blood-pressure-calculation

November 1, 2024

[1]: # This Python 3 environment comes with many helpful analytics libraries␣
↪installed

# It is defined by the kaggle/python Docker image: https://github.com/kaggle/

↪docker-python

# For example, here's several helpful packages to load

import numpy as np # linear algebra

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory

# For example, running this (by clicking run or pressing Shift+Enter) will list␣
↪all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname , filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that␣

↪gets preserved as output when you create a version using "Save & Run All"

# You can also write temporary files to /kaggle/temp/, but they won't be saved␣
↪outside of the current session

/kaggle/input/BloodPressureDataset/part_4.mat
/kaggle/input/BloodPressureDataset/part_9.mat
/kaggle/input/BloodPressureDataset/part_10.mat
/kaggle/input/BloodPressureDataset/part_11.mat
/kaggle/input/BloodPressureDataset/part_3.mat
/kaggle/input/BloodPressureDataset/part_1.mat
/kaggle/input/BloodPressureDataset/part_8.mat
/kaggle/input/BloodPressureDataset/part_5.mat
/kaggle/input/BloodPressureDataset/part_6.mat
/kaggle/input/BloodPressureDataset/part_7.mat
/kaggle/input/BloodPressureDataset/part_2.mat
/kaggle/input/BloodPressureDataset/part_12.mat
/kaggle/input/BloodPressureDataset/Samples/rec_235.csv
/kaggle/input/BloodPressureDataset/Samples/rec_389.csv
/kaggle/input/BloodPressureDataset/Samples/rec_397.csv

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/kaggle/input/BloodPressureDataset/Samples/rec_55.csv
/kaggle/input/BloodPressureDataset/Samples/rec_143.csv
/kaggle/input/BloodPressureDataset/Samples/rec_372.csv
/kaggle/input/BloodPressureDataset/Samples/rec_36.csv
/kaggle/input/BloodPressureDataset/Samples/rec_92.csv
/kaggle/input/BloodPressureDataset/Samples/rec_350.csv
/kaggle/input/BloodPressureDataset/Samples/rec_331.csv
/kaggle/input/BloodPressureDataset/Samples/rec_10.csv
/kaggle/input/BloodPressureDataset/Samples/rec_416.csv
/kaggle/input/BloodPressureDataset/Samples/rec_392.csv
/kaggle/input/BloodPressureDataset/Samples/rec_415.csv
/kaggle/input/BloodPressureDataset/Samples/rec_199.csv
/kaggle/input/BloodPressureDataset/Samples/rec_344.csv
/kaggle/input/BloodPressureDataset/Samples/rec_279.csv
/kaggle/input/BloodPressureDataset/Samples/rec_375.csv
/kaggle/input/BloodPressureDataset/Samples/rec_479.csv
/kaggle/input/BloodPressureDataset/Samples/rec_108.csv
/kaggle/input/BloodPressureDataset/Samples/rec_471.csv
/kaggle/input/BloodPressureDataset/Samples/rec_277.csv
/kaggle/input/BloodPressureDataset/Samples/rec_98.csv
/kaggle/input/BloodPressureDataset/Samples/rec_225.csv
/kaggle/input/BloodPressureDataset/Samples/rec_148.csv
/kaggle/input/BloodPressureDataset/Samples/rec_95.csv
/kaggle/input/BloodPressureDataset/Samples/rec_107.csv
/kaggle/input/BloodPressureDataset/Samples/rec_64.csv
/kaggle/input/BloodPressureDataset/Samples/rec_41.csv
/kaggle/input/BloodPressureDataset/Samples/rec_202.csv
/kaggle/input/BloodPressureDataset/Samples/rec_427.csv
/kaggle/input/BloodPressureDataset/Samples/rec_214.csv
/kaggle/input/BloodPressureDataset/Samples/rec_335.csv
/kaggle/input/BloodPressureDataset/Samples/rec_265.csv
/kaggle/input/BloodPressureDataset/Samples/rec_209.csv
/kaggle/input/BloodPressureDataset/Samples/rec_141.csv
/kaggle/input/BloodPressureDataset/Samples/rec_198.csv
/kaggle/input/BloodPressureDataset/Samples/rec_5.csv
/kaggle/input/BloodPressureDataset/Samples/rec_215.csv
/kaggle/input/BloodPressureDataset/Samples/rec_258.csv
/kaggle/input/BloodPressureDataset/Samples/rec_252.csv
/kaggle/input/BloodPressureDataset/Samples/rec_32.csv
/kaggle/input/BloodPressureDataset/Samples/rec_326.csv
/kaggle/input/BloodPressureDataset/Samples/rec_364.csv

