WO2010038217A1 - Dispositif de surveillance du bien-être d’un cerveau de nouveau-né - Google Patents

Dispositif de surveillance du bien-être d’un cerveau de nouveau-né Download PDF

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Publication number
WO2010038217A1
WO2010038217A1 PCT/IB2009/054316 IB2009054316W WO2010038217A1 WO 2010038217 A1 WO2010038217 A1 WO 2010038217A1 IB 2009054316 W IB2009054316 W IB 2009054316W WO 2010038217 A1 WO2010038217 A1 WO 2010038217A1
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subject
data
emotional
ecg
emotional state
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PCT/IB2009/054316
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English (en)
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Eli Morgan Barak
Johannes Bergman Nils
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University Of Cape Town
South African Medical Research Council
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Publication of WO2010038217A1 publication Critical patent/WO2010038217A1/fr
Priority to ZA2011/03203A priority Critical patent/ZA201103203B/en

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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
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • 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
    • 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
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • 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]

Definitions

  • THIS invention relates to a method of, and apparatus for, monitoring emotional or brain well-being in infants.
  • the mammalian brain interprets mother-infant skin-to-skin contact as a safe, rewarding environment and orchestrates an approach-type neuroaffective state comprising nutritive (feeding and growth) and social (bonding and communication) components accompanied by pleasurable feelings.
  • the mammalian brain interprets mother-infant separation as an unsafe, threatening environment and orchestrates an avoidance-type neuroaffective state comprising withdrawal (immobilisation and resource-conservation) and social shutdown components accompanied by unpleasant feelings.
  • a method of monitoring the emotional state of an infant including:
  • ECG data indicative of heart rate and heart rate variability (HRV) in the subject;
  • analysing the ECG data to determine a plurality of parameters including heart rate and at least one further parameter related to the interval between R-waves in an ECG waveform of the subject, and generating second output data related thereto; and processing the first and second output data to provide an indication in real time or near-real time of the emotional state of the subject.
  • the at least one further parameter determined from the ECG data may include one or more of the following:
  • SDNN Standard deviation of normal R-R intervals
  • the method may include measuring and analysing at least one of the following parameters to determine the emotional state of the subject:
  • ABSS Infant behavioural/vigilance state using the ABSS (eg. Asleep, quiet awake, crying);
  • the indication of the emotional state of the subject may comprise quantitative values obtained from recorded data, time lines of such data over a selected measurement period, or text-format interpretations of the subject's current state.
  • apparatus for monitoring the emotional state of an infant including: an input circuit for receiving and pre-processing EEG and ECG data acquired from a subject;
  • a processor arranged to analyse the EEG data to determine asymmetry of the frontal lobe activity and to generate first output data related thereto; to analyse the ECG data to determine a plurality of parameters including heart rate and at least one further parameter related to the interval between R-waves in an ECG waveform of the subject, and to generate second output data related thereto; and to process the first and second output data to generate display data; and
  • At least one display responsive to the display data to provide an indication in real time or near-real time of the emotional state of the subject.
  • the apparatus preferably includes at least one module for analysing the EEG and ECG data, wherein a module for analysing the ECG data is arranged to determine at least one of the following further parameters from the ECG data:
  • SDNN Standard deviation of normal R-R intervals
  • At least one module of the apparatus is arranged to measure and analyse one or more of the following parameters to determine the emotional state of the subject:
  • Said at least one display is preferably capable of displaying a graphical representation suggesting at least one of at least two possible emotional states.
  • said at least one display is preferably capable of displaying an indication of the emotional state of the subject comprising quantitative values obtained from recorded data, time lines of such data over a selected measurement period, or text-format interpretations of the subject's current state.
  • Figure 1 is a simplified schematic block diagram of apparatus for monitoring the emotional state of an infant according to the invention
  • Figures 2 and 3 are sets of graphs showing displays of ECG parameters generated by the apparatus of Figure 1 ;
  • FIG. 5 is a schematic diagram illustrating operational principles of the method and apparatus of the invention. DESCRIPTION OF AN EMBODIMENT
  • the present invention aims to provide a means of looking at the emotional well-being of an infant subject, that is, the well-being of the subject's brain, utilising both EEG and ECG data.
