# Predictive and diagnosis models of stroke from hemodynamic signal monitoring

Luis García-Terriza  José L. Risco-Martín  Gemma Reig Roselló  José L. Ayala
Dpt. of Computer Architecture and Automation, Complutense University of
Madrid, Spain  Stroke Care Unit, Hospital Universitario de La Princesa, Spain

###### Abstract

This work presents a novel and promising approach to the clinical management
of acute stroke. Using machine learning techniques, our research has succeeded
in developing accurate diagnosis and prediction real-time models from
hemodynamic data. These models are able to diagnose stroke subtype with 30
minutes of monitoring, to predict the exitus during the first 3 hours of
monitoring, and to predict the stroke recurrence in just 15 minutes of
monitoring. Patients with difficult access to a CT scan, and all patients that
arrive at the stroke unit of a specialized hospital will benefit from these
positive results. The results obtained from the real-time developed models are
the following: stroke diagnosis around $98\%$ precision ($97.8\%$ Sensitivity,
$99.5\%$ Specificity), exitus prediction with $99.8\%$ precision ($99.8\%$
Sens., $99.9\%$ Spec.) and $98\%$ precision predicting stroke recurrence
($98\%$ Sens., $99\%$ Spec.).

Graphical abstract depicting the complete process since a patient is monitored
until the data collected is used to generate models

Total words of the manuscript: 5694 words
The number of words of the abstract: 124
Number of figures: 5
Number of tables: 12

###### keywords:

Supervised Machine Learning, Stroke diagnosis, Time series, Hemodynamic
Monitoring, Exitus Forecasting, Rebleeding prediction

††journal: Medical & Biological Engineering & Computing

##  1 Introduction

Stroke is a cerebrovascular disease that has great health and social impact
due to its high incidence and prevalence. It is one of the main death causes
in the world and produces serious long-term disabilities. It is a great
burden, not only from the health point of view but also personal, family and
health/social cost, because of its impact on peoples life’s, patients and
caregivers [1].

There are different stroke subtypes, what implies different clinical
management and therapeutic approaches. Intracerebral hemorrhage needs early
admission in the stroke unit for blood pressure monitoring. All stroke
patients need early evaluation by a neurologist to have access to prompt
reperfusion therapy. Large vessel occlusion stroke needs prompt
neurointerventional evaluation.

Acute stroke management implies a complex healthcare organization. In the
acute phase, pre-hospital emergency units are aware and trained to detect a
stroke. Depending on the clinical features, patients will be sent to the
closest stroke unit hospital or stroke center (if large vessel occlusion is
suspected). Once patients arrive at the closest and correct hospital, a
neurologist is waiting in the emergency box. A neurological, cardiological and
respiratory examination is performed, and fast transfer to the CT scan is
done.

A stroke unit is an organized in-hospital facility that is fully devoted to
care for patients with stroke. The semi-intensive monitoring system during
acute stroke phase, and a well trained team, allows to detect and sometimes
control all possible neurological complications, which depending on subtype
stroke diagnosis are: i) rebleeding in cerebral hemorrhage; ii) recurrence
and/or hemorrhagic transformation in ischemic stroke cases.

During the stay of the patients in the stroke units, patients are monitored
for a long time, generating a large amount of heterogeneous data that,
currently, are not exploited to help on the diagnosis, evolution, or treatment
of the patients.

Applying machine learning methods and algorithms over biomedical records is
nowadays providing remarkable results. Machine learning ensures unbiased
decisions that are only made according to the state of the patient, profiled
by the gathered data. Machine learning is more than a suitable option for
performing predictions as patients are monitored continuously since the
hospital admission, and data are stored ordered and structured in a database.

There are more cases where machine learning has been used to aid or improve
the diagnosis or detection of diseases, like Parkinson’s disease [2], or to
predict the results of coronary angiography [3].

In this research, we develop and apply machine learning algorithms and methods
to perform predictions over data from patients who have suffered a stroke.
Stroke monitoring data have a great temporal component, what is a difficult
factor to deal with in machine learning and deep learning analysis. There is a
challenge as well with the balance of classes, as every predictive target is
totally off-balance. This may incur in an over-fitting and bad
sensitivity/specificity. Therefore, it must be considered and managed from the
problem conception.

Being able to predict stroke subtype in a hospital setting, especially when
brain CT-scan is possible, may not seem very useful. However, this result is
extremely interesting to be applied in other settings like rural areas that
are far away from local hospitals, which not always have access to CT image.
In these cases, a prompt screening of the patients is mandatory. Additionally,
the prediction of acute neurological complications, such as rebleeding,
recurrence or hemorrhagic transformation, could allow health professionals to
react in time in order to avoid them or at least minimize adverse effects.
Once in the hospital, being able to predict death not only gives opportunities
to doctors to change the therapeutic approach but, in cases that this is not
possible, giving an accurate prognosis risk to patients and/or families is
relevant.

The goals of our research are:

  * 1.

efficient preprocessing of real-time data to increase the quality of the
acquired data and allow knowledge extraction;

  * 2.

development of machine-learning approaches for modeling of stroke subtype,
exitus caused by stroke, and recurrence of the episode;

  * 3.

selection of best model strategy, and validation of results from a
computational and clinical perspective.

