Note

Click here to download the full example code

Extracting Shock Data for Feature Extraction - Initial Plots for BPF

Fetching shock instances (including symptoms that indicate severe dengue) and plotting the data. Processing of data will follow this step

# Importing Libraries, Definitions and Data Loading

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 # Generic
 import os
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 from datetime import datetime
 import glob
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 import plotly.io as pio

 Terminal = True

 SQI_clinical_file = r'..\..\..\..\OUCRU\Outputs\Complete_SQIs_with_Clinical.csv'
 Raw_signals = r'..\..\..\..\OUCRU\Outputs\Raw_signals.csv'


 #Loading SQI Matched with clinitcal to the Dataframe
 SQI_C = pd.read_csv(SQI_clinical_file)
 Raw = pd.read_csv(Raw_signals)

 if Terminal:
     print("\n SQI with Clinical Match:")
     print(SQI_C)
     print("\n Raw Signals")
     print(Raw)

 #List of events explored
 event = ['event_shock', 'reshock24','diagnosis_admission',\
      'ascites', 'respiratory_distress', 'ventilation_cannula', \
      'ventilation_mechanical', 'ventilation_ncpap', 'bleeding_severe', \
      'cns_abnormal', 'liver_mild', 'pleural_effusion', 'skidney']

 shock_ad = 'shock_admission' #excluding it from the prior list as we are treating this differently

 #Study_no list for ease of use
 patient_list = ['003-2162']
 #['003-2009', '003-2012','003-2023','003-2028','003-2103','003-2104','003-2109', '003-2110', '003-2162']

Out:

 SQI with Clinical Match:
       Unnamed: 0               timedelta                  PPG_w_s                  PPG_w_f  ...  cns_abnormal  liver_mild  pleural_effusion  skidney
0               0  0 days 00:05:00.010000  2020-07-28 16:04:20.104  2020-07-28 16:04:50.094  ...           NaN         NaN               NaN      NaN
1               1  0 days 00:05:30.010000  2020-07-28 16:04:50.104  2020-07-28 16:05:20.094  ...           NaN         NaN               NaN      NaN
2               2  0 days 00:06:00.010000  2020-07-28 16:05:20.104  2020-07-28 16:05:50.094  ...           NaN         NaN               NaN      NaN
3               3  0 days 00:06:30.010000  2020-07-28 16:05:50.104  2020-07-28 16:06:20.094  ...           NaN         NaN               NaN      NaN
4               4  0 days 00:07:00.010000  2020-07-28 16:06:20.104  2020-07-28 16:06:50.094  ...           NaN         NaN               NaN      NaN
...           ...                     ...                      ...                      ...  ...           ...         ...               ...      ...
12698       12698  0 days 15:03:30.010000  2020-07-21 05:08:37.203  2020-07-21 05:09:07.193  ...           NaN         NaN               NaN      NaN
12699       12699  0 days 15:04:00.010000  2020-07-21 05:09:07.203  2020-07-21 05:09:37.193  ...           NaN         NaN               NaN      NaN
12700       12700  0 days 15:04:30.010000  2020-07-21 05:09:37.203  2020-07-21 05:10:07.193  ...           NaN         NaN               NaN      NaN
12701       12701  0 days 15:05:00.010000  2020-07-21 05:10:07.203  2020-07-21 05:10:37.193  ...           NaN         NaN               NaN      NaN
12702       12702  0 days 15:05:30.010000  2020-07-21 05:10:37.203  2020-07-21 05:10:52.883  ...           NaN         NaN               NaN      NaN

[12703 rows x 42 columns]

