Vagal withdrawal as a function of audience

1996 ◽  
Vol 270 (4) ◽  
pp. H1381-H1383 ◽  
Author(s):  
R. De Meersman ◽  
S. Reisman ◽  
M. Daum ◽  
R. Zorowitz
Keyword(s):  
Cardiac Electrophysiology ◽  
Real Life ◽  
Cardiac Activity ◽  
Monitor System ◽  
Current Investigation ◽  
Holter Monitor ◽  
Power Spectral ◽  
Psychosocial Influences ◽  
System Power ◽  
Ecg Derived Respiration

This investigation examined the effects of psychosocial influences upon vagal cardiac activity. In this crossover, counter-balanced study, 15 subjects were assessed for vagal cardiac activity before and during a presentation in the presence and/or absence of an audience. Electrocardiograms (ECG) were collected throughout the epochs of interest, using a portable holter monitor system. Power spectral density analyses were used to decompose autonomic rhythmicities of heart rate variability. Significantly diminished vagal power was noted before and during presentation episodes with an audience compared with vagal power during a presentation without an audience (P < 0.05). Because respiration modulates autonomic outflow, ECG-derived respiration was derived and compared for all epochs, and no significant differences were noted. The real-life findings in the current investigation are strongly suggestive of the modulating effects of psychosocial interactions upon vagal cardiac electrophysiology and should be considered when assessing autonomic status.

scholarly journals Computational approaches to understand cardiac electrophysiology and arrhythmias

2012 ◽  
Vol 303 (7) ◽  
pp. H766-H783 ◽  
Author(s):  
Byron N. Roberts ◽  
Pei-Chi Yang ◽  
Steven B. Behrens ◽  
Jonathan D. Moreno ◽  
Colleen E. Clancy
Keyword(s):  
Ion Channels ◽  
Electrical Activity ◽  
Cardiac Electrophysiology ◽  
Cardiac Activity ◽  
Computational Approaches ◽  
Cardiac Electrical Activity ◽  
Whole Heart ◽  
Arrhythmia Therapy ◽  
Emergent Behaviors ◽  
The Brain

Cardiac rhythms arise from electrical activity generated by precisely timed opening and closing of ion channels in individual cardiac myocytes. These impulses spread throughout the cardiac muscle to manifest as electrical waves in the whole heart. Regularity of electrical waves is critically important since they signal the heart muscle to contract, driving the primary function of the heart to act as a pump and deliver blood to the brain and vital organs. When electrical activity goes awry during a cardiac arrhythmia, the pump does not function, the brain does not receive oxygenated blood, and death ensues. For more than 50 years, mathematically based models of cardiac electrical activity have been used to improve understanding of basic mechanisms of normal and abnormal cardiac electrical function. Computer-based modeling approaches to understand cardiac activity are uniquely helpful because they allow for distillation of complex emergent behaviors into the key contributing components underlying them. Here we review the latest advances and novel concepts in the field as they relate to understanding the complex interplay between electrical, mechanical, structural, and genetic mechanisms during arrhythmia development at the level of ion channels, cells, and tissues. We also discuss the latest computational approaches to guiding arrhythmia therapy.

scholarly journals Robust Acoustic Scene Classification in thePresence of Active Foreground Speech

2021 ◽  
Author(s):  
Siyuan Song ◽  
Brecht Desplanques ◽  
Celest De Moor ◽  
Kris Demuynck ◽  
Nilesh Madhu
Keyword(s):  
Single Channel ◽  
Real Life ◽  
Scene Classification ◽  
Noise Floor ◽  
Percentage Points ◽  
Power Spectral ◽  
Covariance Models ◽  
Scene Information ◽  
Resource Constrained Devices ◽  
Constrained Devices

We present an iVector based Acoustic Scene Clas-sification (ASC) system suited for real life settings where activeforeground speech can be present. In the proposed system, eachrecording is represented by a fixed-length iVector that modelsthe recording’s important properties. A regularized Gaussianbackend classifier with class-specific covariance models is usedto extract the relevant acoustic scene information from theseiVectors. To alleviate the large performance degradation when aforeground speaker dominates the captured signal, we investigatethe use of the iVector framework on Mel-Frequency CepstralCoefficients (MFCCs) that are derived from an estimate of thenoise power spectral density. This noise-floor can be extracted in astatistical manner for single channel recordings. We show that theuse of noise-floor features is complementary to multi-conditiontraining in which foreground speech is added to training signalto reduce the mismatch between training and testing conditions.Experimental results on the DCASE 2016 Task 1 dataset showthat the noise-floor based features and multi-condition trainingrealize significant classification accuracy gains of up to more than25 percentage points (absolute) in the most adverse conditions.These promising results can further facilitate the integration ofASC in resource-constrained devices such as hearables.