In the following section the state of the art is related, where the current
techniques, methods, researches and approaches are presented and explained.
Next, the methodology section will present the methods, techniques and
procedures followed in this research. Finally, results and clinical conclusion
will end this article showing the results obtained and the clinical and
computational discussion.

##  2 Approach

Recent research in the field of artificial intelligence has been incorporated
into the clinical field, targeting neurological diseases like stroke.

Some works have aimed to predict strokes by modeling over demographic and
public datasets. [4] used the Cardiovascular Health Study (CHS)dataset to
predict eventual stroke by modeling Support Vector Machine (SVM).[5] developed
a generative model called Bayesian List Machine capable of predicting stroke
in atrial fibrillation patients.

Machine learning models and techniques applied to time-series variable data is
an area of research in continuous development. [6] trained a SVM model by
using the ECG data from the MIT-BIH database that analyzes ECG and classifies
the different sorts of heartbeats, including Ppremature ventricular
contraction (PVC). Another research that used the MIT-BIH Arrhythmia Database
was [7], who developed an FSVM (Fuzzy SVM) model to detect Arrhythmia. [8]
sought to use natural language processing techniques of electronic health
records to determine ischemic stroke sub-type treatment.

Brain image analysis is currently the state-of-art in stroke sub-type
diagnosis according to [9]. AI in the brain image area has been deeply
developed in the last years, obtaining relevant results ([10]). There have
been researches that apply machine learning algorithms on imaging data to
predict or diagnose outcomes related to strokes. [11] used SVM models to
predict SICH outcomes by using CT images of patients who suffered an acute
ischemic stroke. [12] sought to predict the stroke itself by applying PCA plus
Deep Neural Networks by using medical service data and health behavior data
from more than $15000$ patients.

[13] used machine learning algorithms to predict the outcomes of patients
after being treated with intra-arterial therapy. [14] applied regression-based
algorithms to predict outcomes of the patients three months after suffering
the incident by using data within the first 48 hours. [15] aims to predict
outcome out of a Rankin Scale after stroke by comparing the performance of
different machine learning methods like Regression models, Random Forests and
neural networks, obtaining accuracy near to $90\%$.

Our work, on the other hand, applies machine learning models over hemodynamic,
real-time data, for the first time in literature, to achieve several novel
goals as stroke sub-type diagnosis, prediction of the eventual death of the
patient caused by the stroke, and prediction of stroke recurrence. Previous
works have never addressed these goals with such clinical relevance, and have
not dealt with the complexity of processing the real-time hemodynamic data.
Our results not only represent significant advances in the field of applied
machine learning techniques in the biomedical field, but also open new
therapeutic perspectives in the management of stroke.

##  3 Methods

###  3.1 Dataset

The dataset used in this study was acquired with a Philips PIIC iX monitoring
center installed in the stroke care unit of the Hospital Universitario La
Princesa. Hemo-dynamic data collection was performed from Spring 2017 until
October 2019, and 798 patients were successfully monitored.

Patients were monitored from the moment they were admitted to the Stroke Care
Unit until they were transferred to the observation floor. All patients
admitted in the stroke unit were monitored. Inclusion criteria for stroke unit
monitoring included all types of stroke in the acute phase (mostly within
24-48 hours after the stroke onset), but longer monitoring is possible if
close neurological and semi-intensive monitoring is needed. No age limit is
used as exclusion criteria. The only exclusion criteria for monitoring is
immediate death or previous severe dependence (modified ranking scale mRS 4-5,
GDS - Global Deterioration Scale - 6-7). Some patients with mRS 4 due to
musculoskeletal disease but good cognitive state are also admitted. Before
stroke care unit admission, patients are evaluated by a neurologist on call in
the emergency room (mostly in emergency box). A CT scan is performed. After
ruling out hemorrhage, in the cases that an ischemic stroke is suspected, a
perfusion CT and an angio-CT (cerebral and supraortic vessels) is performed.
In the cases of intracerebral hemorrhage, a post-contrast CT is done, in order
to evaluate spot sign and evaluate active bleeding and rebleeding risk. In
cases that a stroke mimic is suspected, the patient will be transferred to the
emergency care unit and more tests (EEG, lumbar puncture,…) will be done to
achieve a final diagnosis. The information recorded from the patients is based
on non-invasive measures of hemodynamic variables, as described in Table 1.
Timestamps for the acquired data are also considered in the analysis, as well
as demographic information like age and gender.

The clinical variables that were registered are the type of stroke (ischemic
vs hemorrhage), type of hemorrhage (intraparenchymal vs non), death and time
to death since admission, time to any acute neurological complication that
includes early recurrence stroke, hemorrhagic transformation in acute ischemic
stroke, and rebleeding in acute intraparenchymal hemorrhage. Exclusion
criteria after monitoring analysis were patients with non-stroke final
diagnosis (i.e. seizures, CNS infection, migraine, …) or scheduled hospital
admission to perform carotid angioplasty or aneurysm embolization.

