Heart Controller Abstract
I present a concept for autonomic cardiac pacing as a method to augment existing physiological pacing for both ventricular assist devices (VAD) and heart transplantations. The following development represents a vision and reflects an area that has yet to be fully exploited in the field. Therefore, the analysis is meant to be a starting point for further study in this area. Furthermore, an automatic control system methodology for both heart rate and contractile force (stroke volume) of patients having either an artificial left ventricular assist device (LVAD) or who have experienced degenerative performance of the Sinoatrial node is suggested. The methodology is described both in terms of a device and associated operational framework, and is based on the use of the naturally-occurring hormones epinephrine, norepinephrine, and dopamine contained in the return blood flow through the superior vena cava. The quantities of these hormones measured in the blood stream are used to derive a proportional response in terms of contractile force and pacing of the Sinoatrial node. The method of control suggests features normally described using cyclic voltammetry, expert systems, and feedback to pacing an artificial assist device.
AVN, Atrioventricular Node
CO, Cardiac Output
FDA, Food and Drug Administration
LVAD, Left Ventricular Assist Device
NHLBI, National Heart, Lung, and Blood Institute
SAN, Sinoatrial Node
SV, Stroke Volume
TAH, Total Artificial Heart
TCI, Threshold Crossing Intervals
VAD, Ventricular Assist Device
Artificial assist devices that exist today normally operate by controlling heart rate based on Proprioceptors–position of limbs & muscles during physical activity, or Baroreceptors–monitoring blood pressure in major arteries & veins. None to date operate on the basis of Chemoreceptors: monitoring changes in chemical makeup of the blood stream in direct response to epinephrine production by the adrenal medulla. Such changes are, however, more consistent with the operation of the human heart. For instance, heart rate varies not only according to mechanical movement of limbs, but also as the result of changes in emotion. Such changes manifest themselves as increases and decreases in sympathetic and parasympathetic hormones. Sympathetic hormones (epinephrine, norepinephrine) tend to increase stroke work and heart rate, whereas parasympathetic hormones (acetylcholine) tend to lower heart rate. These two hormones operate to control the resting rate of the heart and its changes as a result of higher-brain center changes (including production of hormone by the adrenal medulla). Benefits of achieving this capability include more naturally-behaving artificial hearts, or, in the case in which a human heart is merely being paced by an assist device, to control normal heart function in relation to changes in hormone production. Thus, the controller described herein provides an adjunct to existing controllers. Specific methods outlining artificial control mechanisms for affecting heart rate and contractility have not specifically been described in the literature, although related concepts have been suggested .
Autonomic Heart Regulation
Autonomic regulation of heart rate is controlled via one of the following specific systems within the body [2, 3, 4, 5, 6].
- Proprioceptors – monitor general movement—position of limbs & muscles during physical activity
- Chemoreceptors – monitor chemical changes in blood
- Baroreceptors – monitor blood pressure in major arteries & veins
- Chemical regulation (Hypoxia, Acidosis, Alkalosis)
- Hormones (Catecholamines & thyroid)
- Cations (balance of K+, Na+, Ca2+)
Current artificial heart assist devices operate using feedback from items (1) and (3) alone. Long-term (weeks to months) artificial heart assist devices popularly used for ventricular support, all under the auspices of NHLBI , include:
- Abiomed extracorporeal, pneumatically-driven, pulsatile, left, right, or biventricular, introduced in 1988, FDA approved for in-hospital use for low output syndrome.
- Thoratec extracorporeal, pneumatically-driven, pulsatile, left, right, or biventricular approved for in-hospital use for post cardiotomy low output and as a bridge to transplantation.
- TCI (Heart rate) implantable , , pulsatile, pneumatically-driven, approved for in-hospital use as a bridge to transplantation. The electrically powered totally implanted configuration with a wearable power source, transcutaneous power lead and vent, is approved for in-hospital as well as out-of-hospital use for bridging to transplantation and is currently used under IDE in the randomized REMATCH trial.
- Novacor  implantable electric, pulsatile, left ventricular (LV), wearable power source, transcutaneous power lead and vent, is approved for in-hospital and out-of-hospital use for bridge to transplantation.
