Cardiac ECG Research Evidence Assurance
Assess supplied ECG evidence through bounded research screening while keeping diagnostic and clinical decisions under qualified human authority.
Who this is for
Cardiac-signal research leads, biomedical engineering teams and ECG research program reviewers evaluating bounded arrhythmia evidence.
The problem you already have
A rhythm label can hide whether the underlying cardiac sequence was actually coherent
Part 38 does not treat ECG review as a single morphology label. Its cardiac evidence model preserves cycle timing, conduction order and window-level consistency so a research team can distinguish a stable sequence from one whose supplied evidence is internally irregular.
One lead or one abnormal interval should not silently become the whole conclusion
The documented implementation separates per-window rhythm state from multi-lead consistency and named interval evidence. That matters when a research package contains conflicting lead behavior, variable RR timing, prolonged intervals or possible artifact: the disagreement should remain visible rather than being collapsed into one opaque classification.
Research ECG evidence is not a diagnosis
Part 38 explicitly labels these outputs as computational research proxies. The product therefore keeps cardiology interpretation, diagnosis, live patient monitoring and treatment decisions under qualified human authority.
Real-World Operational Scenario: The Abnormal Episode That Disappeared When the Leads Were Compared
Scenario: One ECG lead shows an irregular episode, while the broader supplied package is mixed.
A biomedical team is reviewing a candidate episode before escalating it into a validation claim. One lead looks abnormal, but the remaining lead evidence does not cleanly agree and the team needs to know whether the event is a coherent research finding or a lead-specific inconsistency.
WITHOUT this check: The single abnormal trace can dominate the review and become the basis for an overstated arrhythmia conclusion even though the cross-lead evidence is inconsistent.
WITH this check: The supplied episode is evaluated as a bounded rhythm window, and lead agreement remains part of the evidence record. A coherent result can support a research classification such as RHYTHM_NORMAL, RHYTHM_IRREGULAR or RHYTHM_PATHOLOGICAL; conflicting leads remain visibly inconsistent and are routed to human review rather than treated as diagnosis.
What BivectorAI Provides
Window-Level Rhythm Evidence
Evaluates the supplied episode as a bounded sequence rather than a detached waveform snapshot, preserving the temporal relationship between cycles and the resulting research classification.
Multi-Lead Consistency Review
Keeps agreement and disagreement across supported lead forms visible so lead-specific artifact or inconsistent evidence does not silently become a stronger conclusion.
Named Interval and Variability Evidence
Preserves customer-safe findings linked to supplied QT/QTc, RR variability and morphology or duration flags where those fields are part of the agreed research package.
Research-Only Clinical Boundary
Every result remains evidence for scientific review. It does not become an ECG interpretation, clinical diagnosis, live monitoring decision or substitute for qualified cardiology review.
Prove the Gate Before Broader Integration
Back-test the screen against ECG episodes with blinded expert annotations.
Freeze the supported format, episode window, lead-consistency rule and acceptance criteria before revealing the reference labels. The evaluation is useful only if the same contract reproducibly separates coherent normal, irregular and pathological research cases while preserving inconsistent or unsupported submissions as review-required rather than forcing a classification.
What you provide
A bounded caller-supplied ECG evidence package in a supported research format, including the agreed single- or multi-lead episode observations and, where already available in the research package, cycle timing, QT/QTc, RR variability and lead-level morphology or duration flags. Live monitoring authority and patient-care decisions are not required.
What BivectorAI actually hands you
Rhythm-state classification
A bounded research classification tied to the exact supplied episode window and evidence package.
Lead-consistency result
A clear record of whether the supported leads agree or whether the episode remains inconsistent and requires human review.
Interval / variability evidence
Input-linked QT/QTc, RR-variability and supported morphology or duration findings preserved separately from the overall rhythm state.
Research evidence record
A reviewable result with explicit limitations, human-owned follow-up and fail-closed handling when the supplied contract cannot support a valid classification.
What we will never ask you for
This is bounded ECG research evidence review, not live clinical monitoring or diagnostic authority, so patient-care decisions remain outside the engagement.
- Live clinical monitoring authority.
- Patient treatment decisions.
- A diagnostic certification claim.
- A claim that computational ECG screening replaces cardiology review.
How it actually runs
Start with a bounded evaluation
Fixed scope / quoted per engagement. Inputs, outputs, evidence boundary and acceptance criteria are agreed before work begins.
We do not open with an annual license. If the bounded ECG review does not establish that the research evidence is useful for the agreed cardiac-signal question, there is no reason to move into a broader commercial commitment.
Cardiac ECG assessment operations are implemented within a broader biomedical research dispatcher, including supported multi-lead forms. Results are research evidence, not clinical diagnosis or live patient monitoring.