This SQLite-driven database contains 2510 respiratory signals (uni-variate time series) of 419 patients (total acquisition time > 325.000 s). We hope that this data will be widely used for respiratory motion prediction as well as uni-variate time series forecasting in general. To this end, we provide data splitting (train, val and test set), preprocessing functionality and a pytorch dataset/loader template, which may be helpful in your machine or deep learning project.
We advise to use https://sqlitebrowser.org/ as additional database browsing tool. Currently, only python is supported. However, we hope that we can provide a Rest-API in the near future (approx. December 2023) for non-python user. If you experience any troubles, feel free to open an issue.
Author: Lukas Wimmert ([email protected])
Download sql database from our nextcloud server: https://icns-nas2.uke.uni-hamburg.de/drive/d/s/wFCsYhokwsJ9hLp9CHqBoHidu91yXfSk/lvQnG6BiEbfM2OqIOEXhaonW4vilyWKK-07sgZW6pCws.
Clone this repository to your local machine:
git clone https://github.com/IPMI-ICNS-UKE/respiratory-signal-database.gitcd into the repo and install necessary dependencies and the package itself:
pip install -r requirements.txt
pip install -e .Tested with Python 3.9.11.
Always start by connecting to the database:
from pathlib import Path
from resp_db.client import RpmDatabaseClient
db_root = Path("example_database_path.db") # change to the downloaded db-file
client = RpmDatabaseClient(db_filepath=db_root)# query a signal
with client:
df_signal, signal = client.get_specific_signal(
research_number=5708019,
modality="4DCT", # or "CBCT" or "LINAC"
fraction= 0, # "4DCT" = 0, "LINAC" fractions between 1 and 5
origin="UKE")
# preprocess
sampling_rate = 25 # sampling rate in HZ, i.e. points per second.
df = RpmDatabaseClient.preprocess_signal(
df_signal=df_signal,
sampling_rate=sampling_rate,
only_beam_on=True, # strongly recommended
remove_offset=True, # strongly recommended
)
# df.columns = [time, amplitude]with client:
query = client.get_signals_of_dl_dataset(
dl_dataset="train", project="short-term-prediction"
)
query = list(query) # query contains all train signals
# preprocess all signals
query = [RpmDatabaseClient.preprocess_signal(df_signal=signal.df_signal,
sampling_rate=25,
only_beam_on=True, # strongly recommended
remove_offset=True # strongly recommended
)
for signal in query]
# query is a list of dataframes. Feel free to write more query functions and add them to the RpmDatabaseClient.
Have a look at our pytorch template at "scripts/dataloader.py" for machine/deep learning projects.
All respiratory signals were recorded during radiotherapy treatment (i.e. 4D CT, 4D CBCT and dose delivery) of patients with thoracic lesions between February 2013 and May 2022 at the clinic of Radiotherapy and Radiation Oncology of the University Medical Center Hamburg-Eppendorf. Data acquisition was performed using the Varian RPM System, i.e. monitoring an external marker block placed on the patient's chest wall with an infrared camera system (uni-variate time series). Check out our acquisition images. All patients did not receive any visual guidance or coaching and, therefore, can be considered as free-breathing patients.
| modality | # signals | # patients | mean signal length (s) |
|---|---|---|---|
| 4DCT | 481 | 419 | 98.1 |
| 4D CBCT | 251 | 52 | 59.6 |
| dose delivery | 1778 | 357 | 145.6 |
| sum | 2510 | 129.3 |
Data splitting was performed on patient level. Thus, all signals of one patient belong to one set.
| set | proportion of data | # signals | # patients |
|---|---|---|---|
| training | 50 % | 1262 | 215 |
| validation | 20 % | 514 | 84 |
| testing | 30 % | 726 | 117 |
| total | 100 % | 2502 | 416 |
Note that, eight signals (corresponding to three patients) were retrospectively classified as corrupted and, therefore, they were removed from the database.
Lastly, open access of retrospectively collected and anonymized respiratory signals was approved by the local ethics board and the need to obtain written informed consent was waived [2023-300334-WF].