  • the ECG data is complemented and corroborated by looking at upstream factors (EEG) in the frontal lobe (the heart being a downstream effect), the highest part of the "emotional brain” (the neural structures and networks responsible for processing emotional stimuli and generating emotional responses).
  • EEG upstream factors
  • the method of the invention gathers signals from these two sources, interprets them according to known as well as novel principles of "affective neuroscience” (the neurobiology of emotion), and provides real time or near-real time information to clinicians in order to guide newborn / infant care.
  • the first "emotional brain” to evolve was the autonomic nervous system which ensures appropriate behavioural responses and internal (metabolic, physiological) homeostasis necessary for survival. Survival, however, is more than momentary homeostasis, but involves procurement of resources for growth and development and avoidance of danger.
  • the first component of the autonomic nervous system (ANS) to evolve (called the “old vagus") was therefore capable of orchestrating two emotional response modes: “nutrition & growth” in response to safe conditions and "immobilisation” in response to threat. This deepest layer is exemplified in reptiles and amphibians.
  • the emotional brain therefore comprises more primitive autonomic and more recent social-emotional components.
  • These three systems generate distinctly different behaviours and make distinctly different metabolic demands on the body as a whole.
  • the vegetative system switches between immobilisation (a passive withdrawal response to threat) and nutritive (feeding/growth response in absence of threat) modes.
  • the SNS triggers an active fight or flight response to threat while the social system mediates internal and behavioural responses to changes in the social environment.
  • the three systems communicate in bottom-up and top-down ways and mediate increasingly sophisticated and subtle physiological, behavioural and subjective (motivational/emotional) approach/avoid response patterns to rewarding or threatening situations.
  • the heart plays a crucial role in adapting the body according to the metabolic demands of each neuroaffective (emotional brain) subsystem.
  • the emotional brain therefore sends appropriate messages to the heart through nerve pathways.
  • Signals from the new social vagus have been extensively studied. At rest (safe conditions) it is actively on and produces a slowing effect on the heart (if it was not there, human hearts would beat a lot faster than they do.) This effect is extremely finely tuned, and can change the interval from one beat to the next with microsecond precision. Metabolic demand changes can therefore be managed extremely efficiently.
  • This control mechanism is also directly linked to the breathing centre of the brain as evidenced by heart rate increases during inspiration and decreases during expiration (a pattern known as respiratory sinus arrhythmia, RSA).
  • RSA and heart rate are so closely coupled that the breathing rate can be calculated accurately from a particular frequency band component in the heart's beat to beat variation.
  • Variation of the interbeat interval on a beat-to-beat basis in the high frequency (HF) component of heart rate variability (HRV) reflects activity of the most recent layer of the emotional brain, the new social vagus.
  • the sympathetic nervous system represented a significant advance in terms of defence. It allows for very rapid mobilisation of resources, and a rapid increase in heart rate. But the social vagus has branches that switch this off (allowing for more subtle responses to a social threat, which does not necessarily mean a physical threat, where a fight or flight response is appropriate). The SNS can therefore only show its effect once the social vagus is inactive. In the presence of real danger, the social vagus does indeed switch off, allowing for an increase in heart rate (vagal brake is off), and on top of this increase, sympathetic activation increases heart rate as required. Whether in safe conditions or under threat, mammalian research shows that all the above systems interact in various ways. For example, the old vagus, while presumed to have no cardiac effect, will under threat actively shut off the gut.
  • the social vagus allows for the rapid emotional assessment of faces which requires rapid approach or avoid decisions, whether to engage or disengage. This involves both cortical or conscious judgement and subcortical unconscious responses.
  • the subcortical amygdala is a key part of the emotional brain, and sends emotion-charged information to the frontal lobe.
  • the left frontal lobe is responsible for approach to reward, and the right responsible for avoidance of threat.
  • This activity can be measured in the EEG, and by comparing left and right a measure called "Frontal EEG Asymmetry" can be calculated (Davidson, 1998).
  • Approach asymmetry has been shown to correlate well with psychological and physiological health in adults, and also in infants more than four weeks old. It has mostly been interpreted as a personality trait, rather than as an emotional state.
  • the incubator was invented some 100 years ago, and active management of prematurity started some 50 years ago.
  • technologies were developed that supported temperature regulation, breathing and cardiac functions, and metabolic and nutritional support.
  • a variety of monitoring techniques developed with these technologies.
  • Increasingly sophisticated technology is now able to achieve survival at extreme low birth weights and gestational ages.
