Automatic Classification of Sleep Apnea Type and Severity using

EEG Signals

Maryam Alimardani

and Guido de Moor

Department of Cognitive Science and Artificial Intelligence, Tilburg University, Warandelaan 2, Tilburg, The Netherlands

Keywords: Sleep Apnea Disorder, Apnea Severity, Apnea Type, Automatic Diagnosis, Artificial Intelligence, Machine

Learning, EEG, Brain-Computer Interface (BCI).

Abstract: Sleep apnea is a potentially fatal disorder that causes frequent breathing pauses during sleep. Prior research

has shown that monitoring of EEG signals during sleep can contribute to automatic detection of apnea events.

However, a more comprehensive classification of specific apnea types and their severity is required for

accurate clinical diagnosis and real-time detection of critical apnea episodes. In this study, we employed

annotated EEG signals from 25 apnea patients and constructed two distinct classifiers using EEG frequency

domain and non-linear features for binary classification of apnea severity and multiclass classification of

apnea types. In both classification problems, three models i.e. Support Vector Machine (SVM), Linear

Discriminant analysis (LDA) and Naive Bayes (NB) were evaluated and compared. Results showed that SVM

model performed the best in both classification problems reaching accuracy higher than the baseline level.

The SVM performance in the binary classification of apnea severity was acceptable (76% mean accuracy)

however in the case of multiclass classification of apnea types, the SVM classifier did not reach acceptable

performance for all apnea types (48% mean accuracy). Our findings illustrate that in addition to the detection

of apnea episodes, EEG signals can be used in classification of apnea severity, which could lead to

development of accurate diagnostic systems for automatic assessment and management of sleep disorders.

1 INTRODUCTION

A major proportion of our day is devoted to sleep and

hence it is fundamental to our wellbeing and health.

Sleep Apnea is a respiratory sleep disorder

characterized by shallow breaths or intermittent stops

of the breathing process, which manifests clinically

with snoring, gasping or chocking during sleep and

hence results in poor sleep quality (Altevogt &

Colten, 2006). According to American Sleep Apnea

Association

, it is estimated that in the US alone, 22

million people suffer from sleep apnea, with majority

of the moderate and severe cases undiagnosed.

Research shows that prevalence of sleep apnea has

increased in the past two decades in part due to

increasing rates of obesity (Senaratna et al., 2017).

This has created a concern for undiagnosed apnea

patients as cessation of breathing during sleep can

lead to severe respiratory and cardiovascular

https://orcid.org/0000-0003-3077-7657

https://www.sleepapnea.org/learn/sleep-apnea-

information-clinicians/

disorders as well as cognitive impairment (Fonseca et

al., 2015; Senaratna et al., 2017).

Given that apnea episodes occur during sleep

when patients have no control over events, the most

frequently used tool for diagnosis of sleep apnea is

through polysomnography, in which multiple

physiological measurements, such as heart rhythm

(measured by ECG), brain activity (measured by

EEG), muscle activation (measured by EMG) and

respiratory flow are collected during sleep and

analyzed by sleep physicians (Tan et al., 2014).

Although this method provides reliable results, it is

complicated and requires extensive time and labour

from sleep specialists to conduct visual inspection

and manual labelling of the patients’ data collected at

sleep labs. Therefore, there is an eminent demand for

AI-supported techniques that automatically process

long durations of physiological signals and detect

Alimardani, M. and de Moor, G.

Automatic Classiﬁcation of Sleep Apnea Type and Severity using EEG Signals.

DOI: 10.5220/0010288301210128

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 1: BIODEVICES, pages 121-128

ISBN: 978-989-758-490-9

121

sleep apnea at an early stage of the disorder (Zhou et

al., 2015).

