"Frequencies" are just one piece of the puzzle

This is a process post. It is about building a list, why the list turned out to be the useful artefact, and what checking it against non-living waves did to it.

The starting observation is unremarkable once stated: an ECG is not only a record of a heart contracting. The published literature has read identity, sex, age and health out of the same trace. If that is true of one signal, the obvious question is what every biological signal carries, and whether the same structural features carry it each time.

Answering that properly starts with a boring inventory.

The signals catalogued so far

Six, named by the tissue they come from rather than the application they serve. These are representative textbook ranges, not hard bounds.

signal	source	band	amplitude	what its shape is like
ECG	myocardium	~0.05–100 Hz	1–10 mV	strongly periodic, one sharp recurring event, very consistent beat shape
EEG	cortex	~0.5–100 Hz	2 µV – 0.1 mV	broadband, oscillatory, band-structured, weakly event-locked
EMG	skeletal muscle	~2–500 Hz	50 µV – 5 mV	burst-structured, high-frequency, amplitude tracks effort
EOG	corneo-retinal dipole	~DC–10 Hz	10 µV – 5 mV	slow steps and ramps — saccades, blinks — essentially DC
EDA	sweat glands	~DC–2 Hz	µS-scale	a slow level with responses on top; not oscillatory at all
EGG	stomach smooth muscle	~0.03–0.15 Hz	10–500 µV	a very-low-frequency near-sinusoidal pacemaker rhythm

Writing that table was the first useful thing, and not for the reason I expected. Laid out together, the six are separated by properties that have nothing to do with physiology: how periodic they are, how sharp their events are, where their spectral weight sits, how much DC they carry. You can tell them apart without knowing what tissue you are looking at. That is either an interesting fact about biological signals or an artefact of how they are all recorded, and I genuinely do not know which yet.

The axes, and where each one comes from

The second list is the map: what kind of information is a biological wave known to carry? Each row below is an entry point to a literature, not a claim of mine.

Identity. Attributing a trace to a specific person is a mature field for ECG ¹, and there is a parallel line of work on individuating signatures in EEG ².

Sex and age. Both have been read from short clinical recordings ³, and the discrepancy between an age estimated from the signal and the person’s actual age has itself been studied as a marker ⁴.

Autonomic and affective state. Heart-rate variability is the standard non-invasive window onto autonomic balance, and electrodermal activity indexes a sympathetic pathway directly.

Intention. Muscle activity decodes motor intent; cortical activity carries potentials that precede movement rather than follow it.

Health. The axis with the most literature behind it, and the only one this bench has measured anything on directly.

I want to be careful here, because this is exactly the point where a survey turns into an overclaim: I have not read any of those papers at full text. They are cited as the entry point to their topic. What I can say is that these axes exist and are established; what I cannot say from this page is what any individual study found.

The part I did not expect

Building the list changed what I thought the project was.

I had been treating this as signal processing — a question about extracting a feature from a trace. Laying the axes out next to each other makes it look like a taxonomy problem instead. Identity, sex and age are properties that barely change. State and intention change minute to minute. Health drifts. Those are not the same kind of quantity, and lumping them together as “information in the signal” hides the most important distinction between them.

That reframing is the actual output of the exercise, and it came from making a table, not from running anything.

Pointing it at waves with nothing alive in them

Here is the part that keeps the whole thing honest, and it is the reason I would defend this method to anyone building something similar.

If you build a reader that finds rich structure in biological signals, you have to ask whether it finds rich structure in everything. So the same reader gets pointed at waves with no organism anywhere in them:

• Tide gauges — two stations, 61 days. A blind read recovered the principal lunar semidiurnal period and correctly separated the moon’s contribution from the weather’s.
• Solar flux against cosmic-ray flux — 14.6 years. It recovered the known anticorrelation, but only when given the full cycle. That makes it a result about how much data you need, not about the sun.
• Seismic, infrasound, ocean sound and the geomagnetic field — it recovered the secondary microseism and the daily solar-quiet variation, blind.

The standing rule on that log is the sentence I would put on the wall:

A high structure score on a signal with no organism in it is a caution about the instrument, not a finding about nature.

Reading tides well is partly bad news. It means the structure the reader is finding might be a property of the reader rather than of life. Every one of those runs was a test of the instrument that could have embarrassed it, and two of them did — one exposed silent bugs at low sample rates, another showed a standard statistical null to be invalid on deterministic signals.

Sound, light, and water

The obvious next question is whether the same characteristics carry information in non-biological waves, and here I want to be honest about the state of it: this is mapped, not tested.

The structural properties in question — how loud, how bright, how periodic, how consistent the repeating unit is, whether a slow rhythm modulates a fast one — all have natural readings in sound and in light. Loudness and timbre in acoustics; brightness and colour in optics; amplitude and morphology in a bioelectric trace. Water gave the cleanest test so far precisely because a tide is so well characterised that the reader had nowhere to hide.

But a correspondence you can write down is not a correspondence you have shown. Applying these characteristics across modalities is, until it is tested the same way the cardiac work was, a hypothesis carried by analogy. I would rather say that plainly than let a tidy table imply otherwise.

What would make this wrong

If the structural characteristics that separate biological signals turn out to separate any filtered time series equally well, then the shared-grammar idea is an artefact of everyone using similar analysis pipelines, and this survey is describing the pipeline rather than describing life. That is a real possibility, it is the reason the non-biological waves get read at all, and nothing on this page rules it out.