Microphone based Smartphone enabled Spirometry Data Augmentation using Information Maximizing Generative Adversarial Network

Spirometry is gaining popularity in the primary care and point-of-care scenarios in the medical community for constant monitoring of lung condition and early diagnosis of chronic lung ailments. Accurate classification of spirometry data is necessary to diagnose lung ailments properly. Deep learning-based classification models, free from manual feature extraction, are quite accurate. One of the major challenges of deep learning-based classification approach in spirometry is its small size of the datasets. In this paper, a user-friendly smartphone application that performs spirometry using built-in mobile microphone has been developed and an information maximizing generative adversarial network (InfoGAN) model has been proposed that can generate spirometry signal to augment the dataset and enhance the performance of the deep learning-based classification models.

[1]  Miad Faezipour,et al.  Lung capacity estimation through acoustic signal of breath , 2012, 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE).

[2]  Khashayar Khorasani,et al.  Deep Convolutional Neural Networks and Learning ECG Features for Screening Paroxysmal Atrial Fibrillation Patients , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[3]  Tonio Ball,et al.  EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals , 2018, ArXiv.

[4]  Eric C. Larson,et al.  SpiroSmart: using a microphone to measure lung function on a mobile phone , 2012, UbiComp.

[5]  T. Seemungal,et al.  Time course and recovery of exacerbations in patients with chronic obstructive pulmonary disease. , 2000, American journal of respiratory and critical care medicine.

[6]  Shahrokh Valaee,et al.  Generalization of Deep Neural Networks for Chest Pathology Classification in X-Rays Using Generative Adversarial Networks , 2017, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  Seiichi Uchida,et al.  Biosignal Data Augmentation Based on Generative Adversarial Networks , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[8]  Ugur Halici,et al.  A novel deep learning approach for classification of EEG motor imagery signals , 2017, Journal of neural engineering.

[9]  Juan Manuel Ramírez-Cortés,et al.  Digital Spirometer with LabView Interface , 2008, 18th International Conference on Electronics, Communications and Computers (conielecomp 2008).

[10]  Lakshminarayanan Nandakumar,et al.  A novel algorithm for spirometric signal processing and classification by evolutionary approach and its implementation on an ARM embedded platform , 2013, 2013 International Conference on Control Communication and Computing (ICCC).

[11]  Mario Cifrek,et al.  Classification of asthma using artificial neural network , 2016, 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[12]  Ramon Gisbert,et al.  Pharmacoeconomic evaluation of acute exacerbations of chronic bronchitis and COPD. , 2002, Chest.

[13]  Saifuddin Hitawala,et al.  Comparative Study on Generative Adversarial Networks , 2018, ArXiv.

[14]  Kostas Stamatis,et al.  Development of a smartphone-enabled spirometer for personalised respiratory health , 2014, 2014 4th International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH).

[15]  Germain Forestier,et al.  Deep Neural Network Ensembles for Time Series Classification , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

[16]  P. Brand,et al.  Home spirometry and asthma severity in children , 2006, European Respiratory Journal.

[17]  Gretchen A. Piatt,et al.  Patients with Complex Chronic Diseases: Perspectives on Supporting Self-Management , 2007, Journal of General Internal Medicine.

[18]  Ali Hakan Isik,et al.  Feature selection in pulmonary function test data with machine learning methods , 2013, 2013 21st Signal Processing and Communications Applications Conference (SIU).

[19]  Martyn R Partridge,et al.  Patient understanding, detection, and experience of COPD exacerbations: an observational, interview-based study. , 2006, Chest.

[20]  Xiao Liu,et al.  mCOPD: mobile phone based lung function diagnosis and exercise system for COPD , 2013, PETRA '13.

[21]  J. Scott,et al.  The use of home spirometry in detecting acute lung rejection and infection following heart-lung transplantation. , 1990, Chest.

[22]  Yun Luo,et al.  EEG Data Augmentation for Emotion Recognition Using a Conditional Wasserstein GAN , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[23]  Hasan Mir,et al.  MobSpiro: Mobile based spirometry for detecting COPD , 2017, 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC).

[24]  Assim Sagahyroon,et al.  Diagnosing COPD Using Mobile Phones , 2015 .

[25]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[26]  R. Puers,et al.  A Differential Pressure Approach to Spirometry , 2007, 2007 IEEE Biomedical Circuits and Systems Conference.

[27]  Manisha R. Mhetre,et al.  Spirometric data analysis by support vector machine , 2012, 2012 1st International Symposium on Physics and Technology of Sensors (ISPTS-1).

[28]  P Zabel,et al.  [Chronic obstructive pulmonary disease (COPD)]. , 2006, Der Internist.

[29]  Ki H. Chon,et al.  Estimation of Respiratory Rates Using the Built-in Microphone of a Smartphone or Headset , 2016, IEEE Journal of Biomedical and Health Informatics.

[30]  Pieter Abbeel,et al.  InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.

[31]  A. Kassem,et al.  A smart spirometry device for asthma diagnosis , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).