el estado actual de la técnica de auscultación pulmonar y su rendimiento objetivo basado en la nomenclatura actual aceptada para los ruidos respiratorios, además de resumir la evidencia principal de estudios de concordancia de auscultación pediátrica y su análisis objetivo a través de nueva tecnología computacional.
... 8 Many investigators have worked to reach a common vocabulary for respiratory sounds, [9][10][11][12] and the emergence of computerized lung sound analysis, which provides for a more precise description of respiratory sounds, has resulted in progress in this area. 13 Some studies [16][17][18] found fair to moderate inter-observer agreement between health professionals (eg physicians, nurses, physiotherapists) for the recognition of lung sounds. However, these studies were designed to test clinical scores that were limited to wheezes as the only lung sound. ...
Background:
Auscultation is a fundamental part of the physical examination, but its utility has been questioned due to the low inter-rater concordance. We therefore sought to evaluate the concordance of the discrimination of lung sound recordings between experienced physiotherapists.
Methods:
Lung sound recordings were selected and validated by an expert panel when Fleiss κ concordance was > 0.75. Eleven recordings were played for subject recognition using a portable computer in their workplace. Results were analyzed using Fleiss κ when looking for concordance between physiotherapists. Univariate regression was performed to determine if there was an association with clinical training, years of experience, academic accomplishment, or university affiliation.
Results:
Sixty-nine physiotherapists with a median of 4 years of working experience (interquartile range 2-6 y) completed the study. There was moderate concordance (κ = 0.562; 95% CI 0.462-0.605) for overall lung sound recording discrimination. For continuous and noncontinuous lung sound recordings, discrimination concordance was substantial (κ = 0.63 and κ = 0.76, respectively). A bivariate analysis revealed that years of experience presented an inverse association with stridor recognition.
Conclusion:
Concordance between physiotherapists in discriminating recorded lung sounds was moderate. The ability to recognize stridor was inversely associated with years of work experience.
... Moreover, the air resonance caused by high-frequency components creates an audible sound and can be analyzed to determine the ectopic functioning of lungs. Time-frequency analysis has been performed to extract the wheeze inside normal and abnormal breath sounds [35,36]. Pneumonia has been automatically detected in breath sounds via shorttime Fourier transform and machine intelligence [37]. ...
Biological and medical diagnoses depend on high-quality measurements. A wearable device based on Internet of Things (IoT) must be unobtrusive to the human body to encourage users to accept continuous monitoring. However, unobtrusive IoT devices are usually of low quality and unreliable because of the limitation of technology progress that has slowed down at high peak. Therefore, advanced inference techniques must be developed to address the limitations of IoT devices. This review proposes that IoT technology in biological and medical applications should be based on a new data assimilation process that fuses multiple data scales from several sources to provide diagnoses. Moreover, the required technologies are ready to support the desired disease diagnosis levels, such as hypothesis test, multiple evidence fusion, machine learning, data assimilation, and systems biology. Furthermore, cross-disciplinary integration has emerged with advancements in IoT. For example, the multiscale modeling of systems biology from proteins and cells to organs integrates current developments in biology, medicine, mathematics, engineering, artificial intelligence, and semiconductor technologies. Based on the monitoring objectives of IoT devices, researchers have gradually developed ambulant, wearable, noninvasive, unobtrusive, low-cost, and pervasive monitoring devices with data assimilation methods that can overcome the limitations of devices in terms of quality measurement. In the future, the novel features of data assimilation in systems biology and ubiquitous sensory development can describe patients’ physical conditions based on few but long-term measurements.
... devices include wheeze detectors, which so far have only been studied in the inpatient setting, and wearable devices (31). Although wearables have the potential to provide a broad range of physiologic detail, they are inherently limited by the virtue that they must be worn, and there are a lot of confounding variables during the day that may make data analysis challenging. ...
Rationale:
Asthma management depends on prompt identification of symptoms, which challenges both patients and providers. In asthma, a misapprehension of health between exacerbations can compromise compliance. Thus, there is a need for a tool that permits objective longitudinal monitoring without increasing the burden of patient compliance.
