Artificial intelligence identifies worsening heart failure by voice analysis: a pilot study

TL;DR

This study shows that analyzing a person's voice with AI can help track heart failure, potentially allowing remote monitoring at home.

Contribution

The study introduces a reliable set of voice tasks and acoustic features for detecting worsening heart failure using mobile phone recordings.

Findings

Four speech tasks and specific acoustic features were identified as effective for monitoring heart failure.

Mobile phone recordings proved reliable across different brands for capturing relevant voice biomarkers.

Machine learning models achieved high accuracy in distinguishing heart failure patient statuses.

Abstract

Effective out-of-hospital monitoring is essential for reducing heart failure (HF) readmission rates. Emerging evidence suggests that voice biomarkers may serve as non-invasive indicators for HF monitoring. The objectives of this study were to (1) identify HF-specific speech tasks and acoustic features; and (2) validate the reliability of mobile phone recordings in HF monitoring. Recordings were collected in three phases from 160 HF patients. Phase I focused on simplifying speech tasks based on feasibility and correlation with NT-proBNP levels, with recordings taken on the same day as NT-proBNP testing. Phase II aimed to assess the cross-device stability of acoustic features through simultaneous recordings using both professional equipment and smartphones. Phase III sought to develop machine learning models to differentiate patient statuses at admission, mid-hospitalization, and discharge, and identify speech tasks and acoustic features most relevant to HF. Out of 11 tasks, 4 were retained for the final model. The accuracy, sensitivity, specificity, and area under the curve for distinguishing admission and discharge were 0.82, 0.85, 0.78, and 0.85, respectively; for mid-hospitalization and discharge, these were 0.82, 0.79, 0.85, and 0.88. Key features included Mel-frequency cepstral coefficients, chroma, fundamental frequency, frequency-domain, energy, and glottal features. No significant acoustic differences were observed between mobile phone brands. This study established a feasible, reliable set of speech tasks, identified HF-specific acoustic features, and validated mobile phone recordings for HF monitoring, supporting the potential for remote, at-home HF patient monitoring.