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EDFZ: working with compressed EDFs

Luna supports reading and writing compressed EDFs, i.e. with decompression performed on-the-fly. We call such files EDFZ files. Specifically, Luna uses the BGZF library (developed to support high throughput sequencing alignment/map files), which itself takes advantage of the gzip file format's ability to concatenate compressed files in a way that allows for random access of small blocks. This means it is possible to allow random access into part of a compressed file without having to decompress the entire file.

For large projects, there can be considerable savings in terms of disk space from working with compressed files. Importantly, the format is simply a special case of the gzip format, so an EDFZ file can be easily decompressed with the standard gunzip tool, e.g.:

cat my.edfz | gunzip > my.edf 

Here we consider two use-cases (PSGs from the NSRR, and a hdEEG study), to consider the space/time trade-offs in working with EDFZ instead of EDF. We'll also consider how this plays with Luna's RECORD-SIZE command, which allows you to change the internals of the EDF format. Because Luna reads an EDF one record at a time, different records sizes can have different impacts on different workflows. Here we'll consider streaming over the entire EDF.

These tests were performed on an iMac with a 4 GHz Intel Core i7 processor and 32 GB RAM.


Naturally, if you are routinely using other tools to work with the EDFs alongside Luna, there will be limited value in using compressed files, unless those other tools can read gzipped files directly. That is, you would not want to have to retain uncompressed copies of the original EDF too... On the other hand, if you are moving EDFs to a separate system (e.g. a Linux compute cluster) specifically for Luna analyses, this might be a really good use-case for creating and migrating EDFZ files only.

Use-case #1, NSRR PSG

  • ~10 hours with 14 signals
  • 54Mb file
  • two EEG (125 Hz) channels, ECG, EMG, EOG, respiratory and other signals
  • original EDF record size of 1 second

From the original EDF, we'll obtain three other files, using compression and/or a different record-size, using the following commands. To write a compressed (EDFZ) file, add the edfz option to WRITE:

luna t1.lst -s "WRITE edfz edf-dir=test/ edf-tag=z1 sample-list=z1.lst" 

As well as the original 1 second record size, we'll try working with a 30-second record size. Because epochs cannot be smaller than the EDF record size, we do not want to set this to be any larger. To increase the record size to 30 seconds, but keep an uncompressed EDF, we'd use the RECORD-SIZE command (which also expects the same parameters are WRITE, as it forces an immediate write of the reformatted EDF):

luna t1.lst -s "RECORD-SIZE dur=30 edf-dir=test/ edf-tag=t30 sample-list=t30.lst" 

Finally, to change both record size and write as an EDFZ:

luna t1.lst -s "RECORD-SIZE dur=30 edfz edf-dir=test/ edf-tag=z30 sample-list=z30.lst" 

Here are the resulting file sizes (in bytes) for EDF and EDFZ:

          EDF       EDFZ      IDX     EDFZ+IDX  RATIO
RS = 1    54495840  24578092  476909  25055001  46%
RS = 30   54495840  23660147  15873   23676020  43%

Using the EDFZ format reduces file size by more than half: from 54Mb to around 24Mb. In general, PSG files compress quite well. As expected, changing EDF record size has no impact on the EDF file size, whereas there is a small difference in the efficiency of EDFZ compression.

.edfz.idx files

Saving as an EDFZ also produces an index file (IDX above), i.e. if we save test/my.edfz Luna will additionally generate a file test/my.edfz.idx. The index files needs to be kept with the EDFZ file; they are simple text files that specify the EDF total record size (in bytes), and the offsets for each record in the EDFZ. You can safely ignore the contents of these files.

But how much of a price do we pay (if any) for having to decompress on-the-fly, in terms of speed? In theory, working with compressed files could actually be faster than uncompressed files, depending on disk speed, CPU speed and the achieved compression ratio, but in general we expect it will slow things down a little.