Objectives:
We sought to determine whether changes in nocturnal physiology are associated with asthma symptoms in pediatric patients.
Methods:
Using a contactless bed sensor, nocturnal heart rate, respiratory rate, relative stroke volume, and movement in asthmatic children 5-18 years old (n=16) were recorded. Asthma symptoms and Asthma Control Test score were reported every two weeks. Random forest model was used to identify physiologic parameters associated with asthma symptoms. Elastic net regression was used to identify variables associated with Asthma Control Test score.
Measurements and main results:
The model on the full cohort performed with sensitivity of 47.2%, specificity of 96.3%, and accuracy of 87.4%; heart rate and respiratory parameters were the most important variables in this model. The model predicted asthma symptoms 35% of the time on the day prior to perception of symptoms, and 100% of the time for a select subject for which the model performed with greater sensitivity. Multivariable and bivariable analyses demonstrated significant association between heart rate and respiratory rate parameters and Asthma Control Test score.
Conclusions:
Nocturnal physiologic changes correlate with asthma symptoms, supporting the notion that nocturnal physiologic monitoring represents an objective diagnostic tool capable longitudinally assessing disease control and predicting asthma exacerbations in asthmatic children at home.
... 19 While forced expiratory wheezing has been attractive for theoretical modelling and physiologic experimentation in humans, inspiratory wheezes are also a common finding in medical practice. [25][26][27][28] However, aside from laryngeal noises, they cannot be generated by healthy subjects even with maximum effort. Forgacs recognized the theoretical problems of wheezing during inspiration when airways should be pulled open. ...
Wheezing is the most widely reported adventitious lung sound in the English language. It is recognized by health professionals as well as by lay people, although often with a different meaning. Wheezing is an indicator of airway obstruction and therefore of interest particularly for the assessment of young children and in other situations where objective documentation of lung function is not generally available. This review summarizes our current understanding of mechanisms producing wheeze, its subjective perception and description, its objective measurement, and visualization, and its relevance in clinical practice.
... A respiration belt was strapped around each infant's chest to detect the inspiratory and expiratory phases of the breathing cycle (figure 1). A detailed description of the measurement protocol was presented previously (Puder et al 2014). Briefly, respiratory sounds were recorded for 10 min in sleeping infants who were clinically stable and free of respiratory infections during the 3 previous weeks. ...
... The main strengths include the use of a relative large sample size and the same equipment and protocol for all patients. The equipment we used has recently been validated for young infants (Puder et al 2014, Fischer et al 2015. One of the limitations of this study is that the retrospective nature of the evaluation did not allow investigation of the causes of disturbances in the lung sounds. ...
Computerized wheeze detection is an established method for objective assessment of respiratory sounds. In infants, this method has been used to detect subclinical airway obstruction and to monitor treatment effects. The optimal location for the acoustic sensors, however, is unknown. The aim of this study was to evaluate the quality of respiratory sound recordings in young infants, and to determine whether the position of the sensor affected computerized wheeze detection. Respiratory sounds were recorded over the left lateral chest wall and the trachea in 112 sleeping infants (median postmenstrual age: 49 weeks) on 129 test occasions using an automatic wheeze detection device (PulmoTrack(®)). Each recording lasted 10 min and the recordings were stored. A trained clinician retrospectively evaluated the recordings to determine sound quality and disturbances. The wheeze rates of all undisturbed tracheal and chest wall signals were compared using Bland-Altman plots. Comparison of wheeze rates measured over the trachea and the chest wall indicated strong correlation (r ⩾ 0.93, p < 0.001), with a bias of 1% or less and limits of agreement of within 3% for the inspiratory wheeze rate and within 6% for the expiratory wheeze rate. However, sounds from the chest wall were more often affected by disturbances than sounds from the trachea (23% versus 6%, p < 0.001). The study suggests that in young infants, a better quality of lung sound recordings can be obtained with the tracheal sensor.
... Similar or somewhat stronger agreements on the presence of crackles and wheezes were found in this study compared to most previous studies. [18][19][20][21][22][23][24][25][26][27][28][29] In most previous studies, the observers listened to real patients and registered crackles and wheezes without specifying the respiratory phase. Moderate agreement with κ values between 0.41 and 0.60 have usually been found, also in a study where a teaching stethoscope was used, allowing four observers to listen simultaneously. ...
