Heartbeat Control Research Articles

Adaptive Circadian Rhythms for Autonomous and Biologically Inspired Robot Behavior.

Biological rhythms are periodic internal variations of living organisms that act as adaptive responses to environmental changes. The human pacemaker is the suprachiasmatic nucleus, a brain region involved in biological functions like homeostasis or emotion. Biological rhythms are ultradian (<24 h), circadian (∼24 h), or infradian (>24 h) depending on their period. Circadian rhythms are the most studied since they regulate daily sleep, emotion, and activity. Ambient and internal stimuli, such as light or activity, influence the timing and the period of biological rhythms, making our bodies adapt to dynamic situations. Nowadays, robots experience unceasing development, assisting us in many tasks. Due to the dynamic conditions of social environments and human-robot interaction, robots exhibiting adaptive behavior have more possibilities to engage users by emulating human social skills. This paper presents a biologically inspired model based on circadian biorhythms for autonomous and adaptive robot behavior. The model uses the Dynamic Circadian Integrated Response Characteristic method to mimic human biology and control artificial biologically inspired functions influencing the robot's decision-making. The robot's clock adapts to light, ambient noise, and user activity, synchronizing the robot's behavior to the ambient conditions. The results show the adaptive response of the model to time shifts and seasonal changes of different ambient stimuli while regulating simulated hormones that are key in sleep/activity timing, stress, and autonomic basal heartbeat control during the day.

HRV based feature selection for congestive heart failure and normal sinus rhythm for meticulous presaging of heart disease using machine learning

Heart rate variability (HRV) for subjects of Congestive heart rate failure and normal sinus rhythm is structured to be subjected to turbulence studies. When checking the scaling property for NSR subjects, it indicates the presence of measured heartbeat control mechanisms, and concerning CHF subjects found the scaling property is partially lost. So the absence of scaling properties indicates heart rate variations. Various factors regularize the regular pulse of the heart. It is observing the methods for noticing the interaction between the thoughtful and parasympathetic sensory systems. The versatility of the heart to outside also inner boosts is reflected by the pulse changeability (HRV) diminished. HRV is a key indicator that shows if patients have negative HD results. Given the Time domain, frequency domain, and nonlinear and profoundly complex elements parameters for heart disease. It has restricted the measure's ability to precisely investigate the basic elements. In this examination, we propose a framework to detect HRV by feature selection methods that highlight relevant features, and complex elements and the best method. This paper uses HRV features and the dataset of HRV for 419 instances which is real dataset (Max Hospital mohali) to be taken and feature selection methods are to be used such as the filter method, Wrapper method, and embedded method to select the relevant feature set. This paper's implementation is done by using a Machine learning classifier, for example, Random forest (RF), KNN, support vector machine, Naïve Bayes, and Decision tree be utilized to assess the identification execution. The experiment's result is best for the filtering method in requisites of Accuracy, and F-measure. The resulting performance on the data available is better than previous feature selection models that were used. The current work is implemented on the Python Jupyter notebook, it enables the evaluation and provides excellent performance. The results for the current work the filtering methods are considered the best method for feature selection the average accuracy without without-FS is 86.3492064, the filtering method is 95.3968254, the Wrapper method is 94.9206348, and the embedded method 93.8095236., the average score of F measure for without FS is 83.7949952, Filtering Method 95.2867576, Wrapper Method 94.8161982, Embedded Method 93.4696154, the average score is AUC for without-FS 85.2,650,446, by Filtering method is 95.0550574, for Wrapper method 94.6734954, for Embedded Method 93.0729834, the basic approach of feature extraction is also shown for this current work. From the health point of view in this study, the HRV parameters using machine learning technologies could easily predict heart disease at early stages, and accuracy in results achieved higher.

