Physiological data is one of the most important—and increasingly valuable—sources of information for healthcare AI.
While medical AI is often associated with images such as X-rays, CT scans and digital pathology slides, healthcare systems also generate continuous streams of physiological signals. These include electrocardiograms (ECG), electroencephalograms (EEG), vital-sign recordings and data from wearable monitoring devices.
For AI systems to learn from these signals, however, raw recordings must first be transformed into structured, reliable and clinically meaningful training data.
That transformation depends on high-quality signal annotation.
What Are Physiological Signals?
Physiological signals are measurements that capture processes occurring within the human body over time. Depending on the project, datasets may include:
- ECG recordings showing the electrical activity of the heart
- EEG recordings showing electrical activity associated with the brain
- Heart-rate and rhythm data
- Blood oxygen and respiratory signals
- Blood pressure and other vital-sign streams
- Holter monitor recordings
- Wearable device data
- Remote patient-monitoring streams
Unlike a single medical image, these datasets are often time-based. A clinically important event may occur at a specific moment, continue for several seconds or minutes, or appear repeatedly throughout a long recording.
This makes accurate annotation essential.
From Raw Signal to Structured Data
A physiological recording does not automatically tell an AI model what is clinically important.
For example, a 24-hour ECG recording may contain thousands of heartbeats. Before the data can support a specific AI task, the relevant events must be identified according to a clearly defined annotation protocol.
A typical workflow may look like this:
Raw Physiological Signal → Clinical Review → Event Identification → Annotation → Quality Assurance → Expert Validation → AI-Ready Dataset
Each stage helps transform complex recordings into structured data that can be used for AI development, research or model evaluation.
Beat Labelling and Rhythm Classification
One common application of signal annotation is identifying individual beats and classifying cardiac rhythms.
Depending on the project guideline, annotators may identify:
- normal beats;
- abnormal beats;
- rhythm changes;
- irregular patterns; or
- other project-defined cardiac events.
The exact labels must always be determined by the project’s clinical definitions. Annotators should not create categories based on assumption or personal interpretation.
Clear guidelines are particularly important because a model can only learn consistently when similar events are labelled consistently across the dataset.
Event Marking
Some projects focus on identifying when a clinically relevant event begins and ends.
For example, an annotation task may require the reviewer to mark:
Start time → Event duration → End time → Event category
This can be important in ECG, EEG, wearable and remote-monitoring datasets where the timing of an event is part of the information required for AI development.
Accurate event boundaries help create structured datasets for models designed to detect, classify or analyse changes over time.
EEG and Seizure Annotation
EEG datasets present another important area for healthcare AI.
An EEG recording may contain long periods of continuous signal data. Depending on the project requirements, annotation may involve identifying specific patterns, events or time intervals of interest.
For seizure-related projects, the annotation protocol may define how potential events are reviewed, what boundaries should be marked and which categories should be assigned.
Because these tasks can involve complex clinical interpretation, clinically informed review and clearly documented escalation procedures are important. When an annotator encounters uncertainty, the correct approach is not to guess—it is to follow the project’s escalation and disagreement-resolution process.
Wearable and Remote Monitoring Data
Wearable technology is generating increasing volumes of healthcare-related data.
Depending on the device and project, datasets may contain information related to heart activity, movement, oxygen levels, sleep, respiratory patterns or other physiological measurements.
However, real-world wearable data can be complex.
Signals may contain interruptions, noise, missing sections or other factors that affect usability. Before data becomes useful for AI training, projects may require annotation of relevant events, signal segments and data-quality characteristics.
This creates opportunities to develop structured datasets for areas such as:
- remote patient monitoring;
- digital therapeutics;
- cardiovascular AI;
- wearable health technology; and
- longitudinal health analysis.
Artefact Rejection and Signal Quality
Not every part of a recording should necessarily be treated as usable clinical signal.
Physiological data can contain artefacts caused by factors such as movement, poor sensor contact, recording interference or device-related issues. Where required by the project guideline, these sections can be identified and labelled separately.
This is important because poor-quality data can affect downstream model performance.
A high-quality annotation workflow should clearly distinguish between:
Usable signal
and
Project-defined artefact or low-quality signal
The definitions should come from the approved annotation guideline, ensuring that different annotators apply the same criteria.
Why Disagreement Is Valuable
Clinical interpretation is not always completely uniform. Two qualified reviewers may occasionally disagree about an event, its boundary or its classification.
Rather than simply ignoring disagreement, a robust annotation workflow should manage it systematically.
Depending on the project, this may involve:
- Independent annotation or review.
- Identification of disagreements.
- Comparison against the project guideline.
- Consensus review or escalation to an appropriately qualified expert.
- Documentation of the final decision where required.
The disagreement itself can also be useful information. It may reveal ambiguous definitions, difficult signal patterns or areas where the annotation guideline needs clarification.
The goal is not simply to force agreement. The goal is to create a transparent and clinically informed process for resolving uncertainty.
Quality Assurance Matters
High-quality physiological datasets require more than completing annotations.
Quality assurance may include checks for:
- correct event labels;
- accurate event boundaries;
- consistency across annotators;
- compliance with project guidelines;
- missing annotations;
- incorrect classifications;
- duplicate or inconsistent labels; and
- appropriate escalation of uncertain cases.
At SCILabel, we view annotation as part of a broader data-quality workflow. The objective is to support the development of structured datasets that are clinically informed, traceable and aligned with the requirements of each AI or research project.
Turning Complex Signals Into AI-Ready Data
ECG, EEG and wearable signals contain valuable information for the future of healthcare AI. But raw physiological recordings alone are not automatically ready for model development.
They require a defined objective, appropriate annotation protocols, clinically informed reviewers and structured quality processes.
From individual beat labels to rhythm classification, seizure and event marking, artefact identification and expert review, every stage contributes to transforming complex time-series data into meaningful training and evaluation datasets.
As healthcare moves further toward remote monitoring, connected devices and continuous data collection, the ability to structure and validate physiological signals will become increasingly important.
Better healthcare AI depends on more than collecting more data. It depends on making that data meaningful, reliable and fit for purpose.
Work With SCILabel
Do you have ECG, EEG, Holter, wearable or remote-monitoring data that needs structured annotation, clinical review or quality assurance?
SCILabel works with healthcare AI companies, research organisations, hospitals and digital health innovators to support clinically informed data annotation and evaluation.
Discuss your signal annotation project with us:
Email: info@shevslabel.com
Turn complex physiological signals into structured, AI-ready data.