Linear and non-linear heart rate metrics for the assessment of anaesthetists’ workload during general anaesthesia

Martin, Jan; Schneider, F; Kowalewskij, A; Jordan, Denis; Hapfelmeier, Alexander; Kochs, Eberhard; Wagner, Klaus J.; Schulz, Christian

doi:10.1093/bja/aew342

Cited by 13 publications

(44 citation statements)

References 40 publications

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“…PE has also been used to characterize the RRI time series. Actually, PE of HRV was confirmed to separate workload levels between different stages of anesthesia [20]. Aziz and Arif [21] found that the PE of the RRI time series of normal sinus rhythm subjects becomes greater than that of the RRI time series from congestive heart failure subjects.…”

Section: Introductionmentioning

confidence: 98%

Application of Permutation Entropy and Permutation Min-Entropy in Multiple Emotional States Analysis of RRI Time Series

Xia

Yang

Zunino

et al. 2018

Entropy

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This study's aim was to apply permutation entropy (PE) and permutation min-entropy (PME) over an RR interval time series to quantify the changes in cardiac activity among multiple emotional states. Electrocardiogram (ECG) signals were recorded under six emotional states (neutral, happiness, sadness, anger, fear, and disgust) in 60 healthy subjects at a rate of 1000 Hz. For each emotional state, ECGs were recorded for 5 min and the RR interval time series was extracted from these ECGs. The obtained results confirm that PE and PME increase significantly during the emotional states of happiness, sadness, anger, and disgust. Both symbolic quantifiers also increase but not in a significant way for the emotional state of fear. Moreover, it is found that PME is more sensitive than PE for discriminating non-neutral from neutral emotional states.

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Section: Introductionmentioning

confidence: 98%

Application of Permutation Entropy and Permutation Min-Entropy in Multiple Emotional States Analysis of RRI Time Series

Xia

Yang

Zunino

et al. 2018

Entropy

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“…[ 4 – 6 ] Therefore, in order to enable an early identification of situations at risk for cognitive over-load, the objective assessment of workload remains of great interest for research and clinical practice. [ 1 , 7 , 8 ]…”

Section: Introductionmentioning

confidence: 99%

“…[ 9 – 11 ] Recently, several linear and non-linear metrics of heart rate variability (HRV) have been identified to be promising tools to discriminate different levels of anesthetists’ workload in the operation theatre. [ 8 ] Among them, mean HR and permutation entropy (PeEn) performed best according to their area under the receiver operating characteristics curves (AUC). However, the aforementioned study was limited to the highly-standardized work environment of the operation theatre with healthy patients presenting for minor limb surgery under general anesthesia.…”

Section: Introductionmentioning

confidence: 99%

The validity of linear and non-linear heart rate metrics as workload indicators of emergency physicians

et al. 2017

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BackgroundIt has been shown that linear and non-linear heart rate variability (HRV) metrics are suitable to assess workload of anesthetists administering anesthesia. In pre-hospital emergency care, these parameters have not yet been evaluated. We hypothesized that heart rate (HR) and HRV metrics discriminate between differing workload levels of an emergency physician.MethodsElectrocardiograms were obtained from 13 emergency physicians. Mean HR, ten linear and seven non-linear HRV metrics were analyzed. For each sortie, four different levels of workload were defined. Mixed-effects models and the area under the receiver operating characteristics curve (AUC) were used to test and quantify the HR and HRV metrics’ ability to discriminate between levels of workload. This was conducted for mean HR and each HRV metric as well as for groups of metrics (time domain vs. frequency domain vs. non-linear metrics).ResultsThe non-linear HRV metric Permutation entropy (PeEn) discriminated best between the time before the alarm and primary patient care (AUC = 0.998, 1st rank of 18 HRV metrics). In contrast, AUC of the mean HR was low (0.558, 17th rank). In the multivariable approach, the non-linear HRV metrics provided a higher AUC (0.998) compared to the frequency domain (0.677) and to the time domain metrics (0.680).ConclusionNon-linear heart rate metrics and, specifically, PeEn provided good validity for the assessment of different levels of a physician’s workload in the setting of pre-hospital emergency care. In contradiction to earlier findings, the physicians’ mean HR was not a valid marker of workload.

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“…Variations in NASA-TLX were comparable with the Borg rating scale and/or physiological monitoring tools,18 28 when both tools were used. When used with physiological measuring tools, significant positive correlation was observed between physiological outcome measures (heart rate, heart rate variability and pupil size) and NASA-TLX scores 28. For NASA-TLX domains across all included studies, higher workload scores were consistently observed in temporal, mental and physical domains not changed.…”

Section: Resultsmentioning

confidence: 90%

“…Heart rate alone had inconsistent relationship with workload with marked interindividual variability. More advanced metrics of autonomic function (derived from heart rate variability) showed better discriminative ability in one study 28. Pupil changes were inconsistently related to workload with marked interindividual variability.…”

Section: Resultsmentioning

confidence: 97%

Anaesthesia workload measurement devices: qualitative systematic review

2018

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BackgroundManagement of mental workload is a key aspect of safety in anaesthesia but there is no gold-standard tool to assess mental workload, risking confusion in clinical and research use of such tools.ObjectiveThis review assessed currently used mental workload assessment tools.MethodsA systematic literature search was performed on the following electronic databases; Cochrane, EMBASE, MEDLINE, SCOPUS and Web of Science. Screening and data extraction were performed individually by two authors. We included primary published papers focusing on mental workload assessment tools in anaesthesia.ResultsA total of 2331 studies were screened by title, 32 by full text and 24 studies met the inclusion criteria. Six mental workload measurement tools were observed across included studies. Reliability for the Borg rating scales and Vibrotactile device was reported in two individual studies. The rest of the studies did not record reliability of the tool measurements used. Borg rating scales, NASA-TLX and task-oriented mental work load measurements are subjective, easily available, readily accessible and takes a few minutes to complete. However, the vibrotactile and eye-tracking methods are objective, require more technical involvement, considerable time for the investigator and moderately expensive, impacting their potential use.ConclusionWe found that the measurement of mental workload in anaesthesia is an emerging field supporting patient and anaesthetist safety. The self-reported measures have the best evidence base.

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Linear and non-linear heart rate metrics for the assessment of anaesthetists’ workload during general anaesthesia

Abstract: In this exploratory study based on short ECG segment analysis, PeEn and HR seem to be promising to separate workload levels between different stages of anaesthesia. The multiparametric analysis of the regression models favours non-linear heart rate metrics over linear metrics.

Cited by 13 publications

References 40 publications

Application of Permutation Entropy and Permutation Min-Entropy in Multiple Emotional States Analysis of RRI Time Series

Application of Permutation Entropy and Permutation Min-Entropy in Multiple Emotional States Analysis of RRI Time Series

The validity of linear and non-linear heart rate metrics as workload indicators of emergency physicians

Anaesthesia workload measurement devices: qualitative systematic review

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