Large-Scale HRV Dataset Analysis for Cardiovascular Research
In partnership with a university research team, we built a scalable Heart Rate Variability (HRV) analysis platform capable of processing large, heterogeneous ECG datasets from specific patient cohorts. The platform extracted clinically meaningful cardiovascular parameters across hundreds of recordings, transforming raw multi-hour ECG archives into structured, publication-ready statistical outputs that directly fuelled peer-reviewed journal articles and conference presentations.
Category:
MedicalIndustry:
Cardiovascular Research / Academic MedicineClient:
University PartnerYear:
2022–2024The Challenge
The university research team held a substantial archive of long-term ECG recordings from a defined patient population, but lacked the computational tooling to process the data at scale. Manual review was prohibitively slow, existing off-the-shelf HRV tools could not handle dataset heterogeneity (varying sampling rates, electrode configurations, recording durations), and the team required reproducible, auditable analysis pipelines that would withstand peer review scrutiny. A custom platform was the only viable path to turning raw data into publishable science.
Our Solution
We developed a batch-capable HRV analysis pipeline that ingested multi-format ECG archives, performed automated signal quality screening, and then applied a standardised feature-extraction workflow across the entire dataset. The pipeline computed the full HRV feature set, time-domain, frequency-domain, and nonlinear indices, for every recording, storing results in structured formats ready for statistical analysis. Close collaboration with the university team allowed iterative refinement of inclusion/exclusion criteria and feature definitions, ensuring results met the reproducibility and transparency standards required for scientific publication. Findings were published in recognised cardiovascular research journals and presented at international academic conferences.
Key Features
- Large-Scale Batch Processing: Automated ingestion and analysis of hundreds of long-term ECG recordings from heterogeneous sources: handling variable sampling rates, recording durations, and electrode configurations without manual preprocessing.
- Comprehensive HRV Feature Extraction: Full computation of time-domain (SDNN, RMSSD, pNN50), frequency-domain (VLF, LF, HF power, LF/HF ratio), and nonlinear indices (SampEn, DFA, Poincaré SD1/SD2) across each dataset recording.
- Automated Signal Quality Screening: Integrated quality assessment layer detects noise segments, baseline wander, and ectopic beats, flagging low-quality records and applying corrective filtering before feature extraction.
- Reproducible, Auditable Pipeline: Parameterised workflow with version-controlled configuration ensures all analysis steps are fully reproducible: an explicit requirement for peer-reviewed publication.
- Peer-Reviewed Publications: Research findings contributed to recognised cardiovascular science journals, advancing the academic community's understanding of HRV as a cardiovascular risk marker in the studied patient cohort.
- Conference Presentations: Results were showcased at international academic events, facilitating knowledge exchange between clinical cardiologists, biomedical engineers, and HRV researchers.
Technologies
- Python
- MATLAB
- Pandas / NumPy / SciPy
- HRV analysis libraries
- ECG databases (MIT-BIH format)
- Digital Signal Processing
- Power Spectral Density
- Nonlinear dynamics (SampEn, DFA)
Results
The platform enabled the university team to extract statistically rigorous cardiovascular insights from a large patient dataset in a fraction of the time manual analysis would have required. The resulting peer-reviewed publications and conference contributions demonstrate KeySoft's capacity to bridge embedded biophysics hardware expertise with the data science depth demanded by modern cardiovascular research.
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