The monitoring center stores all the information in a relational database,
where each variable measured compounds a row in the relational database. Each
row is structured as follows: variable ID, variable value, variable unit,
timestamp, and patient ID. Therefore, several rows must be preprocessed
together to obtain a complete observation of the patient. Observations do not
include demographic variables yet because, as previously mentioned, training
instances are compounded by several observations. This observation is acquired
and stored in the database every $30$ seconds.

One observation may provide a significant amount of information from the
patient as it represents the state of the patient for 30 seconds; but in a
time series prediction or diagnosis where the patient is monitored for hours,
it is not enough for machine learning algorithms to provide reliable
predictions or diagnosis. Henceforth, we have put together several consecutive
observations shaping a training or testing instance. The number of
observations per instance was a relevant part of the study, where the minimum
number of observations compounding an instance were five observations (what
represents $2.5$ minutes of monitoring time) and the maximum number was one
hundred and twenty observations (what represents $1$ hour of monitoring time).
In this study, the number of observations for all the results is five, as it
was the time interval that performed best. Training instances do include as
well demographic variables (age, gender) and the timestamp of the first
observation measured.

From the original dataset that includes 798 patients, several datasets have
been created as required by the experimental work. This happens because
diagnostics were not available for all of the patients by the time these
experiments were performed. In the case of stroke subtype diagnosis, the
dataset includes 468 ischemic patients and 80 hemorrhagic patients. Regarding
exitus statistics, 43 out of 504 passed away due to stroke during their
hospitalization, and 34 out of 500 suffered a stroke recurrence episode during
their hospitalization.

Table 1: Definition of variables used in this study Variable | Abbreviation | Type | Role | Description
---|---|---|---|---
Type of Stroke | TS | Categorical | Ischemic/Hemorrhagic | Target
Risk Prediction | RP | Categorical | Exitus/Non Exitus | Target
Rhythm Estimation | RE | Numerical | Input | A Rhythm Indicator
VE | VE | Numerical | Input | Ventricular extra systole
CF | CF | Numerical | Input | Cardiac Frequency
Breathing Frequency | BF | Numerical | Input | Respiratory Rate
Perf | - | Numerical | Input | Pulmonar Perfusion
SpO2 | - | Numerical | Input | Oxygen Saturation
ST-II | - | Numerical | Input | Syst. Time Interval Index

###  3.2 Preprocessing

Figure 1: Data preprocessing: data cleaning and imputation phases

The first preprocessing phase consisted of removing outlier values, being
replaced by the last correct value or the mean of the previous recordings,
depending on the variable type. In the case of numerical data (SPO2, EV, etc.)
the value was replaced by the mean of the previous recordings of that patient
when the value was greater or equal to four times the standard deviation. On
the other hand, categorical values (Rhythm Estimation) were replaced by the
last value when the value was not measured correctly. The same procedure was
followed for missing values.

This preprocessing phase is graphically depicted in Figure 1.

Finally, the last preprocessing phase consisted on standardizing data. As each
feature has different ranges and different measurement units, data must be
standardized in order to prevent unbalance in the algorithm’s internal
weights. Therefore, Z-Score standardization was performed for every feature,
where each value is subtracted the mean value ($\mu$) and then that result is
divided by the standard deviation ($\sigma$):

$\mathrm{Z}=\frac{X-\mu}{\sigma}$ (1)

Signal preprocessing was not performed since that task was efficiently done by
the monitoring center.

###  3.3 Algorithms

One of the main goals of this study was to discover which learning algorithms
achieve the best performance in terms of prediction capability. Initially, we
evaluated at least one algorithm for each sort of ML model but, after several
tests, some of them were discarded due to their bad performance. The criteria
to discard algorithms was as follows: each algorithm was compared against the
best performing algorithm and, if the F-Measure difference was more than
$10\%$, the algorithm was discarded. Discarded algorithms were Logistic
Regression, SVM, Naive Bayes, Multi-Layer Perceptron Neural Network, Long-
Short Term Memory Deep Neural Network, and Convolutional 1-D Deep Neural
Network.

Thus, the algorithms chosen and shown in this research are the following:

  * 1.

Decision Tree: a non-parametric model that create a model targeting the value
of each variable by simple decision rules inferred from data. The optimal
parameters found were 17 as maximum depth, gini as split function, and a
minimum of 2 samples to split a node.

  * 2.

Random Forests: a ensemble algorithm typically used for classification and
regression. It fits several classifiers (usually decision trees), also known
as estimator, and the output of the algorithm is obtained by taking the most
repeated output of the classifiers. It performs especially well because it
avoids over-fitting due to the way dataset is distributed between the
indicators. Optimal parameters were $1000$ estimators, maximum depth of $23$
and gini as entropy algorithm. Rest of parameters were set to their default
values.

  * 3.

Gradient Boosting: a ensemble algorithm that builds the model in stages, also
known as estimators. In each stage, it builds $N$ regression trees and
finally, it generalizes all of them by allowing optimization of an arbitrary
loss function (e.g., Deviance Loss Function). The optimal parameters were 100
estimators, maximum depth of 5, $1.0$ as learning rate and Deviance Loss as
loss metric. The rest of the parameters were set as default.

