Example of utilizing AWARE dataset

There are three ways to use this dataset:

• Raw WAV files
• Segmented and aligned signals
• Processed airway cross-sectional area curve

Read raw WAV files

First, Unzip the "aware_raw.zip" to the same folder. After then:

import os
from scipy import io
import numpy as np
import matplotlib.pyplot as plt

plt.figure(figsize=(8,6))
path = './aware_raw/1/' # Take subject #1 as an example
for idx, filename in enumerate(os.listdir(path)):
    fs, data = io.wavfile.read(path+filename)
    data = data/(2**15) # Normalize from 16-bit PCM to [-1,1]
    plt.subplot(3,3,idx+1)
    plt.plot(np.array(range(len(data)))/fs, data)
    plt.title(filename)
    plt.xlabel('Time (sec)')
    plt.ylabel('Amplitude')
plt.tight_layout()
plt.show()

Read segmented and aligned signals

For PICKLE file, use this:

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_pickle('aware_segmented.pkl')
display(df.loc[[100]])  # Take record #0 as an example

data = df['Inhale_1'][100] # Let's try to see how record #100's signals in phase "Inhale_1" looks like
data = data/(2**15) # Normalize from 16-bit PCM to [-1,1]
plt.figure(figsize=(12,9))
for idx in range(data.shape[0]):
    plt.subplot(5, 5, idx+1)
    plt.plot(data[idx,:])
plt.show()

	AWARE STUDY ID:	Calculated age (years):	Sex:	Race/ethnicity: (choice=White)	Race/ethnicity: (choice=Black / African American)	Race/ethnicity: (choice=Hispanic / Latino)	Race/ethnicity: (choice=Asian)	Race/ethnicity: (choice=Other)	Height (cm):	Weight (kg):	...	Calibration_1	Calibration_2	Calibration_3	Nasal	Inhale_1	Inhale_2	Inhale_3	Exhale_1	Exhale_2	Exhale_3
100	28	38.7	Male	Checked	Unchecked	Unchecked	Unchecked	Unchecked	177.8	87.5	...	[[-2, -4, -4, -3, -1, 0, -2, -1, -3, -1, -1, -...	[[-21, -18, -15, -16, -21, -22, -21, -18, -15,...	[[-7, -5, -6, -5, -5, -5, -3, -3, -2, -1, -2, ...	[[5, 7, 3, 3, 2, 1, 0, 0, -3, -6, -7, -9, -8, ...	[[21, 23, 28, 31, 31, 30, 31, 32, 36, 39, 39, ...	[[-13, -12, -11, -10, -10, -11, -12, -13, -11,...	[[-39, -46, -45, -40, -32, -31, -37, -38, -39,...	[[-7, -7, -6, -5, -6, -6, -4, -3, -2, -5, -6, ...	[[198, 185, 171, 154, 136, 122, 110, 102, 86, ...	[[-54, -48, -43, -38, -31, -26, -22, -20, -16,...

1 rows × 56 columns

For CSV file, use this:

NOTE: Reading whole file is time consuming

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('aware_segmented.csv', header=None, skiprows=100, nrows=1) # Read the 101-th line (record #100) for example
display(df)

data = df.iloc[0, 292846:412846].to_numpy() # 292846:412846 correspond to phase "Inhale_1", see README.md
data = data.reshape(25,-1)
data = data/(2**15) # Normalize from 16-bit PCM to [-1,1]
plt.figure(figsize=(12,9))
for idx in range(data.shape[0]):
    plt.subplot(5, 5, idx+1)
    plt.plot(data[idx,:])
plt.show()

	0	1	2	3	4	5	6	7	8	9	...	1012836	1012837	1012838	1012839	1012840	1012841	1012842	1012843	1012844	1012845
0	28	38.7	Male	Checked	Unchecked	Unchecked	Unchecked	Unchecked	177.8	87.5	...	-60	-59	-55	-50	-52	-58	-61	-62	-56	-53

1 rows × 1012846 columns

Processed airway cross-sectional area curve

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('aware_csa.csv')
display(df)

plt.plot(df.iloc[0:4, 46:].T.to_numpy())  # Let's take a look at subject #70's airway CSA curve (record #0 to #3)
plt.grid('on')
plt.show()

	AWARE STUDY ID:	Calculated age (years):	Sex:	Race/ethnicity: (choice=White)	Race/ethnicity: (choice=Black / African American)	Race/ethnicity: (choice=Hispanic / Latino)	Race/ethnicity: (choice=Asian)	Race/ethnicity: (choice=Other)	Height (cm):	Weight (kg):	...	CSA_75	CSA_76	CSA_77	CSA_78	CSA_79	CSA_80	CSA_81	CSA_82	CSA_83	CSA_84
0	70	30.7	Female	Checked	Unchecked	Unchecked	Unchecked	Unchecked	160.02	80.0	...	3.810551	3.913885	3.957559	3.970113	4.004002	4.103503	4.280572	4.504991	4.712451	4.833306
1	70	30.7	Female	Checked	Unchecked	Unchecked	Unchecked	Unchecked	160.02	80.0	...	4.490665	4.274807	4.022432	3.807408	3.686022	3.682560	3.784819	3.943260	4.080129	4.119503
2	70	30.7	Female	Checked	Unchecked	Unchecked	Unchecked	Unchecked	160.02	80.0	...	3.219514	3.399397	3.595761	3.768916	3.900902	3.999388	4.086103	4.178455	4.277437	4.368749
3	70	30.7	Female	Checked	Unchecked	Unchecked	Unchecked	Unchecked	160.02	80.0	...	4.317590	4.736081	5.098569	5.315797	5.369527	5.325483	5.294827	5.382279	5.654416	6.126306
4	72	19.6	Female	Checked	Checked	Unchecked	Unchecked	Unchecked	152.40	50.6	...	4.063526	3.973345	3.926251	3.907793	3.893328	3.864602	3.824250	3.801414	3.845476	4.012830
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1053	66	18.0	Male	Unchecked	Checked	Unchecked	Unchecked	Unchecked	177.20	100.9	...	4.317066	4.419419	4.453888	4.432593	4.382772	4.338273	4.329225	4.376190	4.486610	4.652911
1054	66	18.0	Male	Unchecked	Checked	Unchecked	Unchecked	Unchecked	177.20	100.9	...	5.058151	5.262232	5.407480	5.526130	5.672834	5.898386	6.228152	6.649212	7.106533	7.515035
1055	66	18.0	Male	Unchecked	Checked	Unchecked	Unchecked	Unchecked	177.20	100.9	...	5.700953	5.885066	5.989681	6.028290	6.022268	6.000034	5.994446	6.038448	6.157008	6.358157
1056	68	19.9	Male	Unchecked	Checked	Unchecked	Unchecked	Unchecked	182.30	85.0	...	5.396123	5.364110	5.456679	5.680443	6.016404	6.422066	6.833527	7.169713	7.347199	7.307952
1057	68	19.9	Male	Unchecked	Checked	Unchecked	Unchecked	Unchecked	182.30	85.0	...	6.678286	6.753770	6.664450	6.450338	6.209805	6.062980	6.115728	6.443510	7.083921	8.019280

1058 rows × 130 columns