kitlawes / edfize

Ruby gem used to load, validate, and parse European Data Format files. Used for batch testing EDFs for errors.

Edfize

Ruby gem used to load, validate, and parse European Data Format files. Used for batch testing EDFs for errors. Ruby 2.2+ compatible.

Installation

Use gem install edfize to update Edfize to the latest stable

Use gem install edfize --pre to update Edfize to the latest prerelease

Usage

Validate EDFs

Use edfize test to test that EDFs stored in the current directory have a valid format.

cd <edf-directory>
edfize test

A list of validations performed is:

• Expected Length Check: Compares the calculated size of the file based on signal sizes defined in the header with the actual file size. A failure may indicate corruption in the header (if the expected is less than the actual file size), or a partial/truncated file (if the expected is more than the actual file size).
• Reserved Area Checks: Check that reserved areas are blank. Non-blank reserved areas can indicate a sign of header or EDF file corruption.

Flags that can be added to the test command include:

• --failing: Only display EDFs with failing tests
• --quiet: Suppress detailed failure descriptions that show the expected versus the actual result of the test

Print Signal Header information

Use edfize run to print out signal header information for each EDF in the current directory.

cd <edf-directory>
edfize run

View A List of All Available Commands for Edfize

Use edfize help to list available commands and descriptions for Edfize.

edfize help

View Current Version of Edfize

Use edfize version to check the version of Edfize.

edfize version

Example of how to rewrite the start date of recording for a folder of EDFs

This will update all EDFs in the current directory and subdirectories with a start date of 1st Jan 1985.

rewrite_signal_date.rb

# gem install edfize --no-document
# ruby rewrite_signal_date.rb

require 'rubygems'
require 'edfize'

CLIPPING_DATE = '01.01.85'

Edfize.edfs do |edf|
  edf.update(start_date_of_recording: CLIPPING_DATE)
end

Example of how to Load and Analyze EDFs in a Ruby Script

The following Ruby file demonstrates how to make use of the Edfize gem to load EDF signals into arrays for analysis.

tutorial_01_load_edf_and_signals.rb

# Tutorial 01 - Load EDF and Signals
#
#   gem install edfize --no-document
#
#   ruby tutorial_01_load_edf_and_signals.rb
#
# The EDF exists at:
#
#    https://sleepdata.org/datasets/shhs/files/edfs/shhs1?f=shhs1-200001.edf
#

require 'rubygems'
require 'edfize'

# Loads the file and reads the EDF Header
edf = Edfize::Edf.new('shhs1-200001.edf')

# Loads the data section of the EDF into Signal objects
edf.load_signals

# Print out information on the signals
puts "EDF #{edf.filename} contains the following #{edf.signals.count} signal#{'s' unless edf.signals.count == 1}:\n\n"

edf.signals.each do |signal|
  puts "Signal"
  puts "  Label                    : #{signal.label}"
  puts "  Samples Per Data Record  : #{signal.samples_per_data_record}"
  puts "  First 10 Physical Values : #{(signal.physical_values[0..10] + ['...']).inspect}\n\n"
end

When run, the code above will output the following:

EDF shhs1-200001.edf contains the following 14 signals:

Signal
  Label                    : SaO2
  Samples Per Data Record  : 1
  First 10 Physical Values : [95.31242847333486, 95.31242847333486, 95.31242847333486, 95.31242847333486, 95.31242847333486, 95.31242847333486, 95.31242847333486, 95.31242847333486, 94.14053559166858, 94.14053559166858, 94.14053559166858, "..."]

Signal
  Label                    : H.R.
  Samples Per Data Record  : 1
  First 10 Physical Values : [77.34416723887999, 77.34416723887999, 77.34416723887999, 76.56595712214848, 76.56595712214848, 76.56595712214848, 75.00190737773708, 75.00190737773708, 75.00190737773708, 75.00190737773708, 75.00190737773708, "..."]