  * 4.

AdaBoost: a meta-estimator that builds the model by fitting a classifier
(typically a CART Decision Tree Classifier) and comparing to another fitted
classifier, where weights of incorrectly classified examples are adjusted such
that subsequent classifiers are focused on wing cases. Optimal parameters were
$400$ estimators, $1.0$ as learning rate, Decision Trees Classifier as base
estimator and SAMME.R as base algorithm.

  * 5.

K-Nearest Neighbors: a non-parametric and distance-based algorithm where input
consists the base knowledge, i.e, training examples. When K-NN is used for
classifying, each output is chosen by the closest K examples of the base
knowledge, calculating the distance with the chosen distance metric algorithm.
The optimal parameters were $K=17$, Ball Tree as compute algorithm and
Minkowski distance, P=2 as distance metric.

  * 6.

Dynamic Time Warping: an algorithm that measures the distance between two time
series signals. This algorithm used with a simple K-Nearest-Neighbors with
$K=1$ and adapted to multivariate (i.e. applied to measure the distance to
several features) it may be used on classification problems. There are two
types of multivariate DTW algorithms: dependent and independent ([16]). Since
dependent DTW has provided slightly better results than the independent
algorithm it is the method shown in this research.

###  3.4 Experimental evaluation

In our experimental work, we have performed three sets of experiments related
to stroke events. The first consists on diagnosing the type of stroke suffered
by the patient (ischemic vs hemorrhagic) within a short temporary window (less
than 60 minutes). As the treatment of both types of stroke is totally
opposite, an early guess of the event is desired for proper management. It is
known that stroke diagnosis gold standard procedure is a brain image scan (CT
or MRI). But in country areas, having access to a CT is difficult, even more
to MRI studies. Most rural medical centers will not have advanced imaging
techniques, and alternatives are desired to avoid delays in treatment options.

The second experiment is to predict a patient’s death after stroke, according
to incremental time windows from the event. The initial results of these two
goals were already published in [17]. From that preliminary work, we have
improved the data pre-processing, added demographic variables, and extended
the refinement of machine learning algorithms. These improvements, and a
larger dataset, have allowed us to obtain better performance in the
algorithms. Death prediction allows clinicians to adopt a different
therapeutic approach in order to prevent, and if not possible minimize, any
possible future and immediate adverse event due to any clinical complication
(neurological, respiratory, cardiological, …). Additionally, being able to
give this information to patients and family is also relevant in clinical
practice as relatives require information about risks and prognosis.

Finally, the third experiment consists on predicting any neurological
complication of a stroke patient during acute setting monitoring, which
includes: early recurrence in ischemic stroke, hemorrhagic transformation in
ischemic stroke and rebleeding in intraparenchymal hemorrhage. This is a
critical situation for the patient since it drastically decreases the chances
of survival. So far, clinicians have not been able to understand the
mechanisms that drive a patient into recurrence or rebleeding, and our work is
a novel and promising advance in that direction. The development of a model
that can predict neurological complications of patients allows the medical
staff to take preventive measures to avoid the event or to reduce its
severity.

Figure 2: K-Fold cross validation method

Our target application demands unbiased results and models objectively
evaluated. As depicted in Figure 2, we have performed a K-fold cross-
validation methodology with K=5 to avoid over-fitting and obtain results
independent from the training and test datasets. The code and libraries used
in this study are open source and widely-used: Python with Sci-kit Learn, and
Keras. Some of the algorithms used were not available in public libraries
repositories, therefore, they were coded for these experiments. The evaluation
of the models was measured by the following metrics: accuracy, sensitivity,
specificity, F-Measure (F1-Score) and both the areas under the Receiver
Operating Characteristic (ROC)curve and the Precision Recall Curve (PRC).
These metrics are calculated as follows:

$\mathrm{Accuracy}=\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{TN}+\mathrm{FP}+\mathrm{FN}}$
(2)

$\mathrm{Sensitivity}=\mathrm{Recall}=\frac{\mathrm{TP}}{\mathrm{FN}+\mathrm{TP}}$
(3)

$\mathrm{Specificity}=\frac{\mathrm{TN}}{\mathrm{FP}+\mathrm{TN}}$ (4)

$\mathrm{F-Measure}=\frac{2\cdot\mathrm{Precision}\cdot\mathrm{Recall}}{\mathrm{Precision}+\mathrm{Recall}}$
(5)

where $\mathrm{Precision}=\mathrm{TP}/(\mathrm{TP+FP})$, $\mathrm{TP}$ is the
number of true positives, $\mathrm{TN}$ is the number of true negatives,
$\mathrm{FP}$ is the number of false positives, and $\mathrm{FN}$ is the
number of false negatives

##  4 Results

As aforementioned, we have performed three different experiments: Stroke type
diagnosis, Exitus prediction, and Stroke recurrence prediction. These three
experiments were trained, validated and tested with the same initial dataset,
being slightly smaller or greater regarding the number of patients as
explained before. The dataset features are the same except in the case of
Exitus and recurrence predictions where the dataset includes as well the
stroke type diagnosed.