9
/kaggle/input/BloodPressureDataset/Samples/rec_442.csv
/kaggle/input/BloodPressureDataset/Samples/rec_316.csv
/kaggle/input/BloodPressureDataset/Samples/rec_489.csv
/kaggle/input/BloodPressureDataset/Samples/rec_403.csv
/kaggle/input/BloodPressureDataset/Samples/rec_310.csv
/kaggle/input/BloodPressureDataset/Samples/rec_488.csv
/kaggle/input/BloodPressureDataset/Samples/rec_244.csv
/kaggle/input/BloodPressureDataset/Samples/rec_79.csv
/kaggle/input/BloodPressureDataset/Samples/rec_493.csv
/kaggle/input/BloodPressureDataset/Samples/rec_185.csv
/kaggle/input/BloodPressureDataset/Samples/rec_203.csv
/kaggle/input/BloodPressureDataset/Samples/rec_444.csv
/kaggle/input/BloodPressureDataset/Samples/rec_458.csv
/kaggle/input/BloodPressureDataset/Samples/rec_156.csv
/kaggle/input/BloodPressureDataset/Samples/rec_58.csv
/kaggle/input/BloodPressureDataset/Samples/rec_320.csv
/kaggle/input/BloodPressureDataset/Samples/rec_268.csv
/kaggle/input/BloodPressureDataset/Samples/rec_478.csv
/kaggle/input/BloodPressureDataset/Samples/rec_481.csv
/kaggle/input/BloodPressureDataset/Samples/rec_229.csv
/kaggle/input/BloodPressureDataset/Samples/rec_31.csv
/kaggle/input/BloodPressureDataset/Samples/rec_25.csv
/kaggle/input/BloodPressureDataset/Samples/rec_200.csv
/kaggle/input/BloodPressureDataset/Samples/rec_432.csv
/kaggle/input/BloodPressureDataset/Samples/rec_17.csv
/kaggle/input/BloodPressureDataset/Samples/rec_257.csv
/kaggle/input/BloodPressureDataset/Samples/rec_461.csv
/kaggle/input/BloodPressureDataset/Samples/rec_443.csv
/kaggle/input/BloodPressureDataset/Samples/rec_428.csv
/kaggle/input/BloodPressureDataset/Samples/rec_402.csv
/kaggle/input/BloodPressureDataset/Samples/rec_498.csv
/kaggle/input/BloodPressureDataset/Samples/rec_263.csv
/kaggle/input/BloodPressureDataset/Samples/rec_167.csv
/kaggle/input/BloodPressureDataset/Samples/rec_116.csv
/kaggle/input/BloodPressureDataset/Samples/rec_362.csv
/kaggle/input/BloodPressureDataset/Samples/rec_405.csv
/kaggle/input/BloodPressureDataset/Samples/rec_89.csv
/kaggle/input/BloodPressureDataset/Samples/rec_400.csv
/kaggle/input/BloodPressureDataset/Samples/rec_387.csv
/kaggle/input/BloodPressureDataset/Samples/rec_500.csv
/kaggle/input/BloodPressureDataset/Samples/rec_276.csv
/kaggle/input/BloodPressureDataset/Samples/rec_110.csv
/kaggle/input/BloodPressureDataset/Samples/rec_483.csv
/kaggle/input/BloodPressureDataset/Samples/rec_186.csv
/kaggle/input/BloodPressureDataset/Samples/rec_434.csv
/kaggle/input/BloodPressureDataset/Samples/rec_271.csv
/kaggle/input/BloodPressureDataset/Samples/rec_283.csv
/kaggle/input/BloodPressureDataset/Samples/rec_456.csv

