Last week I was reading in Science et Vie to be stunned by an article on predicting heart attacks mathematically from cardiac monitoring after people visit an emergency room. The article cites with lead author involving work completed recently both in North America, England and Germany by Zeesham Syed currently working at the University of Michigan.
In medicine we normally use blood bio-markers as protein signals that with time plus with much analysis become predictive for assessing risk outcome, especially with something critical like following a heart attack. From this work these researchers were able to use regular electro cardiogram (EKG) recordings to evaluate the underlying risk of patients who had just suffered a heart attack then following them after a year, checking these same patients to see how they were doing. Unfortunately some of these people died, so the researchers asked: is there a way to measure the EKG in some way to spot those patients who are at risk before they die way back a year earlier when they showed up at the Emergency Room? Is there some sort of reliable indicator to extract from the EKG that accurately evaluates the impending cardiovascular crash, can there be a neurocardiac indicator that essentially is a flashing warning light of the future crash? It appears there is just such a computed neurocardiac bio-marker-a critical status of metabolic state of the brain control parameter that relates to the outcome of the performance of the heart beat pacing control running on its own. Zeesham Syed has demonstrated, especially after his own father suffered a heart attack, realized he wanted to understand the future risk toward potential deterioration of his father after the heart attack. Mathematically treating the EKG in a novel way, with these other German collaborators, they concluded that impending failure of the heart is happening as a slow crash within the brain at the heart pacing control messaging to the heart. It’s as if the central brain message slowly stops properly texting its pacing synchronization app to the heart, to rhythmically beat as it were. It’s all part of the balance sequence within the autonomic nervous system that controls our heart beating every single beat of our life. It’s at the center of what is termed the sympathetic versus the parasympathetic balance of central cardiac control. It’s where we think concussions go off balance too as our working hypothesis towards developing a diagnostic.
So what are these new measurements all about involving the EKG? Cardiologists, because of time constraints usually take snap shots of the EKG trying to value this rapid assessment as a good way toward assessing how the heart is actually behaving. It’s a very flawed picture for a variety of reasons. The EKG has the capacity to give back a huge amount of data on the overall health of he heart. A days worth of EKG gives a huge chunk of this cardiac, performance especially balancing information, because the pacing is at a potential tipping point all the time. But the internal central priming of pacing always varies moment to moment. It’s like driving in heavy traffic, instead of car speed it’s heart speed, speeding up the heart rate then slowing down the heart, it’s all part of everyday existence for everyone, this moment to moment pacing control. There are built-in preferences for our daylight heart pacing and for our nighttime heart pacing. What these researchers have evaluated is: what speeding up of the heart on its own or slowing down of the heart on its own can be tracked as a measure of the central control balance going off following a minor heart attack? Here in Syed’s words are what happens with a heart decreasing on its own, what is termed, deceleration capacity. The opposite effect of a heart rate increasing on its own is termed, acceleration capacity. “Deceleration capacity attempts to make a distinction of vagal (slowing down)and sympathetic (speeding up) effects on cardiac activity by separate assessment of deceleration-related and acceleration-related variability in the heart rate. An impaired deceleration capacity, suggests cardiac unresponsiveness to vagal modulation, and has been shown to indicate a lack of ability of the autonomic nervous system to counteract the effects of sympathetic upregulation.” ref: published 28 September 2011, Sci. Transl. Med. 3, 102ra95 (2011) in Supplimentary Materials for Computationally generated Cardiac Biomarkers for risk Stratificationm After Acute Coronary Syndrome by Zeeshan Syed, Collin M. Stulz, benjamin M. Scirica, and John V. Gut. The autonomic control system behaves kind of like an erratic driver surging forward or suddenly braking depending on the demand of traffic in his lane. Traffic is chaotic and so is the central pacing control of the heart, its how Nature regulates the pacing character.
