SpeechSpiro: Lung Function Assessment from Speech Pattern as an Alternative to Spirometry for Mobile Health Tracking


Abstract—Respiratory illnesses are common in the United States and globally which people deal with in various forms, such as asthma, chronic obstructive pulmonary diseases or infectious respiratory diseases (e.g. from coronavirus). Lung function of the subjects affected by these illnesses is compromised due to infection and/or inflammation in their respiratory airways. There are clinically-validated tests to assess lung function using in-clinic medical equipment, and quite recently, via portable spirometry devices. Research has shown that the obstruction and restriction in the respiratory airways affect individuals’ voice characteristics, where the audio features could be analyzed to predict the lung function and severity of the obstruction. In this paper, we go beyond well-known voice audio features and create a hybrid deep learning model using CNN-LSTM to discover spatiotemporal patterns in speech and predict the lung function parameters with accuracy comparable to conventional devices. We validate the performance and generalizability of our method using the data collected from 200 subjects enrolled in two studies internally and in collaboration with a pulmonary hospital. SpeechSpiro measures lung function parameters (e.g. FEV1, FVC, FEV1/FVC) with mean RMSE of 12% and R2 of up to 76% using 60-second phone audio recording of individuals reading a passage.

Clinical relevance — Speech-based spirometry (SpeechSpiro) eliminates the need for an additional device and carries out the lung function assessment outside the clinical settings using a smartphone; hence, enabling continuous mobile health tracking for the individuals, healthy or with a respiratory illness.

Author: Korosh Vatanparvar, Viswam Nathan, Ebrahim Nematihosseinabadi, Mahbubur Rahman, Daniel McCaffrey, Jilong Kuang, Alex Gao

Published: Engineering in Medicine and Biology Conference (EMBC)

Date: Oct 31, 2021