Several models were trained for a large distribution of time windows. Before
the analysis of the obtained results, we will present how the models (and the
time windows) were selected.

###  4.1 Model selection

In order to compare and rank the set of algorithms used, we use the
hypervolume indicator $I_{H}^{-}$ in the metrics space. This indicator
calculates the volume (in the metrics space) covered by members of a non-
dominated set of solutions $W$ ([18]). Note that a solution in our case is
defined as the set of metrics associated with each time window. For instance,
in the case of the stroke sub-type classification, each algorithm has 94
solutions, i.e., 94 time windows with their four metrics. With this in mind,
let $v_{i}$ be the volume enclosed by solution $w_{i}\in W$ . Then, a union of
all hypercubes is found and its hypervolume ($I_{H}$) is calculated.

$I_{H}(W)=\bigcup_{1}^{\left|W\right|}v_{i}$ (6)

The hypervolume of a set is measured relative to a reference point, usually
the anti-optimal point or “worst possible” point in space. We do not address
here the problem of choosing a reference point; if the anti-optimal point is
not known or does not exist, one suggestion is to take in each objective the
worst value from any of the fronts being compared. If a set $W_{i}$ has a
greater hypervolume than a set $W_{j}$, then $W_{i}$ is taken to be a better
set of solutions than $W_{j}$. In this work, we consider the hypervolume
difference to a reference set $R$, defined as

$I_{H}^{-}(W)=I_{H}(R)-I_{H}(W)$ (7)

where smaller values correspond to higher quality. If the reference set is not
given, we take $I_{H}(R)=0$.

Table 2: Hypervolumes computed in the case of the stroke sub-type classification system. Algorithm | HyperVolume
---|---
Logistic Regression | -0.24
Support Vector Machines(SVC) | -0.17
K-Nearest Neighbour | -1.23
MLP | -0.98
Decision Tree | -1.22
AdaBoost | -1.12
LSTM | -0.89
Random Forests | -1.38
Naive Bayes | -0.25
Gradient Boosting | -1.46
Dependent Dynamic Time Warping 1-NN | -1.14

Table 2 illustrates the set of hypervolumes $I_{H}^{-}(W)$ calculated for the
stroke sub-type prediction system (with $I_{H}(R)=0$). As Table 2 shows, the
best indicator is reached by the Gradient Boosting algorithm, with
$I_{H}^{-}(W)=-1.46$ followed by Random Forest ($I_{H}^{-}(W)=-1.38$) and the
K-Nearest Neighbour ($I_{H}^{-}(W)=-1.23$). Clearly, according to this
indicator the Gradient Boosting algorithm is the best algorithm for stroke
sub-type classification.

As stated above, all the hypervolumes are computed over the non-dominated set
of solutions, or in other words, the non-dominated set of time windows. A
solution $w_{i}\in W$ is said to dominate another solution $w_{j}\in W$
(denoted as $w_{i}\prec w_{j}$) if the following two conditions are satisfied.

$\displaystyle\forall
i\in\left\\{1,2,\ldots,m\right\\},f_{i}\left(w_{i}\right)\geq
f_{i}\left(w_{j}\right)$ $\displaystyle\exists
i\in\left\\{1,2,\ldots,m\right\\},f_{i}\left(w_{i}\right)>f_{i}\left(w_{j}\right)$
(8)

A solution $w$ is non-dominated if another $w^{\prime}$ such that
$w^{\prime}\prec w$ does not exist. Hence, the set of non-dominated solutions
of each algorithm will give us the best time windows to be used in the
prediction or the classification system. This is usually known as the Pareto
front or more precisely, an approximation to the Pareto front.

Table 3: Non-dominated solution set of Gradient Boosting and their respective
metric values.

Time Window | F1-Score | Specificity | Sensitivity | Accuracy
---|---|---|---|---
5-(0,30) | 0.96 | 0.98 | 0.96 | 0.97
5-(0,65) | 0.99 | 1.0 | 0.98 | 0.99
5-(0,110) | 0.99 | 1.0 | 0.99 | 0.99
5-(0,115) | 0.99 | 1.0 | 1.0 | 1.0
5-(0,120) | 0.99 | 1.0 | 0.98 | 0.99

Table 3 illustrates the non-dominated solution set for stroke sub-type
classification with the Gradient Boosting algorithm. As can be seen, the best
time window candidate to be used for the classification system remains within
the one or two first hours after the patient was admitted in the Stroke Care
Unit with only 5 observations per instance. However, a very good result is
also obtained for the observations during the first 30 minutes, achieving a
much more useful clinical result.

Table 4 illustrates the set of hypervolumes calculated for the exitus
prediction system, where Random Forest and Gradient Boosting again achieve
similar results ($I_{H}^{-}(W)=-1.46$).

