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Miyashita H, Nakamura M, Svensson AK, Nakamura M, Tokuno S, Chung UI, Svensson T. Association Between Electroencephalogram-Derived Sleep Measures and the Change of Emotional Status Analyzed Using Voice Patterns: Observational Pilot Study. JMIR Form Res 2020; 4:e16880. [PMID: 32515745 PMCID: PMC7312246 DOI: 10.2196/16880] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 04/01/2020] [Accepted: 04/02/2020] [Indexed: 11/20/2022] Open
Abstract
Background Measuring emotional status objectively is challenging, but voice pattern analysis has been reported to be useful in the study of emotion. Objective The purpose of this pilot study was to investigate the association between specific sleep measures and the change of emotional status based on voice patterns measured before and after nighttime sleep. Methods A total of 20 volunteers were recruited. Their objective sleep measures were obtained using a portable single-channel electroencephalogram system, and their emotional status was assessed using MIMOSYS, a smartphone app analyzing voice patterns. The study analyzed 73 sleep episodes from 18 participants for the association between the change of emotional status following nighttime sleep (Δvitality) and specific sleep measures. Results A significant association was identified between total sleep time and Δvitality (regression coefficient: 0.036, P=.008). A significant inverse association was also found between sleep onset latency and Δvitality (regression coefficient: –0.026, P=.001). There was no significant association between Δvitality and sleep efficiency or number of awakenings. Conclusions Total sleep time and sleep onset latency are significantly associated with Δvitality, which indicates a change of emotional status following nighttime sleep. This is the first study to report the association between the emotional status assessed using voice pattern and specific sleep measures.
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Affiliation(s)
- Hirotaka Miyashita
- Precision Health, Department of Bioengineering, Graduate School of Engineering, University of Tokyo, Tokyo, Japan.,Internal Medicine, Mount Sinai Beth Israel, New York, NY, United States
| | - Mitsuteru Nakamura
- Voice Analysis of Pathophysiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Akiko Kishi Svensson
- Precision Health, Department of Bioengineering, Graduate School of Engineering, University of Tokyo, Tokyo, Japan.,Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden.,Department of Diabetes and Metabolic Diseases, University of Tokyo, Tokyo, Japan
| | - Masahiro Nakamura
- Precision Health, Department of Bioengineering, Graduate School of Engineering, University of Tokyo, Tokyo, Japan
| | - Shinichi Tokuno
- Voice Analysis of Pathophysiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.,School of Health Innovation, Kanagawa University of Human Services, Kawasaki-ku, Kawasaki-shi, Kanagawa, Japan
| | - Ung-Il Chung
- Precision Health, Department of Bioengineering, Graduate School of Engineering, University of Tokyo, Tokyo, Japan.,School of Health Innovation, Kanagawa University of Human Services, Kawasaki-ku, Kawasaki-shi, Kanagawa, Japan.,Clinical Biotechnology, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Thomas Svensson
- Precision Health, Department of Bioengineering, Graduate School of Engineering, University of Tokyo, Tokyo, Japan.,Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden.,School of Health Innovation, Kanagawa University of Human Services, Kawasaki-ku, Kawasaki-shi, Kanagawa, Japan
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