We'll use the STATS command to calculate summary statistics for every channel in the EDF or EDFZ, which ensures that all data from the EDF is read into memory:

luna t1.lst -s STATS

The times taken (in seconds) to load the data and complete this command are:

          EDF    EDFZ
RS = 1    2.8    10.5
RS = 30   1.9    2.3

In this case, we see that working with EDFZ and small record sizes (1 second) incurs noticeable overhead (in relative terms), taking 10.5 instead of 2.8 seconds. However, using the larger 30-second record size speeds things up in both cases: in fact, the compressed EDFZ is now faster than the original EDF, but only using 43% of the disk space. You do pay a 0.4 second cost per-file on loading, but for any non-trivial analysis, this will be negligible in comparison to the overall processing time.

Importantly, identical results (from the STATS command) were obtained for all four analyses. That is, EDFZ is a lossless compression format. We can also check that after using gunzip on the EDFZ, we obtain an EDF that is identical to the original one (for the same record size). For example, if test/my-t30.edf was the original EDF:

cat test/my-t30.edfz | gunzip > test/my-t30-v2.edf 
and we'll find that:
diff -q test/my-t30.edf test/my-t30-v2.edf
yields no differences.

Use-case #2, sleep hdEEG

For the second example we consider a significantly larger, different type of EDF file: a hdEEG sleep dataset more than 70 times the size of the previous PSG file.

  • ~9 hours with 63 signals
  • 4 Gb file
  • EEG channels sampled at 1000 Hz
  • original EDF record size of 1 second

Using the same approach as above, here are the file sizes (in bytes) and the compression ratios, as a function of record size (RS):

         EDF         EDFZ        IDX     EDFZ+IDX    RATIO
RS = 1   4022440384  2568741043  489936  2569230979  64%
RS = 30  4021936384  2532976251  16321   2532992572  63%

These EEG files tend not to compress quite as well as the PSG files: after all, the brain is a more complex organ and so EEG has a higher information content. Nonetheless, if we are saving around 1.45 Gb per EDF, in a project with 100s of recordings, these differences will quickly become non-trivial.

In terms of speed, naturally everything takes longer (this is a much larger dataset, to be fair):

          EDF   EDFZ
RS = 1    168   201
RS = 30   140   151

We see a similar pattern to above, however, in that larger EDF record sizes increase speed overall, as well as making the speed difference between EDF and EDFZ files relatively trivial.


Overall, the combination of large (i.e. epoch-length) record sizes and EDFZ appears to result in a favourable combination of performance in terms of both space and time. If working with large datasets, or making copies of original EDFs that are used by Luna only, you may want to consider the edfz option of WRITE and RECORD-SIZE.

Naturally, there may be other considerations that impact performance. The potential drawbacks are a) using larger record size currently precludes the ability to look at epochs smaller than the record size, b) if using other tools on the same set of EDFs that do not support on-the-fly compression, there is no point in compressing EDFs (with the caveat that it is easy to decompress with gunzip, e.g. if you only want to occasionally use another tool to look at one or two EDFs).


We have not performed any type of exhaustive test, but purely in terms of time to load an EDF, Luna seems to compare favorably to the couple of other tools we've used. Taking the default (EDF, 1-second record size) hdEEG dataset above:

  • edfReader R package: 8 minutes
  • EEGLAB Matlab toolbox: 8 minutes 30 seconds
  • lunaC: 2 minutes 48 seconds
  • lunaR: 2 minutes 45 seconds

Although not formally checked, EEGLAB (using the Biosig toolbox) also appeared to use more than double the amount of RAM compared to Luna and edfReader (peaking at ~45 GB).

Context for comparisons

These other tools likely have options (e.g. memory mapping files in EEGLAB) that might greatly enhance performance: we have not investigated any of those options. Naturally, EEGLAB is a wonderfully powerful tool that is quite possibly doing a number of other checks, etc, behind the scenes, not to mention the fact that it encompasses a whole suite of EEG-based methods that are not even part of Luna, which has a very different and focussed use-case. The point of these comparisons is merely to note that Luna's baseline performance is at least comparable with other tools that are designed to analyse EDF files, which can be useful if wanting to perform standard types of analyses on large numbers of EDFs.