WO2004028362A1 - Detection de la fatigue utilisant un electroencephalogramme - Google Patents

Detection de la fatigue utilisant un electroencephalogramme Download PDF

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Publication number
WO2004028362A1
WO2004028362A1 PCT/AU2003/001248 AU0301248W WO2004028362A1 WO 2004028362 A1 WO2004028362 A1 WO 2004028362A1 AU 0301248 W AU0301248 W AU 0301248W WO 2004028362 A1 WO2004028362 A1 WO 2004028362A1
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Prior art keywords
state
data
user
eeg
fatigue
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PCT/AU2003/001248
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English (en)
Inventor
Saroj Lal
Ashley Craig
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University Of Technology, Sydney
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Application filed by University Of Technology, Sydney filed Critical University Of Technology, Sydney
Priority to AU2003266812A priority Critical patent/AU2003266812A1/en
Publication of WO2004028362A1 publication Critical patent/WO2004028362A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves

Definitions

  • the invention concerns a method and a system for computing a state of fatigue whilst a user carries out a task.
  • Driver fatigue is a significant cause of traffic accidents, and is a persistent occupational hazard for professional or long-distance drivers who are involved in shift-work, Fatigue related accidents have potentially catastrophic personal consequences and are a substantial financial burden on the community. Cognitive skills are impaired by fatigue. An adverse effect of fatigue is a drivers' limited ability to assess their own level of alertness. This affects a drivers' ability to continue to drive safely. It is therefore desirable to develop countermeasures to driver fatigue.
  • the EEG records the electrical activity generated in the brain of the individual and may be used to define which stage of alertness sleep the Individual is experiencing.
  • Ninomija et al 2 developed a system which detects sleepy states of drivers using grouped EEG alpha waves to warn the driver of such a state.
  • the error in their system has a reported order of magnitude of 25- 35%.
  • the system is cumbersome as extra electrodes are required to monitor separate physiological signals.
  • a further proposal described a system based on detecting grouped alpha waves.
  • the system incorporates a convolution with weighting factors such as moving average methods. Such a system separates grouped alpha waves from various kinds of noise and detects low awakening levels as soon as grouped alpha waves appear.
  • the inventors have described the importance of using EEG as an indicator of fatigue to reduce fatigue related errors and accidents (Lai & Craig) 3,4 ' 5 .
  • the invention is a method for computing a state of fatigue whilst a user carries out a task, the method comprising the steps of : sampling EEG data from a user when the user is performing a task; performing frequency domain analysis of the sampled data to derive the magnitude of EEG in a plurality of frequency bands; computing the magnitude simultaneously in each of the bands; and comparing the magnitude in each of the bands against pre-determi ⁇ ed standards to determine a corresponding state of fatigue.
  • the method may further alert the user as to their determined state of fatigue.
  • the predetermined standards may be determined according to the steps of: sampling EEG data obtained from a user, wherein the data is representative of when the user is in an alert state; performing frequency domain analysis of the sampled data to compute a mean and a standard deviation of the EEG magnitude simultaneously in each of a plurality of frequency bands; computing at least a first threshold coefficient for each band in terms of the mean and the standard deviation of the respective band; and computing a plurality of standards in terms of the EEG magnitude in each frequency band and the relation of each magnitude to the respective coefficient such that each standard is representative of a state of fatigue.
  • An advantage of at least one embodiment of the invention is that the determination of fatigue utilises detection of simultaneous changes that occur in a plurality of frequency bands rather than a single frequency band.
  • a further advantage of at least one embodiment is that the determination of fatigue detection in three stages i.e. early, medium and extreme is based on brain activity changes.
  • the method may use an FFT to perform the frequency domain analysis, Sampling data representative of an alert state may be derived from a single user.
  • the sampling data may be derived from a sample of users who may perform similar tasks whereby an average is taken of the sample set. Either way, the sampling data representing an 'alert state' may be acquired 'on-line', whilst carrying out a task.
  • the data may be acquired 'off-line', and stored for future use. In obtaining such data, video data, or audio data, may be simultaneously acquired for confirmation that the, or each user is in an alert state.
  • the sampled data may be classified into four frequency bands comprising frequencies within the range of delta waves, theta waves, alpha waves and beta waves.
  • Delta, theta, alpha and beta waves may be within the ranges of about 0 to 4 Hz, about 4 to 8 Hz, about 8 to 13 Hz, and about 13 to
  • a first and a second threshold coefficient may be assigned for each band, the first and second coefficients representing an upper bound and a lower bound respectively. Further coefficients may be defined.
  • Boolean logic may be applied in order to define the respective standards. Aside from an alert state, the states of fatigue may correspond, in increasing order, to a transition state, a transitional to post-transitional state and a post-transitional state.
  • the EEG data may be obtained using a single or a multi channel physiological monitor. Data may be sampled at 256 Hz.
  • the EEG magnitude may be computed as the sum of the values within each frequency band.
