We ran PhysioSim's API against two data types: real daily vitals from actual individuals (smartwatch-style readings), and a high-frequency ECG/waveform dataset from PhysioNet — 242,815 observations. Different formats, same engine. Below is the actual output.
PhysioSim was run against actual daily vital sign readings collected from two real individuals — the kind of data a smartwatch or consumer health device produces. Two people, two datasets, different signals. Same API, same individualized baseline engine.
⚠ Data collected with consent for development purposes. De-identified. For research and pilot use only.
Repeated or multi-signal deviation detected across 3 concurrent signals, 4 multi-signal deviation days. Trend acceleration flagged in systolic and diastolic BP. SpO₂ deviation detected — monitoring for rising oxygen requirement indicated. Pattern warrants monitoring; routed to Tier 2 remote monitoring command center within 24h.
| Signal | Individual Median | IQR | Mean | Std Dev | Triggers | Accelerating? |
|---|---|---|---|---|---|---|
| Resting Heart Rate | 70.0 bpm | 6.0 bpm | 72.6 bpm | 4.5 bpm | 1 | No |
| SpO₂ | 98.0% | 1.5% | 97.6% | 1.5% | 3 | No |
| Systolic BP | 123.0 mmHg | 6.5 mmHg | 119.3 mmHg | 7.2 mmHg | 4 | YES ↑ |
| Diastolic BP | 84.0 mmHg | 6.0 mmHg | 80.0 mmHg | 6.9 mmHg | 6 | YES ↑ |
Green line = observed · Dashed = individual baseline median · Shaded = IQR · Amber/red dots = flagged deviations
Repeated deviation pattern detected in resting heart rate despite low trigger count. Trend acceleration flagged — rate of change in HR is increasing. Tachycardia is nonspecific; HR elevation may reflect pain, anxiety, fever, dehydration, or stress. Routed to Tier 2 remote monitoring command center for review within 24h.
| Signal | Individual Median | IQR | Mean | Std Dev | Triggers | Accelerating? |
|---|---|---|---|---|---|---|
| Resting Heart Rate | 73.0 bpm | 3.5 bpm | 73.0 bpm | 4.6 bpm | 1 | YES ↑ |
Green line = observed · Dashed = individual baseline (73.0 bpm) · Shaded = IQR · Amber dot = flagged deviation
Two real individuals. Two different signal sets. The same individualized baseline engine assessed each against their own physiological normal — not a population average. Sparse daily readings, real deviation patterns, real alert routing. Below, the same engine processes 242,815 high-frequency waveform observations.
ecg, pleth, temperature, accelerometerPOST /v1/patients/analyze — uploaded via multipart/form-dataBefore flagging anything, PhysioSim builds a statistical baseline from an initial longitudinal window of this individual's own data — specific to them. This is what makes deviation detection meaningful: deviation from their normal, not a population average.
| Signal | Median (individual) | IQR | Mean | Std Dev | Observations | Unit |
|---|---|---|---|---|---|---|
| ECG | 31,849 | 3,900 | 32,624 | 5,839 | 242,815 | ADC units* |
| Plethysmogram | 64,766 | 558 | 64,790 | 400 | 242,815 | ADC units |
| Temperature | 32.94 | 0.38 | 33.00 | 0.24 | 242,815 | °C |
| Accelerometer | 4.57 | 4.81 | 5.17 | 3.03 | 242,815 | g |
* PhysioNet MIMIC ECG values are raw ADC (analog-to-digital converter) units, not millivolts. To convert to mV, divide by the gain factor (typically ~200 ADC/mV). PhysioSim correctly labels these as ADC units in v1.3+.
The chart below shows actual temperature values from the dataset versus the individualized baseline median (dashed) and IQR band (shaded). The distinctive U-shaped pattern shows the patient's temperature dipping during the mid-recording period and recovering — a real longitudinal physiological signal, not synthetic data.
Green line = observed values · Dashed = individual baseline median · Shaded = baseline IQR · Reproduced from actual API output
PhysioSim applies a proprietary frequency-domain algorithm to detect when signal rhythms shift — not just whether they did. This provides clinically precise time-localized detection for non-stationary physiological waveforms.
| Signal | Baseline Pattern | Recent Pattern | Pattern Shift | Spectral Score | Deviated? | Clinical Significance |
|---|---|---|---|---|---|---|
| ECG | Stable | Shifted | Significant ↑ | Elevated | YES | Meaningful shift in cardiac rhythm pattern |
| Plethysmogram | Stable | Reduced | Moderate ↓ | Reduced | YES | Energy reduction in PPG waveform pattern |
| Temperature | Stable | Shifted | Moderate | Reduced | YES | Spectral pattern shift vs baseline window |
| Accelerometer | Stable | Minimal shift | Minimal | Reduced | YES | Minimal pattern shift, spectral change detected |
Note: PhysioSim's proprietary algorithm flags both increases and decreases in spectral pattern energy as warranting clinical review — reduced activity patterns (e.g., sedation, sleep, reduced movement) are as clinically meaningful as elevated ones.
Sustained multi-signal deviation detected across 4 signals. Pattern is persistent versus individual baseline and consistent across signals — not consistent with short-term noise. Proprietary frequency-domain analysis additionally flagged spectral pattern shifts in ECG, plethysmogram, temperature, and accelerometer. Trend velocity analysis shows accelerating deviation trajectory.
High-confidence alert routes directly into clinical workflow. Nurse review within 4–8h. Do not route through intermediate layers — bedside team awareness required.
Remote monitoring command center. Escalate to EHR if pattern worsens over 24h.
Routine monitoring queue. No immediate outreach required. Re-evaluate if signals continue trending.
242,815 observations processed, baselined, analyzed with PhysioSim's proprietary algorithm, and risk-stratified in under 3 seconds. The engineering is real and deployed on Railway.
The baseline is built from this individual's own longitudinal data — not a population average. Every deviation threshold is calibrated to their specific physiological normal range.
PhysioSim's proprietary algorithm detected spectral pattern shifts across all 4 signals including ECG and PPG waveforms — with time-localized precision that standard frequency methods cannot provide.
HIGH risk correctly routed to Tier 1 (EHR/Epic direct) — bypassing remote monitoring intermediate layers for high-confidence sustained multi-signal alerts.
The API generated a clinician-readable PDF report with baseline tables, trend charts, frequency analysis, and flagged events — downloadable, no dashboard required.
Input: a CSV file. Output: structured JSON + PDF report. No device required. No proprietary format. Any data source that can export CSV works with PhysioSim.
Important context: This waveform dataset (125Hz ICU data) represents the most technically demanding input PhysioSim handles. The same engine works equally well on sparse daily vitals — as shown above with the smartwatch datasets. The individualized baseline approach applies regardless of data density or signal type.