Conference PaperPDF Available

Hilbert-Huang Dönüşümü ve Derin Öğrenme Kullanarak Akciğer Seslerinde Astım Teşhisi (The Diagnosis of Asthma using Hilbert-Huang Transform and Deep Learning on Lung Sounds)

October 2017

Conference: Akıllı Sistemlerde Yenilikler ve Uygulamalar (ASYU2017)
At: Alanya, Antalya, Turkey

Authors:

Iskenderun Technical University

Iskenderun Technical University, Hatay, Turkey

Lung auscultation is the most effective and indispensable method for diagnosing various respiratory disorders by using the sounds from the airways during inspirium and exhalation using stethoscope. In this study, the statistical features are calculated from intrinsic mode functions that are extracted by applying the Hilbert-Huang Transform to the lung sounds from 12 different auscultation regions on the chest and back. The classification of the lung sounds from asthma and healthy subjects is performed using Deep Belief Networks (DBN). The DBN classifier model with two hidden layers has been tested using 5-fold cross validation method. The proposed DBN separated lung sounds from asthmatic and healthy subjects with high classification performance rates of 84.61%, 85.83%, and 77.11% for overall accuracy, sensitivity, and selectivity, respectively using frequency-time analysis.

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Project

Feature Extraction Methods

Gokhan Altan

-Extracting features for EEG, ECG, Lung Sounds and other non-stationary signals. -Deep Learning Encoder -Feature reduction on Deep Learning

Project

Design of Fuzzy Expert Systems

Project

Detailed Power Spectrum Analysis to Distinguish Motor and Executed EEG Signals

Main goal is detecting subject-specific EEG bands that are related to imagery.

Project

Respiratory Database (RespiratoryDatabase@TR)

Article

Full-text available

The Diagnosis of Asthma using Hilbert-Huang Transform and Deep Learning on Lung Sounds

October 2018

Lung auscultation is the most effective and indispensable method for diagnosing various respiratory disorders by using the sounds from the airways during inspirium and exhalation using a stethoscope. In this study, the statistical features are calculated from intrinsic mode functions that are extracted by applying the HilbertHuang Transform to the lung sounds from 12 different auscultation ... [Show full abstract] regions on the chest and back. The classification of the lung sounds from asthma and healthy subjects is performed using Deep Belief Networks (DBN). The DBN classifier model with two hidden layers has been tested using 5-fold cross validation method. The proposed DBN separated lung sounds from asthmatic and healthy subjects with high classification performance rates of 84.61%, 85.83%, and 77.11% for overall accuracy, sensitivity, and selectivity, respectively using frequencytime analysis

Preprint

Full-text available

The Diagnosis of Asthma using Hilbert-Huang Transform and Deep Learning on Lung Sounds

January 2021

Lung auscultation is the most effective and indispensable method for diagnosing various respiratory disorders by using the sounds from the airways during inspirium and exhalation using a stethoscope. In this study, the statistical features are calculated from intrinsic mode functions that are extracted by applying the HilbertHuang Transform to the lung sounds from 12 different auscultation ... [Show full abstract] regions on the chest and back. The classification of the lung sounds from asthma and healthy subjects is performed using Deep Belief Networks (DBN). The DBN classifier model with two hidden layers has been tested using 5-fold cross validation method. The proposed DBN separated lung sounds from asthmatic and healthy subjects with high classification performance rates of 84.61%, 85.83%, and 77.11% for overall accuracy, sensitivity, and selectivity, respectively using frequencytime analysis.

Article

Full-text available

The Diagnosis of Asthma using Hilbert-Huang Transform and Deep Learning on Lung Sounds

December 2019

Lung auscultation is the most effective and indispensable method for diagnosing various respiratory disorders by using the sounds from the airways during inspirium and exhalation using a stethoscope. In this study, the statistical features are calculated from intrinsic mode functions that are extracted by applying the Hilbert-Huang Transform to the lung sounds from 12 different auscultation ... [Show full abstract] regions on the chest and back. The classification of the lung sounds from asthma and healthy subjects is performed using Deep Belief Networks (DBN). The DBN classifier model with two hidden layers has been tested using 5-fold cross validation method. The proposed DBN separated lung sounds from asthmatic and healthy subjects with high classification performance rates of 84.61%, 85.83%, and 77.11% for overall accuracy, sensitivity, and selectivity, respectively using frequencytime analysis.

Article

Deep learning with 3D-second order difference plot on respiratory sounds

August 2018 · Biomedical Signal Processing and Control

The second order difference plot (SODP) is a nonlinear signal analysis method that visualizes two consecutive data points for many types of biomedical signals. The proposed method is based on analysing quantization of 3D-space which is originated using three consecutive data points in signal. The obtained 3D-SODP space was segmented into 3-10 spaces using octants, spheres and cuboid polyhedrons ... [Show full abstract] of which centroids are at the origin. Lung sound is an indispensable tool for respiratory and cardiac diseases. The study is focused on classifying the lung sounds from at risk level and the interior level of chronic obstructive pulmonary disease (COPD). The COPD is one of the most deadliest and common respiratory diseases which come into existence as a consequence of smoking. The smokers for a few years are qualified as at risk level of COPD (COPD-0). The 12 channels of lung sounds from the RespiratoryDatabase@TR were utilized in the analysis of the proposed 3D-SODP quantization method. The lung sounds are auscultated synchronously from posterior and anterior sides of subjects using two digital stethoscopes by a pulmo-nologist clinician in Antakya State Hospital, Turkey. Deep Belief Networks (DBN) algorithm was preferred in the classification stage. It has a greedy layer-wise pre-training which is based on restricted Boltzmann machines and optimizes the pre-trained weights using supervised iterations. The proposed DBN model had 2 hidden layers with 270 and 580 neurons, respectively. The conjunction usage of 3D-SODP quantiza-tion features with the DBN separated the lung sounds from different levels of COPD with high classification performance rates of 95.84%, 93.34% and 93.65% for accuracy, sensitivity and specificity, respectively. The results indicate that the 3D-SODP quantization on respiratory sounds has ability to diagnose the levels of the COPD using the deep learning model. Especially, the octant-based quantization is effective on lung sounds with high generalization capability using a small number of feature set dimension.