  • the EEG magnitude may be computed as an average of each of the recording channels.
  • Equipment indicators may alert a user as to their current state of fatigue, for example, a green indicator may indicate to a user that they are performing a task in an alert state. Yellow may indicate a transitional state, orange a transitional to post -transitional state, and red a post-transitional state.
  • the invention is a system for computing a state of fatigue whilst a user carries out a task, the system comprising: sampling means for sampling EEG data from a user when the user is performing a task; analysing means to perform frequency domain analysis of the sampled data to derive the EEG magnitude in a plurality of frequency bands; computing means for classifying the spectrum and simultaneously computing the magnitude in each of the bands; and memory means to compare the magnitude in each of the bands against a pre-determined standard to determine a corresponding state of fatigue
  • the pre-determined standard may be determined according to the method described above.
  • the sampling data representative of an alert state may be derived from a single user.
  • the sampling data representative of an alert state may be derived from a sample of users who perform similar tasks and whereby an average is taken of the sample set.
  • the sampling data representing an 'alert state' may be acquired 'on-line', whilst the, or each user is carrying out a task.
  • the sampling data may be acquired 'off-line' and stored for future use.
  • sampling data may be simultaneously acquired for confirmation that the, or each user is in an alert state.
  • the sampled data may be classified into four frequency bands comprising frequencies within the range of delta waves, theta waves, alpha waves and beta waves.
  • the delta, theta, alpha and beta waves may be within the ranges of 0 to about 4 Hz, about 4 to about 8 Hz, about 8 to about 13 Hz, and about 13 to about 20 Hz respectively.
  • At least a first and a second threshold coefficient may be assigned for each band, the first and second coefficients representing an upper bound and a lower bound respectively. Further coefficients may be defined.
  • Boolean logic may be applied in order to define the respective standards. Aside from an alert state, the states of .fatigue may correspond, in increasing order, to a transition state, a transitional to post-transitional state and a post-transitional state.
  • EEG data may be obtained using a single or a multi channel physiological monitor,
  • the EEG magnitude is the sum of the values within each frequency band.
  • the EEG magnitude may be computed as an average of the separate individual recording channels.
  • the EEG magnitude may be computed as an average of a particular site on the brain, for example, but not limited to, the temporal, parietal, or central site.
  • the system may further include an alert means to alert the user as to their determined state of fatigue.
  • auditory indicators may alert a user as to their current state of fatigue
  • the system may include a first indicator which indicates to a user that they are performing a task in an alert state, a second indicator which indicates a transitional state, a third indicator which indicates a transitional to post -transitional state, and a fourth indicator which indicates a post-transitional state.
  • Each indicator may be identified by a different sound feedback.
  • Figure 1 which illustrates steps to compute a state of fatigue whilst a user performs a task
  • Figure 2 schematically illustrates a software panel for monitoring fatigue
  • Figure 3 schematically illustrates data detection during computation of a state of fatigue.
  • Figure 1 illustrates a sequence of steps 10 used to compute a state of fatigue whilst a user performs a task.
  • the partial sequence of steps 12 indicates the steps required in order to determine a plurality of standards, each corresponding to a different state of fatigue.
  • Four different fatigue states were identified, these were alert phase, transitional phase or early fatigue phase, the transitional-post transitional phase referred to as medium levels of fatigue, and the post-transitional phase in other words extreme levels of fatigue.
  • alert state a user is essentially non-fatigued.
  • the user participates in a trial, and data is taken over a period of time that is representative of the user's alert state 14. This data is taken from the beginning of the trial before the user develops signs of fatigue and recorded on a spectrum analyser. Video footage of the user's face is used to confirm that the user shows signs of being in the alert state. This alert state data is referred to as 'baseline data'.
  • the magnitude for each second of data, in each of the bands is calculated as the sum of the values in microvolts. From the baseline data, the mean and standard deviation of the magnitudes in each frequency band are calculated 18.
  • the spectrum analyser has multiple recording channels and for each recording channel the following values are computed: D m , Ds , T m> T sci ,
  • a m , A ⁇ d , B m , B__ where D, T, A and B represent the magnitude in the delta, theta, alpha and beta bands respectively, and subscript m and subscript _. respectively represent the mean and standard deviation of those magnitudes.
  • Figure 2 illustrates a software controlled panel 50 into which the user is able to change the conditional and combinatorial logic.
  • the right hand column specifies the frequency ranges for each of delta 52, theta 54, alpha 56 and beta 58.
  • Boolean logic is used to define standards representing the four states of fatigue in terms of the instantaneous magnitude in each frequency band and the relation of those magnitudes to the thresholds 22. For example: (D & T) & A
  • the left hand column in figure 2 represents specified algorithms to detect specific states. Testing
  • the methodology was tested on EEG data collected from ten subjects.
  • the drivers were between thirty three and fifty five years of age and all gave written consent for the test.
