Highly sensitive index of cardiac autonomic control based on time-varying respiration derived from ECG

Gilfriche, Pierre; Arsac, Laurent M.; Daviaux, Yannick; Diaz-Pineda, Jaime; Miard, Brice; Morellec, Olivier; André, Jean‐Marc

doi:10.1152/ajpregu.00057.2018

Cited by 5 publications

(3 citation statements)

References 71 publications

(92 reference statements)

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“…These confounding factors make it more challenging to distinguish ictal patterns from non-ictal ones, resulting in lower accuracy [ 48 ]. Also, similar activation of the autonomic nervous system can occur before physiological arousal or other sleep-related movements [ 49 ].…”

Section: Discussionmentioning

confidence: 99%

Ictal autonomic changes as a tool for seizure detection: a systematic review

et al. 2018

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Purpose Adequate epileptic seizure detection may have the potential to minimize seizure-related complications and improve treatment evaluation. Autonomic changes often precede ictal electroencephalographic discharges and therefore provide a promising tool for timely seizure detection. We reviewed the literature for seizure detection algorithms using autonomic nervous system parameters. Methods The PubMed and Embase databases were systematically searched for original human studies that validate an algorithm for automatic seizure detection based on autonomic function alterations. Studies on neonates only and pilot studies without performance data were excluded. Algorithm performance was compared for studies with a similar design (retrospective vs. prospective) reporting both sensitivity and false alarm rate (FAR). Quality assessment was performed using QUADAS-2 and recently reported quality standards on reporting seizure detection algorithms. Results Twenty-one out of 638 studies were included in the analysis. Fifteen studies presented a single-modality algorithm based on heart rate variability ( n = 10), heart rate ( n = 4), or QRS morphology ( n = 1), while six studies assessed multimodal algorithms using various combinations of HR, corrected QT interval, oxygen saturation, electrodermal activity, and accelerometry. Most studies had small sample sizes and a short follow-up period. Only two studies performed a prospective validation. A tendency for a lower FAR was found for retrospectively validated algorithms using multimodal autonomic parameters compared to those using single modalities (mean sensitivity per participant 71–100% vs. 64–96%, and mean FAR per participant 0.0–2.4/h vs. 0.7–5.4/h). Conclusions The overall quality of studies on seizure detection using autonomic parameters is low. Unimodal autonomic algorithms cannot reach acceptable performance as false alarm rates are still too high. Larger prospective studies are needed to validate multimodal automatic seizure detection. Electronic supplementary material The online version of this article (10.1007/s10286-018-0568-1) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Ictal autonomic changes as a tool for seizure detection: a systematic review

et al. 2018

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“…Respiratory rates were determined via chest belt as well as calculated from the ECG registration as previously described [12,13]. Corresponding hardware and software were included in the HRV-Scanner (Biosign ® ).…”

Section: Measurements Of Respiratory Ratesmentioning

confidence: 99%

Effects of Respiratory Rate on Heart Rate Variability in Neurologic Outpatients with Epilepsies or Migraine: A Preliminary Study

Druschky

Lorenz²,

Druschky

2019

Med Princ Pract

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Background: Variation of spontaneous respiratory rates and influences of spontaneous and paced breathing rates on heart rate variability (HRV) were assessed in patients with epilepsy or migraine, and HRV parameters were compared between the groups. Materials and Methods: Thirty neurologic outpatients, 16 diagnosed with epilepsies and 14 with migraine, were included. Autonomic testing consisted of short-term HRV, the deep breathing test (DBT), and measurement of HRV at systematically changed breathing rates (paced breathing, 5–18 breaths per minute, bpm). Results: Spontaneous respiratory rate during short-term HRV varied from 9 to 23 bpm in the epileptic group and from 5 to 21 bpm in migraine patients and was significantly and negatively correlated with SD of all normal RR intervals (SDNN) and total power (TP) in epileptic patients but not in migraine patients. Paced breathing rate had a significant effect on all HRV parameters assessed in both groups. HRV (SD1, SDNN, TP) and DBT (E-I, SD1, SDNN) parameters were significantly lower in the epileptic group. Group differences were significantly greater during slow compared to fast breathing. Conclusions: An important and new finding is the wide variation of spontaneous respiratory rate in both groups, along with the significant negative correlation with the assessed HRV parameters. The reduction of HRV during slow breathing in epileptic patients may indicate a diminished cardiorespiratory coupling caused by a probable loss of sensitivity within the cardiovagal brainstem circuitry.

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“…The results showed that the multiscale features obtain a better mental workload recognition effect. In [15], the respiratory and HRV signals were extracted using 5 min scale electrocardiogram (ECG) signals. This study introduced a novel method that fused respiratory and HRV signals to assess subtle variations in sympathovagal balance using ECG recordings during the MATA-II mission.…”

Section: Introductionmentioning

confidence: 99%

Comparison Analysis of Different Time‐Scale Heart Rate Variability Signals for Mental Workload Assessment in Human‐Robot Interaction

Shao

Wang

et al. 2021

Wireless Communications and Mobile Computing

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Excessive mental workload affects human health and may lead to accidents. This study is motivated by the need to assess mental workload in the process of human-robot interaction, in particular, when the robot performs a dangerous task. In this study, the use of heart rate variability (HRV) signals with different time scales in mental workload assessment was analyzed. A humanoid dual-arm robot that can perform dangerous work was used as a human-robot interaction object. Electrocardiogram (ECG) signals of six subjects were collected in two states: during the task and in a relaxed state. Multiple time-scale (1, 3, and 5 min) HRV signals were extracted from ECG signals. Then, we extracted the same linear and nonlinear features from the HRV signals at different time scales. The performance of machine learning algorithms using the different time-scale HRV signals obtained during the human-robot interaction was evaluated. The results show that for the per-subject case with a 3 min HRV signal length, the K -nearest neighbor classifier achieved the best mental workload classification performance. For the cross-subject case with a 5 min time-scale signal length, the gentle boost classifier achieved the best mental workload classification accuracy. This study provides a novel research idea for using HRV signals to measure mental workload during human-robot interaction.

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Highly sensitive index of cardiac autonomic control based on time-varying respiration derived from ECG

Cited by 5 publications

References 71 publications

Ictal autonomic changes as a tool for seizure detection: a systematic review

Ictal autonomic changes as a tool for seizure detection: a systematic review

Effects of Respiratory Rate on Heart Rate Variability in Neurologic Outpatients with Epilepsies or Migraine: A Preliminary Study

Comparison Analysis of Different Time‐Scale Heart Rate Variability Signals for Mental Workload Assessment in Human‐Robot Interaction

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