Underlying Motivation for the Heart Control Method
The suggested device measures the chemical content of the blood flow past the sinoatrial node, processes the chemical content via cyclic voltammetry [11, 12, 13, 14] to determine the amount of epinephrine (EPI) and norepinephrine (NE), and uses this information to establish the resting and various pacing heart rates of a left ventricular assist device. Furthermore, researchers  focusing on advances in heart assist devices have identified characteristics desired for upcoming artificial organs. These include (1) miniaturization, (2) interfaces with nerves for automatic control, (3) control systems that are acceptable for both the living body and the embedded artificial organs, and (4) harmonization with the living body in various ways, including interfaces with higher brain centers and reduction of thrombus (and the associated foreign body rejection issues).
“the neuronal and hormonal control of the circulation, including the control of the heart, is mainly effectuated by the autonomic nervous system and its hormonal transmitters, the catecholamines. Autonomic control of the circulation primarily operates through the sympathetic system, though to a slight extent through parasympathetic signals to the heart. These have been lumped together, and there are basically three separate feedback mechanisms in this computational block. These are (1) feedback from the baroreceptor control system; (2) feedback from the peripheral chemoreceptors in the carotid and aortic bodies, and (3) feedback control of the circulator system caused by central nervous system ischemia, that is, ischemia of the vasomotor center in the brainstem. Another input that affects the autonomic nervous system is also included: The activation of the autonomic nervous system during exercise.” 
Methods for measuring serum epinephrine levels exist. WPI  suggests that sensitive, low noise carbon fiber (CF) and carbon disk (CD) electrodes can be employed in the electrochemical detection of catecholamines (e.g.: EPI, NE, Dopamine). As reported on their Web Site , the CF30-500 class of Carbon Fiber Disk Microelectrodes shows current output in Pico Amperes versus Dopamine concentration (nanograms/milliliter). In this analysis, WPI sites work by D. Yeomans and X. T. Wang of the University of Illinois. The analysis shows excellent linearity characteristics for CF filaments ranging in size from 10 to 30 microns to compounds with…detection limit[s] as low as 0.2 nanoMoles. Figure 1 illustrates this linear relationship .
Figure 1: Carbon Fiber Current Measurement versus Dopamine Concentration.
WPI reports  that “longer CF electrodes…provide higher sensitivity and larger signal to noise ration. They are hence very suitable for in vitro amperometric and differential pulse voltammetry (DPV) in which…voltage scan rates are much lower.”
WPI further suggests  “extracellular recordings using CD electrodes (CD-30) in CA1 region of the hippocampus in an anesthetized rat shows ultra-low noise (<5 microVolts).” Voltage response in same region varied between +50 and –100 microVolts with response time less than 5 milliseconds. This is important from the real-time implantable device perspective as changes in responses need to be measured rapidly in order to correctly mimic actual behavior. A suggested bio-sensing device based on those implemented by WPI and others [22, 23] is illustrated in Figure 2.
Figure 2: Author’s biosensing device based on technology suggested by WPI literature to measure current response to catecholamines using cyclic voltammetry.
While WPI stated  at the time of publication that there was no existing electrochemical method that exhibited selectivity among the members of the catecholamines or indolamines, others , reporting later, suggest that there may be ways to do this.
The Heart Conduction System
Now that the electrochemical mechanism has been suggested, let’s proceed with the automatic control methodology. Figure 3 indicates the hierarchy in which electrical signals and pacing are conducted throughout the human heart .
Figure 3: Major heart rate pacing centers.
The Sinoatrial Node: Primary Heart Pacemaker
The SAN, the primary pacemaker for the heart, is located in the rear wall of the right atrium near the opening of the superior vena cava. The SAN has the fastest rhythm producing, on average, from 60-70 action potentials per minute. SAN pacing overrides all others. The action potential originated within the SAN travels through walls of the atria causing contraction. Internodal pathways connect the SAN to the atrioventricular node (AVN). The AVN is the located in the right posterior portion of interatrial septum. In the absence of SAN pacing, the AVN can take over, although the resting rate is slower (40-60 action potentials per minute). Next, the AV Bundle of His divides into both right and left bundle branches in the ventricular septum and is the only electrical connection between atria & ventricles. The Bundle of His is capable of generating from 30-40 action potentials per minute. The Purkinje fibers (4) are distributed throughout the ventricular myocardium and synchronize ventricular contraction. Ventricular muscles can generate from 20-30 action potentials per minute.