  • the present invention utilises both frontal EEG data which is analysed to provide an indication of dynamic frontal lobe asymmetry, and ECG data from which heart rate variability (HRV) data is extracted and analysed, to determine an overall emotional state of the subject.
  • frontal EEG data which is analysed to provide an indication of dynamic frontal lobe asymmetry
  • ECG data from which heart rate variability (HRV) data is extracted and analysed, to determine an overall emotional state of the subject.
  • HRV heart rate variability
  • a Place Model was developed. This states that the newborn in skin-to-skin contact with the mother (SSC; the expected place) expresses a nutritional and neurodevelopmental program that leads to good quality attachment, breastfeeding and optimal development. When separated from the mother (Maternal-Infant Separation or MIS; any other place) a survival program is expressed, in which psycho-physiological dysregulation is accompanied by poor quality attachment (e.g. dissociation), which hampers neurocognitive, emotional and social development.
  • SSC skin-to-skin contact with the mother
  • MIS Major-Infant Separation
  • the HRV data has in the past been regarded as a static model, though recent research has shown there are dynamic aspects.
  • the novel approach to making this data useful entails a synthesis of three things: firstly, emotional brain effects on the heart are dynamic. Secondly, they reflect the real time inputs and outputs of the emotional brain. Thirdly, the deeper and older vegetative vagus was identified at very low frequencies (VLF) buried in the HRV recording.
  • VLF very low frequencies
  • the Heart Rate Variability (HRV) frequencies which best reflect the different components of the emotional brain in neonates were predicted and explored in the data, after the data sets had been cleaned using a manual visual filtering technique. Following spectrographic analysis and adjustment of epoch lengths, the data was extracted and digitalised for further analysis. Correlations were made with the Anderson Behaviour State Scale or ABSS (Anderson 1986), heart rate and standard deviation of inter beat intervals of infants studied in cots and during maternal infant skin- to-skin contact.
  • ABSS Anderson Behaviour State Scale
  • the apparatus was designed to utilise output signals from currently available EEG and ECG equipment, and to process these signals using algorithms based on the findings of the pilot study, to provide a real time or near-real time indication of the emotional state of an infant subject.
  • the prototype apparatus is shown in a simplified schematic form in Figure 1. The apparatus is utilised with a conventional EEG data acquisition unit 10, and a conventional ECG data acquisition unit 12.
  • the EEG data acquisition unit used in the pilot study was a battery powered research-only EEG unit such as units made by Biosemi of Amsterdam, The Netherlands or by G-Tec of Graz, Austria.
  • the electrodes of the unit are easily adhered onto the mid-frontal areas of the scalp (F3 and F4) and left and right mastoids (as reference) using disposable adhesive rings or elasticised Velcro (trade mark) bands (e.g. Biosemi infant electrode caps). If the Biosemi-type active electrode system is used, a CMS (common mode signal) and a DRL (driven right leg) reference electrode will be attached to the scalp as well.
  • the electrodes are washed in warm water and air dried between each subject.
  • the ECG data acquisition unit 12 can be the equivalent to an ambulatory ECG data acquisition system (e.g. a unit made by AMS of Vrije Universiteit, Netherlands) customised to work with three disposable neonatal ECG electrodes placed on the chest at the left mid-clavicular line (4 th intercostal space), at the sternal notch, and at the right mid-axillary line at the level of the last rib.
  • an ambulatory ECG data acquisition system e.g. a unit made by AMS of Vrije Universiteit, Netherlands
  • the outputs of the EEG and ECG data acquisition units 10 and 12 are fed to an A/D converter 14 for digitization.
  • the output of the A/D converter is fed to a digital signal processing (DSP) circuit 16, which pre-processes the digitized data and utilises noise separation software to clean the acquired data.
  • DSP digital signal processing
  • the output of the DSP circuit is fed to a microprocessor 18 running proprietary software which implements the algorithms of the invention.
  • the software provides an output which can drive a display 22 and an associated alarm or indicator 24.
  • the apparatus of Figure 1 may include a plurality of components or modules which correspond to the functional tasks to be performed by the apparatus.
  • module in the context of the specification will be understood to include an identifiable portion of code, computational or executable instructions, data, or computational object to achieve a particular function, operation, processing, or procedure. It follows that a module need not be implemented in software; a module may be implemented in software, hardware, or a combination of software and hardware. Further, the modules need not necessarily be consolidated into one device but may be spread across a plurality of devices in the apparatus. In the prototype apparatus, the modules were implemented in proprietary software, as indicated above.
  • the software running on the microprocessor 18 calculates EEG asymmetry on-line using standard power - frequency spectrum density (PSD) extraction methods (FFT and/or wavelet analyses).
  • PSD power - frequency spectrum density
