Autonomie Correlates of Antidepressant Treatment Using Heart-Rate Variability Analysis

1998 ◽  
Vol 43 (2) ◽  
pp. 183-186 ◽  
Author(s):  
Yaariv Khaykin ◽  
Paul Dorian ◽  
Brian Baker ◽  
Colin Shapiro ◽  
Paul Sandor ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Standard Deviation ◽  
Rating Scale ◽  
Antidepressant Treatment ◽  
Response To Therapy ◽  
Heart Rate Variability Analysis ◽  
Randomized Evaluation ◽  
Temporal Domain ◽  
Percentage Difference

Objective: To assess the 24-hour temporal-domain heart-rate variability correlates of treatment with fluoxetine or doxepinfor depression. Method: A randomized evaluation of fluoxetine and doxepin measured a 50% change in the Hamilton Depression Rating Scale (HDRS) score as a response to therapy and was correlated with measures of standard deviation of the mean of all 5-minute segments of normal electrocardiographic R-R intervals (SDANN), standard deviation of all normal R-R intervals (SDNN), root mean square of successive differences in R-R intervals (r-MSSD), and percentage difference between adjacent normal R-R intervals that are greater than 50 msec (pNN50)from 24-hour electrocardiogram (ECG) tapes. Results: Ten out of 14 patients responded. Response was associated with an increase in SDANN of 17% (P < 0.05). Nonresponse was associated with a 17% decrease in SDANN and a 22% decrease in SDNN (both P < 0.05). No other measures correlated with therapeutic response. No heart-rate variability (HRV) differences between the 2 drug therapies were observed. Conclusion: Twenty-four-hour HRV measures may be useful in assessing response to antidepressant therapy.

scholarly journals On the Minimal Adequate Sampling Frequency of the Photoplethysmogram for Pulse Rate Monitoring and Heart Rate Variability Analysis in Mobile and Wearable Technology

2019 ◽  
Vol 19 (5) ◽  
pp. 232-240 ◽  
Author(s):  
Szabolcs Béres ◽  
Lőrinc Holczer ◽  
László Hejjel
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Standard Deviation ◽  
Pulse Rate ◽  
Sampling Error ◽  
Sampling Theorem ◽  
Sampling Frequency ◽  
Heart Rate Variability Analysis ◽  
Poincaré Plot ◽  
Adequate Sampling

Abstract Recently there has been great interest in photoplethysmogram signal processing. However, its minimally necessary sampling frequency for accurate heart rate variability parameters is ambiguous. In the present paper frequency-modulated 1.067 Hz cosine wave modelled the variable PPG in silico. The five-minute-long, 1 ms resolution master-signals were decimated (D) at 2-500 ms, then cubic spline interpolated (I) back to 1 ms resolution. The mean pulse rate, standard deviation, root mean square of successive pulse rate differences (RMSSD), and spectral components were computed by Varian 2.3 and compared to the master-series via relative accuracy error. Also Poincaré-plot morphology was assessed. Mean pulse rate is accurate down to 303 ms (D) and 400 ms (I). In low-variability series standard deviation required at least 5 ms (D) and 100 ms (I). RMSSD needed 10 ms (D), and 303 ms (I) in normal, whereas 2 ms (D) and 100 ms (I) in low- variability series. In the frequency domain 5 ms (D) and 100 ms (I) are required. 2 ms (D) and 100 ms (I) preserved the Poincaré-plot morphology. The minimal sampling frequency of PPG for accurate HRV analysis is higher than expected from the signal bandwidth and sampling theorem. Interpolation improves accuracy. The ratio of sampling error and expected variability should be considered besides the inherent sensitivity of the given parameter, the interpolation technique, and the pulse rate detection method.

scholarly journals Correlates of Therapeutic Response in Panic Disorder Presenting with Palpitations: Heart Rate Variability, Sleep, and Placebo Effect

2003 ◽  
Vol 48 (6) ◽  
pp. 381-387 ◽  
Author(s):  
Brian Baker ◽  
Yaariv Khaykin ◽  
Gerald Devins ◽  
Paul Dorian ◽  
Colin Shapiro ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Panic Disorder ◽  
Therapeutic Response ◽  
Rating Scale ◽  
Placebo Response ◽  
Structural Heart Disease ◽  
Response To Therapy ◽  
Double Blinded ◽  
Stage 1