scholarly journals Application of wearable EEG sensors for indoor thermal comfort measurements

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 214
Author(s):  
Silvia Angela Mansi ◽  
Ilaria Pigliautile ◽  
Camillo Porcaro ◽  
Anna Laura Pisello ◽  
Marco Arnesano
Keyword(s):  
Thermal Comfort ◽  
Brain Activity ◽  
Low Cost ◽  
Wearable Sensors ◽  
Real Life ◽  
Thermal Conditions ◽  
Ambient Temperatures ◽  
Human Thermal Comfort ◽  
Power Spectral ◽  
Wearable Eeg

Multidomain comfort theories have been demonstrated to interpret human thermal comfort in buildings by employing human-centered physiological measurements coupled with environmental sensing techniques. Thermal comfort has been correlated with brain activity through electroencephalographic (EEG) measurements. However, the application of low-cost wearable EEG sensors for measuring thermal comfort has not been thoroughly investigated. Wearable EEG devices provide several advantages in terms of reduced intrusiveness and application in real-life contexts. However, they are prone to measurement uncertainties. This study presents results from the application of an EEG wearable device to investigate changes in the EEG frequency domain at different indoor temperatures. Twenty-three participants were enrolled, and the EEG signals were recorded at three ambient temperatures: cold (16 °C), neutral (24 °C), and warm (31 °C). Then, the analysis of brain Power Spectral Densities (PSDs) was performed, to investigate features correlated with thermal sensations. Statistically significant differences of several EEG features, measured on both frontal and temporal electrodes, were found between the three thermal conditions. Results bring to the conclusion that wearable sensors could be used for EEG acquisition applied to thermal comfort measurement, but only after a dedicated signal processing to remove the uncertainty due to artifacts.

scholarly journals Effect of long-term training on heart rate variability, central hemodynamics and physical working capacity in female swimmers with different sports qualifications

2021 ◽  
Vol 23 (5) ◽  
Author(s):  
Ye. L. Mykhaliuk ◽  
V. V. Syvolap ◽  
Ye. Yu. Horokhovskyi
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Cardiac Activity ◽  
Mean Value ◽  
Stress Index ◽  
Physical Working Capacity ◽  
Central Hemodynamics ◽  
Working Capacity ◽  
Power Spectral

The aim of this study was to compare the indices of heart rate variability, central hemodynamics and physical working capacity in female swimmers with different sports qualifications. Materials and methods. The indices of heart rate variability (HRV), central hemodynamics (CH) and physical development (PD) were studied in 44 female swimmers (mean age 15.00 ± 0.36 years, swimming experience – 7.40 ± 0.35 years) depending on their sports qualifications (MSIC, MS, CMS, first- and second-class athletes). To analyze the autonomic regulation of cardiac activity, power spectral and time-domain indices of HRV were used. CH were examined by the method of automated tetrapolar rheography according to W. Kubiček et al. (1970) in Y. T. Pushkar’s et al. modification (1970). Physical working capacity was measured according to the generally accepted technique on a cycling ergometer using the PWC170 submaximal test. The functional state index (FSI) was calculated using the formula patented by authors. Results. Significant differences were found between the indices of HRV, CH and PD in female swimmers with different qualifications. Thus, in the athletes with the MSIC–MS sports qualifications, heart rate was 61.0 ± 3.8 bpm, cardiac index (CI) – 2.978 ± 0.098 L·min-1·m-2 (there was a trend towards the eukinetic type of hemodynamics (TH)), stress index (SI) – 51.16 ± 12.66 relative units (r.u.), PWC170/kg – 16.98 ± 1.22 kgm·min-1·kg-1, FSI – 6.511 ± 0.422 r.u. A decrease in heart rate among them was correlated with a decrease in SI, and an increase in Mo – with a decrease in CI. In female CMS swimmers, heart rate was 61.37 ± 2.83 beats/min-1, CI – 3.021 ± 0.112 l -1min -1·m -2 , a trend towards the predominantly eukinetic TH, SI – 53.73 ± 9.41 r.u., PWC170 /kg– 14.66 ± 0.683 kgm·min-1·kg -1, FSI – 5.683 ± 0.324 r.u. Reduced values of SI and CI were associated with increased values of Mo and PWC170/kg. In first- and second-class female swimmers, heart rate was 63.05 ± 2.22 beats/min, SI – 50.62 ± 6.4 r.u. This group tended to be eytonic and eukinetic. The mean value of the PWC170/kg was 14.19 ± 0.589 kgm·min-1·kg-1 and FSI – 5.953 ± 0.337 r.u. Correlation analysis confirmed the relationship between the decrease in heart rate and CI and the increase in Mo and PWC170/kg. Conclusions. Long-term training in female swimmers at the distance of 50 to 200 meters is accompanied by the significant increase in the PWC170/kg values with qualification improving, 14.19 ± 0.589 kgm·min-1·kg-1, 14.66 ± 0.683 kgm·min-1·kg-1; 16.98 ± 1.22 kgm·min-1·kg-1, respectively, improvements in HRV (decrease in stress index and increase in Mo) and decrease in CI.