  • To qualify for the test the drivers had to have no medical contraindications such as severe concomitant disease, alcoholism, drug abuse, psychological or intellectual problems likely, to limit compliance. This was determined during the initial interview on a separate day prior to the test.
  • the test was conducted in a temperature-controlled laboratory and each driver performed a standardised sensory motor driver simulator task.
  • the driving task consisted of ten minutes of active driving to familiarise each driver, followed by a maximum of two continuous hours of driving with a speed less than eighty km/hr, till the respective driver showed physical signs of fatigue.
  • Simultaneous EEG and EOG measures were obtained during the driving task.
  • the EOG or electrooculogram detected the muscle movement of the subject's eyes due to movement of eye muscles, for example, from blink activity.
  • EEG EEG activity was recorded in relation to a linked-ear reference.
  • Left eye EOG measurements were obtained with electrodes positioned above and below the eye with a ground electrode on the masseter.
  • the EOG signal was used to identity blink artefacts in the EEG data as well as changes in blink types such as the small and slow blinks that characterise fatigue.
  • Physical signs of fatigue were identified using a video image of the driver's face, linked in real time with the EEG and EOG measures.
  • Specific facial features that were used to identify fatigue included changes in facial tone, blink rate, eye activity and mannerisms such as nodding and yawning.
  • the video image, and the EOG were used to validate the EEG changes associated with fatigue. The driving task was concluded when the specific facial signs of fatigue appeared. Data Acquistion
  • the EEG and EOG data were acquired using a multi channel physiological monitor.
  • An individual EEG data point was classified as an epoch; a basic unit for stored EEG data. Data was sampled at 256 Hz, 24. The total sample time was dependent on the subject and lasted until arousal from fatigue by a verbal interaction from a test investigator.
  • An FFT was performed on the EEG data using a spectral analysis package, 26.
  • the EEG was defined in terms of the pre-categorised frequency bands. For each band the average EEG magnitude measured in microvolts was computed as an average of the nineteen channels representative of the entire head of the subject, 28.
  • the EEG of fatigue was classified into the first appearance of transitional phase, between awake and absence of alpha, the transitional-post transitional phase which has characteristics of both, and post transitional phase followed by self-arousals, 30.
  • the four different fatigue phases were classified according to the simultaneous video analysis of facial features in the EOG measurements. Physical signs of fatigue were identified using a video image of each driver's face, linked in real time with the respective physiological measures. The video analysis served as an independent variable for fatigue assessment. The identification of fatigue from the video and EOG had excellent reliability, demonstrated by a high inter- observer and intra-observer agreement, 88% between three trained observers. On appearance of fatigue as classified from the video and EOG measures, thirty epochs that spanned the range of each of the alert and three fatigue phases were recorded to test the ability of the software to allocate each epoch into the correct phase.
  • a fatigue monitor in an off-line summary, 80 outputs EEG data in the four phases beginning with the alert state were categorised into four channels represented by colour panels, which were green , yellow, orange and red respectively.
  • a colour scale indicated green, 82 as a 'safe' level i.e alert and red 88 as a 'dangerous level of fatigue post- transitional phase.
  • Yellow, 84 and orange, 86 denoted early transitional phase and medium transitional-post transitional phase levels of fatigue, respectively.
  • Figure 3 illustrate data collection from a singular channel only, constituting one side of the brain.
  • the thirty epochs identified as representing each of the fatigue phases from the video and EOG measures were tested.
  • the testing involved identifying the proportion of epochs that were in each fatigue phase and allocating the data to the respective colour panels.
  • the data could also be viewed graphically with a line indicating in which panel i.e. alert or one of the fatigue states, a particular epoch had been allocated.
  • a repeated analysis of variance (ANOVA) was performed to identify if differences existed in the means of the four states detected by the software.
  • a Scheffe test then identified where the differences existed in the comparison of the means. The significance level was set at p ⁇ 0.05 for all analyses performed.
  • the data was categorised into each state of fatigue. Twenty five percent of the total epochs were allocated in each of the four states according to the video and EOG analysis which acted as the control against which the allocation of the epochs were compared. The ability of the software to detect fatigue, validated by the video analysis of fatigue, was demonstrated by the fact that the software detected no false positives. A false positive was defined as detecting fatigue in the absence of facial/EEG signs of fatigue. Table 1 demonstrates the allocation by the software of the total number of epochs to each fatigue phase for each subject,
  • the video and EOG analysis had identified subjects as being in the alert phase for an average of 40% of the time, in the transitional phase for 25% of the time, in the transitional to post- transitional for 20% and in the post transitional state for 15% of the total study time.
  • the largest difference in the two methods of detection was observed for the transitional and transitional to post-transitional phases with error rates in the order of ten. Data channels were output to the user to indicate their status of fatigue.