Objective: To examine the correlates of therapeutic response of patients with panic disorder presenting with palpitations, we hypothesized that therapeutic response would correlate with heart rate variability (HRV) and sleep measures. Methods: After a 1-week placebo washout, 27 patients free of structural heart disease and not on cardioactive drugs were randomized in a double-blinded fashion to 4 weeks of treatment with clonazepam (a known antipanic agent) or placebo. We performed standard sleep measures and recorded HRV from 24-hour Holter acquisitions at baseline and end of study. We defined response to therapy as a 50% improvement in the Hamilton Anxiety Rating Scale (HARS) score, confirmed by questionnaires and reaction to sodium lactate infusion. Results: There were 12 responders and 15 nonresponders. Normalization of sleep pattern (including less stage 1 and rapid eye movement [REM] sleep) was observed in both drug and placebo responders ( P = 0.011 and P = 0.05, respectively) and in placebo responders alone, compared with nonresponders ( P = 0.006 and P = 0.013, respectively). Placebo responders were more likely to show less depression, but even after we controlled for depression, main sleep effects remained. None of the HRV measures correlated with response, but compared with placebo, clonazepam led to a decrease in all the time and frequency domain measures of HRV (all P < 0.05). Conclusions: Central mechanisms are related to the therapeutic response of patients with panic disorder presenting with palpitations, but this does not directly correlate with HRV. Larger and longer studies may allow objective explanations of placebo response in panic disorder.

scholarly journals Field Monitoring the Effects of Overnight Shift Work on Specialist Tactical Police Training with Heart Rate Variability Analysis

2021 ◽  
Vol 13 (14) ◽  
pp. 7895
Author(s):  
Colin Tomes ◽  
Ben Schram ◽  
Robin Orr
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Shift Work ◽  
Emergency Response ◽  
Police Training ◽  
Heart Rate Variability Analysis ◽  
Response Team ◽  
Hrv Analysis ◽  
And Performance ◽  
Personnel Allocation

Police work exposes officers to high levels of stress. Special emergency response team (SERT) service exposes personnel to additional demands. Specifically, the circadian cycles of SERT operators are subject to disruption, resulting in decreased capacity to compensate in response to changing demands. Adaptive regulation loss can be measured through heart rate variability (HRV) analysis. While HRV Trends with health and performance indicators, few studies have assessed the effect of overnight shift work on HRV in specialist police. Therefore, this study aimed to determine the effects overnight shift work on HRV in specialist police. HRV was analysed in 11 SERT officers and a significant (p = 0.037) difference was found in pRR50 levels across the training day (percentage of R-R intervals varying by >50 ms) between those who were off-duty and those who were on duty the night prior. HRV may be a valuable metric for quantifying load holistically and can be incorporated into health and fitness monitoring and personnel allocation decision making.

scholarly journals Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adriana Leal ◽  
Mauro F. Pinto ◽  
Fábio Lopes ◽  
Anna M. Bianchi ◽  
Jorge Henriques ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Linear Time ◽  
Epileptic Seizures ◽  
Heart Rate Variability Analysis ◽  
Drug Resistant ◽  
Clustering Methods ◽  
Temporal Lobe Seizures ◽  
New Perspective ◽  
Drug Resistant Epilepsy

AbstractElectrocardiogram (ECG) recordings, lasting hours before epileptic seizures, have been studied in the search for evidence of the existence of a preictal interval that follows a normal ECG trace and precedes the seizure’s clinical manifestation. The preictal interval has not yet been clinically parametrized. Furthermore, the duration of this interval varies for seizures both among patients and from the same patient. In this study, we performed a heart rate variability (HRV) analysis to investigate the discriminative power of the features of HRV in the identification of the preictal interval. HRV information extracted from the linear time and frequency domains as well as from nonlinear dynamics were analysed. We inspected data from 238 temporal lobe seizures recorded from 41 patients with drug-resistant epilepsy from the EPILEPSIAE database. Unsupervised methods were applied to the HRV feature dataset, thus leading to a new perspective in preictal interval characterization. Distinguishable preictal behaviour was exhibited by 41% of the seizures and 90% of the patients. Half of the preictal intervals were identified in the 40 min before seizure onset. The results demonstrate the potential of applying clustering methods to HRV features to deepen the current understanding of the preictal state.

