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%matplotlib inline

Sleep stage classification from polysomnography (PSG) data

Note

This code is taken from the analysis code used in [3]_. If you reuse this code please consider citing this work.

This tutorial explains how to perform a toy polysomnography analysis that answers the following question:

.. important:: Given two subjects from the Sleep Physionet dataset [1] [2], namely Alice and Bob, how well can we predict the sleep stages of Bob from Alice's data?

This problem is tackled as supervised multiclass classification task. The aim is to predict the sleep stage from 5 possible stages for each chunk of 30 seconds of data. :depth: 2



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# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#          Stanislas Chambon <stan.chambon@gmail.com>
#          Joan Massich <mailsik@gmail.com>
#
# License: BSD Style.

import numpy as np
import matplotlib.pyplot as plt

import mne
from mne.datasets.sleep_physionet.age import fetch_data
from mne.time_frequency import psd_welch

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import FunctionTransformer

Load the data

Here we download the data from two subjects and the end goal is to obtain :term:epochs and its associated ground truth.

MNE-Python provides us with :func:mne.datasets.sleep_physionet.age.fetch_data to conveniently download data from the Sleep Physionet dataset [1] [2]. Given a list of subjects and records, the fetcher downloads the data and provides us for each subject, a pair of files:

-PSG.edf containing the polysomnography. The :term:raw data from the EEG helmet,
-Hypnogram.edf containing the :term:annotations recorded by an expert.

Combining these two in a :class:mne.io.Raw object then we can extract :term:events based on the descriptions of the annotations to obtain the :term:epochs.

Read the PSG data and Hypnograms to create a raw object ~~~~~~~~~~~



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ALICE, BOB = 0, 1

[alice_files, bob_files] = fetch_data(subjects=[ALICE, BOB], recording=[1])

mapping = {'EOG horizontal': 'eog',
           'Resp oro-nasal': 'misc',
           'EMG submental': 'misc',
           'Temp rectal': 'misc',
           'Event marker': 'misc'}

raw_train = mne.io.read_raw_edf(alice_files[0])
annot_train = mne.read_annotations(alice_files[1])

raw_train.set_annotations(annot_train, emit_warning=False)
raw_train.set_channel_types(mapping)

# plot some data
raw_train.plot(duration=60, scalings='auto')

Extract 30s events from annotations ~~~~~~~

The Sleep Physionet dataset is annotated using 8 labels <physionet_labels>: Wake (W), Stage 1, Stage 2, Stage 3, Stage 4 corresponding to the range from light sleep to deep sleep, REM sleep (R) where REM is the abbreviation for Rapid Eye Movement sleep, movement (M), and Stage (?) for any none scored segment.

We will work only with 5 stages: Wake (W), Stage 1, Stage 2, Stage 3/4, and REM sleep (R). To do so, we use the event_id parameter in :func:mne.events_from_annotations to select which events are we interested in and we associate an event identifier to each of them.



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annotation_desc_2_event_id = {'Sleep stage W': 1,
                              'Sleep stage 1': 2,
                              'Sleep stage 2': 3,
                              'Sleep stage 3': 4,
                              'Sleep stage 4': 4,
                              'Sleep stage R': 5}

events_train, _ = mne.events_from_annotations(
    raw_train, event_id=annotation_desc_2_event_id, chunk_duration=30.)

# create a new event_id that unifies stages 3 and 4
event_id = {'Sleep stage W': 1,
            'Sleep stage 1': 2,
            'Sleep stage 2': 3,
            'Sleep stage 3/4': 4,
            'Sleep stage R': 5}

# plot events
mne.viz.plot_events(events_train, event_id=event_id,
                    sfreq=raw_train.info['sfreq'])

# keep the color-code for further plotting
stage_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']

Create Epochs from the data based on the events found in the annotations ~~~~~~~~~~~~~~~~



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tmax = 30. - 1. / raw_train.info['sfreq']  # tmax in included

epochs_train = mne.Epochs(raw=raw_train, events=events_train,
                          event_id=event_id, tmin=0., tmax=tmax, baseline=None)

print(epochs_train)

Applying the same steps to the test data from Bob ~~~~~~~~~~~~~



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raw_test = mne.io.read_raw_edf(bob_files[0])
annot_test = mne.read_annotations(bob_files[1])
raw_test.set_annotations(annot_test, emit_warning=False)
raw_test.set_channel_types(mapping)
events_test, _ = mne.events_from_annotations(
    raw_test, event_id=annotation_desc_2_event_id, chunk_duration=30.)
epochs_test = mne.Epochs(raw=raw_test, events=events_test, event_id=event_id,
                         tmin=0., tmax=tmax, baseline=None)

print(epochs_test)

Feature Engineering

Observing the power spectral density (PSD) plot of the :term:epochs grouped by sleeping stage we can see that different sleep stages have different signatures. These signatures remain similar between Alice and Bob's data.

