An Expert Diagnostic System to Automatically Identify Asthma and Chronic Obstructive Pulmonary Disease in Clinical Settings

Badnjević, Almir; Gurbeta, Lejla; Čustović, Edhem

doi:10.1038/s41598-018-30116-2

Cited by 116 publications

(27 citation statements)

References 36 publications

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“…Badnjevic et al combined fuzzy logic with ANNs, an approach they call a “neuro-fuzzy” system to obtain 99.41% classification accuracy for asthma patients and 99.19% classification accuracy for COPD patients, showing that a combination of methods can yield better accuracy [29]. They furthered the ANNFL logic into an expert diagnostic system (EDS), yielding a sensitivity of 96% and specificity of 98% in a prospective system [156]. Barua et al were also able to achieve a high classification accuracy using ANN, but recommended further development to extract expert rules to obtain a human understanding of the network’s knowledge.…”

Section: Signal Processingmentioning

confidence: 99%

Acoustic Methods for Pulmonary Diagnosis

Rao

Huynh

Royston

et al. 2019

IEEE Rev. Biomed. Eng.

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Recent developments in sensor technology and computational analysis methods enable new strategies to measure and interpret lung acoustic signals that originate internally, such as breathing or vocal sounds, or are externally introduced, such as in chest percussion or airway insonification. A better understanding of these sounds has resulted in new instrumentation that allows for highly accurate as well as portable options for measurement in the hospital, in the clinic, and even at home. This review outlines instrumentation for acoustic stimulation and measurement of the lungs. We first review the fundamentals of acoustic lung signals and the pathophysiology of the diseases that these signals are used to detect. Then, we focus on different methods of measuring and creating signals that have been used in recent research for pulmonary disease diagnosis. These new methods, combined with signal processing and modeling techniques, lead to a reduction in noise, and allow improved feature extraction and signal classification. We conclude by presenting the results of human subject studies taking advantage of both the instrumentation and signal processing tools to accurately diagnose common lung diseases. This paper emphasizes the active areas of research within modern lung acoustics, and encourages the standardization of future work in this field.

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Section: Signal Processingmentioning

confidence: 99%

Acoustic Methods for Pulmonary Diagnosis

Rao

Huynh

Royston

et al. 2019

IEEE Rev. Biomed. Eng.

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“…ANN-based models have recently been used as a robust technique to classify patients with asthma, chronic obstructive pulmonary disease, or normal lung function based on measurement of lung condition and symptoms [ 7 , 32 , 33 ].…”

Section: Discussionmentioning

confidence: 99%

Neural Network–Based Clinical Prediction System for Identifying the Clinical Effects of Saffron (Crocus sativus L) Supplement Therapy on Allergic Asthma: Model Evaluation Study

et al. 2020

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Background Asthma is commonly associated with chronic airway inflammation and is the underlying cause of over a million deaths each year. Crocus sativus L, commonly known as saffron, when used in the form of traditional medicines, has demonstrated anti-inflammatory effects which may be beneficial to individuals with asthma. Objective The objective of this study was to develop a clinical prediction system using an artificial neural network to detect the effects of C sativus L supplements on patients with allergic asthma. Methods A genetic algorithm–modified neural network predictor system was developed to detect the level of effectiveness of C sativus L using features extracted from the clinical, immunologic, hematologic, and demographic information of patients with asthma. The study included data from men (n=40) and women (n=40) individuals with mild or moderate allergic asthma from 18 to 65 years of age. The aim of the model was to estimate and predict the level of effect of C sativus L supplements on each asthma risk factor and to predict the level of alleviation in patients with asthma. A genetic algorithm was used to extract input features for the clinical prediction system to improve its predictive performance. Moreover, an optimization model was developed for the artificial neural network component that classifies the patients with asthma using C sativus L supplement therapy. Results The best overall performance of the clinical prediction system was an accuracy greater than 99% for training and testing data. The genetic algorithm–modified neural network predicted the level of effect with high accuracy for anti–heat shock protein (anti-HSP), high sensitivity C-reactive protein (hs-CRP), forced expiratory volume in the first second of expiration (FEV1), forced vital capacity (FVC), the ratio of FEV1/FVC, and forced expiratory flow (FEF25%-75%) for testing data (anti-HSP: 96.5%; hs-CRP: 98.9%; FEV1: 98.1%; FVC: 97.5%; FEV1/FVC ratio: 97%; and FEF25%-75%: 96.7%, respectively). Conclusions The clinical prediction system developed in this study was effective in predicting the effect of C sativus L supplements on patients with allergic asthma. This clinical prediction system may help clinicians to identify early on which clinical factors in asthma will improve over the course of treatment and, in doing so, help clinicians to develop effective treatment plans for patients with asthma.

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“…(a) For all weights w i initialize the step sizes ∆ (1) i = ∆ 0 , Repeat (b) For all weights w i compute the SA-DRPROP error gradient:…”

Section: The Learning Algorithmmentioning

confidence: 99%

“…Nowadays, the field of bioinformatics has gained a lot of research interest, with two of the most active hottest topics being the study of biomedical signals and the development of processing tools [1]. The analysis of lung sounds constitute an important issue in the area, and a series of identification and separation techniques regarding lung sounds have been proposed [2]- [3].…”

Section: Introductionmentioning

confidence: 99%

Neurofuzzy modelling of lung sounds

Kandilogiannakis¹,

Mastorocostas²,

Varsamis³

et al. 2018

ces

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In this paper a computational intelligence-based filter for real-time separation the adventitious discontinuous lung sounds from the vesicular sounds is proposed. The filter uses two Dynamic Fuzzy Neural Networks to perform the task of separation of the lung sounds, obtained from patients with pulmonary pathology. The networks are trained by the Simulated Annealing Dynamic Resilient Propagation algorithm and the resulting filter is applied to three major classes of lung sounds. In order to highlight the learning characteristics and the performance of the proposed separation scheme, extensive experimental analysis is conducted, where a comparison with other filters is given.

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An Expert Diagnostic System to Automatically Identify Asthma and Chronic Obstructive Pulmonary Disease in Clinical Settings

Cited by 116 publications

References 36 publications

Acoustic Methods for Pulmonary Diagnosis

Acoustic Methods for Pulmonary Diagnosis

Neural Network–Based Clinical Prediction System for Identifying the Clinical Effects of Saffron (Crocus sativus L) Supplement Therapy on Allergic Asthma: Model Evaluation Study

Neurofuzzy modelling of lung sounds

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