Table 4: Hypervolumes computed in the case of the exitus prediction system. Algorithm | HyperVolume
---|---
Logistic Regression | -0.68
Support Vector Machines(SVC) | -1.35
K-Nearest Neighbour | -1.37
MLP | -1.44
Decision Tree | -1.45
AdaBoost | -1.42
LSTM | -1.16
Random Forests | -1.46
Naive Bayes | -0.67
Gradient Boosting | -1.46
Dependent Dynamic Time Warping 1-NN | -1.16

Tables 5 and 6 illustrate the non-dominated solution set for exitus prediction
with the Random Forest and Gradient Boosting algorithms, respectively. As can
be seen, the results for the exitus prediction are highly positive from a
clinical perspective during the first 3 hours and 5 observations per sample
(in the case of the Random Forest), although they increase slightly in the
next hours. In the case of the Gradient Boost, results are pretty similar and
the same window could be used.

Table 5: Non-dominated solution set of Random Forests and their respective
metric values.

Time Window | F1-Score | Specificity | Sensitivity | Accuracy
---|---|---|---|---
5-(0,180) | 0.99 | 1.0 | 0.98 | 0.99
5-(0,360) | 0.99 | 1.0 | 0.99 | 1.0
5-(0,540) | 1.0 | 1.0 | 0.99 | 1.0
5-(0,2880) | 1.0 | 1.0 | 1.0 | 1.0
5-(0,3600) | 1.0 | 1.0 | 1.0 | 1.0

Table 6: Non-dominated solution set of Gradient Boost and their respective
metric values.

Time Window | F1-Score | Specificity | Sensitivity | Accuracy
---|---|---|---|---
5-(0,180) | 0.99 | 0.99 | 0.99 | 0.99
5-(0,360) | 0.99 | 1.0 | 1.0 | 1.0
5-(0,3600) | 1.0 | 1.0 | 1.0 | 1.0
5-(0,4320) | 1.0 | 1.0 | 1.0 | 1.0

Similar approach has been followed for the stroke recurrence in Tables 7 and
8, where DTW 1-NN algorithm has been selected with a time window of 30 minutes
and 5 observations per instance.

Table 7: Stroke recurrence Algorithm | HyperVolume
---|---
Logistic Regression | -0.61
Support Vector Machines(SVC) | -0.01
K-Nearest Neighbour | -1.38
MLP | -1.24
Decision Tree | -1.36
AdaBoost | -1.4
LSTM | -1.23
Random Forests | -1.44
Naive Bayes | -0.46
Gradient Boosting | -1.44
Dependent Dynamic Time Warping 1-NN | -1.46
Table 8: Non-dominated solution set of Dependent Dynamic Time Warping 1-NN and
their respective metric values.

Time Window | F1-Score | Specificity | Sensitivity | Accuracy
---|---|---|---|---
5-(0,10) | 0.92 | 0.963 | 0.8846 | 0.9245
5-(0,30) | 0.9505 | 0.9896 | 0.923 | 0.9662
5-(0,50) | 0.9090 | 0.939 | 0.946 | 0.941

###  4.2 Performance Results for Stroke Diagnosis Models

Table 9: Stroke diagnosis model: performance metrics Perf. metric | DTW | Nearest Neighbour | Gradient Boost | Random Forests | Decision Tree
---|---|---|---|---|---
Sensitivity | 0.8736 | 0.9134 | 0.9783 | 0.9567 | 0.8881
Specificity | 0.9509 | 0.9701 | 0.9957 | 0.9893 | 0.9594
F-Measure | 0.8930 | 0.9301 | 0.9855 | 0.9689 | 0.9077
Accuracy | 0.9221 | 0.9490 | 0.9893 | 0.9772 | 0.9329
ROC Area | 0.9123 | 0.9417 | 0.9870 | 0.9730 | 0.9237
PRC Area | 0.9169 | 0.9794 | 0.9994 | 0.9975 | 0.9290
Avg | 0.9115 | 0.9473 | 0.9892 | 0.9771 | 0.9235
Figure 3: Diagnosis detection: all the performance metrics of each model
together.

In this set of experiments, the aim is not to classify for making a prediction
but to achieve a diagnosis or detection of the stroke type. As aforementioned,
the stroke can be either ischemic or hemorrhagic and detecting the type of
stroke is crucial for the proper treatment.

The results presented in this section were obtained after training and testing
with data from 548 patients, where 468 patients were ischemic and the
remaining 80 patients were hemorrhagic. The tests were performed through
several temporal windows and the results shown here correspond to data within
the first 30 minutes of monitoring, which is the minimum time required to
obtain these results. Extending the monitoring time does not improve results,
and reducing the monitoring time decreases scores markedly. The first tests
showed that the unbalance of classes was causing an under-fit of the smallest
class hence we decided to balance classes to a proportion where the smallest
class formed the 35% of the dataset.

