Physiological Harmony and Dissonance: The Confluence of Math, Music, and Medicine
An algorithmic musical exploration of physiological state transitions
Understanding the universe around us has historically been a shared aspiration among philosophers, mathematicians, and scientists. Central to this perennial exploration is an intricate concept that aims to bind the mathematical accuracy of the universe to the innate resonance of music. This principle, known as Musica Universalis or the Music of the Spheres, has its roots deeply embedded in ancient thought.
In antiquity, Pythagoras made substantial contributions to integrating the quantitative and empirical study of music with an expansive philosophical inquiry into the fundamental constitution of reality. Utilizing formal experimentations with musical instruments, coupled with rigorous numerical analyses, Pythagoras established quantifiable mathematical correlations between music, arithmetic, and the natural order of the universe. Within the Pythagorean paradigm, harmony manifested as a universal construct, regulating not just musical aspects but also the mathematical, medical, psychological, aesthetic, and cosmological counterparts. The universe, in this view, was conceived as a structure of harmony and numerical relations, reflecting a fundamental belief in the interconnectedness of all entities, the pursuit of which persists to this day in the grand unification schemas of quantum and classical physics.
Pythagoras's work was likely rooted in the geometric and harmonic principles advanced by Plato and Aristotle. This intellectual foundation blossomed in the Baroque era, with composers like Bach attempting to capture planetary harmony in their works. The principles of Musica Universalis were later directly applied by astronomer Johannes Kepler in Harmonices Mundi, linking astronomical proportions with musical theory, a notion regarded as a significant scientific achievement and embraced by Galileo, Newton, and even 20th-century thinkers like Heisenberg, who made reference to such analyses as being among the most important developments in the annals of human scientific achievement. Composers such as Gustav Holst and Nicolas Slonimsky drew inspiration from these ideas as well, reflecting the enduring connection between the mathematical harmony of the universe and artistic expression.
In the early 2000s, as a musician with a strong mathematical bent and a deep understanding of advanced analytics and AI/ML, I ventured into medical school and quickly became fascinated by the nexus of mathematics, music theory, and the pathophysiology of diseases. Drawing inspiration from the principles of Musica Universalis and Pythagorean examinations of harmonic ratios, I crafted a relevant thesis. My medical school advisors and mentors, graciously indulged my exploration, in which I sought to intertwine the foundational mathematics of AI and ML research of the time with Pythagorean insights into natural harmonic rhythms. My thesis introduced methods to translate pathophysiological patterns into mathematical models, and subsequently, into musical compositions.
Central to the thesis was a hypothesis postulating that underlying and objectively assessable human pathophysiological patterns could be potent predictors of diverse health states. It further argued that AI and ML techniques could substantially contribute to the objective prediction of health outcomes and transitions between disease states. Although constrained by the technological limitations of the time, I hoped to illuminate new pathways for a more precise and quantitatively rigorous approach to biomedical research and patient care.
The postulated hypothesis was based on the premise that every human physiological condition, ranging from a state of balanced homeostasis to terminal disturbances manifested by phenomena such as agonal respiration and cardiac arrhythmias, can be described by quantifiable mathematical units. These units could be used create a multi-instrumental composition arranged in harmonious segments and precise tonal keys corresponding to each physiological state.
Under this framework, conditions representing various physiological states (eg, homeostatic order versus pathological imbalance) could be articulated in different musical key and time signatures, each governed by a coherent mathematical relationship. Within this model, each physiological state could be characterized by a central axis or tonal center, commonly referred to in music theory as a key in addition to rhythmic patterns and various time signatures. Transitions between states, such as progression from health to disease, could be audibly heard as a change in the central tonal axis (key), the time signature, and/or the rhythm.
The practical implication of this theoretical framework is as follows: when a human physiological state is objectively quantified and articulated in this mathematical-musical language, deviations indicative of the signs and symptoms of disease would emerge in a musical composition as audible changes. These tones would be discordant and out of sync with the baseline harmonic structure derived from the healthy homeostatic state. As a result, computational methods, especially AI/ML algorithms, could be honed to recognize these mathematical ratios and rhythmic patterns to not only predict the likelihood but also the exact sequence of transitions between physiological states (eg, from homeostasis to disease or from illness to mortality).
These transition states, discernible through computational methods and audible as dissonant shifts in musical compositions, might be detected before traditional diagnostic methods could identify overt clinical signs and symptoms. My hypothesis rested on two main pillars: firstly, the emphasis on objective quantification of patient signs and symptoms; and secondly, the advocacy for computations derived from these objective data to guide clinical decisions. This method contrasts with the traditional practice of gathering subjective insights from patients followed by the application of clinical judgment and intuition for decision making. By intertwining mathematical, musical, and medical perspectives, the thesis proposed an accessible way to comprehend and anticipate early changes in human physiological states. Specifically, those not well-versed in mathematical predictions could intuit changes through music, akin to how large language models today make AI accessible, allowing those without a deep mathematical foundation to effortlessly engage with complex algorithms and understand their behaviors and patterns of predictions.
What follows is an analysis of a composition called Perpetual Drift, written and recorded when I was in medical school to test the hypothesis of my thesis.
Analyzing the Composition: Perpetual Drift
The compostion begins with the sound of agonal breathing and hypoventilatory gasps, set against the backdrop of a heartbeat, marked by heart sounds referred to as S1 (first heart sound, typically associated with the closing of the mitral and tricuspid valves) and S2 (second heart sound, commonly linked to the closing of the aortic and pulmonic valves). All of this unfolds within the context of a second-degree heart block.
Between the timestamps 1:01:12 and 1:01:42 in the composition, the ECG rhythm transitions to a more advanced atrioventricular (AV) block.
Approximately 2:33 minutes into the composition, bursts of polymorphic ventricular tachycardia (PVT) can be heard. As PVT manifests, the QRS complexes, signifying the electrical depolarization of the heart's ventricles, exhibit shifts in amplitude, axis, and duration. This devolution of the pathophysiological state into a terminal phase causes the tonality (key) of the song to change, introducing a pronounced musical dissonance. The disparity becomes especially evident around the 3:30 and 3:53-minute marks when the vocalist continues to sing in the original key against the shifted tonality of the song, creating a stark and audible contrast.
The composition draws to a close in a state of physiological homeostasis, with the harmonious sound of a classical guitar playing chords based on EEG alpha waves. These waves are emblematic of a state of tranquil wakefulness, often experienced during moments of serene reflection or meditation. As the resonant chords linger, they evoke a sense of peace and calm, contrasting the earlier turbulent phases of the piece. The entire narrative of Perpetual Drift, with its myriad physiological transitions and musical evolutions, leaves listeners with a possibility: Perpetual Drift could've all been a dream.
Recorded in 2002. Originally released by Azure. Rereleased in 2021.
Composer: Sean Khozin
Vocals: S. Gunn
Special thanks to my med school mentors and advisors who tolerated and supported this project.