Autonomic modulation of chronic kidney patients on hemodialysis

Chronic kidney Disease may progress to cardiovascular complications just as much as these complications can result in further deterioration of the renal function, with dialysis as an extreme therapeutical option. Heart Rate Variability is a noninvasive method for the evaluation of sympathovagal imbalance of the heart beat control, aiding on the early detection of cardiovascular malfunctions. Our purpose was to identify heart rate variability patterns in chronic kidney disease patients undergoing hemodialysis treatment, both by linear and nonlinear analysis.Were sampled a group of 27 renal failure patients under hemodialysis care, and a control group of 21 healthy individuals. The RR intervals were captured with a Polar® device from subjects lying in supine position for 20 minutes. A total of 1000 consecutive intervals were selected, and both linear and nonlinear parameters were considered for the heart rate variability analysis. Were used Shapiro-Wilk test, Mann-Whitney test, and Spearman test. Among the findings with statistical significance, reductions were found in RMSSD and pNN50, and SD1 and SD2 indexes whereas increases in the recurrence percentage and in entropy could be observed. A significant negative correlation between age and the values of SD1 index was also found. Patients with renal failure present a lower autonomic modulation of the parasympathetic branch, as highlighted by both the linear and nonlinear analysis. The nonlinear indexes in the Chaos domain were more precise in identifying autonomic dysfunctions, can reflect more clearly the complexity of the organic systems.

Synaptic Strengths Dominate Phasing of Motor Circuit: Intrinsic Conductances of Neuron Types Need Not Vary across Animals.

Identified neurons and the networks they compose produce stereotypical, albeit individually unique, activity across members of a species. We propose, for a motor circuit driven by a central pattern generator (CPG), that the uniqueness derives mainly from differences in synaptic strength rather than from differences in intrinsic membrane conductances. We studied a dataset of recordings from six leech (Hirudo sp.) heartbeat control networks, containing complete spiking activity patterns from inhibitory premotor interneurons, motor output spike patterns, and synaptic strength patterns to investigate the source of uniqueness. We used a conductance-based multicompartmental motor neuron model to construct a bilateral motor circuit model, and controlled it by playing recorded input spike trains from premotor interneurons to generate output inhibitory synaptic patterns similar to experimental measurements. By generating different synaptic conductance parameter sets of this circuit model, we found that relative premotor synaptic strengths impinging onto motor neurons must be different across individuals to produce animal-specific output burst phasing. Obtaining unique outputs from each individual's circuit model did not require different intrinsic ionic conductance parameters. Furthermore, changing intrinsic conductances failed to compensate for modified synaptic strength patterns. Thus, the pattern of synaptic strengths of motor neuron inputs is critical for the phasing of this motor circuit and can explain individual differences. When intrinsic conductances were allowed to vary, they exhibited the same conductance correlations across individuals, suggesting a motor neuron "type" required for proper network function. Our results are general and may translate to other systems and neuronal networks that control output phasing.

A pilot study of heartbeat control by heartbeat-music feedback system

A pilot study of heartbeat control by heartbeat-music feedback system

SLA-based management of software licenses as web service resources in distributed computing infrastructures

Until recently the use of applications requiring a software license for execution was quite limited in distributed environments. Due to the mandatory centralised control of license usage at application runtime, e.g. heartbeat control by the license server running at the home site of a user, traditional software licensing practices are not suitable especially when the distributed computing infrastructure stretches across administrative domains. In this paper we present a novel approach for managing software licenses as web service resources in distributed service oriented environments. Licenses become mobile objects, which may travel to the environment where required to authorise the execution of a license protected application. A first implementation has been realised for dynamic Grid environments in the European SmartLM project co-funded by the European Commission. The SmartLM solution decouples authorisation for license usage from authorisation for application execution. All authorisations are expressed as and guaranteed by Service Level Agreements. We will present the core technology, discuss various security aspects and how they are addressed in the SmartLM prototype, and present the evaluation of the prototype through a number of usage scenarios. Finally, we will give an outlook on specific issues and current work extending the solution to Clouds and service based systems in general.

レートレスポンスパルスジェネレーター組込み型ホルター心電計の心拍コントロール評価に対する有用性 慢性心房細動での検討

レートレスポンスパルスジェネレーター組込み型ホルター心電計の心拍コントロール評価に対する有用性 慢性心房細動での検討

Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics.