scholarly journals Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3235
Author(s):  
Koichi Fujiwara ◽  
Shota Miyatani ◽  
Asuka Goda ◽  
Miho Miyajima ◽  
Tetsuo Sasano ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Mean Squared Error ◽  
False Positive Rate ◽  
Premature Ventricular Contraction ◽  
Heart Rate Variability Analysis ◽  
Unified Framework ◽  
Atrial Contraction ◽  
Premature Atrial Contraction ◽  
Positive Rate

Heart rate variability, which is the fluctuation of the R-R interval (RRI) in electrocardiograms (ECG), has been widely adopted for autonomous evaluation. Since the HRV features that are extracted from RRI data easily fluctuate when arrhythmia occurs, RRI data with arrhythmia need to be modified appropriately before HRV analysis. In this study, we consider two types of extrasystoles—premature ventricular contraction (PVC) and premature atrial contraction (PAC)—which are types of extrasystoles that occur every day, even in healthy persons who have no cardiovascular diseases. A unified framework for ectopic RRI detection and a modification algorithm that utilizes an autoencoder (AE) type of neural network is proposed. The proposed framework consists of extrasystole occurrence detection from the RRI data and modification, whose targets are PVC and PAC. The RRI data are monitored by means of the AE in real time in the detection phase, and a denoising autoencoder (DAE) modifies the ectopic RRI caused by the detected extrasystole. These are referred to as AE-based extrasystole detection (AED) and DAE-based extrasystole modification (DAEM), respectively. The proposed framework was applied to real RRI data with PVC and PAC. The result showed that AED achieved a sensitivity of 93% and a false positive rate of 0.08 times per hour. The root mean squared error of the modified RRI decreased to 31% in PVC and 73% in PAC from the original RRI data by DAEM. In addition, the proposed framework was validated through application to a clinical epileptic seizure problem, which showed that it correctly suppressed the false positives caused by PVC. Thus, the proposed framework can contribute to realizing accurate HRV-based health monitoring and medical sensing systems.

scholarly journals Heart Rate Variability Analyses in Parkinson’s Disease: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 11 (8) ◽  
pp. 959
Author(s):  
Konstantin G. Heimrich ◽  
Thomas Lehmann ◽  
Peter Schlattmann ◽  
Tino Prell
Keyword(s):  
Parkinson’S Disease ◽  
Heart Rate ◽  
Parkinson's Disease ◽  
Heart Rate Variability ◽  
Meta Analysis ◽  
Cardiac Activity ◽  
Heart Rate Variability Analysis ◽  
Short Term ◽  
Variability Analysis ◽  
Parasympathetic Regulation

Recent evidence suggests that the vagus nerve and autonomic dysfunction play an important role in the pathogenesis of Parkinson’s disease. Using heart rate variability analysis, the autonomic modulation of cardiac activity can be investigated. This meta-analysis aims to assess if analysis of heart rate variability may indicate decreased parasympathetic tone in patients with Parkinson’s disease. The MEDLINE, EMBASE and Cochrane Central databases were searched on 31 December 2020. Studies were included if they: (1) were published in English, (2) analyzed idiopathic Parkinson’s disease and healthy adult controls, and (3) reported at least one frequency- or time-domain heart rate variability analysis parameter, which represents parasympathetic regulation. We included 47 studies with 2772 subjects. Random-effects meta-analyses revealed significantly decreased effect sizes in Parkinson patients for the high-frequency spectral component (HFms2) and the short-term measurement of the root mean square of successive normal-to-normal interval differences (RMSSD). However, heterogeneity was high, and there was evidence for publication bias regarding HFms2. There is some evidence that a more advanced disease leads to an impaired parasympathetic regulation. In conclusion, short-term measurement of RMSSD is a reliable parameter to assess parasympathetically impaired cardiac modulation in Parkinson patients. The measurement should be performed with a predefined respiratory rate.

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