Adding CRP

Hypnogram -> Annotation
readthedocs fix
0.1.0a

.readthedocs.yml

@@ -17,10 +17,18 @@ sphinx:
 formats:
   - pdf
 
+# Optionally set the version of Python and requirements required to build your docs
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.6"
+
+sphinx:
+  configuration: docs/source/conf.py
+
 # Optionally set the version of Python and requirements required to build your docs
 python:
-  version: 3.6
   install:
     - requirements: docs/requirements.txt
     - method: pip
-      path: .
+      path: .

README.rst

@@ -29,25 +29,47 @@ Installation
 
 Cite
 """""""""""""""""""""""""""
-When use whole, parts, or are inspired by, we appreciate you acknowledge and refer these journal papers:
+This toolbox was developed during multiple projects we appreciate you acknowledge when using or inspired by this toolbox.
+The toolbox as a whole was developed during the following projects:
 
+| F. Mivalt et V. Kremen et al., “Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans,” J. Neural Eng., vol. 19, no. 1, p. 016019, Feb. 2022, doi: 10.1088/1741-2552/ac4bfd.
 
+| V. Sladky et al., “Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation,” Brain Commun., vol. 4, no. 3, May 2022, doi: 10.1093/braincomms/fcac115.
 
+For different modules, please see below.
 
-| Mivalt F, Kremen V, Sladky V, Balzekas I, Nejedly P, Gregg N, Lundstrom BN, Lepkova K, Pridalova T, Brinkmann BH, et al. Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans. J Neural Eng (2022) Available at: http://iopscience.iop.org/article/10.1088/1741-2552/ac4bfd
+Sleep classification and feature extraction
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+| F. Mivalt et V. Kremen et al., “Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans,” J. Neural Eng., vol. 19, no. 1, p. 016019, Feb. 2022, doi: 10.1088/1741-2552/ac4bfd.
 
-| Gerla, V., Kremen, V., Macas, M., Dudysova, D., Mladek, A., Sos, P., & Lhotska, L. (2019). Iterative expert-in-the-loop classification of sleep PSG recordings using a hierarchical clustering. Journal of Neuroscience Methods, 317(February), 61?70. https://doi.org/10.1016/j.jneumeth.2019.01.013
+| F. Mivalt et V. Sladky et al., “Automated sleep classification with chronic neural implants in freely behaving canines,” J. Neural Eng., vol. 20, no. 4, p. 046025, Aug. 2023, doi: 10.1088/1741-2552/aced21.
 
+| Gerla, V., Kremen, V., Macas, M., Dudysova, D., Mladek, A., Sos, P., & Lhotska, L. (2019). Iterative expert-in-the-loop classification of sleep PSG recordings using a hierarchical clustering. Journal of Neuroscience Methods, 317(February), 61?70. https://doi.org/10.1016/j.jneumeth.2019.01.013
 
 | Kremen, V., Brinkmann, B. H., Van Gompel, J. J., Stead, S. (Matt) M., St Louis, E. K., & Worrell, G. A. (2018). Automated Unsupervised Behavioral State Classification using Intracranial Electrophysiology. Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/aae5ab
 
-
 | Kremen, V., Duque, J. J., Brinkmann, B. H., Berry, B. M., Kucewicz, M. T., Khadjevand, F., G.A. Worrell, G. A. (2017). Behavioral state classification in epileptic brain using intracranial electrophysiology. Journal of Neural Engineering, 14(2), 026001. https://doi.org/10.1088/1741-2552/aa5688
 
 
+Seizure detection
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+| V. Sladky et al., “Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation,” Brain Commun., vol. 4, no. 3, May 2022, doi: 10.1093/braincomms/fcac115.
+
+Artificial Signal Generation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+| F. Mivalt et al., “Deep Generative Networks for Algorithm Development in Implantable Neural Technology,” in 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Oct. 2022, pp. 1736–1741, doi: 10.1109/SMC53654.2022.9945379.
+
+
+Evoked Response Potential Analysis
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+| K. J. Miller et al., “Canonical Response Parameterization: Quantifying the structure of responses to single-pulse intracranial electrical brain stimulation,” PLOS Comput. Biol., vol. 19, no. 5, p. e1011105, May 2023, doi: 10.1371/journal.pcbi.1011105.
+
+
 Acknowledgement
-"""""""""
+"""""""""""""""""""
 