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Abstract

L'invention concerne un procédé et un système de calcul d'un état de fatigue alors qu'un utilisateur effectue une tâche. Dans une première étape du procédé, les données de l'électroencéphalogramme sont échantillonnées à partir d'un utilisateur lorsque celui-ci exécute une tâche. On effectue ensuite l'analyse du domaine de fréquence sur les données échantillonnées afin de déduire l'intensité de l'électroencéphalogramme dans une pluralité de bandes de fréquence. L'intensité est ensuite calculée simultanément dans chaque bande avant de la comparer dans chaque bande avec des valeurs standard prédéterminées de manière à déterminer un état correspondant de fatigue.
PCT/AU2003/001248 2002-09-24 2003-09-24 Detection de la fatigue utilisant un electroencephalogramme WO2004028362A1 (fr)

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Cited By (16)

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Publication number Priority date Publication date Assignee Title
WO2006096135A1 (fr) * 2005-03-08 2006-09-14 National University Of Singapore Systeme et procede pour controler la fatigue mentale
CN101859473A (zh) * 2009-04-07 2010-10-13 陈耕田 疲劳驾驶预警及自动控制装置(器)
CN102184415A (zh) * 2011-05-17 2011-09-14 重庆大学 一种基于脑电信号的疲劳状态识别方法
US20110288424A1 (en) * 2009-10-29 2011-11-24 Etsuko Kanai Human fatigue assessment device and human fatigue assessment method
CN102657526A (zh) * 2012-04-23 2012-09-12 中国医学科学院生物医学工程研究所 观看3d影像致不适感的脑电信号功率谱与r值评价方法
CN103606245A (zh) * 2013-11-08 2014-02-26 北京工业大学 基于蓝牙脑电耳机和安卓手机的疲劳驾驶检测预警***
US8731736B2 (en) 2011-02-22 2014-05-20 Honda Motor Co., Ltd. System and method for reducing driving skill atrophy
CN103989471A (zh) * 2014-05-08 2014-08-20 东北大学 一种基于脑电图识别的疲劳驾驶检测***及方法
CN104605866A (zh) * 2015-01-21 2015-05-13 中煤科工集团西安研究院有限公司 基于脑电检测的矿工生理与心理疲劳监测方法
US20150223743A1 (en) * 2014-02-12 2015-08-13 Wipro Limited Method for monitoring a health condition of a subject
US9538949B2 (en) 2010-09-28 2017-01-10 Masimo Corporation Depth of consciousness monitor including oximeter
CN106504475A (zh) * 2016-10-15 2017-03-15 北海益生源农贸有限责任公司 基于脑电信号的疲劳驾驶检测方法
US9775545B2 (en) 2010-09-28 2017-10-03 Masimo Corporation Magnetic electrical connector for patient monitors
US10154815B2 (en) 2014-10-07 2018-12-18 Masimo Corporation Modular physiological sensors
WO2020151075A1 (fr) * 2019-01-23 2020-07-30 五邑大学 Procédé d'identification de fatigue d'un conducteur basé sur un modèle d'apprentissage profond cnn-lstm
CN112450933A (zh) * 2020-11-10 2021-03-09 东北电力大学 一种基于人体多类特征的驾驶疲劳监测方法