scholarly journals Power Spectral Differences between Transient Epileptic and Global Amnesia: An eLORETA Quantitative EEG Study

2020 ◽  
Vol 10 (9) ◽  
pp. 613
Author(s):  
Jacopo Lanzone ◽  
Claudio Imperatori ◽  
Giovanni Assenza ◽  
Lorenzo Ricci ◽  
Benedetto Farina ◽  
...  
Keyword(s):  
Real Life ◽  
Power Spectral Analysis ◽  
Transient Global Amnesia ◽  
Epileptic Focus ◽  
Problem Solution ◽  
Beta Band ◽  
Power Spectral ◽  
Beta Power ◽  
Global Amnesia ◽  
Clinical Records

Transient epileptic amnesia (TEA) is a rare epileptic condition, often confused with transient global amnesia (TGA). In a real-life scenario, differential diagnosis between these two conditions can be hard. In this study we use power spectral analysis empowered by exact Low Resolution Brain Electromagnetic Tomography (eLORETA) to evidence the differences between TEA and TGA. Fifteen patients affected by TEA (64.2 ± 5.2 y.o.; 11 female/4 male; 10 left and 5 right temporal epileptic focus) and 15 patients affected by TGA (65.8 ± 7.2 y.o.; 11 females/4 males) were retrospectively identified in our clinical records. All patients recorded EEGs after symptoms offset. EEGs were analyzed with eLORETA to evidence power spectral contrast between the two conditions. We used an inverse problem solution to localize the source of spectral differences. We found a significant increase in beta band power over the affected hemisphere of TEA patients. Significant results corresponded to the uncus and para-hippocampal gyrus, respectively Brodmann’s Areas: 36, 35, 28, 34. We present original evidence of an increase in beta power in the affected hemisphere (AH) of TEA as compared to TGA. These differences involve key areas of the memory network located in the mesial temporal lobe. Spectral asymmetries could be used in the future to recognize cases of amnesia with a high risk of epilepsy.

scholarly journals Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study (Preprint)

2020 ◽  
Author(s):  
Luc JW Evers ◽  
Yordan P Raykov ◽  
Jesse H Krijthe ◽  
Ana Lígia Silva de Lima ◽  
Reham Badawy ◽  
...  
Keyword(s):  
Spectral Density ◽  
Validation Study ◽  
Real Life ◽  
Motor Fluctuations ◽  
Total Power ◽  
Data Set ◽  
Dopaminergic Medication ◽  
Medication Intake ◽  
Power Spectral ◽  
Sensor Locations

BACKGROUND Wearable sensors have been used successfully to characterize bradykinetic gait in patients with Parkinson disease (PD), but most studies to date have been conducted in highly controlled laboratory environments. OBJECTIVE This paper aims to assess whether sensor-based analysis of real-life gait can be used to objectively and remotely monitor motor fluctuations in PD. METHODS The Parkinson@Home validation study provides a new reference data set for the development of digital biomarkers to monitor persons with PD in daily life. Specifically, a group of 25 patients with PD with motor fluctuations and 25 age-matched controls performed unscripted daily activities in and around their homes for at least one hour while being recorded on video. Patients with PD did this twice: once after overnight withdrawal of dopaminergic medication and again 1 hour after medication intake. Participants wore sensors on both wrists and ankles, on the lower back, and in the front pants pocket, capturing movement and contextual data. Gait segments of 25 seconds were extracted from accelerometer signals based on manual video annotations. The power spectral density of each segment and device was estimated using Welch’s method, from which the total power in the 0.5- to 10-Hz band, width of the dominant frequency, and cadence were derived. The ability to discriminate between before and after medication intake and between patients with PD and controls was evaluated using leave-one-subject-out nested cross-validation. RESULTS From 18 patients with PD (11 men; median age 65 years) and 24 controls (13 men; median age 68 years), ≥10 gait segments were available. Using logistic LASSO (least absolute shrinkage and selection operator) regression, we classified whether the unscripted gait segments occurred before or after medication intake, with mean area under the receiver operator curves (AUCs) varying between 0.70 (ankle of least affected side, 95% CI 0.60-0.81) and 0.82 (ankle of most affected side, 95% CI 0.72-0.92) across sensor locations. Combining all sensor locations did not significantly improve classification (AUC 0.84, 95% CI 0.75-0.93). Of all signal properties, the total power in the 0.5- to 10-Hz band was most responsive to dopaminergic medication. Discriminating between patients with PD and controls was generally more difficult (AUC of all sensor locations combined: 0.76, 95% CI 0.62-0.90). The video recordings revealed that the positioning of the hands during real-life gait had a substantial impact on the power spectral density of both the wrist and pants pocket sensor. CONCLUSIONS We present a new video-referenced data set that includes unscripted activities in and around the participants’ homes. Using this data set, we show the feasibility of using sensor-based analysis of real-life gait to monitor motor fluctuations with a single sensor location. Future work may assess the value of contextual sensors to control for real-world confounders.

