Medical & biomedical signals

Four worked examples on physiological data. Each pairs a measure to the kind of change it detects best. Runnable end-to-end in examples/medical.py (uses synthetic data so it runs with no files — swap in pd.read_csv(...) for your own recordings).

Heart rate / HRV — sample entropy

Reduced beat-to-beat variability is clinically meaningful. Sample entropy drops when a healthy, variable heart rate becomes abnormally regular.

import pandas as pd
from entroscope import sample

bpm = pd.read_csv("ecg.csv")["bpm"]
regularity = sample.rolling(bpm, window=100, m=2, r=0.2)
# healthy-phase mean ~2.34 -> regular-phase mean ~1.32 : a clear drop

EEG seizure onset — permutation & spectral entropy

A seizure shows up as the trace collapsing onto a dominant rhythm. Both permutation and spectral entropy fall as broadband background activity gives way to a single frequency.

from entroscope import permutation, spectral

eeg = pd.read_csv("eeg.csv")["uv"]
perm = permutation.rolling(eeg, window=100, order=4)
spec = spectral.rolling(eeg, window=100, sf=50.0)   # sf = sampling frequency (Hz)
# background -> seizure: permutation 4.44 -> 2.68, spectral 5.03 -> 0.63

Respiration regularity — approximate entropy

Approximate entropy rises as a steady breathing cycle breaks down into irregular, labored breathing.

from entroscope import approximate

chest = pd.read_csv("respiration.csv")["expansion"]
irregularity = approximate.rolling(chest, window=80, m=2, r=0.2)
# steady-phase mean ~0.32 -> irregular-phase mean ~0.49 : a rise

Continuous glucose — differential entropy

Differential entropy is a function of spread, so it rises when glucose variability increases — a shift from well-controlled to a volatile regime.

from entroscope import differential

glucose = pd.read_csv("cgm.csv")["mgdl"]
variability = differential.rolling(glucose, window=48, dist="normal")
# controlled-phase mean ~2.98 -> volatile-phase mean ~5.29 : a clear rise

⚠️ Medical / biomedical disclaimer

These examples are provided for research, educational, and signal-analysis purposes only. entroscope is a general-purpose time series analysis toolkit and is not a medical device, diagnostic tool, or substitute for professional medical evaluation.

Entropy measures can produce false positives, false negatives, or misleading signals depending on the data, preprocessing, sampling rate, parameter choices, noise, and underlying physiology. A detected change in entropy should not be interpreted as evidence of a medical condition or used to make clinical decisions without appropriate validation by qualified medical and/or biomedical professionals.

The authors and contributors of entroscope are not responsible for medical decisions, diagnoses, treatments, or other consequences resulting from the use or misuse of these signals. Always validate biomedical applications against appropriate clinical datasets and established medical methods before drawing conclusions.