Cardiology
The Cardiology area is a web workspace for multimodal cardiovascular and physiological signal research. Upload ECG, RR intervals, blood pressure, PPG, and respiration recordings; run validated preprocessing and metrics pipelines; link data to subjects; compare cohorts; and generate interpretations grounded in computed metrics.
Start from a research scenario
Guided workflows map common questions to data requirements, analysis steps, and documentation — pick the scenario closest to your study.
Holter HRV analysis
Research question: What are the time-domain, frequency-domain, and nonlinear HRV characteristics of this recording, and is the signal quality sufficient for reliable metrics?
Analyze a single ECG or Holter recording — preprocess the waveform, compute HRV metrics, inspect signal quality in the waveform explorer, and generate analytical interpretation.
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ECG morphology phenotyping
Research question: What are the ECG interval and morphology characteristics (PR, QRS duration, QT/QTc, ST level) of this recording, and are the fiducial landmarks placed reliably enough to trust the summaries?
Move beyond rhythm and rate into waveform shape — delineate P/Q/R/S/T landmarks on a recording, derive PR, QRS, QT/QTc, ST-level, and T-wave metrics per beat, and verify the placement visually in the waveform explorer.
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Longitudinal wearable HRV
Research question: How does HRV change over the course of a long recording, and does its level *or* its temporal variability differ across subjects or groups?
Turn a long ECG, Holter, or wearable recording into a time-series of HRV instead of a single number — slide overlapping windows across the recording, track how autonomic state evolves, and compare temporal variability across groups.
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Stress and activity
Research question: What do skin-conductance responses and physical activity add to the autonomic picture, and how much of the HRV signal is contaminated by movement?
Extend autonomic analysis beyond the heart — add electrodermal activity (the sympathetic "stress" axis) and accelerometer-derived movement, and use motion to judge when ECG/HRV can be trusted.
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Pulse-wave and transit-time analysis
Research question: What do the PPG pulse-wave morphology and the ECG-to-PPG timing reveal about vascular state, and how do they track over a recording or across a cohort?
Go beyond pulse rate into the *shape* and *timing* of the pulse — characterize each PPG pulse waveform, and, when ECG and PPG are recorded together, measure pulse transit / arrival time as a surrogate for cuffless blood pressure.
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Wearable RR cohort
Research question: How do HRV metrics differ across participants, treatment arms, or diagnostic groups when only RR interval data is available?
Process RR interval exports from wearables or Holter summary files across a cohort, batch HRV computation, and compare metrics between groups.
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Multimodal autonomic profiling
Research question: How do heart rate variability, blood pressure dynamics, and respiration interact for this participant? What do baroreflex sensitivity and respiratory sinus arrhythmia reveal about autonomic regulation?
Combine ECG, blood pressure, and respiration recordings for a single subject — compute cross-modal coupling metrics and generate mechanistic AI interpretation.
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Cohort outcomes
Research question: Do HRV, blood pressure, or other cardiovascular metrics differ significantly between groups, and do signal-derived indices associate with clinical outcomes in this cohort?
Compare physiological metrics across treatment groups, identify outliers, and explore research-only signal-derived risk stratification tied to outcomes metadata.
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Platform features
Everything available in the Cardiology workspace today — pipelines, explorers, exports, and provenance.
- Experiment containers for cardiovascular signal studies
- ECG upload in WFDB, CSV, and EDF waveform formats
- ECG preprocessing with R-peak detection, RR intervals, and signal quality index
- Time-domain, frequency-domain, and nonlinear HRV metrics
- Advanced HRV: SDANN, DFA, entropy, fragmentation, PRSA acceleration/deceleration capacity, and Lomb/AR spectra
- Full ECG analysis pipeline chaining preprocess and HRV in one run
- ECG delineation of P, Q, R, S, and T fiducial landmarks from detected beats
- Per-beat and recording-level morphology: PR, QRS, QT/QTc, ST level, and T-wave metrics
- RR-only CSV upload for Holter and wearable exports
- Arrhythmia burden metrics: irregularity percentage, pause burden, and beat flags
- Sliding-window HRV with per-window metrics, quality/missingness, trends, and feature matrices
- Electrodermal activity (EDA): tonic/phasic decomposition and skin conductance responses
- Accelerometer activity: ENMO intensity, motion burden, and per-window activity
- Cross-modal motion quality flagging ECG/HRV beats and segments during movement
- PPG preprocessing with pulse peak detection and inter-beat intervals
- Pulse rate variability (PRV) computed from PPG preprocess runs
- PPG pulse-wave morphology: amplitude, rise/decay time, width, area, and dicrotic notch
- Pulse transit / arrival time (PAT, PTT) from paired ECG and PPG recordings
- Blood pressure variability, dipping classification, and MAP statistics
- Arterial pressure (ABP) waveform: beat-level SBP/DBP/MAP, pulse pressure, and upstroke dP/dt
- Respiration rate, breath timing, and quality summaries
- Respiratory sinus arrhythmia (RSA) cross-modal coupling from ECG and respiration
- Baroreflex sensitivity by sequence method and cross-spectral transfer function (gain, coherence, phase, latency)
- Subject import and cohort metadata linking across recordings
- Cohort comparison with group means, standard deviations, and statistical tests
- Windowed-feature cohort comparison on per-subject medians and temporal variability (CV)
- Batch HRV jobs across multiple datasets in one request
- Research-only signal-derived risk stratification from cohort outcomes metadata
- Interactive waveform explorer for QC and visual context, with an optional P/Q/R/S/T landmark overlay
- AI interpretation with descriptive and mechanistic modes citing recorded metrics
- Methods report generation from experiment bundles