Honghuang Lin1,2, Cody Karjadi2,3, Ting F A Ang2,3,4,5, Joshi Prajakta2,3, Chelsea McManus2,3, Tuka W Alhanai6, James Glass7, Rhoda Au2,3,4,5,8. 1. Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA. 2. The Framingham Heart Study, Boston University School of Medicine, Boston, MA 02118, USA. 3. Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA 02118, USA. 4. Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA. 5. Slone Epidemiology Center, Boston University School of Medicine, Boston, MA 02118, USA. 6. Department of Electrical and Computer Engineering, New York University Abu Dhabi, Abu Dhabi, UAE. 7. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. 8. Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA.
Abstract
AIM: Human voice contains rich information. Few longitudinal studies have been conducted to investigate the potential of voice to monitor cognitive health. The objective of this study is to identify voice biomarkers that are predictive of future dementia. METHODS: Participants were recruited from the Framingham Heart Study. The vocal responses to neuropsychological tests were recorded, which were then diarized to identify participant voice segments. Acoustic features were extracted with the OpenSMILE toolkit (v2.1). The association of each acoustic feature with incident dementia was assessed by Cox proportional hazards models. RESULTS: Our study included 6, 528 voice recordings from 4, 849 participants (mean age 63 ± 15 years old, 54.6% women). The majority of participants (71.2%) had one voice recording, 23.9% had two voice recordings, and the remaining participants (4.9%) had three or more voice recordings. Although all asymptomatic at the time of examination, participants who developed dementia tended to have shorter segments than those who were dementia free (P < 0.001). Additionally, 14 acoustic features were significantly associated with dementia after adjusting for multiple testing (P < 0.05/48 = 1 × 10-3). The most significant acoustic feature was jitterDDP_sma_de (P = 7.9 × 10-7), which represents the differential frame-to-frame Jitter. A voice based linear classifier was also built that was capable of predicting incident dementia with area under curve of 0.812. CONCLUSIONS: Multiple acoustic and linguistic features are identified that are associated with incident dementia among asymptomatic participants, which could be used to build better prediction models for passive cognitive health monitoring.
AIM: Human voice contains rich information. Few longitudinal studies have been conducted to investigate the potential of voice to monitor cognitive health. The objective of this study is to identify voice biomarkers that are predictive of future dementia. METHODS: Participants were recruited from the Framingham Heart Study. The vocal responses to neuropsychological tests were recorded, which were then diarized to identify participant voice segments. Acoustic features were extracted with the OpenSMILE toolkit (v2.1). The association of each acoustic feature with incident dementia was assessed by Cox proportional hazards models. RESULTS: Our study included 6, 528 voice recordings from 4, 849 participants (mean age 63 ± 15 years old, 54.6% women). The majority of participants (71.2%) had one voice recording, 23.9% had two voice recordings, and the remaining participants (4.9%) had three or more voice recordings. Although all asymptomatic at the time of examination, participants who developed dementia tended to have shorter segments than those who were dementia free (P < 0.001). Additionally, 14 acoustic features were significantly associated with dementia after adjusting for multiple testing (P < 0.05/48 = 1 × 10-3). The most significant acoustic feature was jitterDDP_sma_de (P = 7.9 × 10-7), which represents the differential frame-to-frame Jitter. A voice based linear classifier was also built that was capable of predicting incident dementia with area under curve of 0.812. CONCLUSIONS: Multiple acoustic and linguistic features are identified that are associated with incident dementia among asymptomatic participants, which could be used to build better prediction models for passive cognitive health monitoring.
Entities:
Keywords:
Digital voice; acoustic features; dementia; epidemiology; prediction
Authors: Juan Rafael Orozco-Arroyave; Elkyn Alexander Belalcazar-Bolaños; Julián David Arias-Londoño; Jesús Francisco Vargas-Bonilla; Sabine Skodda; Jan Rusz; Khaled Daqrouq; Florian Hönig; Elmar Nöth Journal: IEEE J Biomed Health Inform Date: 2015-08-12 Impact factor: 5.772
Authors: J Kaye; P Aisen; R Amariglio; R Au; C Ballard; M Carrillo; H Fillit; T Iwatsubo; G Jimenez-Maggiora; S Lovestone; F Natanegara; K Papp; M E Soto; M Weiner; B Vellas Journal: J Prev Alzheimers Dis Date: 2021
Authors: Larry Zhang; Anthony Ngo; Jason A Thomas; Hannah A Burkhardt; Carolyn M Parsey; Rhoda Au; Reza Hosseini Ghomi Journal: Explor Med Date: 2021-06-30