Deep Multivariate Domain Translation for Device Invariant Pulmonary Patient Identification from Cough and Speech Sounds.

Abstract

Pulmonary audio sensing from cough and speech sounds in commodity mobile and wearable devices is increasingly used for remote pulmonary patient monitoring, home healthcare, and automated disease analysis. Patient identification is important for such applications to ensure system accuracy and integrity, and thus avoiding errors and misdiagnosis. Widespread usage and deployment of such patient identification models across various devices are challenging due to domain shift of acoustic features because of device heterogeneity. Because of this phenomenon, a patient identification model developed using audio data collected with one type of device is not usable when deployed in another type of device, which is a concern for model portability and general usability. This paper presents a framework utilizing a multivariate deep neural network regressor as a feature translator between source device and target device domains to reduce the effect of domain shift for better model portability. Extensive and empirical experiments of our translation framework consisting of two different human sound (speech and cough) based pulmonary patient identification tasks using audio data collected from 91 real patients demonstrate that it can recover up to 64.8% of lost accuracy due to domain shift across two common and widely used mobile and wearable devices: smartphone and smartwatch. Clinical Relevance- The methods presented in this paper will enable efficient and easy portability of pulmonary patient identification models from cough and speech across various mobile and wearable devices used by a patient.