A cutting-edge, non-invasive technique that can predict chronic high blood pressure (hypertension) with a high degree of accuracy using just a person’s voice has been unveiled by researchers.
The findings hold huge potential for advancing early detection of chronic high blood pressure and provides a novel way to harness vocal biomarkers for better health outcomes.
The study’s 245 participants were asked to record their voices up to six times daily for two weeks by speaking into a proprietary mobile app, developed by scientists at Klick Labs, which detected high blood pressure with accuracies up to 84 per cent for females and 77 per cent for males.
The app uses machine learning to analyse hundreds of vocal biomarkers that are indiscernible to the human ear, including the variability in pitch (fundamental frequency), the patterns in speech energy distribution (Mel-frequency cepstral coefficients), and the sharpness of sound changes (spectral contrast).
“By leveraging various classifiers and establishing gender-based predictive models, we discovered a more accessible way to detect hypertension, which we hope will lead to earlier intervention for this widespread global health issue. Hypertension can lead to a number of complications, from heart attacks and kidney problems to dementia,” said Yan Fossat, senior vice president of Klick Labs and principal investigator of the study.
The “Silent Killer”
The World Health Organization (WHO) refers to hypertension as the ‘‘silent killer,’’ as well as a global public health concern that affects over 25 percent of the global population. Half are unaware of their condition, and more than 75 per cent of those diagnosed live in low- or middle-income countries.
Conventional methods of measuring blood pressure (and, accordingly, identifying hypertension) include using an arm cuff (sphygmomanometry) or an automatic blood pressure measurement device.
However, these methods may require technical expertise, specialised equipment, and may not be readily accessible to people in underserved areas.
This study marks Klick Labs’ first venture into using voice technology to identify conditions beyond diabetes, as the company expands its research to assess its AI algorithms’ effectiveness in detecting and managing a broader range of health conditions.
Klick Labs has been collaborating with hospitals, academic institutions, and public health authorities worldwide since its research revealed that voice analysis combined with AI can accurately screen for Type 2 diabetes.
“Voice technology has the potential to exponentially transform healthcare, making it more accessible and affordable, especially for large, underserved populations,” said Jaycee Kaufman, Klick Labs research scientist and co-author of the study.
“Our ongoing research increasingly demonstrates the significant promise of vocal biomarkers in detecting hypertension, diabetes, and a growing list of other health conditions.”