Several past studies have conducted research on

detection of sleep apnea using EEG signals and

obtained promising results (Almuhammadi et al.,

2015; Goshvarpour et al., 2013; Hassan & Bhuiyan,

2017; Kumari et al., 2020; Vimala et al., 2019; Zhou

et al., 2015). However, almost the entire scope of

previous research is focused on the detection, rather

than classification of sleep apnea. This means that,

even though sleep apnea exists in a variety of types,

namely central, obstructive and mixed apnea, and in

different severity level, such as severe apnea and mild

hypopnea, the majority of the prior research have not

made this distinction. The prediction problem in these

studies is based on whether the subject has or does not

have the apnea disorder. Those studies that did make

the distinction, only focused on obstructive sleep

apnea, which is a very severe type of the disorder and

is accompanied by prominent physiological features

(Almuhammadi et al., 2015; Kumari et al., 2020; Lee

et al., 2019; Tan et al., 2014). Therefore, despite

impressive results of these classifiers in detection of

apnea vs. non-apnea events, they have failed to grasp

the complexity of the sleep apnea disorder and its

severity level in different patients (Goshvarpour et al.,

2013). This gap in research is also identified by

previous scholars, highlighting the importance of

such classification in better comprehension of the

disorder (Goshvarpour et al., 2013) as well as in early

diagnosis of high-priority cases that might bear fatal

consequences (Leppänen et al., 2017).

This study attempts to approach this gap in the

literature by expanding the existing apnea detection

models to an EEG-based classification system that

recognizes apnea severity and apnea type among

patients. Earlier research has established three types

of sleep apnea based on respiratory effort;

obstructive, central and mixed apnea (Vimala et al.,

2019). “Obstructive sleep apnea”, which is a frequent

and serious type of sleep disorder, relaxes the throat

muscles during sleep and causes a complete blockage

of upper airways. In “central sleep apnea”, the brain

stops to send proper signals to the muscles that

control respiration and therefore the breathing stops

and starts repeatedly during sleep. Finally, “mixed

sleep apnea” which is also known as “complex sleep

apnea” is a combination of obstructive and central

apnea types, carrying the symptoms of both disorders

in the same episode. On the other hand, all apnea

symptoms indicated above could happen on a less

severe level, in which case the episode in called a

hypopnea. Unlike apnea episodes that contain periods

of no breathing, hypopneas are usually accompanied

by abnormally slow or shallow breathing (a reduction

rather than absence in airflow). Therefore, apneas are

considered as the “Severe” level of the disorder while

hypopneas are the “Mild” subcategory. Similar to

apneas, hypopneas consist of three types of

obstructive, central and mixed. Table 1 summarizes

the description of all apnea types and severity

categories based on the American Academy of Sleep

Medicine criteria for diagnosing sleep apnea disorder

(Kagawa et al., 2016).

Based on the existing knowledge with regard to

apnea severity and types, two research questions were

formulated:

RQ1: To what extent can a binary classification

model distinguish between mild and severe cases of

sleep apnea disorder based on EEG signals?

RQ2: To what extent can a multiclass

classification model distinguish between multiple

types of sleep apnea and hypopnea based on EEG

signals?

We believe that our attempt to answer these

questions

in this study provides new insights with

Table 1: Description of apnea severity levels and apnea types as established in previous research.

Severity level Type Label Symptoms

Severe

(Apnea)

Obstructive APNEA-O Obstruction of the upper airways, complete cessation airflow

Central APNEA-C No obstruction upper airways, complete cessation airflow

Mixed APNEA-M

Central respiratory pause is quickly followed by obstructive ventilatory

effort, complete cessation airflow

Mild

(Hypopnea)

Obstructive HYP-O Obstruction of the upper airways, incomplete cessation airflow

Central HYP-C No obstruction upper airways, incomplete cessation airflow

Mixed HYP-M

Central respiratory pause is quickly followed by obstructive ventilatory

effort, incomplete cessation airflow

BIODEVICES 2021 - 14th International Conference on Biomedical Electronics and Devices

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respect to different neurophysiological underpinnings

of apnea disorder. In addition, our AI-based approach

for detection of apnea severity and types will put

forward cost-efficient support systems such as home-

based brain-computer interfaces (BCIs) that assist

sleep therapists in their diagnosis of disorder and

monitoring of the patients’ treatment process (Penzel

et al., 2018).

2 METHODS

2.1 Dataset

We employed the St. Vincent's University Hospital

database, which can be found online on PhysioNet

repository (Goldberger et al., 2000). The dataset

contains full overnight polysomnograms from 25 adult

subjects (21 men, 4 female; all above 18 years old) with

sleep-disordered breathing but no known cardiac

disease or medication to interfere with the experiment.