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/kaggle/input/BloodPressureDataset/Samples/rec_74.csv
/kaggle/input/BloodPressureDataset/Samples/rec_255.csv
/kaggle/input/BloodPressureDataset/Samples/rec_183.csv
/kaggle/input/BloodPressureDataset/Samples/rec_181.csv
/kaggle/input/BloodPressureDataset/Samples/rec_342.csv
/kaggle/input/BloodPressureDataset/Samples/rec_184.csv
/kaggle/input/BloodPressureDataset/Samples/rec_435.csv
/kaggle/input/BloodPressureDataset/Samples/rec_355.csv
/kaggle/input/BloodPressureDataset/Samples/rec_254.csv
/kaggle/input/BloodPressureDataset/Samples/rec_473.csv
/kaggle/input/BloodPressureDataset/Samples/rec_337.csv
/kaggle/input/BloodPressureDataset/Samples/rec_424.csv
/kaggle/input/BloodPressureDataset/Samples/rec_464.csv
/kaggle/input/BloodPressureDataset/Samples/rec_208.csv
/kaggle/input/BloodPressureDataset/Samples/rec_485.csv
/kaggle/input/BloodPressureDataset/Samples/rec_445.csv
/kaggle/input/BloodPressureDataset/Samples/rec_132.csv
/kaggle/input/BloodPressureDataset/Samples/rec_243.csv
/kaggle/input/BloodPressureDataset/Samples/rec_245.csv
/kaggle/input/BloodPressureDataset/Samples/rec_97.csv
/kaggle/input/BloodPressureDataset/Samples/rec_447.csv
/kaggle/input/BloodPressureDataset/Samples/rec_311.csv
/kaggle/input/BloodPressureDataset/Samples/rec_49.csv
/kaggle/input/BloodPressureDataset/Samples/rec_366.csv
/kaggle/input/BloodPressureDataset/Samples/rec_179.csv
/kaggle/input/BloodPressureDataset/Samples/rec_377.csv
/kaggle/input/BloodPressureDataset/Samples/rec_241.csv
/kaggle/input/BloodPressureDataset/Samples/rec_6.csv
/kaggle/input/BloodPressureDataset/Samples/rec_8.csv
/kaggle/input/BloodPressureDataset/Samples/rec_160.csv
/kaggle/input/BloodPressureDataset/Samples/rec_261.csv
/kaggle/input/BloodPressureDataset/Samples/rec_140.csv
/kaggle/input/BloodPressureDataset/Samples/rec_87.csv
/kaggle/input/BloodPressureDataset/Samples/rec_96.csv
/kaggle/input/BloodPressureDataset/Samples/rec_260.csv
/kaggle/input/BloodPressureDataset/Samples/rec_99.csv
/kaggle/input/BloodPressureDataset/Samples/rec_133.csv
/kaggle/input/BloodPressureDataset/Samples/rec_290.csv
/kaggle/input/BloodPressureDataset/Samples/rec_422.csv
/kaggle/input/BloodPressureDataset/Samples/rec_106.csv
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/kaggle/input/BloodPressureDataset/Samples/rec_182.csv
/kaggle/input/BloodPressureDataset/Samples/rec_149.csv
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/kaggle/input/BloodPressureDataset/Samples/rec_348.csv
/kaggle/input/BloodPressureDataset/Samples/rec_48.csv