Background:
The European Respiratory Society (ERS) lung sounds repository contains 20 audiovisual recordings of children and adults. The present study aimed at determining the interobserver variation in the classification of sounds into detailed and broader categories of crackles and wheezes.
Methods:
Recordings from 10 children and 10 adults were classified into 10 predefined sounds by 12 observers, 6 paediatricians and 6 doctors for adult patients. Multirater kappa (Fleiss' κ) was calculated for each of the 10 adventitious sounds and for combined categories of sounds.
Results:
The majority of observers agreed on the presence of at least one adventitious sound in 17 cases. Poor to fair agreement (κ<0.40) was usually found for the detailed descriptions of the adventitious sounds, whereas moderate to good agreement was reached for the combined categories of crackles (κ=0.62) and wheezes (κ=0.59). The paediatricians did not reach better agreement on the child cases than the family physicians and specialists in adult medicine.
Conclusions:
Descriptions of auscultation findings in broader terms were more reliably shared between observers compared to more detailed descriptions.
... 17 This is regrettable, as CORSA may be useful in testing bronchial responsiveness 12 and in monitoring the progression of obstructive airway disease from birth. Recently, a commercial device was validated for automatic wheeze detection in neonates and young infants, 18 with CORSA shown to provide quantitative information about the duration of wheezing in this age group. However, it was not known whether automatically detected wheeze was associated with abnormal LFT or was merely a sound phenomenon. ...
... Respiratory sounds were recorded and analyzed using the PulmoTrack 1 monitor (Model 2020, KarmelSonix Ltd., Israel), a commercial device designed to automatically analyze breath sounds and continuously quantify wheeze duration, as described previously. 18 In brief, two phonopneumographic piezoelectric contact sensors are attached to the manubrium and the left axillary line using adhesive foam pads. An air-coupled microphone was placed next to each infant to record ambient noises, thus reducing the signal-to-noise ratio. ...
... Suitable cut-off values for automatically detected wheeze duration were previously established in a validation study. 18 According to these thresholds, infants were classified as inspiratory "wheezers" if inspiratory wheeze exceeded 2% of inspiratory time and as expiratory "wheezers" if expiratory wheeze exceeded 3% of expiratory time. ...
Objective:
Computerized respiratory sound analysis (CORSA) has been validated in the assessment of wheeze in infants, but it is unknown whether automatically detected wheeze is associated with impaired lung function. This study investigated the relationship between wheeze detection and conventional lung function testing (LFT) parameters.
Methods:
CORSA was performed using the PulmoTrack® monitor in 110 infants, of median (interquartile range) postmenstrual age 50 (46-56) weeks and median body weight 4,810 (3,980-5,900) g, recovering from neonatal intensive care. In the same session, LFT was performed, including tidal breathing measurements, occlusion tests, body plethysmography, forced expiratory flow by rapid thoracoabdominal compression, sulfur hexafluoride (SF6 ) multiple breath washout (MBW), and capillary blood gas analysis. Infants were classified as wheezers or non-wheezers using predefined cut-off values for the duration of inspiratory and expiratory wheeze.
Results:
Wheezing was detected in 72 (65%) infants, with 43 (39%) having inspiratory and 53 (48%) having expiratory wheezing. Endotracheal mechanical ventilation in the neonatal period for > 24 hr was associated with inspiratory wheeze (P = 0.009). Airway resistance was increased in both inspiratory (P = 0.02) and expiratory (P = 0.004) wheezers and correlated with the duration of expiratory wheeze (r = 0.394, P < 0.001). Expiratory wheezers showed a significant increase in respiratory resistance (P = 0.001), time constant (0.012), and functional residual capacity using SF6 MBW (P = 0.019). There was no association between wheezing and forced expiratory flow or blood gases.
Conclusion:
CORSA can help identify neonates and young infants with subclinical airway obstruction and may prove useful in the follow-up of high-risk infants. Pediatr Pulmonol. © 2015 Wiley Periodicals, Inc.