The included EEG signals consisted of two channels in

the left and right central area (C3 and C4) referenced

to the earlobes. The recordings had an average duration

of six hours and contained annotations by a sleep

technologist who labelled different apnea episodes

based on their type and severity. There were two

severity levels; Mild (hypopnea) and Severe (apnea)

each including three categories; Obstructive (labelled

“O”), Central (labelled “C”), and Mixed (labelled “M”)

(see Table 1 for a full description of labels and

symptoms associated with each apnea severity

category and apnea type).

Figure 1 illustrates the distribution of available

apnea episodes and their labels in the dataset with

respect to each classification problem. As can be seen

in this figure, the number of “Mild” hypopnea

episodes was considerably larger than the number of

“Severe” apnea episodes (Figure 1a). Also among six

classes of apnea and hypopnea types (Figure 1b), the

central hypopnea “HYP-C” and obstructive hypopnea

“HYP-O” episodes occurred more frequently than

other apnea and hypopnea types. This imbalance in

the dataset could introduce a bias in the performance

whereby prediction of the majority class would

maximize accuracy. Therefore, the majority class in

each classification problem was downsampled so that

every class held the same number of occurrences

during training and test of the models.

2.2 Data Pre-processing

EEG signals were pre-processed in MATLAB using

EEGLAB toolbox (Delorme & Makeig, 2004). First

the signals were imported at a sampling rate of 128

Hz, which was the original sampling rate at the time

of recording, and band-pass filtered between 0.5 to 30

Hz. Then, filtered EEG signals were segmented into

apnea epochs using the event markers in the data.

Each apnea epoch was used to extract EEG features

associated with that apnea episode. In total, the data

provided 3318 EEG epochs with durations ranging

between 10 to 20 seconds.

2.3 Feature Extraction

There are three types of features, which are

commonly used in sleep classification; time domain

features, frequency domain features and non-linear

features (Koley & Dey, 2012). In the case of sleep

apnea classification, the features that are found the

most relevant are frequency domain and non-linear

features (Almuhammadi et al., 2015; Goshvarpour et

al.,

2013). Therefore, in this research we used

Figure 1: Distribution of the apnea episodes in the dataset based on (a) severity and (b) type.

Automatic Classiﬁcation of Sleep Apnea Type and Severity using EEG Signals

123

previously reported frequency domain and non-linear

features as the input for the classification algorithms.

For frequency domain features, mean spectral powers

were computed in four frequency bands of delta (1-4

Hz), theta (4-8 Hz), alpha (8-12 Hz) and beta (12-30

Hz) through Fast-Fourier Transform (FFT).

For the second non-linear feature category,

approximate entropy, which is a measure of system

complexity, was computed using EntroPy package

Approximate entropy quantifies the unpredictability

of fluctuations and the regularity in a time series data.

A smaller approximate entropy value means that the

data performs well in terms of regularity and

prediction. It can be obtained using equation 1

(Goshvarpour et al., 2013), where m is the pattern

length, r is the effective filter and L is the total number

of data points in the data. In this research, we chose

m = 2 and r was set to 15% of the standard deviation

of each EEG segment.

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Additionally, two statistical measures, i.e. mean

and standard deviation of the amplitudes, were

extracted from the EEG signal segments as time

domain features. These statistical measures were

included to feed the algorithm a more comprehensive

selection of information content from the data as such

features offer information about the shape and density

of the EEG signal during sleep (Koley & Dey, 2012).

The obtained spectral powers, approximate entropy

and statistical measures were then passed to the

feature selection step in order to construct an

optimized feature space for each classification

algorithms.

2.4 Feature Selection

In order to obtain the most optimal input features for

the classification algorithms, the leave-one-out

technique was employed (Feng et al., 2013). This

method consists of dropping one individual feature

per run to examine how the outcome of the classifier

is influenced. In this way, individual importance of

each selected feature is evaluated while interactions

between features during selection process is

preserved which, in turn, results in a more optimal

https://github.com/raphaelvallat/entropy

and unified selection of features. For this study, non-

linear features as well as frequency band powers were

dropped individually to investigate what the effect

was on the evaluation metrics.

2.5 Classification

Following our research questions in this study, two

classification problems were investigated; 1) binary

classification of severe apnea episodes (APNEA) vs.

mild hypopnea episodes (HYP), and 2) multiclass

classification of apnea and hypopnea types which

included six classes of obstructive sleep apnea

(APNEA-O), central sleep apnea (APNEA-C), mixed

sleep apnea (APNEA-M), obstructive sleep hypopnea

(HYP-O), central sleep hypopnea (HYP-C), and

mixed sleep hypopnea (HYP-M) (see Table 1).

