Computer-aided diagnosis of pneumonia in patients with chronic obstructive pulmonary disease

Morillo, Daniel; Jiménez, Antonio León; Moreno, Sonia Astorga

doi:10.1136/amiajnl-2012-001171

Cited by 55 publications

(18 citation statements)

References 49 publications

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“…Therefore, a resulting subset of 26 parameters features was obtained. This features subset have been previously used by the authors of this study in the analysis of respiratory sounds in COPD patients [ 29 , 30 ].…”

Section: Methodsmentioning

confidence: 99%

“…Abnormal respiratory sounds like wheezes and rhonchi are a manifestation of the latter conditions [ 27 ] and appear as key symptoms related to the pathophysiology of exacerbations of COPD. Consequently, changes in respiratory sounds are a clinical sign commonly reported during exacerbation episodes and have been analysed in scientific literature in different contexts [ 28 , 29 , 30 ]. In addition, the results of a very recent review suggest that adventitious respiratory sounds are mainly characterised by inspiratory and coarse crackles and expiratory wheezes in patients with COPD [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computerised Analysis of Telemonitored Respiratory Sounds for Predicting Acute Exacerbations of COPD

Fernández-Granero

Morillo

León‐Jiménez

2015

Sensors

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Chronic obstructive pulmonary disease (COPD) is one of the commonest causes of death in the world and poses a substantial burden on healthcare systems and patients’ quality of life. The largest component of the related healthcare costs is attributable to admissions due to acute exacerbation (AECOPD). The evidence that might support the effectiveness of the telemonitoring interventions in COPD is limited partially due to the lack of useful predictors for the early detection of AECOPD. Electronic stethoscopes and computerised analyses of respiratory sounds (CARS) techniques provide an opportunity for substantial improvement in the management of respiratory diseases. This exploratory study aimed to evaluate the feasibility of using: (a) a respiratory sensor embedded in a self-tailored housing for ageing users; (b) a telehealth framework; (c) CARS and (d) machine learning techniques for the remote early detection of the AECOPD. In a 6-month pilot study, 16 patients with COPD were equipped with a home base-station and a sensor to daily record their respiratory sounds. Principal component analysis (PCA) and a support vector machine (SVM) classifier was designed to predict AECOPD. 75.8% exacerbations were early detected with an average of 5 ± 1.9 days in advance at medical attention. The proposed method could provide support to patients, physicians and healthcare systems.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computerised Analysis of Telemonitored Respiratory Sounds for Predicting Acute Exacerbations of COPD

Fernández-Granero

Morillo

León‐Jiménez

2015

Sensors

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“…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 short-time Fourier transform and machine intelligence [ 37 ]. Abnormal respiration sounds have been detected by tracking instantaneous frequencies, which can be similarly obtained by previous approaches, and envelop, which can be obtained from ensemble empirical mode decomposition [ 38 ].…”

Section: Data Collection Devices For Bioinformaticsmentioning

confidence: 99%

Data assimilation and multisource decision-making in systems biology based on unobtrusive Internet-of-Things devices

Tang

Chen³

2018

BioMed Eng OnLine

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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.

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“…Furthermore, the device used in that study is not compact, incorporating 40 sensors and a low-suction vacuum unit, components that limit its portability and increase its cost. Another study of a more compact device, which recorded breath sounds with a single microphone, reported a sensitivity of 72% and specificity of 82% for pneumonia diagnosis, with a classification accuracy by a neural network classifier of 77.6% [ 14 ]. In addition to breath sounds, speech can also be analyzed to detect pneumonia.…”

Section: Introductionmentioning

confidence: 99%

Tabla: A Proof-of-Concept Auscultatory Percussion Device for Low-Cost Pneumonia Detection

Rao

Ruiz

Bao

et al. 2018

Sensors

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Pneumonia causes the deaths of over a million people worldwide each year, with most occurring in countries with limited access to expensive but effective diagnostic methods, e.g., chest X-rays. Physical examination, the other major established method of diagnosis, suffers from several drawbacks, most notably low accuracy and high interobserver error. We sought to address this diagnostic gap by developing a proof-of-concept non-invasive device to identify the accumulation of fluid in the lungs (consolidation) characteristic of pneumonia. This device, named Tabla after the percussive instrument of the same name, utilizes the technique of auscultatory percussion; a percussive input sound is sent through the chest and recorded with a digital stethoscope for analysis. Tabla analyzes differences in sound transmission through the chest at audible frequencies as a marker for lung consolidation. This paper presents preliminary data from five pneumonia patients and eight healthy subjects. We demonstrate 92.3% accuracy in distinguishing between healthy subjects and patients with pneumonia after data analysis with a K-nearest neighbors algorithm. This prototype device is low cost and simple to implement and may offer a rapid and inexpensive method for pneumonia diagnosis appropriate for general use and in areas with limited medical infrastructure.

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Computer-aided diagnosis of pneumonia in patients with chronic obstructive pulmonary disease

Abstract: A simple, cost-effective method has been proposed to aid decision-making in areas with no radiological facilities available and in resource-constrained settings, and could have a great diagnostic impact on telemedicine applications.

Cited by 55 publications

References 49 publications

Computerised Analysis of Telemonitored Respiratory Sounds for Predicting Acute Exacerbations of COPD

Computerised Analysis of Telemonitored Respiratory Sounds for Predicting Acute Exacerbations of COPD

Data assimilation and multisource decision-making in systems biology based on unobtrusive Internet-of-Things devices

Tabla: A Proof-of-Concept Auscultatory Percussion Device for Low-Cost Pneumonia Detection

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