Topic 8 – High-Resolution Vital Signs with VitalDB (Python Library)

Dataset & Library

• VitalDB: PhysioNet - https://physionet.org/content/vitaldb/
• Python Library: vitaldb - https://vitaldb.net/docs/

Installation

pip install vitaldb

Data Loading

import vitaldb
import pandas as pd
vf = vitaldb.VitalFile(1)  # example case 1
tracks = ["SNUADC/ECG", "Solar 8000/ART1"]
vals, t = vitaldb.vital_recs(vf, track_names=tracks, return_datetime=True)
df = pd.DataFrame(vals, columns=["ECG", "ART"])
df["Time"] = t
df = df.set_index("Time")

Implementation Steps

1. Library Setup and Data Access

• Install vitaldb library
• Explore available cases and tracks
• Select appropriate case(s) for analysis
• Understand data structure and sampling rates

2. Data Exploration

• Load high-frequency vital signs (ECG, arterial pressure, etc.)
• Inspect sampling rates (typically very high, e.g., 100+ Hz)
• Examine data quality and artifacts
• Identify available time ranges

3. Data Preprocessing

• Downsampling (if needed):
• High-frequency data may need downsampling for time series analysis
• Use appropriate resampling method
• Artifact Removal:
• Identify and handle artifacts (noise, spikes)
• Apply filtering if needed (low-pass, band-pass)
• Missing Data:
• Handle gaps in recording
• Interpolate or segment appropriately

4. Exploratory Data Analysis (EDA)

• Plot high-resolution signals
• Analyze signal characteristics (amplitude, frequency content)
• Calculate basic statistics
• Identify patterns and events
• Perform time series decomposition (may need adapted methods for high-frequency data)

5. Feature Extraction (Optional)

• Extract relevant features (e.g., heart rate from ECG, systolic/diastolic from arterial pressure)
• Calculate derived metrics
• Create lower-frequency time series for modeling

6. Model Building

• High-Frequency Analysis:
• Consider specialized methods for high-frequency data
• May need to work with derived features rather than raw signals
• Lower-Frequency Models:
• Extract features (e.g., mean HR per minute)
• Apply ARIMA/SARIMA to feature time series
• Event Detection (advanced):
• Detect specific events (arrhythmias, pressure changes)
• Model event occurrences

7. Model Evaluation

• Split data temporally
• Generate forecasts
• Calculate accuracy metrics
• Visualize results
• Compare with clinical expectations

8. Clinical Interpretation

• Interpret results in ICU/OR context
• Discuss implications for monitoring
• Identify clinically relevant patterns
• Compare with medical knowledge

Expected Deliverables

1. EDA Report:

• High-resolution signal plots
• Feature extraction results
• Statistical summaries
• Pattern identification

1. Model Results:

• Selected models and parameters
• Forecast accuracy
• Visualization of results
• Clinical interpretation

1. Code:

• Complete Python notebook
• Functions for data loading and preprocessing
• Feature extraction utilities
• Visualization tools

Tips

• High-frequency data requires different approaches than standard time series
• Consider extracting features (e.g., heart rate) rather than modeling raw signals
• Downsampling may be necessary for standard time series methods
• Handle artifacts carefully - they may be clinically significant
• Use appropriate filtering for signal processing
• Consider event-based analysis in addition to continuous forecasting
• Document sampling rates and preprocessing steps clearly
• High-frequency data can be computationally intensive - optimize code
• Consult clinical literature for appropriate analysis methods