• Journal of Biomedical Engineering Research
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• v.15 no.2
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• pp.159-166
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• 1994

A new pattern classification algorithm using cepstrum to analyze lung sounds for the classification of pattern with pulmonary and bronchial disorders is proposed. To evaluate the perfomance of the proposed method, the results are compared to the pattern classification with the AR modeling method. In the experiment lung sounds recorded for the training of physician used. As a results, the accuracy of the cepstrum classification is 92.3 % and AR modeling is the 53.8 %, therefore cepstrum modeling method has very high performance than AR and it turned out to be a very efficient algorithm.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.568-573
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• 2019

In the diagnosis of pulmonary diseases, auscultation technique is simpler than the other methods, and lung sounds can be used for predicting the types of pulmonary diseases as well as identifying patients with pulmonary diseases. Therefore, in this paper, we identify patients with pulmonary diseases and classify lung sounds according to their sound characteristics using various convolutional neural networks, and compare the classification performance of each neural network method. First, lung sounds over affected areas of the chest with pulmonary diseases are collected by using a single-channel lung sound recording device, and spectral features are extracted from the collected sounds in time domain and applied to each neural network. As classification methods, we use general, parallel, and residual convolutional neural network, and compare lung sound classification performance of each neural network through experiments.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1068-1081
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• 2019

Signal complexity is one point of view to analyze the biological signal. It arises as a result of the physiological signal produced by biological systems. Signal complexity can be used as a method in extracting the feature for a biological signal to differentiate a pathological signal from a normal signal. In this research, Hjorth descriptors, one of the signal complexity measurement techniques, were measured on signal sub-band as the features for lung sounds classification. Lung sound signal was decomposed using two wavelet analyses: discrete wavelet transform (DWT) and wavelet packet decomposition (WPD). Meanwhile, multi-layer perceptron and N-fold cross-validation were used in the classification stage. Using DWT, the highest accuracy was obtained at 97.98%, while using WPD, the highest one was found at 98.99%. This result was found better than the multi-scale Hjorth descriptor as in previous studies.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.2
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• pp.835-838
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• 2016

Background: Lung cancer remains one of the most common cancers in the world, both in terms of new cases (about 13% of total per year) and deaths (nearly one cancer death in five), because of the high case fatality. Errors in lung cancer type or malignant growth determination lead to degraded treatment efficacy, because anticancer strategy depends on tumor morphology. Materials and Methods: We have made an attempt to evaluate effectiveness of machine learning algorithms in the task of lung cancer classification based on gene expression levels. We processed four publicly available data sets. The Dana-Farber Cancer Institute data set contains 203 samples and the task was to classify four cancer types and sound tissue samples. With the University of Michigan data set of 96 samples, the task was to execute a binary classification of adenocarcinoma and non-neoplastic tissues. The University of Toronto data set contains 39 samples and the task was to detect recurrence, while with the Brigham and Women's Hospital data set of 181 samples it was to make a binary classification of malignant pleural mesothelioma and adenocarcinoma. We used the k-nearest neighbor algorithm (k=1, k=5, k=10), naive Bayes classifier with assumption of both a normal distribution of attributes and a distribution through histograms, support vector machine and C4.5 decision tree. Effectiveness of machine learning algorithms was evaluated with the Matthews correlation coefficient. Results: The support vector machine method showed best results among data sets from the Dana-Farber Cancer Institute and Brigham and Women's Hospital. All algorithms with the exception of the C4.5 decision tree showed maximum potential effectiveness in the University of Michigan data set. However, the C4.5 decision tree showed best results for the University of Toronto data set. Conclusions: Machine learning algorithms can be used for lung cancer morphology classification and similar tasks based on gene expression level evaluation.