The Mechanics of Catecholamine Measurement
The SAN is that which is affected by stimuli such as adrenaline, exercise, drugs, etc. As discussed, chemoreceptors that detect changes in catecholamine levels (EPI, NE, Dopamine) translate into changes in pacing and contractility. Studies of environmental stress on epinephrine levels in humans show rather discernable relationships between EPI levels and work-related stress . Table 1 depicts the NE levels in male and female managers as a function of time in the workday .
Table 1: Norepinephrine Change Before & After Work.
Catecholamines: Regulate Heart Function
Catecholamines affect the sinoatrial cells as illustrated within the simplified cartoon of Figure 4 . The medullary cardiovascular control center in the brain contains both sympathetic and parasympathetic neurons that act as agonists to control contractility (ventricles), constriction (veins, arterioles), and control secretion of hormones to which the SAN responds. Carotid and aortic baroreceptors also respond to changes in blood pressure which provides feedback to the medullary control center that, in turn, affects contractility, pulse, and vasoconstriction.
Figure 4: Diagram depicting homeostatic control of pulse and contractility.
The sympathetic and parasympathetic hormones EPI and acetylcholine are secreted from the adrenal medulla and have the effect of causing increased and decreased pulse and contractility, respectively . NE is a principal neurotransmitter in the sympathetic nervous system and is an a-adrenoceptor agonist , implying strong vasoconstrictor response, and, therefore, affects systolic and diastolic blood pressures as well as heart rate and contractility through b1-adrenoceptors . Metabolism of EPI and acetylcholine at the cellular level is illustrated in the diagram of Figure 5. Relationships between stroke volume and contractile force are well known in the literature . Thus, methods of measuring changes in catecholamine concentration must accommodate the combination of both pulsatile rate and contractility. The effects of sympathetic and parasympathetic nervous system on cardiovascular performance are known .
Figure 5: Metabolism method of Epinephrine and Acetylcholine at cellular level.
Changes in sympathetic and parasympathetic hormone affect the arteriolar smooth muscle fibers, the ventricular myocardium, and SAN to affect (as described previously) vasodilation, contractility, and heart rate, respectively. Studies have quantified the effects of changes in mean plasma catecholamine levels both in vivo and in vitro using techniques such as cyclic voltammetry and blood concentration measurement [35, 36]. Heart pacing set by SAN is regulated by antagonistic mechanisms: primarily through sympathetic innervations (release of NE, EPI – increases rate) and parasympathetic innervations (release of acetylcholine—lowers rate). These innervations act to regulate and control mean arterial pressure through heart rate, stroke volume, and constriction and dilation of arterioles, arteries, and veins. Measuring the amount of EPI, NE, and dopamine in vitro has been performed . One measurement approach is via cyclic voltammetry, details of which are available in the literature. Figure 6 depicts the author’s reconstruction of the relationship between carbon fiber peak anode current and Dopamine levels obtained via in vitro measurement using gold-tipped nanotube electrodes . NE and EPI are released from chromaffin cells. Discrimination between NE and EPI from the same cell has been reported to be possible using slow-scan cyclic voltammetry . Furthermore, the use of amperometry enables sub-millisecond release events to be measured . The mechanism for EPI and NE measurement via chromaffin cell ordinarily involves contacting the cell surface via microelectrode. Furthermore, placing microelectrodes at a distance exceeding several microns can result in significant signal loss .
Figure 6: Carbon Fiber Anode Current versus Dopamine Concentration.
Suggested Heart Controller Implementation
A methodology is suggested that provides an initial point-of-departure for refinement and iteration. The starting point depends upon the relationship between plasma concentrations of the sympathetic and parasympathetic hormones. Figure 7, Figure 8 and Figure 9 derive a rather simple relationship between HR and NE levels .
Figure 7: Heart rate variability with NE concentration, 13 patients prior to, during, and post sleep measurement of plasma NE levels.
Figure 8: Heart rate variability with EPI concentration, same sample as in previous figure.
Figure 9: Plasma NE versus EPI relationship.
