Convolutional Dense Neural Network Based Spirometry Variable FVC Prediction Using Sustained Phonations

Yadav, S and Gope, D and Krishnaswamy, UM and Ghosh, PK (2021) Convolutional Dense Neural Network Based Spirometry Variable FVC Prediction Using Sustained Phonations. In: 31st IEEE International Workshop on Machine Learning for Signal Processing, 25-28 Oct 2021, Gold Coast.

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Abstract

Spirometry is a lung function test used to diagnose and monitor lung diseases like asthma, pneumonia, chronic obstructive pulmonary disease, etc. Spirometry measures forced vital capacity (FVC), forced expiratory volume in 1 sec (FEV1), and their ratio to determine lung health. Spirometry is very time-consuming, strenuous, and requires proper training. Alternate methods based on voice for diagnosis and monitoring of lung health are promising because they are faster, easy to do, and require minimal training. Non-speech sounds, namely, cough and wheeze, have been used to predict spirometry variables, but the role of speech sounds that occur in natural speaking for a similar task has not been explored. In this work, the spirometry variable, FVC has been predicted from sustained phonations of vowel sounds using a convolutional dense neural network (CDNN). Mel-spectrogram has been used as a feature. An experiment conducted using 160 subjects indicates, /i/ is the best sound and /u:/ is worst for the prediction task with an average Mean Absolute Error of 0.67l(±. 07l) and 0.70l(± 0.13l) among all sustained phonations of vowels sounds considered in this work. © 2021 IEEE.

Item Type:	Conference Paper
Publication:	IEEE International Workshop on Machine Learning for Signal Processing, MLSP
Publisher:	IEEE Computer Society
Additional Information:	The copyright for this article belongs to IEEE Computer Society
Keywords:	Biological organs; Diagnosis; Forecasting; Linguistics; Pulmonary diseases; Speech, Asthma; Capacity prediction; Convolutional dense neural network; Lung function; Network-based; Neural-networks; Speech sounds; Spirometry; Sustained phonation; Vowel sounds, Convolution
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering Division of Electrical Sciences > Electrical Engineering Division of Interdisciplinary Sciences > Centre for Biosystems Science and Engineering
Date Deposited:	01 Feb 2022 12:40
Last Modified:	01 Feb 2022 12:40
URI:	http://eprints.iisc.ac.in/id/eprint/71210

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