11
 /kaggle/input/BloodPressureDataset/Samples/rec_374.csv
/kaggle/input/BloodPressureDataset/Samples/rec_236.csv
/kaggle/input/BloodPressureDataset/Samples/rec_490.csv
/kaggle/input/BloodPressureDataset/Samples/rec_7.csv
/kaggle/input/BloodPressureDataset/Samples/rec_476.csv
/kaggle/input/BloodPressureDataset/Samples/rec_155.csv
/kaggle/input/BloodPressureDataset/Samples/rec_411.csv
/kaggle/input/BloodPressureDataset/Samples/rec_34.csv
/kaggle/input/BloodPressureDataset/Samples/rec_21.csv
/kaggle/input/BloodPressureDataset/Samples/rec_388.csv
/kaggle/input/BloodPressureDataset/Samples/rec_123.csv
/kaggle/input/BloodPressureDataset/Samples/rec_2.csv
/kaggle/input/BloodPressureDataset/Samples/rec_232.csv
/kaggle/input/BloodPressureDataset/Samples/rec_369.csv
/kaggle/input/BloodPressureDataset/Samples/rec_305.csv
/kaggle/input/BloodPressureDataset/Samples/rec_353.csv
/kaggle/input/BloodPressureDataset/Samples/rec_418.csv

[2]: # importing libraries

import numpy as np # For numerical computation
import pandas as pd # Data manipulation
import seaborn as sns # plotting
import scipy.io # reading matlab files in python
from scipy import signal #signal processing
from scipy.fftpack import fft, dct #signal processing

from sklearn.linear_model import LinearRegression #linear regression model

from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import KFold, train_test_split # cross validation␣
↪split

from sklearn.metrics import mean_squared_error

import warnings
warnings.filterwarnings('ignore')

from matplotlib import pyplot as plt # For plotting graphs(Visualization)

import os # system-wide functions

os.listdir('/kaggle/input/BloodPressureDataset')

[2]: ['part_4.mat',
'part_9.mat',
'part_10.mat',
'Samples',
'part_11.mat',
'part_3.mat',
'part_1.mat',
'part_8.mat',

12
 'part_5.mat',
'part_6.mat',
'part_7.mat',
'part_2.mat',
'part_12.mat']

0.1 Loading Data

[3]: sample_size = 125
ppg = []
ecg = []
bp = []
sbp = []
dbp = []

for i in range(1, 5):

test_sample = scipy.io.loadmat(f'../input/BloodPressureDataset/part_{i}.
↪mat')['p']

for j in range(len(test_sample[0])):
temp_mat = test_sample[0, j]
temp_length = temp_mat.shape[1]

for k in range((int)(temp_length/sample_size)):
temp_ppg = temp_mat[0, k*sample_size:(k+1)*sample_size]
temp_ecg = temp_mat[2, k*sample_size:(k+1)*sample_size]
temp_bp = temp_mat[1, k*sample_size:(k+1)*sample_size]

max_value = max(temp_bp)
min_value = min(temp_bp)

ppg.append(temp_ppg)
ecg.append(temp_ecg)
bp.append(temp_bp)
sbp.append(max_value)
dbp.append(min_value)

[4]: ppg, ecg, bp = np.array(ppg), np.array(ecg), np.array(bp)

sbp, dbp = np.array(sbp).reshape(-1,1), np.array(dbp).reshape(-1,1)

[5]: signal = np.zeros((ppg.shape[0] , 2, 125))

signal[:, 0, :] = ppg[: , :]
signal[:, 1, :] = ecg[: , :]

[6]: bp_combined = np.zeros((sbp.shape[0] , 2))

bp_combined[: , 0] = sbp[:, 0]

13
 bp_combined[: , 1] = dbp[:, 0]

[7]: fig, ax = plt.subplots(3,1, figsize=(12,12), sharex=True)

ax[0].set_title('PPG graph')
ax[0].plot(ppg[0,:], 'r-')
ax[0].legend()
ax[0].grid(True)

ax[1].set_title('ECG graph')
ax[1].plot(ecg[0,:], 'r-')
ax[1].legend()
ax[1].grid(True)

ax[2].set_title('Blood Pressure (BP) graph')

ax[2].set_xlabel('Sample size')
ax[2].plot(bp[0,:], 'r-')
ax[2].legend()
ax[2].grid(True)