The relationship is somewhat misleading in that the implication is that NE is causative with respect to HR. This is only partially true, Figure 9 illustrates with respect to EPI. Again, the relationship provides only a partial picture. Heart rate is only one component affected. It is worthwhile to point out again that these relationships are not necessarily causative. The association between NE and EPI plasma concentrations is shown in Figure 9. Analysis indicates a linear relationship exists in EPI the ranges specified, and the correlation appears to be quite good, suggesting a relatively simple predictive model. While HR relationship to hormone level suggests a linear dependency within the specified NE range, another study , Figure 10 shows a more nonlinear relationship between cardiac output and EPI. This relationship is suggestive of an optimum level of CO change with respect to EPI concentration in dogs. While not conclusive, this relationship serves to illustrate the point that a nonlinear relationship can exist that must be represented in the modeling of plasma hormone levels and the effect on contractility and pulse.
Figure 10: Relationship between cardiac output and EPI levels as a percentage from baseline between 10 & 240 minutes from start of infusion in dogs.
- Determine the resting pulse and cardiac output of a patient, where pulse (/min) x SV (liters) = CO (liters/min).
- Measure the blood plasma EPI, NE, acetylcholine, and dopamine levels. This establishes the baseline state of the patient.
- Measure the patient’s pulse and CO and draw blood samples associated with the plasma levels of the hormones during specific activities, including vigorous exercise, sleeping & awakening. This establishes the training set of inputs (i.e., hormone levels) and outputs (i.e., pulse, CO).
- Build the expert system training model that establishes the inclusive range on hormonal input versus output parameters.
Calibrate the voltammetric sensor (invasive component) for measuring real-time anode current versus hormonal concentration. Anode current levels correlate to different voltage samples using cyclic voltammetry. Peak anode currents vary according to hormone level.
Thus, the specific level of each hormone level would be identified on the basis of the sample voltage value. The training mechanism involved relies on taking known inputs (e.g.: catecholamine levels) and measuring outputs, then using these values to develop a training matrix that establishes the transformation between the input and output (e.g., pulse, stroke volume). So, very crudely, this might be represented as follows:
wherein the xform(training) matrix is determined based on the input and output. Note that this is not a single matrix and not this simplistic in representation: an array of inputs and matching outputs will need to be determined that will translate into classes of transformation matrices. Of course, the viability of this approach would need to be determined. Furthermore, the outputs would provide only one component of input determinant to cardiac behavior. The effects of vasoconstriction, for example, must also be accommodated in terms of its effect on arterial pressure and loading.
In Vivo Operations:
- Hormonal concentration derived value from cyclic voltammetry defines the input parameters (test parameters) used as input to the feed-forward expert system trained using the training set developed above.
Output pulse and CO in terms of pacing trigger voltage to SAN defines the derived pulse and, thus, the appropriate rate for patient heart function based on catecholamine levels.
This new pacing relationship between input hormonal levels and output pacing can be maintained for a specific patient within a processing chip associated or in proximity to a pacemaker unit. The trained relationship then establishes the expected behavior for a cardiovascular pacing or left ventricular assist device. The equation relating pacing to hormone level can be stored in a secure electronic patient record (for instance) for recall, updated training, or for use in data mining to compare and develop more complex relationships with those of other patients. While this methodology does indeed require validation and refinement, it defines a vision for possible implementation. The very nonlinear relationships among the input and output variables cannot be simply represented using one-dimensional mathematical relationships. Furthermore, mechanical and physical issues remain that will be challenging. For example, biofouling of in vivo electrodes must be overcome and represents a formidable technological challenge .
The preceding describes a rough and partially complete model based on lab research that is suggestive of baroceptor measurement of in vivo catecholamine levels. The motivation behind the approach is the lack of capability in current LVAD technologies that focus on this aspect of autonomic pacing. The methodology and concept would also apply in those cases in which patients may have damaged SANs.
As pointed out, detailed training issues related to generalization to any LVAD, validation of the range of catecholamine concentrations and impacts on pacing is that pulse and contractility are well behaved and do not pose a hazard to the patient, issues related to biofouling of sensors and calibration must be addressed. However, even before considering implementation, concept and technology proof-of-principle must be validated. This will require both human and non-human trials. Operationally, manufacturing and implementation challenges must be overcome.
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 Ibid., page 13
 Ibid., page 22.
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[Heart Controller is one area of research interest. See my dissertation for my primary interest area in critical care.]