Signal
  Label                    : EEG(sec)
  Samples Per Data Record  : 125
  First 10 Physical Values : [-4.411764705882348, 5.392156862745111, 2.4509803921568647, 0.49019607843136725, -0.49019607843136725, -10.294117647058826, 3.4313725490196134, 12.25490196078431, -1.470588235294116, -2.4509803921568647, -8.333333333333329, "..."]

Signal
  Label                    : ECG
  Samples Per Data Record  : 125
  First 10 Physical Values : [0.03431372549019618, 0.03431372549019618, 0.03431372549019618, 0.03431372549019618, 0.044117647058823595, 0.044117647058823595, 0.044117647058823595, 0.044117647058823595, 0.044117647058823595, 0.03431372549019618, 0.03431372549019618, "..."]

Signal
  Label                    : EMG
  Samples Per Data Record  : 125
  First 10 Physical Values : [12.622549019607845, 3.7990196078431353, -3.5539215686274517, -2.5735294117647065, 8.455882352941174, 1.5931372549019613, 9.436274509803923, -8.700980392156861, -2.5735294117647065, 13.112745098039213, -12.867647058823529, "..."]

Signal
  Label                    : EOG(L)
  Samples Per Data Record  : 50
  First 10 Physical Values : [28.921568627450966, 17.15686274509804, 25.0, 19.117647058823536, -5.392156862745097, -9.313725490196077, -0.49019607843136725, -1.470588235294116, 1.470588235294116, -1.470588235294116, 0.49019607843136725, "..."]

Signal
  Label                    : EOG(R)
  Samples Per Data Record  : 50
  First 10 Physical Values : [12.25490196078431, 1.470588235294116, 10.294117647058812, 5.392156862745111, 17.15686274509804, 18.137254901960773, 25.980392156862735, 32.84313725490196, 25.0, 26.960784313725497, 22.058823529411768, "..."]

Signal
  Label                    : EEG
  Samples Per Data Record  : 125
  First 10 Physical Values : [-2.4509803921568647, 1.470588235294116, -9.313725490196077, -6.372549019607845, -0.49019607843136725, -10.294117647058826, -12.25490196078431, -12.25490196078431, -7.352941176470594, 1.470588235294116, 6.372549019607845, "..."]

Signal
  Label                    : THOR RES
  Samples Per Data Record  : 10
  First 10 Physical Values : [0.207843137254902, 0.207843137254902, 0.15294117647058825, 0.0980392156862745, 0.03529411764705881, -0.0117647058823529, -0.050980392156862786, -0.08235294117647052, -0.10588235294117654, -0.1215686274509804, -0.13725490196078427, "..."]

Signal
  Label                    : ABDO RES
  Samples Per Data Record  : 10
  First 10 Physical Values : [0.30980392156862746, 0.24705882352941178, 0.16078431372549018, 0.06666666666666665, -0.0039215686274509665, -0.08235294117647052, -0.1607843137254903, -0.2078431372549019, -0.2313725490196079, -0.2549019607843137, -0.2705882352941176, "..."]

Signal
  Label                    : POSITION
  Samples Per Data Record  : 1
  First 10 Physical Values : [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, "..."]

Signal
  Label                    : LIGHT
  Samples Per Data Record  : 1
  First 10 Physical Values : [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, "..."]

Signal
  Label                    : NEW AIR
  Samples Per Data Record  : 10
  First 10 Physical Values : [6.372549019607845, 6.372549019607845, 5.392156862745111, 3.4313725490196134, 7.35294117647058, 6.372549019607845, 8.333333333333343, 9.313725490196077, 6.372549019607845, 6.372549019607845, 7.35294117647058, "..."]

Signal
  Label                    : OX stat
  Samples Per Data Record  : 1
  First 10 Physical Values : [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, "..."]

Contributing

1. Fork it ( https://github.com/sleepepi/edfize/fork )
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create a new Pull Request