PhysioBank & PhysioNet WFDB – Science To Medicine

今回は、データ部分の話を少しまとめておく。
PhysioNetには生理学的な信号情報に関するPhysioBankデータベースがある。
https://physionet.org
解析用にPhysioToolkitが用意されている。WFDB Software PackageはMac用は、PowerPC用でIntelPCには対応していないので、今回は、WFDB Python Packageを用いる。
以下のGItHubからダウンロードして、
https://github.com/MIT-LCP/wfdb-python
Jupiter NotebookでPython 3で動かすことにする。

サンプルデータであるが、MIMIC−III Waveform Database
https://physionet.org/physiobank/database/mimic3wdb/
から取得したいが、
PhysioBankのAutomated Teller Machine
https://physionet.org/cgi-bin/atm/ATM
を利用して各データをzipファイル化して取得もできる。

JupiterでPythonを動かして、MIMICIIIのデータを解析してみる。

from IPython.display import display
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import os
import shutil

import wfdb

from IPython.display import display

import matplotlib.pyplot as plt

%matplotlib inline

import numpy as np

import os

import shutil

import wfdb

例題として、MIMICIIIの39の3900006の0006データを以下のように指定して、

record = wfdb.rdrecord('sample-data/3900006/3900006_0006', channels=[0, 1, 2, 3], sampfrom=0, sampto=400) 
wfdb.plot_wfdb(record=record, title='Record 3900006_0006 from Physionet MIMICIII') 
display(record.__dict__)

record = wfdb.rdrecord('sample-data/3900006/3900006_0006', channels=[0, 1, 2, 3], sampfrom=0, sampto=400)

wfdb.plot_wfdb(record=record, title='Record 3900006_0006 from Physionet MIMICIII')

display(record.__dict__)

Outputを得る。

{'adc_gain': [127.0, 1.28, 128.0, 255.0],
 'adc_res': [8, 8, 8, 8],
 'adc_zero': [0, 0, 0, 0],
 'base_counter': None,
 'base_date': None,
 'base_time': datetime.time(9, 17, 20),
 'baseline': [-64, -109, -65, -128],
 'block_size': [0, 0, 0, 0],
 'byte_offset': [None, None, None, None],
 'checksum': [59043, 5459, 39974, 64305],
 'comments': [],
 'counter_freq': None,
 'd_signal': None,
 'e_d_signal': None,
 'e_p_signal': None,
 'file_name': ['3900006_0006.dat',
  '3900006_0006.dat',
  '3900006_0006.dat',
  '3900006_0006.dat'],
 'fmt': ['80', '80', '80', '80'],
 'fs': 125,
 'init_value': [-8, 112, 127, 127],
 'n_sig': 4,
 'p_signal': array([[   0.44094488,  172.65625   ,    1.5       ,    1.        ],
        [   0.42519685,  168.75      ,    1.46875   ,    1.        ],
        [   0.45669291,  164.84375   ,    1.40625   ,    1.        ],
        ..., 
        [   0.4488189 ,  105.46875   ,   -0.46875   ,    0.69019608],
        [   0.48031496,   99.21875   ,   -0.46875   ,    0.69411765],
        [   0.45669291,   92.96875   ,   -0.484375  ,    0.70196078]]),
 'record_name': '3900006_0006',
 'samps_per_frame': [1, 1, 1, 1],
 'sig_len': 400,
 'sig_name': ['II', 'ABP', 'V', 'RESP'],
 'skew': [None, None, None, None],
 'units': ['mV', 'mmHg', 'mV', 'NU']}

{'adc_gain': [127.0, 1.28, 128.0, 255.0],

'adc_res': [8, 8, 8, 8],

'adc_zero': [0, 0, 0, 0],

'base_counter': None,

'base_date': None,

'base_time': datetime.time(9, 17, 20),

'baseline': [-64, -109, -65, -128],

'block_size': [0, 0, 0, 0],

'byte_offset': [None, None, None, None],

'checksum': [59043, 5459, 39974, 64305],

'comments': [],

'counter_freq': None,

'd_signal': None,

'e_d_signal': None,

'e_p_signal': None,

'file_name': ['3900006_0006.dat',

'3900006_0006.dat',

'3900006_0006.dat'],

'fmt': ['80', '80', '80', '80'],

'fs': 125,

'init_value': [-8, 112, 127, 127],

'n_sig': 4,

'p_signal': array([[ 0.44094488, 172.65625 , 1.5 , 1. ],

[ 0.42519685, 168.75 , 1.46875 , 1. ],

[ 0.45669291, 164.84375 , 1.40625 , 1. ],

...,

[ 0.4488189 , 105.46875 , -0.46875 , 0.69019608],

[ 0.48031496, 99.21875 , -0.46875 , 0.69411765],

[ 0.45669291, 92.96875 , -0.484375 , 0.70196078]]),

'record_name': '3900006_0006',

'samps_per_frame': [1, 1, 1, 1],

'sig_len': 400,

'sig_name': ['II', 'ABP', 'V', 'RESP'],

'skew': [None, None, None, None],

'units': ['mV', 'mmHg', 'mV', 'NU']}

チャンネル２の血圧情報だけにすると、

record = wfdb.rdrecord('sample-data/3900006/3900006_0006', channels=[1], sampfrom=0, sampto=400) 
wfdb.plot_wfdb(record=record, title='Record 3900006_0008 from Physionet Challenge 2018') 
display(record.__dict__)

record = wfdb.rdrecord('sample-data/3900006/3900006_0006', channels=[1], sampfrom=0, sampto=400)

wfdb.plot_wfdb(record=record, title='Record 3900006_0008 from Physionet Challenge 2018')

display(record.__dict__)