We postulated that aging is associated with disruption in the fractallike long-range correlations that characterize healthy sinus rhythm cardiac interval dynamics. Ten young (21-34 yr) and 10 elderly (68-81 yr) rigorously screened healthy subjects underwent 120 min of continuous supine resting electrocardiographic recording. We analyzed the interbeat interval time series using standard time and frequency domain statistics and using a fractal measure, detrended fluctuation analysis, to quantify long-range correlation properties. In healthy young subjects, interbeat intervals demonstrated fractal scaling, with scaling exponents (alpha) from the fluctuation analysis close to a value of 1.0. In the group of healthy elderly subjects, the interbeat interval time series had two scaling regions. Over the short range, interbeat interval fluctuations resembled a random walk process (Brownian noise, alpha = 1.5), whereas over the longer range they resembled white noise (alpha = 0.5). Short (alpha s)- and long-range (alpha 1) scaling exponents were significantly different in the elderly subjects compared with young (alpha s = 1.12 +/- 0.19 vs. 0.90 +/- 0.14, respectively, P = 0.009; alpha 1 = 0.75 +/- 0.17 vs. 0.99 +/- 0.10, respectively, P = 0.002). The crossover behavior from one scaling region to another could be modeled as a first-order autoregressive process, which closely fit the data from four elderly subjects. This implies that a single characteristic time scale may be dominating heartbeat control in these subjects. The age-related loss of fractal organization in heartbeat dynamics may reflect the degradation of integrated physiological regulatory systems and may impair an individual's ability to adapt to stress.

Heartbeat control in the medicinal leech: A model system for understanding the origin, coordination, and modulation of rhythmic motor patterns

We have analyzed in detail the neuronal network that generates heartbeat in the leech. Reciprocally inhibitory pairs of heart interneurons form oscillators that pace the heartbeat rhythm. Other heart interneurons coordinate these oscillators. These coordinating interneurons, along with the oscillators interneurons, form an eight-cell timing oscillator network for heartbeat. Still other interneurons, along with the oscillator interneurons, inhibit heart motor neurons, sculpting their activity into rhythmic bursts. Critical switch interneurons interface between the oscillator interneurons and the other premotor interneurons to produce two alternating coordination states of the motor neurons. The periods of the oscillator interneurons are modulated by endogenous RFamide neuropeptides. We have explored the ionic currents and graded and spike-mediated synaptic transmission that promote oscillation in the oscillator interneurons and have incorporated these data into a conductance-based computer model. This model has been of considerable predictive value and has led to new insights into how reciprocally inhibitory neurons produce oscillation. We are now in a strong position to expand this model upward, to encompass the entire heartbeat network, horizontally, to elucidate the mechanisms of FMRFamide modulation, and downward, to incorporate cellular morphology. By studying the mechanisms of motor pattern formation in the leech, using modeling studies in conjunction with parallel physiological experiments, we can contribute to a deeper understanding of how rhythmic motor acts are generated, coordinated, modulated, and reconfigured at the level of networks, cells, ionic currents, and synapses.

Application of computer averaging techniques to the study of learned volitive heartbeat control

Research on the acquisition of conscious cardiovascular control (Yellin 1984, 1986) utilized computer averaging of electrocardiographic (ECG) cycles to demonstrate and document precise, reliable and sustainable time-locking of heartbeat to external stimuli on demand. ECG cycles were treated as evoked responses, and successive epochs were summated, beginning with Zeitgeber-stimulus (ZS) onset, and with the duration of each epoch equaling the ZS's interstimulus interval. Cardiac synchrony was exhibited by event-modulated (time-locked) cardiac responses (EMCRs) and in average ECGs.

Patterned activity of the buccal ganglion of the nudibranch mollusc Archidoris pseudoargus.

Patterned activity of the buccal ganglion of the nudibranch mollusc Archidoris pseudoargus.