Welcome to BIOBSS’s documentation!
BIOBSS is a Python package for processing signals recorded using wearable sensors, such as Electrocardiogram (ECG), Photoplethysmogram (PPG), Electrodermal activity (EDA) and 3-axis acceleration (ACC).
BIOBSS’s main focus is to generate end-to-end pipelines by adding required processes from BIOBSS or other Python packages. Some preprocessing methods were not implemented from scratch but imported from the existing packages.
Main features:
Applying basic preprocessing steps (*)
Assessing quality of PPG and ECG signals
Extracting features for ECG, PPG, EDA and ACC signals
Performing Heart Rate Variability (HRV) analysis using PPG or ECG signals
Extracting respiratory signals from PPG or ECG signals and estimating respiratory rate (*)
Calculating activity indices from ACC signals
Generating and saving pipelines
(*): Not all methods were implemented from scratch but imported from existing packages.
The table shows the capabilites of BIOBSS and the other Python packages for physiological signal processing.
Preprocessing
BIOBSS has modules with basic signal preprocessing functionalities. These include:
Resampling
Segmentation
Normalization
Filtering (basic filtering functions with commonly used filter parameters for each signal type)
Peak detection
Visualization
BIOBSS has basic plotting modules specific to each signal type. Using the modules, the signals and peaks can be plotted using Matplotlib or Plotly packages.
Signal Quality Assessment
Signal quality assessment steps listed below can be used with PPG and ECG signals.
Clipping detection
Flatline detection
Physiological checks
Morphological checks
Template matching
Feature Extraction
Heart Rate Variability Analysis
Heart rate variability analysis can be performed with BIOBSS and the parameters given below can be calculated for PPG or ECG signals.
Activity Indices
BIOBSS has functionality to calculate activity indices from 3-axis acceleration signals. These indices are:
Proportional Integration Method (PIM)
Zero Crossing Method (ZCM)
Time Above Threshold (TAT)
Mean Amplitude Deviation (MAD)
Euclidian Norm Minus One (ENMO)
High-pass Filtered Euclidian (HFEN)
Activity Index (AI)
Reference: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0261718
The preprocessing steps which should be applied on the raw acceleration differs for each of the activity indices listed above. In other words, each activity index can be calculated only from specific datasets. These datasets can be generated using BIOBSS both independently or as a part of activity index calculation pipeline.
The generated datasets are:
UFXYZ: unfiltered acc signals
UFM: magnitude of unfiltered acc signals
UFM_modified: modified magnitude of unfiltered signals (absolute(UFM-length(UFM)))
UFNM: normalized magnitude of unfiltered acc signals
FXYZ: filtered acc signals
FXYZ_modified: modified filtered acc signals (absolute(FXYZ))
FMpre: magnitude of filtered acc signals
SpecialXYZ: filtered acc signals (special filter parameters)
SpecialM: magnitude of filtered acc signals (special filter parameters)
FMpost: filtered magnitude of acc signals
FMpost_modified: modified of filtered magnitude of acc signals (absolute(FMpost))
Respiratory Analysis
BIOBSS has modules to perform basic respiratory analyses. The functionalities are:
Preprocessing PPG or ECG signals for respiratory analysis using predefined filter parameters
Extracting respiratory signals from modulations (amplitude modulation, frequency modulation, baseline wander) in PPG or ECG signals
Estimating respiratory rate from the extracted respiratory signals
Calculation respiratory quality indices (RQI)
Fusing respiratory rate estimates
Pipeline Generation
The main focus of BIOBSS is to generate and save pipelines for signal processing and feature extraction problems. Thus, it is aimed to :
Simplify the preprocessing procedures by generating signal and event channels
Make it easy to use processes
Decrease the amount of work for repetitive processes and for those who work on multiple datasets
Make it possible to save and share pipelines to compare results of different works