 BEST was developed under projects supported by NIH Brain Initiative UH2&3 NS095495 Neurophysiologically-Based Brain State Tracking & Modulation in Focal Epilepsy, DARPA HR0011-20-2-0028 Manipulating and Optimizing Brain Rhythms for Enhancement of Sleep (Morpheus).
 Filip Mivalt was also partially supported by the grant FEKT-K-22-7649 realized within the project Quality Internal Grants of the Brno University of Technology (KInG BUT), Reg. No. CZ.02.2.69/0.0/0.0/19_073/0016948, which is financed from the OP RDE.

best/__init__.py

@@ -5,7 +5,7 @@
 # LICENSE file in the root directory of this source tree.
 import os
 
-__version__ = '0.0.6'
+__version__ = '0.1.0a'
 
 
 

best/hypnogram/CyberPSG.py → best/annotations/CyberPSG.py

@@ -200,6 +200,12 @@ class CyberPSG_XML_Writter:
         'Sleep stage W':  '00000001-898a-4b80-8ed8-000000000005',
         'IED':  '00000001-898a-4b80-8ed8-000000000010',
         'IED_best':  '00000001-898a-4b80-8ed8-000000000011',
+        'seizure': '00000001-898a-4b80-8ed8-000000000012',
+        'seizure_best': '00000001-898a-4b80-8ed8-000000000013',
+        'seizure_05': '00000001-898a-4b80-8ed8-000000000014',
+        'seizure_05_best': '00000001-898a-4b80-8ed8-000000000015',
+        'seizure_08': '00000001-898a-4b80-8ed8-000000000016',
+        'seizure_08_best': '00000001-898a-4b80-8ed8-000000000017',
     }
 
 

best/hypnogram/__init__.py → best/annotations/__init__.py

@@ -8,7 +8,7 @@
 
 
 """
-A package for a standardized manipulation with hypnogram annotations, saving, loading across different file formats (CyberP{SG, NSRR, ...) but also
+A package for a standardized manipulation with annotations annotations, saving, loading across different file formats (CyberP{SG, NSRR, ...) but also
 basic analysis of hypnograms. The package is based on pandas.DataFrame.
 """
 

best/hypnogram/analysis.py → best/annotations/analysis.py

@@ -14,8 +14,8 @@
 from copy import deepcopy
 from tqdm import tqdm
 
-from best.hypnogram.visualisation import plot_hypnogram
-from best.hypnogram.utils import merge_annotations, filter_by_key
+from best.annotations.visualisation import plot_hypnogram
+from best.annotations.utils import merge_annotations, filter_by_key
 
 
 """

best/hypnogram/io.py → best/annotations/io.py

@@ -18,15 +18,15 @@
 
 from mef_tools.io import MefReader
 
-from best.hypnogram.CyberPSG import CyberPSGFile, CyberPSG_XML_Writter
-from best.hypnogram.NSRR import NSRRSleepFile
-from best.hypnogram.utils import time_to_utc, create_duration, tile_annotations
+from best.annotations.CyberPSG import CyberPSGFile, CyberPSG_XML_Writter
+from best.annotations.NSRR import NSRRSleepFile
+from best.annotations.utils import time_to_utc, create_duration, tile_annotations
 from copy import deepcopy
 
 
 
 """
-Tools for loading and saving of hypnogram files.
+Tools for loading and saving of annotations files.
 """
 
 _hypnogram_colors = {
@@ -141,7 +141,7 @@ def _load_CyberPSG(path, tile=None):
 
 def _load_CyberPSG_dataset(paths: list, tile=None, verbose=True):
     if verbose:
-        print('Loading hypnogram Dataset')
+        print('Loading annotations Dataset')
         return [_load_CyberPSG(pth, tile) for pth in tqdm(paths)]
     else:
         return [_load_CyberPSG(pth, tile) for pth in paths]

best/hypnogram/utils.py → best/annotations/utils.py

@@ -64,7 +64,7 @@ def _convert_to_timezone(x, tzinfo):
 
