Derivation of the respiratory rate from directly and indirectly measured respiratory signals using autocorrelation

Schrumpf, Fabian; Stürm, Matthias; Bausch, Gerold; Fuchs, Mirco

doi:10.1515/cdbme-2016-0054

Cited by 14 publications

(10 citation statements)

References 8 publications

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“…The estimation of RRs using continuous respiratory signals is commonly performed via FFT or the zero‐crossing method [29]. FFT is not able to extract cluttered spectral information as shown in Fig.…”

Section: Discussionmentioning

confidence: 99%

Automatic estimation of respiration rate from oronasal pressure using Ensemble empirical mode decomposition, Butterworth filter, and data fusion

Chung

2020

IET Science, Measurement &Amp; Technology

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Respiratory rate is an essential indicator of serious illness. Compared to heart rate or systolic blood pressure, its change is more evident, hence is a better means to discern stable patients from those at risks. In addition, the respiration rate is a crucial indication of sleep quality associated with sleep disorders such as obstructive sleep apnea. Oronasal pressure, as a clinical respiratory signal for sleep analysis, can be used in polysomnography both in labs of hospital and homes. Besides, it is often taken as a reference signal in research as opposed to the estimated respiration rate. This study aims to provide an automated respiratory rate estimation system with signals taken from oronasal pressure transducer that can cope with noises and is adaptive to various respiratory frequencies. A robust approach is presented here that employs Ensemble empirical mode decomposition method to remove signal noise, together with Butterworth band-pass filter to obtain the breathing frequency by means of zero-crossing. Among 97.6% of the test data, the study yields a root mean square error of 1.031. Compared to other methods, the current approach provides a more accurate respiration rate estimation in the application of orinasal pressure to sleep analysis.

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Section: Discussionmentioning

confidence: 99%

Automatic estimation of respiration rate from oronasal pressure using Ensemble empirical mode decomposition, Butterworth filter, and data fusion

Chung

2020

IET Science, Measurement &Amp; Technology

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“…Resting breathing frequency was analyzed pre- and post-intervention from the ECG. The HRV Scanner Software analyzes respiratory rate from the ECG signal, which is highly correlated with the actual breathing rate ( Schrumpf et al, 2016 ). Thus, ECG-derived breathing frequency has been suggested as an accurate measure of respiration ( Tong et al, 2014 ).…”

Section: Methodsmentioning

confidence: 99%

Implementing Mobile HRV Biofeedback as Adjunctive Therapy During Inpatient Psychiatric Rehabilitation Facilitates Recovery of Depressive Symptoms and Enhances Autonomic Functioning Short-Term: A 1-Year Pre–Post-intervention Follow-Up Pilot Study

Tatschl

Hochfellner²,

Schwerdtfeger

2020

Front. Neurosci.

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Objective: New treatment options for depression are warranted, due to high recurrence rates. Recent research indicates benefits of heart rate variability biofeedback (HRVBF) on symptom recovery and autonomic functioning in depressed individuals. Slowpaced breathing-induced amplification of vagus nerve activity is the main element of HRVBF. Thus, the latter represents a safe and non-invasive complementary depression treatment. However, its efficacy in patients undergoing inpatient psychiatric rehabilitation receiving highly comprehensive treatments has not been evaluated. Methods: Ninety-two inpatients were randomly assigned to an intervention group (IG) or control group (CG). While the latter received the standard treatment only, adjunctive HRVBF was provided to the IG over 5 weeks. Depression severity and heart rate variability (HRV) were assessed before (pre) and after 5 weeks (post). Moreover, 1-year follow-up depression scores were available for 30 participants. Results: Although depression improved in both groups, the IG exhibited significantly larger improvements at post-assessment (η 2 p = 0.065) and significant increases in resting LF-HRV (d = 0.45) and cardiorespiratory coherence (d = 0.61). No significant effects for RMSSD, SDNN, HF-HRV, or HR were found (ps > 0.05). Additionally, the IG showed a medium-to large-sized reduction in resting respiratory rate from 13.2 to 9.8 breaths per minute (p < 0.001, d = 0.86), with the CG exhibiting only a small decrease from 13.5 to 12.4 (p = 0.49; d = 0.35). While the IG exhibited significantly lower depression scores at post-assessment (p = 0.042, d = 0.79), this effect decreased during follow-up (p = 0.195, d = 0.48). Conclusion: HRVBF as adjuvant therapy during inpatient psychiatric rehabilitation facilitated depression recovery. Additionally, amplified LF-HRV as well as

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“…We did not use a breathing belt to minimize the delivery of skin-mediated feedback during capsule vibrations, so we estimated BR from the ECG signal. To do so, we adapted a custom MATLAB script to estimate the BR using the R-R interval peaks via an autocorrelation approach to estimate the periodic maxima corresponding to integer multiples of the signal's fundamental frequency (68). To improve algorithmic estimation of BR we divided each block into 60-second windows while applying detrending of the ECG signal resulting in 27, 13, and 13 windows for the baseline, normal, and enhanced blocks, respectively.…”

Section: Breathing Rate (Br)mentioning

confidence: 99%

Neural indicators of human gut feelings

Mayeli

et al. 2021

Preprint

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Understanding the neural processes that govern the human gut-brain connection has been challenging due to the inaccessibility of the body′s interior. In this study, we aimed to identify neural responses to gastrointestinal sensation (i.e., the neural basis of ′gut feelings′) in healthy humans using a minimally invasive mechanosensory probe. Combining electroencephalography and electrogastrography with signal detection theory measures, we quantified brain, stomach, and perceptual (button-press) responses following the ingestion of a vibrating capsule. The relationship between vibration strength and perceptual sensitivity was evaluated using two stimulation conditions (normal and enhanced). Most individuals successfully perceived capsule stimulation in both conditions, as evidenced by above chance accuracy scores. Perceptual accuracy improved significantly during the enhanced relative to normal stimulation, which was associated with faster reaction time and reduced reaction time variability. Stomach stimulation induced responses in a cluster of parieto-occipital leads near the midline via a late positive potential emerging 300-600 milliseconds after stimulation onset. Moreover, these ′gastric evoked potentials′ showed dose-dependent increases in amplitude and were significantly correlated with perceptual accuracy. Our findings are consistent with recent neurogastric and optogenetic studies demonstrating a role for posteromedial cortices in gastrointestinal interoception and body dissociation and highlight a unique form of enterically-focused sensory monitoring within the human brain. Overall, these results show that this minimally invasive approach could serve as a useful tool for understanding gut-brain interactions in healthy and clinical populations.

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Derivation of the respiratory rate from directly and indirectly measured respiratory signals using autocorrelation

Cited by 14 publications

References 8 publications

Automatic estimation of respiration rate from oronasal pressure using Ensemble empirical mode decomposition, Butterworth filter, and data fusion

Automatic estimation of respiration rate from oronasal pressure using Ensemble empirical mode decomposition, Butterworth filter, and data fusion

Implementing Mobile HRV Biofeedback as Adjunctive Therapy During Inpatient Psychiatric Rehabilitation Facilitates Recovery of Depressive Symptoms and Enhances Autonomic Functioning Short-Term: A 1-Year Pre–Post-intervention Follow-Up Pilot Study

Neural indicators of human gut feelings

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