CardioWave AI transforms ECG recordings and beat-to-beat rhythm data into RR-interval wavelet analysis, helping researchers and partners study HRV patterns, segment changes, and longitudinal cardiac dynamics with a scalable research platform.
Research Use Only. CardioWave v1 is intended for exploratory analysis and validation work, not for clinical decision making, diagnosis, or treatment.
CardioWave focuses on the signal path that matters most for scalable rhythm analytics: extract or ingest RR intervals, analyze them with multi-resolution wavelets, and generate research-ready HRV outputs that can later support personalized monitoring workflows.
The platform converts ECG recordings into beat-to-beat RR intervals or accepts RR interval files directly. This gives CardioWave a compact, physiologically meaningful signal that is practical for long-term monitoring, segmentation, and cross-device analysis.
CardioWave applies multi-resolution wavelet transforms to RR interval segments to surface HRV features, energy patterns, and time-frequency structure that are difficult to capture with simple aggregate metrics alone.
Future phases will compare each person against their own longitudinal baseline, support selective escalation from lightweight RR monitoring to ECG capture, and explore machine learning only after research validation and larger datasets are in place.
The current platform is focused on research-ready RR-interval analysis. The broader CardioWave vision extends that foundation into continuous monitoring, personalized baselines, and selective escalation workflows.
CardioWave is looking for aligned collaborators who can help validate RR-interval wavelet analysis, strengthen the research platform, and shape the next phase of scalable cardiac monitoring.
Pre-seed funding will accelerate validation work, harden the cloud platform, and expand the datasets needed to evaluate RR-interval wavelet analysis across more use cases and populations.
We welcome collaboration with universities, clinicians, and data providers on pilots, datasets, and evaluation studies focused on RR-interval features, wavelet outputs, and longitudinal monitoring strategies.
We are interested in collaborators across signal processing, scalable cloud systems, data pipelines, and future wearable integration that can move the platform from research infrastructure toward broader monitoring workflows.
If you work in cardiovascular research, digital health, infrastructure, or early-stage investment, let's talk about validation, pilots, and the next phase of CardioWave.
Project Lead & Independent Researcher: Oleksandr Popov