The rest of this section we will create EEG features based on relative power in specific frequency bands to capture this difference between the sleep stages in our data.



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# visualize Alice vs. Bob PSD by sleep stage.
fig, (ax1, ax2) = plt.subplots(ncols=2)

# iterate over the subjects
stages = sorted(event_id.keys())
for ax, title, epochs in zip([ax1, ax2],
                             ['Alice', 'Bob'],
                             [epochs_train, epochs_test]):

    for stage, color in zip(stages, stage_colors):
        epochs[stage].plot_psd(area_mode=None, color=color, ax=ax,
                               fmin=0.1, fmax=20., show=False,
                               average=True, spatial_colors=False)
    ax.set(title=title, xlabel='Frequency (Hz)')
ax2.set(ylabel='uV^2/hz (dB)')
ax2.legend(ax2.lines[2::3], stages)
plt.show()

Design a scikit-learn transformer from a Python function ~~~~~~~~~~~~

We will now create a function to extract EEG features based on relative power in specific frequency bands to be able to predict sleep stages from EEG signals.



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def eeg_power_band(epochs):
    """EEG relative power band feature extraction.

    This function takes an ``mne.Epochs`` object and creates EEG features based
    on relative power in specific frequency bands that are compatible with
    scikit-learn.

    Parameters
    ----------
    epochs : Epochs
        The data.

    Returns
    -------
    X : numpy array of shape [n_samples, 5]
        Transformed data.
    """
    # specific frequency bands
    FREQ_BANDS = {"delta": [0.5, 4.5],
                  "theta": [4.5, 8.5],
                  "alpha": [8.5, 11.5],
                  "sigma": [11.5, 15.5],
                  "beta": [15.5, 30]}

    psds, freqs = psd_welch(epochs, picks='eeg', fmin=0.5, fmax=30.)
    # Normalize the PSDs
    psds /= np.sum(psds, axis=-1, keepdims=True)

    X = []
    for fmin, fmax in FREQ_BANDS.values():
        psds_band = psds[:, :, (freqs >= fmin) & (freqs < fmax)].mean(axis=-1)
        X.append(psds_band.reshape(len(psds), -1))

    return np.concatenate(X, axis=1)

Multiclass classification workflow using scikit-learn

To answer the question of how well can we predict the sleep stages of Bob from Alice's data and avoid as much boilerplate code as possible, we will take advantage of two key features of sckit-learn: Pipeline , and FunctionTransformer.

Scikit-learn pipeline composes an estimator as a sequence of transforms and a final estimator, while the FunctionTransformer converts a python function in an estimator compatible object. In this manner we can create scikit-learn estimator that takes :class:mne.Epochs thanks to eeg_power_band function we just created.



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pipe = make_pipeline(FunctionTransformer(eeg_power_band, validate=False),
                     RandomForestClassifier(n_estimators=100, random_state=42))

# Train
y_train = epochs_train.events[:, 2]
pipe.fit(epochs_train, y_train)

# Test
y_pred = pipe.predict(epochs_test)

# Assess the results
y_test = epochs_test.events[:, 2]
acc = accuracy_score(y_test, y_pred)

print("Accuracy score: {}".format(acc))

In short, yes. We can predict Bob's sleeping stages based on Alice's data.

Further analysis of the data ~~~~~~~~~

We can check the confusion matrix or the classification report.



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print(confusion_matrix(y_test, y_pred))



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print(classification_report(y_test, y_pred, target_names=event_id.keys()))

Exercise

Fetch 50 subjects from the Physionet database and run a 5-fold cross-validation leaving each time 10 subjects out in the test set.

References

.. [1] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).

.. [2] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000) PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220

.. [3] Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A. (2018)A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series. IEEE Trans. on Neural Systems and Rehabilitation Engineering 26: (758-769).