Hsu, C.M.C.M.HsuLian, F.L.F.L.LianHuang, C.M.C.M.HuangChou, J.H.J.H.ChouFENG-LI LIAN2018-09-102018-09-102014http://www.scopus.com/inward/record.url?eid=2-s2.0-84904430347&partnerID=MN8TOARShttp://scholars.lib.ntu.edu.tw/handle/123456789/387062Many studies show that there are a lot of traffic accidents due to drowsiness while driving. Drowsiness is a complex psychophysiology phenomenon whose mechanism has not been explicitly explored. A variety of psychophysiology parameters have been used in previous researches as indicators of drowsiness. In general, the analysis of heart rate variability (HRV) signals is a major approach for detecting driver drowsiness. That approach can analyse the autonomic nervous system, which allows the evaluation of the balance between the sympathetic and parasympathetic influences on heart rhythm of drivers. Time-Frequency Analysis (TFA) of HRV is a powerful skill to make it easier to evaluate how this balance varies with time. Hilbert-Huang Transform (HHT) is a new method of time-frequency analysis, and is applicable to non-linear and non-stationary processes. This work presents a case study for time-frequency domain analysis of heart rate variability for driver fatigue. The experiment results show that HHT of HRV can be characterized to identify physiological features of human body. ? 2014 IEEE.Empirical Mode Decomposition; Heart Rate Variability; Time-Frequency Analysis[SDGs]SDG3Frequency domain analysis; Heart; Psychophysiology; Autonomic nervous system; Empirical Mode Decomposition; Heart rate variability; Heart rate variability signals; Hilbert Huang transforms; Time frequency analysis; Time-frequency domain analysis; Timefrequency analysis (TFA); Signal processingconference paper10.1109/IS3C.2014.120