 def time_to_local(dfHyp):
     """
-    Converts the time into the local timezone. Default by python and PC. Does not enter the timezone explicitely. Cannot be used for creating a hypnogram figure.
+    Converts the time into the local timezone. Default by python and PC. Does not enter the timezone explicitely. Cannot be used for creating a annotations figure.
     """
     def convert(x, col_key):
         return _convert_to_local(x[col_key])
@@ -90,7 +90,7 @@ def convert(x, col_key):
 
 def time_to_timezone(dfHyp, tzinfo):
     """
-    Converts the hypnogram into a timezone. The timezone has to be from a python library dateutil
+    Converts the annotations into a timezone. The timezone has to be from a python library dateutil
     """
     def convert(x, col_key):
         return _convert_to_timezone(x[col_key], tzinfo)
@@ -102,7 +102,7 @@ def convert(x, col_key):
 
 def time_to_timestamp(dfHyp):
     """
-    Converts the hypnogram time to timestamp.
+    Converts the annotations time to timestamp.
     """
     def convert(x, col_key):
         return _convert_to_timestamp(x[col_key])
@@ -114,7 +114,7 @@ def convert(x, col_key):
 
 def create_duration(dfHyp):
     """
-    Creates duration for each epoch within the hypnogram. (Faster on timestamp)
+    Creates duration for each epoch within the annotations. (Faster on timestamp)
     """
     def duration(x):
         if type(x['start']) in (datetime, Timestamp):
@@ -127,7 +127,7 @@ def duration(x):
 
 def create_day_indexes(dfHyp, hour=12, tzinfo=tz.tzlocal):
     """
-    Creates a day index for each epoch within the hypnogram, given the day-time hour supplied as input parameter.
+    Creates a day index for each epoch within the annotations, given the day-time hour supplied as input parameter.
     The format of start and end has to be an integer or a float in a form representing a timestamp, or a timezone aware datetime  or Timestamp object.
     If the start and end format do not include timezone, the local timezone will be used.
     """

best/hypnogram/visualisation.py → best/annotations/visualisation.py

@@ -18,11 +18,11 @@
 
 def plot_hypnogram(orig_df, hypnogram_values=None, hypnogram_colors=None, fontsize=12, fig=None, night_start=22):
     """
-    Creates a Matplotlib figure of spectrogram from the hypnogram. Time must be in a time-zone aware format.
+    Creates a Matplotlib figure of spectrogram from the annotations. Time must be in a time-zone aware format.
 
     Parameters
     ----------
-    orig_df : hypnogram
+    orig_df : annotations
     hypnogram_values : dict
         dict of a y-axis values for each sleep state
     hypnogram_colors : dict

best/cloud/db.py

@@ -18,7 +18,7 @@
 from sqlalchemy.pool import NullPool
 from sshtunnel import SSHTunnelForwarder
 
-from best.hypnogram.utils import create_day_indexes, time_to_timezone, time_to_timestamp, tile_annotations, create_duration
+from best.annotations.utils import create_day_indexes, time_to_timezone, time_to_timestamp, tile_annotations, create_duration
 from best.cloud._db_connection_variables import *
 
 import pandas as pd
@@ -451,8 +451,7 @@ def load_stimulation_day_info(self, patient_id, start_uutc, stop_uutc):
         freqs = []
         ampls = []
         pws = []
-        for row in df_data.iterrows():
-            row = row[1]
+        for _, row in df_data.iterrows():
             ampl = [row['curr_Prog'+str(k)+'AmpInMilliamps'] for k in range(4) if not isinstance(row['curr_Prog'+str(k)+'AmpInMilliamps'], type(None))]
 
             freq = row['curr_rate_in_hz']
@@ -472,9 +471,9 @@ def load_stimulation_day_info(self, patient_id, start_uutc, stop_uutc):
         if ampls.__len__() == 0: ampls = None
         if pws.__len__() == 0: pws = None
         stim_info = {
-            'freq': np.max(freqs),
-            'ampl': np.max(ampls),
-            'pulsewidth': np.max(pws)
+            'freq': np.nanmax(freqs),
+            'ampl': np.nanmax(ampls),
+            'pulsewidth': np.nanmax(pws)
         }
         return stim_info