  • FFT and/or wavelet analyses The asymmetry score is then taken as the difference in the natural log of the 3-8Hz sub-band (additional sub-bands will possibly yield further useful info) power between left and right mid-frontal (F3, F4, 10-20 International System) electrode sites. (Additional sites, e.g. parietal areas, will possibly yield further useful information.) This gives values in the approximate range 0.5 (extreme right) to -0.5 (extreme left).
  • the software modules implement QRS complex (R-wave) detection algorithms which output the RR interval (interval between successive heartbeats) in milliseconds (ms). This information is then upsampled and transformed into the time domain using MatLab code (e.g. Berger transform, BioSig open source). FFT or wavelet method algorithms extract the PSD which is then analysed to output normalized quantitative power- frequency values for three different components of the emotional brain (old and new vagus, SNS). The algorithms define and interpret these existing PSD sub-bands according to the principles described above. Once done (defined and quantified) the results are easily represented individually and in relation to one another on the monitor display and the data therein are automatically processed and simultaneously represented on the display in a graphic display to reflect emotional or brain well-being. This latter processing is now described in greater detail.
  • QRS complex R-wave
  • the essence of the invention is to gather diverse and previously unconnected information and interpret it in terms of emotional or brain well-being, meaning approach/avoidance modes as described above. Seven core parameters for this purpose are:
  • ABSS Infant behavioural/vigilance state using the ABSS (eg. Asleep, quiet awake, crying).
  • SDNN Standard deviation of normal R-R intervals
  • the top graph shows behavioural state according to the Anderson Behavioural State Scale which has 12 states, the lowest being 1 (quiet sleep), the highest being 12 (severe distress).
  • the middle graph shows instantaneous heart rate (upper trace 26) and standard deviation of instantaneous heart rate (lower trace 28).
  • the lowest graph shows the old vagus 30, new vagus 32 and SNS 34 on a normalised % scale.
  • Cries 1 and 4 are hunger cries: the old vagus (nutritive) 30 is higher than the SNS (fight/flight) 34 and the new vagus (social) 32 is low.
  • the heart rate (blue) is high and the SDNN 28 is low.
  • Cry 2 is a social cry: the old vagus 30 is low, the SNS 34 is low and the new vagus 32 is high.
  • the heart rate 26 is low and the SDNN 28 is high.
  • Cries 3 and 5 are anger/protest cries: the old vagus 30 is lower than the SNS (fight) 34 and the new vagus (social communication) 32 is high. The heart rate 26 is high and the HRV 28 is low.
  • Example 2 Two different breast feeds
  • the first is a hunger feed: the old vagus (nutritive) 30 is high and the SNS 34 and the new vagus (here mother is a feeding object rather than a social object) 32 are low.
  • the second is a comfort feed: the old vagus 30 is low, the SNS 34 is low and the new vagus 32 is high.
  • Example 3 A sleep cycle in SSC
  • FIG 3 shows a full normal sleep cycle in skin-to-skin contact (SSC).
  • SSC skin-to-skin contact
  • a rolling average of the frontal EEG asymmetry score (the 7 th core parameter) will be represented in graph form with the other parameters shown above.
  • right- sided asymmetry (more negative scores) indicating the baby is unhappy invariably occurs in MIS
  • left-sided scores (more positive scores) indicating the baby is happy invariably occurs in SSC, apart from a small number of anomalous cases.
  • EBPs emotional brain patterns
  • the motivational direction will be subtyped into discrete emotional states according to the details of the EBP, e.g. hunger cry, distress cry, lonely cry, comfort-feeding, hungry-feeding, unhappy-sad, unhappy-distressed, unhappy-dissociated, unhappy-lonely, unhappy-hungry, unhappy-angry.
  • EBP EBP
  • states can be represented graphically or in a text format (typically a predetermined text format), for example.
  • FIG. 5 shows in a graphic form how the method and apparatus of the invention (dark arrows) operate in measuring and generating outputs based on the relevant parameters.
  • the optimal interpretation of the monitor outputs at present requires an "input" regarding the behavioural state of the baby (asleep, awake) as observed. This can be automated together with various other parameters that already exist, and only needs to be added to the algorithms to make the interpretations fully automated. Examples include body movements, eye movements and regularity of respiration.
  • SNS from Impedance cardiography (ICG) measures the average thoracic impedance (over a user-defined period, typically 10-60 sees) time-locked to the last R-wave. This ongoing value (if used) will be represented as a fourth component of the emotional brain.
  • the invention allows for the interpretation of the newborn's and the infant's basic needs, which is of fundamental importance for humane care. It allows for direct monitoring of sleep and the quality of sleep, which is increasingly being recognised as fundamental for brain development. Its use may lead to a vastly improved understanding of the newborn and the developing infant. It may be useful in treating colicky infants, feeding disorders, and psychologically disturbed infants. The limited data from the pilot show the infants to be entirely different from adults, and yet the feeding and sleeping schedules imposed on infants are based on adult behaviour patterns. This invention will allow the direct observation at the level of the emotional brain of what a normal infant should do and require thereby allowing for optimal neuroaffective care of ill infants. This is specifically important in the management of premature infants and low birth weight babies.