{'adc_gain': [1.28],
 'adc_res': [8],
 'adc_zero': [0],
 'base_counter': None,
 'base_date': None,
 'base_time': datetime.time(9, 17, 20),
 'baseline': [-109],
 'block_size': [0],
 'byte_offset': [None],
 'checksum': [5459],
 'comments': [],
 'counter_freq': None,
 'd_signal': None,
 'e_d_signal': None,
 'e_p_signal': None,
 'file_name': ['3900006_0006.dat'],
 'fmt': ['80'],
 'fs': 125,
 'init_value': [112],
 'n_sig': 1,
 'p_signal': array([[ 172.65625],
        [ 168.75   ],
        [ 164.84375],
        [ 160.9375 ],
        [ 158.59375],
        [ 157.03125],
        [ 155.46875],
.......
.......
        [ 111.71875],
        [ 105.46875],
        [  99.21875],
        [  92.96875]]),
 'record_name': '3900006_0006',
 'samps_per_frame': [1],
 'sig_len': 400,
 'sig_name': ['ABP'],
 'skew': [None],
 'units': ['mmHg']}

{'adc_gain': [1.28],

'adc_res': [8],

'adc_zero': [0],

'base_counter': None,

'base_date': None,

'base_time': datetime.time(9, 17, 20),

'baseline': [-109],

'block_size': [0],

'byte_offset': [None],

'checksum': [5459],

'comments': [],

'counter_freq': None,

'd_signal': None,

'e_d_signal': None,

'e_p_signal': None,

'file_name': ['3900006_0006.dat'],

'fmt': ['80'],

'fs': 125,

'init_value': [112],

'n_sig': 1,

'p_signal': array([[ 172.65625],

[ 168.75 ],

[ 164.84375],

[ 160.9375 ],

[ 158.59375],

[ 157.03125],

[ 155.46875],

.......

[ 111.71875],

[ 105.46875],

[ 99.21875],

[ 92.96875]]),

'record_name': '3900006_0006',

'samps_per_frame': [1],

'sig_len': 400,

'sig_name': ['ABP'],

'skew': [None],

'units': ['mmHg']}

血圧の数字情報だけを取得するために、

for i in record.p_signal[:]:
    print(i[0])
<pre>

for i in record.p_signal[:]:

print(i[0])

<pre>

172.65625
168.75
164.84375
160.9375
158.59375
157.03125
155.46875
154.6875
153.90625
153.125
…….

Physiologic waveform records in this database contain up to eight simultaneously recorded signals digitized at 125 Hz with 8-, 10-, or (occasionally) 12-bit resolution. Numerics records typically contain 10 or more time series of vital signs sampled once per second or once per minute.

1秒125サンプル

record = wfdb.rdrecord('sample-data/3900006/3900006_0006', channels=[1], sampfrom=0, sampto=125) 
wfdb.plot_wfdb(record=record, title='ABP Record 3900006_0006 from Physionet MIMICIII') 
display(record.__dict__)

record = wfdb.rdrecord('sample-data/3900006/3900006_0006', channels=[1], sampfrom=0, sampto=125)

wfdb.plot_wfdb(record=record, title='ABP Record 3900006_0006 from Physionet MIMICIII')

display(record.__dict__)

for i in record.p_signal[:]:
    print(i[0])

1 2	for i in record.p_signal[:]: print(i[0])

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

172.65625

168.75

164.84375

160.9375

158.59375

157.03125

155.46875

154.6875

153.90625

153.125

150.78125

149.21875

146.09375

142.96875

139.84375

136.71875

132.8125

128.90625

124.21875

118.75

113.28125

107.8125

101.5625

95.3125

89.84375

83.59375

78.90625

74.21875

70.3125

67.1875

64.84375

63.28125

61.71875

60.9375

61.71875

60.9375

60.15625

59.375

58.59375

57.8125

57.03125

56.25

55.46875

54.6875

53.90625

53.125

52.34375

51.5625

50.78125

50.0

49.21875

51.5625

56.25

64.84375

78.90625

97.65625

118.75

139.84375

157.8125

170.3125

178.125

181.25

178.90625

174.21875

170.3125

167.1875

164.0625

162.5

161.71875

160.9375

160.15625

159.375

157.8125

155.46875

153.125

150.0

146.09375

142.96875

138.28125

133.59375

128.90625

123.4375

117.96875

112.5

106.25

100.0

94.53125

89.0625

84.375

79.6875

76.5625

73.4375

Science To Medicine

Just My Daily Study Note by ts.anesth.kpum