• Journal of Korean Academy of Nursing
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• v.23 no.4
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• pp.569-584
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• 1993

This study was done to verify validity of nursing diagnoses related to difficulty in respiratory function. First, content validity was examined by an expert group considering the etiology and the signs / symptoms of three nursing diagnoses - ineffective airway clearance, ineffective breathing pattern, impaired gas exchange. Second, clinical validity was examined by comparing the frequencies of the etiologies and signs / symptoms of the three nursing diagnoses in clinical case studies with the results of the content validity. This study was a descriptive study. The sample consisted of 23 experts (professors, head nurses and clinical instructors) who had had a variety of experiences using nursing diagnoses in clinical practice, and 102 case reports done by senior student nurses of the college of nursing of Y-university. These reports were part of their clinical practice in the ICU. The instrument used for this study was a checklist for etiologies and signs and symptoms based on the literature, Doenges and Moorhouse (1988), Kim, McFarland, McLane (1991), Lee Won Hee et al. (1987), Kim Cho Ja et at. (1988). The data was collected over four month period from May 1992 to Aug. 1992. Data were analyzed using frequencies done with the SPSS / PC＋ package. The results of this study are summarized as follows : 1. General Characteristics of the Expert Group A bachelor degree was held by 43.5% and a master or doctoral degree by 56.5% of the expert group. The average age of the expert group was 35.3 years. Their average clinical experience was 9.3 years and their average experience in clinical practice was 5.9 years. The general characteristics of the patients showed that there were more women than men, that the age range was from 1 to over 80. Most of their medical diagnoses were diagnoses related to the respiratory. system, circulation or neurologic system, and 50% or more of them had a ventilator with intubation or a tracheostomy. The number of cases for each nursing diagnoses was : · Ineffective airway clearance, 92 cases. · Ineffective breathing pattern, 18 cases. · Impaired gas exchange, 22 cases. 2. The opinion of the expert group as to the classification of the etiology, and signs and symptoms of the three nursing diagnoses was as follows : · In 31.8% of the cases the classification of etiology was clear. · In 22.7%, the classification of signs and symptoms was clear. · In 17.4%, the classification of nursing interventions was clear. 3. In the expert group 80% or mere agreed to ‘dysp-nea’as a common sign and symptom of the three nursing diagnoses. The distinguishing signs and symptoms of (Ineffective airway clearance) were ‘sputum’, ‘cough’, ‘abnormal respiratory sounds : rales’. The distinguishing sings and symptoms of (Ineffective breathing pattern) were ‘tachypnea’, ‘use of accessory muscle of respiration’, ‘orthopnea’ and for (Impaired gas exchange) it was ‘abnormal arterial blood gas’, 4. The distribution of etiology, and signs and symptoms of the three nursing diagnoses was as follows : · There was a high frequency of ‘increased secretion from the bronchus and trachea’ in both the expert group and the case reports as the etiology of ineffective airway clearance. · For the etiologies for ineffective breathing pat-tern, ‘rain’, ‘anxiety’, ‘fear’, ‘obstructions of the tract, ca and bronchus’ had a high ratio in the ex-pert group and ‘decreased expansion of lung’ in the case reports. · For the etiologies for impaired gas exchanges, ‘altered oxygen -carrying capacity of the blood’ and ‘excess accumulation of interstitial fluid in lung’ had a high ratio in the expert group and ‘altered oxygen supply’ in the case reports. · For signs and symptoms for ineffective airway clearance, ‘dyspnea’, ‘altered amount and character of sputum’ were included by 100% of the expert group. ‘Abnormal respiratory. sound(rate, rhonchi)’ were included by a high ratio of the expert group. · For the signs and symptoms for ineffective breathing pattern. ‘dyspnea’, ‘shortness of breath’ were included by 100% of the expert group. In the case reports, ‘dyspnea’ and ‘tachypnea’ were reported as signs and symptoms. · For the sign and symptoms for impaired gas exchange, ‘hypoxia’ and ‘cyanosis’ had a high ratio in the expert group. In the case report, ‘hypercapnia’, ‘hypoxia’ and ‘inability to remove secretions’ were reported as signs and symptoms. In summary, the similarity of the etiologies and signs and symptoms of the three nursing diagnoses related to difficulty in respiratory function makes it difficult to distinguish among them But the clinical validity of three nursing diagnoses was established through this study, and at last one sign and symp-tom was defined for each diagnosis.