 Raw Signals
                       timedelta      idx  PLETH  IR_ADC             PPG_Datetime     PLETH_bpf  IR_ADC_bpf  study_no  study_no_rec
0         0 days 00:05:00.010000    29913  10803  135438  2020-07-28 16:04:20.104   -347.936641   -0.439129  003-2009             0
1         0 days 00:05:00.020000    29914  10609  135067  2020-07-28 16:04:20.114  -2051.585777  -16.222886  003-2009             0
2         0 days 00:05:00.030000    29915  10659  134754  2020-07-28 16:04:20.124  -5424.102666  -96.673830  003-2009             0
3         0 days 00:05:00.040000    29916  10983  134473  2020-07-28 16:04:20.134  -8735.859953 -286.584242  003-2009             0
4         0 days 00:05:00.050000    29917  11734  134286  2020-07-28 16:04:20.144 -10096.052116 -548.629881  003-2009             0
...                          ...      ...    ...     ...                      ...           ...         ...       ...           ...
38090494  0 days 15:05:45.650000  5434392  29757  237669  2020-07-21 05:10:52.843   2225.061853  -71.816763  003-2162             0
38090495  0 days 15:05:45.660000  5434393  28948  237769  2020-07-21 05:10:52.853   2087.608183  -63.166235  003-2162             0
38090496  0 days 15:05:45.670000  5434394  28102  237833  2020-07-21 05:10:52.863   1924.112544  -54.688387  003-2162             0
38090497  0 days 15:05:45.680000  5434395  27238  237917  2020-07-21 05:10:52.873   1729.246675  -46.064908  003-2162             0
38090498  0 days 15:05:45.690000  5434396  26337  238017  2020-07-21 05:10:52.883   1506.537732  -44.085682  003-2162             0

[38090499 rows x 9 columns]

Turning signal window rows with an event to “keep = True”

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 SQI_C['keep'] = False
 for i in range(len(event)):
     event_s = event[i]
     SQI_C['keep'][SQI_C[event_s] == True] = True
     print("\n Total ", event[i], " events:")

 #Optional Save
 #SQI_C.to_csv(r'..\..\..\..\OUCRU\Outputs\Complete_SQIs_with_Clinical_keep.csv')

Out:

D:\FILES\Desktop\Dissertation ICL\Git\main\examples\Pre-processing\plot_Extracting_Shock_Samples.py:56: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  SQI_C['keep'][SQI_C[event_s] == True] = True

 Total  event_shock  events:
D:\FILES\Desktop\Dissertation ICL\Git\main\examples\Pre-processing\plot_Extracting_Shock_Samples.py:56: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  SQI_C['keep'][SQI_C[event_s] == True] = True

 Total  reshock24  events:
D:\FILES\Desktop\Dissertation ICL\Git\main\examples\Pre-processing\plot_Extracting_Shock_Samples.py:56: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  SQI_C['keep'][SQI_C[event_s] == True] = True

 Total  diagnosis_admission  events:
D:\FILES\Desktop\Dissertation ICL\Git\main\examples\Pre-processing\plot_Extracting_Shock_Samples.py:56: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  SQI_C['keep'][SQI_C[event_s] == True] = True

 Total  ascites  events:

 Total  respiratory_distress  events:
D:\FILES\Desktop\Dissertation ICL\Git\main\examples\Pre-processing\plot_Extracting_Shock_Samples.py:56: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  SQI_C['keep'][SQI_C[event_s] == True] = True

 Total  ventilation_cannula  events:
D:\FILES\Desktop\Dissertation ICL\Git\main\examples\Pre-processing\plot_Extracting_Shock_Samples.py:56: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  SQI_C['keep'][SQI_C[event_s] == True] = True

 Total  ventilation_mechanical  events:
D:\FILES\Desktop\Dissertation ICL\Git\main\examples\Pre-processing\plot_Extracting_Shock_Samples.py:56: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  SQI_C['keep'][SQI_C[event_s] == True] = True

 Total  ventilation_ncpap  events:
D:\FILES\Desktop\Dissertation ICL\Git\main\examples\Pre-processing\plot_Extracting_Shock_Samples.py:56: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  SQI_C['keep'][SQI_C[event_s] == True] = True