Table 9 shows the detailed results of the performance metrics. The last row of
the table shows the average of the rest of the metrics. The F-Measure results
were $0.9855$ for Gradient Boosting, $0.9689$ for Random Forests, $0.9077$ for
Decision Tree, $0.8930$ for Dynamic Time Warping and $0.9301$ for Nearest
Neighbors. As can be seen, Gradient Boosting algorithm obtained the best
results on each metric. The F-Measure score $0.9855$ is notably higher than
results achieved in previous work [17] where Random Forests performed better.
On the other hand, the algorithm that provided the lowest results was Dynamic
Time Warping. The reason for this behavior can be probably explained by the
observation time (5 observations per instance), that is too short for the
proper performance of DTW.

These results can be seen graphically in Fig. 5.

Being able to detect hemorrhage vs cerebral infarct within only 30 minutes
monitoring allows to apply the correct treatment and/or transfer to the right
medical center (stroke unit vs stroke center) which implies the possibility to
receive early and accurate treatment depending on each medical case.
Therefore, most patients from rural areas, in extra-hospital emergency care or
medical centers without CT scan availability, could benefit from this kind of
analysis.

###  4.3 Performance Results for Exitus Models

Table 10: Exitus prediction: performance metrics Perf. metric | DTW | Nearest Neighbour | GradientBoost | AdaBoost | Random Forests
---|---|---|---|---|---
sensitivity | 0.8757 | 0.9416 | 0.9981 | 0.9718 | 0.9812
Specificity | 0.9407 | 0.9844 | 0.9990 | 0.9825 | 1.0000
F-Measure | 0.8798 | 0.9551 | 0.9981 | 0.9690 | 0.9905
Accuracy | 0.9185 | 0.9699 | 0.9987 | 0.9788 | 0.9936
ROC Area | 0.9082 | 0.9630 | 0.9986 | 0.9771 | 0.9906
PRC Area | 0.9010 | 0.9836 | 1.0000 | 0.9940 | 0.9999
Avg | 0.9040 | 0.9663 | 0.9988 | 0.9789 | 0.9926

The exitus prediction aims to forecast the eventual death of the patient. As
previously mentioned, being able to predict the eventual death of the patient
gives the medical team the possibility of applying in advance a special
treatment and, in some cases, avoid the death of the patient or to increase
his survival probability. The methodology in this experiment is similar to the
previous experiment in terms of preprocessing and most of the features used,
but in this case, the aim is prediction instead of diagnosis. The dataset
employed is composed of monitored data from 504 patients where 43 passed away
by stroke causes. Like the previous section, there is a notable unbalance
between the classes hence a balance was performed by discarding training and
testing instances from the greater class until a balance of 35%-65% was
achieved.

As exposed in the model selection subsection, the results presented in this
study were achieved with a temporal window of the first three hours of
monitoring and size of five observations per training instance.

Being able to predict a patient’s death in 3 hours offers an opportunity for
health professionals to act. Applying different therapeutic tools to save the
patient’s life or avoiding acute stroke complications that could lead to
death. In cases that it is impossible to avoid death, being able to give
certain and accurate prognosis information is very important for patients and
families and allows to plan the therapeutic effort.

Table 10 presents the results of the performance metrics. The algorithm that
performed best was Gradient Boost with $0.9988$ score average, whereas Random
Forests achieved $0.9926$, AdaBoost $0.9789$, Nearest Neighbors $0.9663$ and
Dynamic Time Warping $0.904$. Gradient Boost and Random Forests predict exitus
almost perfect, with very high results in both sensitivity and specificity.

This experiment was performed beforehand as published in our previous work
[17] with fewer patients and fewer features compounding the model. The results
shown in this study are notably better than former results.

Fig. 4 shows graphically all these results together.

Figure 4: Exitus prediction: all the performance metrics of each model
together.

###  4.4 Performance Results for Stroke recurrence Models

Table 11: Stroke recurrence prediction: performance metrics Perf. metric | DTW | Nearest Neighbour | GradientBoost | AdaBoost | Random Forests
---|---|---|---|---|---
Sensitivity | 0.9231 | 0.9038 | 0.9231 | 0.9808 | 0.9808
Specificity | 0.9896 | 0.9792 | 0.9583 | 0.9375 | 0.9896
F-Measure | 0.9505 | 0.9307 | 0.9231 | 0.9358 | 0.9808
Accuracy | 0.9662 | 0.9527 | 0.9459 | 0.9527 | 0.9865
ROC Area | 0.9563 | 0.9415 | 0.9407 | 0.9591 | 0.9852
PRC Area | 0.9648 | 0.9841 | 0.9892 | 0.9919 | 0.9993
Avg | 0.9584 | 0.9487 | 0.9467 | 0.9596 | 0.9870
Figure 5: Stroke recurrence prediction: all the performance metrics of each
model together.

From a dataset composed of 500 patients from which 34 suffered a recurrence
during their hospitalization. The remaining 466 patients did not have any kind
of recurrence of affection. It can be easily spotted that the weights of
classes are not ideal, where only 6.8% had a recurrence. During our tests,
performance decreased substantially when classes were extremely unbalanced.
The best way of achieving a model able to predict both classes with high hit
rates was to balance classes. Due to this, classes were balanced to a
proportion where 35 percent of the observations were from recurrence patients
and the remaining 65 percent were from patients who did not have a recurrence
during hospitalization. In order to do this task properly, observations of
patients belonging to the majority class were discarded.