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Cited By (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2006096135A1 (fr) * 2005-03-08 2006-09-14 National University Of Singapore Systeme et procede pour controler la fatigue mentale
CN101859473A (zh) * 2009-04-07 2010-10-13 陈耕田 疲劳驾驶预警及自动控制装置(器)
US20110288424A1 (en) * 2009-10-29 2011-11-24 Etsuko Kanai Human fatigue assessment device and human fatigue assessment method
US8706206B2 (en) * 2009-10-29 2014-04-22 Panasonic Corporation Human fatigue assessment device and human fatigue assessment method
US11717210B2 (en) 2010-09-28 2023-08-08 Masimo Corporation Depth of consciousness monitor including oximeter
US10531811B2 (en) 2010-09-28 2020-01-14 Masimo Corporation Depth of consciousness monitor including oximeter
US9775545B2 (en) 2010-09-28 2017-10-03 Masimo Corporation Magnetic electrical connector for patient monitors
US9538949B2 (en) 2010-09-28 2017-01-10 Masimo Corporation Depth of consciousness monitor including oximeter
US8731736B2 (en) 2011-02-22 2014-05-20 Honda Motor Co., Ltd. System and method for reducing driving skill atrophy
US9174652B2 (en) 2011-02-22 2015-11-03 Honda Motor Co., Ltd. System and method for reducing driving skill atrophy
CN102184415A (zh) * 2011-05-17 2011-09-14 重庆大学 一种基于脑电信号的疲劳状态识别方法
CN102657526A (zh) * 2012-04-23 2012-09-12 中国医学科学院生物医学工程研究所 观看3d影像致不适感的脑电信号功率谱与r值评价方法
CN103606245B (zh) * 2013-11-08 2015-11-18 北京工业大学 基于蓝牙脑电耳机和安卓手机的疲劳驾驶检测预警***
CN103606245A (zh) * 2013-11-08 2014-02-26 北京工业大学 基于蓝牙脑电耳机和安卓手机的疲劳驾驶检测预警***
US20150223743A1 (en) * 2014-02-12 2015-08-13 Wipro Limited Method for monitoring a health condition of a subject
CN103989471A (zh) * 2014-05-08 2014-08-20 东北大学 一种基于脑电图识别的疲劳驾驶检测***及方法
US10154815B2 (en) 2014-10-07 2018-12-18 Masimo Corporation Modular physiological sensors
US10765367B2 (en) 2014-10-07 2020-09-08 Masimo Corporation Modular physiological sensors
US11717218B2 (en) 2014-10-07 2023-08-08 Masimo Corporation Modular physiological sensor
CN104605866A (zh) * 2015-01-21 2015-05-13 中煤科工集团西安研究院有限公司 基于脑电检测的矿工生理与心理疲劳监测方法
CN106504475A (zh) * 2016-10-15 2017-03-15 北海益生源农贸有限责任公司 基于脑电信号的疲劳驾驶检测方法
WO2020151075A1 (fr) * 2019-01-23 2020-07-30 五邑大学 Procédé d'identification de fatigue d'un conducteur basé sur un modèle d'apprentissage profond cnn-lstm
CN112450933A (zh) * 2020-11-10 2021-03-09 东北电力大学 一种基于人体多类特征的驾驶疲劳监测方法
CN112450933B (zh) * 2020-11-10 2022-09-20 东北电力大学 一种基于人体多类特征的驾驶疲劳监测方法

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