The actual calculation of deceleration capacity was measured for each patient using the libRASCH software generously shared for research use by the inventors of the method, Technische Universitat Munchen, Munich Germany. Grading patients in this manner segregates the patients into high risk groups versus low risk groups for future cardiac death. One of the new methods is actual shape analysis of the EKG signal itself. I find this so ironic that if the underlying changes within the central autonomic nervous system are EKG shape based, tensegrity signal nets behaving as I have described in previous posts as mesh-links. So to use morphology or shape of the ECG signal is amazing. It’s shape in- shape out in order to resolve the organization
Cardiac abnormalities manifest themselves in the dynamics of the 24 hr ECG Holter harness 12 lead of recorded signal affecting a swath-range of electrophysiological characteristics to be associated with the actual shape of the ECG signal given the dynamics of the ECG signal. According to Syed, ‘Data from in-vivo studies following induced experimental myocardial infarction suggest that the cellular abnormalities in the surviving myocardial tissue may produce a wide spectrum of abnormalities during both the acute and the convalescent phases after infarction.’ Syed has hypothesized that high risk patients will have a variety of subtle ECG changes that differ from low risk patients. They also focus their efforts on using advanced analytical methods drawn from machine learning to better support the activity that is normal from the abnormal which is usually unavailable prior knowledge. The analytic methods of Syed plus the collaborators attempt to identify in a population given the incompleteness of characterization how subtle electrophysiological deregulation evolves following an acute coronary event using algorithms involving artificial intelligence, or machine learning, to characterize the signals that support normal activity.
I see this type of analysis as comparable to watching waves on the ocean. There is a regular wave pattern arriving but there are all kinds of other influences that change the character of the arriving waves especially rogue waves coming from a distance, waves from boats traversing, winds swirling changing direction, blowing into waves in different crossing patterns looking for comparable patterns in the chaos, identifying groups of waves who have similar risk of behavior features (good surfing waves) that differ from waves toward developing similar behavior patterns at a lower risk (lousy surfing waves). Imagine sitting on a surfboard: how do you pick a good wave? This is the process to get a good size wave to ride onto. You learn (machine learn?) to pick the appropriate good wave not at its highest, you learn to pick potential good waves (high risk) , given the winds, given the time of day, given the location the beach. So lets back to the analogy, accelerating waves are the becoming big ones that the surfer learns to recognize then there are the decelerating waves that he uses to choose only at the end of the day when he’s too tired to take on another big one. Such is the type of format that applies to recognition for the potential shape change of the EKG discriminating to either a acceleration capacity shape of the EKG or choosing a deceleration capacity shape of the EKG. Capiche ???
Lets go back to previous points that I have brought forward. Seizures are single concussions according to Nigel Shaw. Seizures involve cardiac decoupling, with risk of sudden death: major, major decoupling. Cerebral concussions are major shape changing events within the brain. Remember, because of the architecture of tensegrity holding everything together, Penfield and Foerster noticed triggering, mechanical brain-strain in 1930 to cause seizure initiation. Cardiac pacing happens through the vagal system and the sympathetic autonomic system, losing balance of this dynamic shifts the potential toward erratic arrhythmia involving a newly established less dynamic less chaotic pacing balance. Pudentz noticed dual-hemisphere rotation from high speed cinematography viewing the brain through a plastic see-thru calvarium, in the early 1950’s after induced brain impact. According to Donald Ingber, the brain is a tensegrity organized architectural structure. After impact to the brain from any direction, the brain self-rotates around the central axis during the contre-coup of the rebound because Nature built it that way. Strain is placed on the vagal nerves both at the entry point of the vertebral foramena plus at the medulla insertion point. Cerebrovortex strains the vagal nerves like twisting a towel wringing out the water. Strain is shape change. Vagal nerve stimulation suppresses seizures the gloss-pharyngeal complex too, vagal nerve stimulation returns cardiac pacing into normal patterns. The vagal complex, is involved in cerebral concussions. Our Tamimi hypothesis will evaluate to test sympathetic/parasympathetic decoupling involving net bone increase, a longer time frame parasympathetic surge, tipping the balance point also for cardiac pacing in a much shorter reaction time phase. Hence our latest from last week via yours truly, a second Hollie hypothesis (in honor of my father) : cardiac decoupling following minor traumatic brain injury evaluation using acceleration capacity plus deceleration capacity to detect the impact injury using EKG Holter 24 hr evaluation of cardiac turbulence behavior.