 Total  bleeding_severe  events:
D:\FILES\Desktop\Dissertation ICL\Git\main\examples\Pre-processing\plot_Extracting_Shock_Samples.py:56: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  SQI_C['keep'][SQI_C[event_s] == True] = True

 Total  cns_abnormal  events:
D:\FILES\Desktop\Dissertation ICL\Git\main\examples\Pre-processing\plot_Extracting_Shock_Samples.py:56: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  SQI_C['keep'][SQI_C[event_s] == True] = True

 Total  liver_mild  events:
D:\FILES\Desktop\Dissertation ICL\Git\main\examples\Pre-processing\plot_Extracting_Shock_Samples.py:56: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  SQI_C['keep'][SQI_C[event_s] == True] = True

 Total  pleural_effusion  events:
D:\FILES\Desktop\Dissertation ICL\Git\main\examples\Pre-processing\plot_Extracting_Shock_Samples.py:56: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  SQI_C['keep'][SQI_C[event_s] == True] = True

 Total  skidney  events:

Plotting IR_ADC BPF and PLETH BPF on interactive graphs, together with the events (i.e. event of shock and whether or not the patient was admitted with shock)

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 for i in range(len(patient_list)):
     #Plotting IR_ADC BPF signals with events
     #fig = make_subplots(rows=2, cols=1)
     title_str = 'IR_ADC_BPF over Time for patient ' + str(patient_list[i])
     fig = go.Figure(layout_title_text = title_str)
     fig.add_trace(go.Scatter(x = Raw.PPG_Datetime[Raw.study_no == patient_list[i]], y =  Raw.IR_ADC_bpf[Raw.study_no == patient_list[i]], name='IR_ADC'))#,),row=1,col=1)
     for index, row in SQI_C[(SQI_C.study_no == patient_list[i]) & (SQI_C.keep == True)].iterrows():
         fig.add_vline(x = row.PPG_w_s, line_width=3, line_dash="dot", line_color="red")# annotation_text="Shock")#, annotation_position="top left", annotation_font_size=20, annotation_font_color="red")
         if not row.empty:
             fig.add_trace( go.Scatter(mode='markers', x=[row.PPG_w_s], y=[Raw['PLETH'][(Raw.study_no == patient_list[i]) & (Raw.PPG_Datetime >= row.PPG_w_s) & (Raw.PPG_Datetime < row.PPG_w_f)]], marker=dict(color='red', opacity=1), name = "Shock" ))
     if not SQI_C[(SQI_C.study_no == patient_list[i]) & (SQI_C.shock_admission == True)].empty:
         fig.add_annotation(
         xref="x domain",
         yref="y domain",
         # The arrow head will be 25% along the x axis, starting from the left
         x=0.01,
         # The arrow head will be 40% along the y axis, starting from the bottom
         y=0.01,
         text="Admitted with Shock",
         showarrow=False,
         font=dict(
             family="Courier New, monospace",
             size=20,
             color="RED"
             )
                         )


     fig.update_xaxes(rangeslider_visible=True)
     fig.update_xaxes(title_text="Date")
     fig.update_yaxes(title_text="IR_ADC")
     fig.update_layout(showlegend=True)

     # #IGNORE, this is to save locally
     # imagepath1 = "D:\FILES\Desktop\Dissertation ICL\OUCRU\Outputs\Images_BPF\PNG"
     # imagepath2 = "D:\FILES\Desktop\Dissertation ICL\OUCRU\Outputs\Images_BPF\SVG"
     # imagepath3 = "D:\FILES\Desktop\Dissertation ICL\OUCRU\Outputs\Images_BPF\HTML"
     # img_title1 = os.path.join(imagepath1,title_str)
     # img_title2 = os.path.join(imagepath2,title_str)
     # img_title3 = os.path.join(imagepath3,title_str)
     # img_s1 = img_title1 + ".png"
     # img_s2 = img_title2 + ".svg"
     # img_s3 = img_title3 + ".html"
     # fig.write_image(img_s1)
     # fig.write_image(img_s2)
     # fig.write_html(img_s3)

     fig.show()


