Quickstart

Welcome to the vital_sqi quickstart guide! This section provides step-by-step instructions for setting up and using vital_sqi for signal quality assessment. By the end of this guide, you’ll be able to compute Signal Quality Indexes (SQIs) for ECG and PPG signals and integrate them into your workflow.

Prerequisites

Before starting, ensure you have the following: - Python 3.7+ installed. - vital_sqi installed. Refer to the Installation Guide guide if needed. - Sample ECG or PPG data in a supported format (e.g., .csv, .txt, or .edf).

If you plan to preprocess your data before assessing quality, install and configure the VitalDSP library for seamless integration.

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Getting Started

Follow these steps to quickly set up and run your first SQI computation:

1. Import the Library: Import the required modules from vital_sqi:

   from vital_sqi.sqi.standard_sqi import perfusion_sqi, kurtosis_sqi
   from vital_sqi.sqi.dtw_sqi import dtw_sqi
   import numpy as np
   

2. Load Your Data: Load ECG or PPG data using your preferred method. For example, if your data is stored in a CSV file:

   import pandas as pd
   
   # Load data
   data = pd.read_csv("path/to/your_signal.csv")
   signal = data["PPG"]  # Assuming 'PPG' is the column name
   timestamps = data["Time"]  # Optional: timestamps for alignment
   

3. Compute SQIs: Use vital_sqi to calculate SQIs for the signal:

   # Example: Compute Perfusion Index
   perfusion_score = perfusion_sqi(signal)
   
   # Example: Compute Kurtosis
   kurtosis_score = kurtosis_sqi(signal)
   
   # Example: Compute Dynamic Time Warping (DTW) similarity
   reference_signal = np.sin(np.linspace(0, 10, len(signal)))  # Reference signal
   dtw_score = dtw_sqi(signal, reference_signal)
   
   print("Perfusion SQI:", perfusion_score)
   print("Kurtosis SQI:", kurtosis_score)
   print("DTW Score:", dtw_score)
   

4. Visualize Results: Visualize your signal alongside SQI results for better interpretation:

   import matplotlib.pyplot as plt
   
   plt.figure(figsize=(10, 5))
   plt.plot(timestamps, signal, label="PPG Signal")
   plt.title("PPG Signal with Computed SQIs")
   plt.xlabel("Time")
   plt.ylabel("Amplitude")
   plt.legend()
   plt.show()
   

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Using Preprocessing with VitalDSP

To improve SQI computations, preprocess your signals with VitalDSP. For example:

1. Install VitalDSP: .. code-block:: bash

   pip install vital-DSP

2. Filter and Denoise Your Signal: Use VitalDSP to apply a bandpass filter and remove noise:

   from vitalDSP.filters import bandpass_filter
   
   # Apply bandpass filter (0.5-5 Hz for PPG)
   filtered_signal = bandpass_filter(signal, fs=100, lowcut=0.5, highcut=5)
   

3. Recompute SQIs: Use the filtered signal with vital_sqi:

   perfusion_score = perfusion_sqi(filtered_signal)
   kurtosis_score = kurtosis_sqi(filtered_signal)
   print("Perfusion SQI (filtered):", perfusion_score)
   print("Kurtosis SQI (filtered):", kurtosis_score)
   

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Example Use Case

Here’s a complete example workflow to compute and analyze SQIs:

import pandas as pd
import matplotlib.pyplot as plt
from vitalDSP.filters import bandpass_filter
from vital_sqi.sqi.standard_sqi import perfusion_sqi, kurtosis_sqi

# Load signal
data = pd.read_csv("path/to/your_signal.csv")
signal = data["PPG"]
timestamps = data["Time"]

# Preprocess the signal
filtered_signal = bandpass_filter(signal, fs=100, lowcut=0.5, highcut=5)

# Compute SQIs
perfusion_score = perfusion_sqi(filtered_signal)
kurtosis_score = kurtosis_sqi(filtered_signal)

# Visualize results
plt.figure(figsize=(10, 5))
plt.plot(timestamps, signal, label="Raw Signal")
plt.plot(timestamps, filtered_signal, label="Filtered Signal")
plt.title("PPG Signal with SQI Analysis")
plt.xlabel("Time")
plt.ylabel("Amplitude")
plt.legend()
plt.show()

print("Perfusion SQI:", perfusion_score)
print("Kurtosis SQI:", kurtosis_score)

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Next Steps

Congratulations! You’ve successfully computed Signal Quality Indexes for your physiological signals. To learn more: - Explore the available SQIs in Introduction. - Check out advanced workflows using VitalDSP for preprocessing. - Dive into documentation on pipelines for integrating SQIs into larger projects.

Happy coding!