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Abstract

La présente invention concerne un procédé et un appareil de surveillance de l’état émotionnel d’un nouveau né. L’appareil comprend un circuit d’entrée destiné à recevoir et à prétraiter des données d’EEG et d’ECG acquises d’un sujet. Un processeur exécutant un logiciel propriétaire, mettant en œuvre des modules respectifs de l’appareil, est configuré pour analyser les données d’EEG et les données d’ECG et pour générer des données de sortie respectives associées. Les modules respectifs déterminent l’asymétrie de l’activité du lobe frontal, et analysent les données d’ECG pour déterminer plusieurs paramètres comprenant la fréquence cardiaque et au moins un autre paramètre associé à l’intervalle entre les ondes R dans une forme d’onde d’ECG du sujet. Les premières et deuxièmes données sont traitées pour générer des données d’affichage, et l’appareil comprend au moins un affichage réagissant aux données d’affichage pour fournir une indication en temps réel ou en temps quasi réel de l’état émotionnel du sujet.
PCT/IB2009/054316 2008-10-03 2009-10-02 Dispositif de surveillance du bien-être d’un cerveau de nouveau-né WO2010038217A1 (fr)

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ZA2008/08547 2008-10-03

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EP3263024A1 (fr) * 2016-06-30 2018-01-03 Cal-Comp Electronics & Communications Company Limited Procédé d'analyse d'émotion et appareil électronique correspondant
KR20180007006A (ko) * 2015-07-08 2018-01-19 삼성전자주식회사 감정 평가
US9943237B2 (en) 2013-12-04 2018-04-17 Welch Allyn, Inc. Analysis of direct and indirect heartbeat data variations
CN108937968A (zh) * 2018-06-04 2018-12-07 安徽大学 基于独立分量分析的情感脑电信号的导联选择方法
CN109069081A (zh) * 2015-12-04 2018-12-21 爱荷华大学研究基金会 用于预测、筛查和监测脑病/谵妄的设备、系统和方法
CN109745028A (zh) * 2017-11-07 2019-05-14 财团法人资讯工业策进会 辨识婴儿需求的系统及方法

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Cited By (11)

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Publication number Priority date Publication date Assignee Title
US9943237B2 (en) 2013-12-04 2018-04-17 Welch Allyn, Inc. Analysis of direct and indirect heartbeat data variations
KR20180007006A (ko) * 2015-07-08 2018-01-19 삼성전자주식회사 감정 평가
CN107850940A (zh) * 2015-07-08 2018-03-27 三星电子株式会社 情感评估
EP3320414A4 (fr) * 2015-07-08 2018-06-20 Samsung Electronics Co., Ltd. Évaluation d'émotion
US10285634B2 (en) 2015-07-08 2019-05-14 Samsung Electronics Company, Ltd. Emotion evaluation
KR102104499B1 (ko) * 2015-07-08 2020-04-24 삼성전자주식회사 감정 평가
CN107850940B (zh) * 2015-07-08 2021-12-31 三星电子株式会社 情感评估
CN109069081A (zh) * 2015-12-04 2018-12-21 爱荷华大学研究基金会 用于预测、筛查和监测脑病/谵妄的设备、系统和方法
EP3263024A1 (fr) * 2016-06-30 2018-01-03 Cal-Comp Electronics & Communications Company Limited Procédé d'analyse d'émotion et appareil électronique correspondant
CN109745028A (zh) * 2017-11-07 2019-05-14 财团法人资讯工业策进会 辨识婴儿需求的系统及方法
CN108937968A (zh) * 2018-06-04 2018-12-07 安徽大学 基于独立分量分析的情感脑电信号的导联选择方法

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