scholarly journals Modelling and Simulation for Preclinical Cardiac Safety Assessment of Drugs with Human iPSC-Derived Cardiomyocytes

2020 ◽  
Vol 122 (4) ◽  
pp. 209-257 ◽  
Author(s):  
Philipp Kügler
Keyword(s):  
Drug Safety ◽  
Heart Muscle ◽  
Cardiac Electrophysiology ◽  
Human Heart ◽  
Cardiac Activity ◽  
Muscle Cells ◽  
Estimation Methods ◽  
Proarrhythmic Risk ◽  
Heart Muscle Cells

Abstract As a potentially life threatening side effect, pharmaceutical compounds may trigger cardiac arrhythmias by impeding the heart’s electrical and mechanical function. For this reason, any new compound needs to be tested since 2005 for its proarrhythmic risk both during the preclinical and the clinical phase of the drug development process. While intensive monitoring of cardiac activity during clinical tests with human volunteers constitutes a major cost factor, preclinical in vitro tests with non cardiac cells and in vivo tests with animals are currently under serious debate because of their poor extrapolation to drug cardiotoxicity in humans. For about five years now, regulatory agencies, industry and academia are working on an overhaul of the cardiac drug safety paradigm that is built a) on human heart muscle cells, that can be abundantly bioengineered from donor stem cells without ethical concerns (human induced pluripotent stem cell derived cardiomyocytes, hiPSC-CMs), and b) on computational models of human cardiac electrophysiology both at the cellular and the organ level. The combined use of such human in vitro and human in silico models during the preclinical phase is expected to improve proarrhythmia test specificity (i.e. to lower the false-positive rate), to better inform about the need of thorough heart monitoring in the clinic, and to reduce or even replace animal experiments. This review article starts by concisely informing about the electrical activity of the human heart, about its possible impairment due to drug side effects, and about hiPSC-CM assays for cardiac drug safety testing. It then summarizes the mathematical description of human cardiac electrophysiology in terms of mechanistic ODE and PDE models, and illustrates how their numerical analysis may provide insight into the genesis of drug induced arrhythmias. Finally, this paper surveys proarrhythmic risk estimation methods, that involve the simulation of human heart muscle cells, and addresses opportunities and challenges for future interdisciplinary research.

scholarly journals The Intrinsic Cardiac Nervous System and Its Role in Cardiac Pacemaking and Conduction

2020 ◽  
Vol 7 (4) ◽  
pp. 54
Author(s):  
Laura Fedele ◽  
Thomas Brand
Keyword(s):  
Nervous System ◽  
Cardiac Electrophysiology ◽  
Heart Diseases ◽  
Cardiac Activity ◽  
Physiological Role ◽  
Cardiac Conduction ◽  
Pacemaker Activity ◽  
Cardiac Autonomic Nervous System ◽  
Anatomy And Physiology ◽  
Cardiac Pacemaking

The cardiac autonomic nervous system (CANS) plays a key role for the regulation of cardiac activity with its dysregulation being involved in various heart diseases, such as cardiac arrhythmias. The CANS comprises the extrinsic and intrinsic innervation of the heart. The intrinsic cardiac nervous system (ICNS) includes the network of the intracardiac ganglia and interconnecting neurons. The cardiac ganglia contribute to the tight modulation of cardiac electrophysiology, working as a local hub integrating the inputs of the extrinsic innervation and the ICNS. A better understanding of the role of the ICNS for the modulation of the cardiac conduction system will be crucial for targeted therapies of various arrhythmias. We describe the embryonic development, anatomy, and physiology of the ICNS. By correlating the topography of the intracardiac neurons with what is known regarding their biophysical and neurochemical properties, we outline their physiological role in the control of pacemaker activity of the sinoatrial and atrioventricular nodes. We conclude by highlighting cardiac disorders with a putative involvement of the ICNS and outline open questions that need to be addressed in order to better understand the physiology and pathophysiology of the ICNS.

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