For each classification problem, three models

including Support Vector Machine (SVM), Linear

Discriminant analysis (LDA), and Naive Bayes (NB)

were imported from the scikit-learn package and were

fitted to the input and target data. Feature vectors

were split into train and test set to construct a

supervised learning setting for the classifiers (70%

training data, 30% test data). The training and test

data were subsequently fitted with the use of

StandardScaler from the scitkit-learn package to

standardize the features. Furthermore, LabelEncoder,

which was also derived from scikit-learn, was used to

convert the targets into numerical values.

Finally, for each model, four metrics of accuracy,

precision, recall and F1-score were reported to get a

conclusive view of the model performance. These

metrics are commonly used as evaluation tools for

sleep apnea research (Almuhammadi et al., 2015;

Vimala et al., 2019; Zhou et al., 2015). Accuracy

refers to the ratio of correct predictions to the total

amount of predictions; precision is the ratio of correct

positive predictions to the total of predicted positives;

recall is the ratio of correct positive predictions to the

total of positive cases in the set and the F1-score is

the harmonic mean of precision and recall.

3 RESULTS

The outcomes of classification performances are

presented in two subsections, each associated with

apnea severity and apnea type classification

problems.

BIODEVICES 2021 - 14th International Conference on Biomedical Electronics and Devices

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3.1 Binary Classification for Apnea

Severity Recognition

In the feature selection step for this classification

problem, the leave-one-out method (as described in

2.4) showed that for the SVM model, the performance

was optimal when all spectral band powers were

dropped from the input features (1.19% increase on

accuracy). Also, the performance of LDA was

improved by dropping the delta band power (0.24%

increase on accuracy) and the performance of NB was

enhanced when the theta band was left out of the input

features (0.48% increase on accuracy).

Table 2 demonstrates the outcomes of the binary

classification of apnea severity for the SVM, LDA

and NB classifiers. Boldface denotes the best

performance for each measure. A comparison among

the three classification models shows that the SVM

model reached the highest average performance on all

metrics. All models reached an accuracy level above

the baseline accuracy of 50%, however, the highest

mean accuracy was obtained from the SVM model,

which was 75.90%.

3.2 Multiclass Classification for Apnea

Type Recognition

In the feature selection step for this classification, the

leave-one-out method indicated that for the SVM

model, the performance was optimal when the theta

band power was dropped from the input features

(increase of 2.36% on accuracy). For LDA, the alpha

band power was dropped to strengthen the model

(increase 5.10% on accuracy), and in the case of NB

it turned out that dropping all band power features

was beneficial for the model performance (increase of

1.96% on accuracy).

Table 3 presents the results of the efforts to

classify different types of sleep apnea with the use of

SVM, LDA and NB algorithms. Boldface denotes the

best performance for each measure. As is evident

from the table, again the SVM model surpassed the

other two classifiers in every performance metric as

averaged over multiple classes. All models reached

an accuracy level above the baseline accuracy of

20%, however, the highest mean accuracy was

obtained from the SVM model, which was 48.24%.

Additionally, the highest F1-score was obtained for

the HYP-O class in all classification models.

4 DISCUSSION

Diagnosis of sleep apnea disorder using

polysomnogram signals has become an increasingly

difficult and resourceful task for sleep physicians due

to the prevailing magnitude of the apnea phenomenon

(Altevogt & Colten, 2006). Previous studies have

shown the efficacy of EEG signals in detection of

apnea presence. However, classification of apnea

severity and apnea type based on EEG signals has

never been explored in the past. Therefore, a combined

call from the scientific community (Goshvarpour et al.,

2013) as well as a sense of urgency from the practical

point of view (Goldberger et al., 2003; Koley & Dey,

2012) drove the motivation for this study to explore the

promises of machine learning models in automatic

detection of apnea severity and apnea type from

neurophysiological signals.

In this study, we used annotated EEG recordings from

25 patients who suffered from sleep apnea and

developed classifiers for automatic classification of

two apnea severity levels and three apnea types. Our

results from three classification models showed that

overall EEG signals could be employed in automatic

recognition of apnea severity to a decent extent, but

an optimal performance was not achieved for

classification of apnea types.