14
 0.2 Predicting Blood Pressure using Deep Learning
[8]: X_train, X_val, y_train, y_val = train_test_split(signal, bp_combined ,␣
↪test_size=0.30)

[9]: import tensorflow as tf

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Dropout, Flatten
from tensorflow.keras import optimizers
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.metrics import RootMeanSquaredError
from sklearn.metrics import r2_score

15
 from tensorflow.keras.losses import Huber

2024-11-01 13:08:34.945514: E
external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register
cuDNN factory: Attempting to register factory for plugin cuDNN when one has
already been registered
2024-11-01 13:08:34.945649: E
external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register
cuFFT factory: Attempting to register factory for plugin cuFFT when one has
already been registered
2024-11-01 13:08:35.152714: E
external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to
register cuBLAS factory: Attempting to register factory for plugin cuBLAS when
one has already been registered

[10]: def huber_loss(y_true, y_pred, delta=1.0):

error = y_true - y_pred
is_small_error = tf.abs(error) <= delta
small_error_loss = 0.5 * tf.square(error)
large_error_loss = delta * (tf.abs(error) - 0.5 * delta)
return tf.where(is_small_error, small_error_loss, large_error_loss)

[11]: def combined_loss(y_true, y_pred):

huber_loss = tf.keras.losses.Huber(delta=1.0)
mae_loss = tf.keras.losses.MeanAbsoluteError()
return huber_loss(y_true, y_pred) + mae_loss(y_true, y_pred)

[12]: def Model(input_shape, activation, num_outputs):

model = Sequential()

# Flatten the input if it's not already a flat vector

model.add(tf.keras.layers.Flatten(input_shape=input_shape))
model.add(Dense(1024))
model.add(Activation(activation))
model.add(Dropout(0.5))

model.add(Dense(512))
model.add(Activation(activation))
model.add(Dropout(0.5))

model.add(Dense(64))
model.add(Activation(activation))
model.add(Dropout(0.25))

# Output layer for systolic and diastolic pressures

model.add(Dense(num_outputs))
model.add(Activation('linear'))

16
 # Compile the model
model.compile(loss=combined_loss,
optimizer=optimizers.Adam(learning_rate=0.001),
metrics=[tf.keras.metrics.MeanAbsoluteError(),␣
↪RootMeanSquaredError()])

return model

[13]: input_shape = (2, 125)

num_outputs = 2

# Instantiate the model

model = Model(input_shape=input_shape, activation='relu',␣
↪num_outputs=num_outputs)

model.summary()

Model: "sequential"

����������������������������������������������������������������������������
� Layer (type) � Output Shape � Param # �
����������������������������������������������������������������������������
� flatten (Flatten) � (None, 250) � 0 �
����������������������������������������������������������������������������
� dense (Dense) � (None, 1024) � 257,024 �
����������������������������������������������������������������������������
� activation (Activation) � (None, 1024) � 0 �
����������������������������������������������������������������������������
� dropout (Dropout) � (None, 1024) � 0 �
����������������������������������������������������������������������������
� dense_1 (Dense) � (None, 512) � 524,800 �
����������������������������������������������������������������������������
� activation_1 (Activation) � (None, 512) � 0 �
����������������������������������������������������������������������������
� dropout_1 (Dropout) � (None, 512) � 0 �
����������������������������������������������������������������������������
� dense_2 (Dense) � (None, 64) � 32,832 �
����������������������������������������������������������������������������
� activation_2 (Activation) � (None, 64) � 0 �
����������������������������������������������������������������������������
� dropout_2 (Dropout) � (None, 64) � 0 �
����������������������������������������������������������������������������
� dense_3 (Dense) � (None, 2) � 130 �
����������������������������������������������������������������������������
� activation_3 (Activation) � (None, 2) � 0 �
����������������������������������������������������������������������������

17
 Total params: 814,786 (3.11 MB)

Trainable params: 814,786 (3.11 MB)

Non-trainable params: 0 (0.00 B)

[14]: # Visualizing the model

tf.keras.utils.plot_model(model)
[14]:

18
19
[20]: early_stopping = EarlyStopping(monitor='val_loss', patience=5,␣
↪restore_best_weights=True)

# Assuming X_train and y_train are your training data and labels
# Training the model with explicit validation data and early stopping
history = model.fit(X_train, y_train,
epochs=5,
batch_size=128,
validation_split=0.25,
callbacks=[early_stopping],
verbose=1)

Epoch 1/5
3723/3723 �������������������� 54s 14ms/step -
loss: 31.3529 - mean_absolute_error: 15.9225 - root_mean_squared_error: 21.3379
- val_loss: 25.8113 - val_mean_absolute_error: 13.1509 -
val_root_mean_squared_error: 17.6478
Epoch 2/5
3723/3723 �������������������� 54s 14ms/step -
loss: 30.7538 - mean_absolute_error: 15.6229 - root_mean_squared_error: 20.9788
- val_loss: 25.9571 - val_mean_absolute_error: 13.2239 -
val_root_mean_squared_error: 17.8072
Epoch 3/5
3723/3723 �������������������� 53s 14ms/step -
loss: 30.4432 - mean_absolute_error: 15.4676 - root_mean_squared_error: 20.8128
- val_loss: 25.7271 - val_mean_absolute_error: 13.1087 -
val_root_mean_squared_error: 17.6599
Epoch 4/5
3723/3723 �������������������� 54s 14ms/step -
loss: 30.1240 - mean_absolute_error: 15.3079 - root_mean_squared_error: 20.6043
- val_loss: 25.9630 - val_mean_absolute_error: 13.2268 -
val_root_mean_squared_error: 17.8184
Epoch 5/5
3723/3723 �������������������� 54s 14ms/step -
loss: 29.8120 - mean_absolute_error: 15.1518 - root_mean_squared_error: 20.4283
- val_loss: 25.4623 - val_mean_absolute_error: 12.9763 -
val_root_mean_squared_error: 17.5359

[16]: plt.title('Train loss against mean_absolute_error')

plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.plot(history.history['loss'])
plt.plot(history.history['mean_absolute_error'])
plt.plot(history.history['root_mean_squared_error'])
plt.legend(['Loss', 'Mean_absolute_error', 'Root_mean_squared_error' ])

20
[16]: <matplotlib.legend.Legend at 0x7966f397e680>

[17]: y_pred = model.predict(X_val)

error = combined_loss(y_val, y_pred)
print(f'Neural Combined Loss: {error}')

8509/8509 �������������������� 21s 2ms/step

Neural Combined Loss: 25.284496307373047

[18]: sbp_true = y_val[:, 0]

sbp_pred = y_pred[:, 0]

dbp_true = y_val[:, 1]
dbp_pred = y_pred[:, 1]

# Create an array of sample indices (or sample numbers)

sample_indices = np.arange(len(y_val))

[19]: # Plotting SBP and DBP

plt.figure(figsize=(14, 8))

21
# Plotting Systolic Blood Pressure (SBP)
plt.plot(sample_indices[:10], sbp_true[:10], marker='x', linestyle='--',␣
↪color='blue', label='Actual SBP')

plt.plot(sample_indices[:10], sbp_pred[:10], marker='o', linestyle='-',␣

↪color='green', label='Predicted SBP')

# Plotting Diastolic Blood Pressure (DBP)

plt.plot(sample_indices[:10], dbp_true[:10], marker='x', linestyle='--',␣
↪color='orange', label='Actual DBP')

plt.plot(sample_indices[:10], dbp_pred[:10], marker='o', linestyle='-',␣

↪color='red', label='Predicted DBP')

# Adding labels and title

plt.xlabel('Sample Index')
plt.ylabel('Blood Pressure')
plt.title('Actual vs Predicted Systolic and Diastolic Blood Pressure')
plt.legend()
plt.grid(True)
plt.xticks(sample_indices[:10]) # Ensure all sample indices are displayed on␣
↪x-axis

plt.tight_layout()
plt.show()

22