     #Plotting PLETH BPF and events
     #fig = make_subplots(rows=2, cols=1)
     title_str2 = 'PLETH_BPF over Time for patient ' + str(patient_list[i])
     fig2 = go.Figure(layout_title_text = title_str2)
     fig2.add_trace(go.Scatter(x = Raw.PPG_Datetime[Raw.study_no == patient_list[i]], y =  Raw.PLETH_bpf[Raw.study_no == patient_list[i]], name='PLETH'))#,),row=1,col=1)
     for index, row in SQI_C[(SQI_C.study_no == patient_list[i]) & (SQI_C.keep == True)].iterrows():
         fig2.add_vline(x = row.PPG_w_s, line_width=3, line_dash="dot", line_color="red")#, annotation_text="Shock")#, annotation_position="top left", annotation_font_size=20, annotation_font_color="red")
         if not row.empty:
             fig.add_trace( go.Scatter(mode='markers', x=[row.PPG_w_s], y=[Raw['PLETH'][(Raw.study_no == patient_list[i]) & (Raw.PPG_Datetime >= row.PPG_w_s) & (Raw.PPG_Datetime < row.PPG_w_f)]], marker=dict(color='red', opacity=1), name = "Shock" ))
     if not SQI_C[(SQI_C.study_no == patient_list[i]) & (SQI_C.shock_admission == True)].empty:
         fig2.add_annotation(
         xref="x domain",
         yref="y domain",
         # The arrow head will be 25% along the x axis, starting from the left
         x=0.01,
         # The arrow head will be 40% along the y axis, starting from the bottom
         y=0.01,
         text="Admitted with Shock",
         showarrow=False,
         font=dict(
             family="Courier New, monospace",
             size=20,
             color="RED"
             )
                         )


     fig2.update_xaxes(rangeslider_visible=True)
     fig2.update_xaxes(title_text="Date")
     fig2.update_yaxes(title_text="PLETH")
     fig2.update_layout(showlegend=True)


     # #IGNORE, this is to save locally
     # img_title4 = os.path.join(imagepath1,title_str2)
     # img_title5 = os.path.join(imagepath2,title_str2)
     # img_title6 = os.path.join(imagepath3,title_str2)
     # img_s4 = img_title4 + ".png"
     # img_s5 = img_title5 + ".svg"
     # img_s6 = img_title6 + ".html"
     # fig2.write_image(img_s4)
     # fig2.write_image(img_s5)
     # fig2.write_html(img_s6)

     fig2.show()




 #MULTIPLE plots same figure

 #fig.append_trace(go.Scatter(x = Raw.PPG_Datetime[Raw.study_no == '003-2009'], y =  Raw.PLETH[Raw.study_no == '003-2009'], name='Pleth'), row=2, col=1)
 #for index, row in SQI_C[(SQI_C.study_no == '003-2009') & (SQI_C.keep == True)].iterrows():
 #    fig.add_vline(x = row.PPG_w_s, line_width=3, line_dash="dash", line_color="red", row=2, col=1)


 #fig.update_yaxes(title_text="Infrared", row=1, col=1)
 #fig.update_yaxes(title_text="Pleth", row=2, col=1)

 # fig.update_xaxes(rangeslider_visible=True, row=1, col=1)
 # fig.update_xaxes(title_text="Date", row=1, col=1)
 # fig.update_xaxes(rangeslider_visible=True, row=2, col=1)
 # fig.update_xaxes(title_text="Date", row=2, col=1)

 #fig.update_layout(height=1000, width=1000, title_text=" IR_ADC and PLETH Plots for Patient 003-2009")

Total running time of the script: ( 5 minutes 44.614 seconds)

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