Table 2: Performance results for binary classification of apnea severity with three models of Support Vector Machine, Linear

Discriminant Analysis and Naive Bayes.

Binary Classification for Apnea Severity

Support Vector Machine Linear Discriminant Analysis Naive Bayes

F1-score Precision Recall F1-score Precision Recall F1-score Precision Recall

Mild

0.7624 0.7043 0.8308 0.6916 0.6352 0.7590 0.6509 0.5613 0.7744

Severe

0.7554 0.8254 0.6964 0.6780 0.7473 0.6205 0.5668 0.7067 0.4732

Weighted

Average

0.7587 0.7691 0.7590 0.6844 0.6951 0.6850 0.6059 0.6390 0.6134

Accuracy

75.90% 68.50% 61.34%

Automatic Classiﬁcation of Sleep Apnea Type and Severity using EEG Signals

125

Table 3: Performance results for binary classification of apnea severity with three models of Support Vector Machine, Linear

Discriminant Analysis and Naive Bayes.

With respect to the first classification problem, the

SVM model performed the best on the binary

recognition of mild hypopnea vs. severe apnea

episodes (76% accuracy). A close look at Table 2 and

other performance metrics of each model in this

classification problem revealed that in general the

models obtained a superior precision and an inferior

recall score for the “Severe” class than they did for

the “Mild” class. Also, the precision score was higher

than recall in classification of “Severe” apnea

episodes, while opposite pattern was present for the

“Mild” class, where the recall score was higher than

the precision score. This means that the classifiers

made few mistakes in attribution of mild episodes to

a severe class whereas many severe episodes were

falsely detected as mild. This outcome is

disadvantageous to the classification goal in this

study, as the aim of this research was to detect as

many severe cases as possible. The flagging of severe

cases helps physicians to spot high-risk patients that

require immediate attention. Hence, the recall metric

is an important measure for this classification

problem and thus the scales should be tipped in favour

of detecting as many severe cases as possible, even if

this means that some patients with mild apnea are

classified as severe.

The importance of the recall score has also been

mentioned in previous apnea detection studies, in

view of the fact that the classifier should reduce the

risk of missing the apnea/hypopnea events rather than

reducing the incorrect recognition of non-apnea

events (Xie & Minn, 2012). To that end, Xie and

Minn (2012) proposed a cost-sensitive classification

that would enhance the recall score by imposing a

cost matrix to penalize the FN errors more than the

FP errors. They incorporated this strategy of cost-

sensitive weighting in the feature selection process to

favour highly predictive features. They also found

that this method reduced the computational load by

1/5 of the even cost method (Xie & Minn, 2012). The

same technique could be applied in future research on

the results of this study, in order to improve the

classifier and make it functional for practitioners.

In the second classification problem, the

multiclass classifier did not reach a favourable

performance in apnea type detection, even though the

accuracy obtained from the three models was above

the chance-level baseline. Again, the SVM model

performed the best on the multiclass classification of

apnea types (48% accuracy) and the performance

metrics were relatively high only for the HYP-O and

HYP-C classes; the same classes that originally

provided more instances in the dataset and were

downsampled for training of the model. This means

that although for a few apnea types the model learned

the EEG representations well, the classifier cannot be

put into practice on the basis of this study alone.

Nevertheless, this does not mean that the model

cannot be a starting point for future research and the

further development of an apnea type detection

system. Various strategies can be suggested for future

research to improve the classification performance on

apnea type detection task. For instance,

One of the effective tools in improvement of sleep

data classification is combination of two or more

models (Supratak et al., 2017; Zhang et al., 2016).

