2025/11/18 by Vera Lehmann, Martin Hilpert, Zohreh Mostaani +9 · 1 voice
Engineering · Medicine · Psychology · #ECG Monitoring and Analysis #Emotion and Mood Recognition #Intravenous Infusion Technology and Safety
paper · doi:10.2337/dc25-1680
openalex created_date 2025/11/18 · openalex publication_date 2025/11/18 · openalex updated_date 2026/07/28
OBJECTIVE: Hypoglycemia is a hazardous diabetes-related emergency. We aimed to develop a machine learning (ML) approach for noninvasive hypoglycemia detection using voice data. RESEARCH DESIGN AND METHODS: We collected voice data (540 recordings) with a smartphone in standardized euglycemia and hypoglycemia in two sequential clinical studies in people with type 1 diabetes. Using these data, we trained and evaluated an ML approach to detect hypoglycemia solely based on voice features. RESULTS: Twenty-two individuals were included (11 female, age 37.3 ± 12.4 years, HbA1c 7.1 ± 0.5%). The ML approach detected hypoglycemia noninvasively with high accuracy (area under the receiver operating characteristic curve 0.90 ± 0.12 for reading a text aloud and 0.87 ± 0.15 for rapid repetition of syllables [diadochokinetic task]). CONCLUSIONS: An ML approach exclusively based on voice data allows for noninvasive hypoglycemia detection, corroborating the potential of ML-based approaches to infer acute health states through voice.