This strategy is based on the idea that individual

classifiers offer different perspectives in decision

making and that the combination of different

classifiers would harnesses the complementary

information provided by each of them. In this study,

an improvement of the model can be expected by

Multiclass Classification for Apnea Types

Support Vector Machine Linear Discriminant Analysis Naive Bayes

F1-score Precision Recall F1-score Precision Recall F1-score Precision Recall

APNEA-C

0.4200 0.3559 0.5122 0.3855 0.3810 0.3902 0.1509 0.3333 0.0976

APNEA-M

0.4615 0.4865 0.4390 0.4471 0.4318 0.4634 0.4706 0.3590 0.6829

APNEA-O

0.2973 0.5000 0.2115 0.2558 0.3235 0.2115 0.1972 0.3684 0.1346

HYP-C

0.5321 0.5088 0.5577 0.5000 0.4821 0.5192 0.4306 0.3370 0.5964

HYP-M

0.3019 0.3200 0.2857 0.2388 0.2051 0.2857 0.1860 0.2667 0.1429

HYP-O

0.7500 0.6545 0.8780 0.7160 0.7250 0.7073 0.6000 0.6154 0.5854

Weighted

Average

0.4646 0.4815 0.4824 0.4293 0.4341 0.4314 0.3448 0.3834 0.3843

Accuracy

48.24% 43.14% 38.43%

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merging of the SVM and LDA models, since the LDA

precision scores for “APNEA-C” and “HYP-O” offer

additional value for the classification performance.

Another direction for future research could be

investigation of appropriate EEG features for apnea

type and severity classification. In this study, we

mainly relied on previously reported EEG features

that were employed in the field of apnea detection

research, however, it is conceivable that the best

features for apnea detection are not necessarily the

optimal features for apnea classification. For

instance, past research shows that sample entropy has

an advantage over approximate entropy as it yields

more consistent results and is less affected by the

choice of parameters in the model (Richman &

Moorman, 2000). Nonetheless, both are sensitive to

spikes and noise in the EEG signals (Molina-Picó et

al., 2011). Further research should be conducted in

order to estimate what other non-linear, time domain

or frequency domain features could be used to

strengthen the model and its performance.

Alternatively, deep learning models can be employed

for automatic learning of the EEG signal

characteristics without utilizing any hand-engineered

features (Zhang et al., 2016).

Increasing the size of the dataset will also benefit

the performance of the model although insufficient

data is a problem that is often faced in sleep research

since collection and annotation of polysomnogram

data is a very costly and time-consuming process. It

is worth noting that not only the number of the

recorded patients, but also the unbalanced frequency

in the occurrence of apnea and hypopnea episodes

imposed a limitation on the final data employed in

this study. Due to the imbalance of the Vincent’s

dataset, we had to deploy a downsampling technique,

which meant a large part of the data segments could

not be used in the training and test of the models.

Consequently, we had to combine all EEG epochs

from all subjects and employ cross validation over

events rather than subjects. Although, this approach

is ideal for development of a one-fit-all solution that

makes diagnosis without system calibration possible,

it comes at the expense of accuracy for long-term

monitoring and treatment. With extended and more

frequent EEG recordings from more apnea patients,

future research can investigate the inter-subject

variability in classifiers’ performance and develop a

personalized BCI system that learns from the same

patient’s EEG signals and provides a more reliable

prediction.

In sum, our study showed that machine learning

methods combined with EEG monitoring sensors can

provide a prominent evidence for automated

classification of apnea severity. Determining the

severity of apnea disorder is a key aspect of accurate

diagnosis and the first step toward development of

home testing and treatment devices for apnea

disorder. Apnea can have very serious health

consequences and, therefore, the severe cases need to

be detected and treated promptly. Development of

AI-driven home-based apnea management systems

will have three major impacts; 1) they would alleviate

the burden of lengthy diagnosis procedures from

overworked physicians, 2) they would relieve a

patient from the intrusive data collection process at a

sleep lab, and 3) they would make the diagnosis of

sleep apnea and follow-up monitoring of treatment

cost-efficient and widely accessible to the public.

Therefore, future research should continue to explore

methods for improvement of the apnea classifier

performance and pragmatically investigate the

benefits of real-time BCI applications in sleep health

research and clinical practice.

5 CONCLUSIONS

This study attempted the classification of sleep apnea

severity and apnea types from EEG signals of 25

patients. Our results showed promising findings with

respect to recognition of apnea severity (mild vs.

severe), which could be of significant interest to sleep

specialists. Additionally, our comparison of three

machine learning algorithms confirmed that the SVM

model performed better than LDA and NB models in

both classifications of apnea severity and apnea type.

These findings hold promise for future development

of EEG-based apnea diagnosis technologies as well

as home-based apnea monitoring and management

systems (e.g. smartphone apps) that can automatically

detect apnea episodes in real-time and provide

immediate care.

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