Table 11 shows the detailed metric scores for each algorithm tested, where
Random Forests is the algorithm that obtains the best results with a $0.98$
percent, followed with $0.9504$ by DTW, $0.9357$ for AdaBoost, $0.9307$ for
K-Nearest Neighbors and $0.923$ for Gradient Boosting. Random Forests was the
algorithm with best average results and, on the opposite side, DTW obtained
the worst results.

The results shown correspond to observations from the first 15 minutes of
monitoring of the patients, which is enough to obtain these results. Creating
the model with a longer time window provides similar results, hence this is
the minimum time window necessary for predicting recurrences on patients.
Predicting any kind of acute stroke neurological complications (i.e.:
rebleeding in cerebral hemorrhage, stroke recurrence or hemorrhagic
transformation in ischemic stroke) in such a short period of time gives
multiple therapeutic opportunities to avoid or at least minimize adverse
events.

Figure 5 depicts the results achieved in this experiment together.

Table 12: Comparative between other invasive and non-invasive hemodynamic monitoring studies Reference | N.∘ Patients | Non-Invasive | Rebleeding | Diagnosis required | Real-Time | Mean Precision
---|---|---|---|---|---|---
Hatib et al.[19] | 1.538 | Yes | No | No | Yes | 0.88
Kendale et al.[20] | 13.323 | Yes | No | Yes | N/A | 0.76
Convertino et al.[21] | 190 | Yes | No | No | Yes | 0.965
Shoemaker et al.[22] | 661 | Yes | No | Yes | Yes | 0.89
Prasad et al.[23] | 101 | No | No | Yes | No | 0.82
Our work | 548 | Yes | Yes | No | Yes | 0.98

##  5 Discussion

This study demonstrated that the use of machine learning models can accurately
predict the outcomes in acute stroke patients and serve to identify the stroke
subtype. Many factors influence stroke outcomes, and these variables may have,
even a slight, impact on prediction. Indeed, based on the data acquired from
798 patients in the Stroke Unit of a large national hospital, this study has
demonstrated that machine learning-based models can be derived to help on the
right management of this serious neurological disease.

Our research work has created models for stroke subtype diagnosis, recurrence
prediction, and exitus prediction, from hemodynamic data acquired in real time
by a traditional monitor. These models can be incorporated into the existing
monitors in the Stroke unit to help on neurologist’s decisions, or can be used
by the emergency services to advance the treatment of the patients.

This study has derived and evaluated several machine learning-based approaches
and, in order to compare and rank the set of algorithms used, we have proposed
the hypervolume indicator in the metrics space.

Gradient Boosting algorithm was able to detect hemorrhage vs cerebral infarct
within only 30 minutes monitoring, and it achieved an F-Measure of 0.9855.
Also, Gradient Boosting algorithm was able to predict a patient’s death in 3
hours with an F-Measure of 0.9988. Finally, Random Forests algorithm obtained
an F-Measure of 0.98 to predict a stroke recurrence from 15 minutes of
monitoring.

Table 12 compares our study with respect to other similar approaches from the
state-of-the-art. The table shows qualitative and quantitative data, and
allows to evaluate the achieved accuracy of the models, if the monitoring
approach requires invasive data acquisition, and if the prediction model is
able to predict the rebleeding effect in stroke disease.

As can be seen in the table, our approach not only achieves better accuracy
for the developed prediction models, but it is also able to work with non-
invasive data and predict the rebleeding events in stroke disease.For the
first time in literature, our real-time models from hemodynamic data have
opened a new trend in the clinical management of stroke, providing an accurate
and efficient approach from the perspective of data processing.

Our results are a straightforward complement to other approaches in this field
that apply ML techniques to predict the stroke event from more complex tests
like MRI [24]. Our approach, affordable and reliable, could in the future
reduce the need of costly neuroimage tools and achieve faster diagnosis.

The present study has several limitations. First, the patient population was
not calculated before conduction of the study. However, it should be noted
that the current patient number has been enough the validate the
generalization capabilities of our approach. Second, all the patients had come
from one stroke unit. Although these patients may represent the actual
characteristics of acute stroke patients, parameters for ML algorithms may
have been optimized and determined for the current population only, suggesting
that the results of this study should be interpreted with caution. In a
multicenter setting, with different patient populations, further validation is
warranted. Finally, patients who arrived at the stroke unit have different
evolution time, some of them could have started the stroke episode a few hours
ago. This approach should be tested in the initial stages of the event.

##  6 Conclusions

A correct and early stroke type diagnosis is very important in order not to
lose the therapeutic window and to correctly send patients to the appropriate
medical center.

The prediction of short-term outcomes in ischemic stroke patients may be
useful in treatment decisions. In situations where imminent neurological
complications could happen, it will allow us to react and change treatment in
order to try to avoid it or minimize adverse effects. When death is predicted
we can evaluate if there is any therapeutic chance to change it, or if not
viable, decide to the adequate therapeutic effort.

In this study, we have also been able to predict the type of stroke with
simple semi-intensive routine monitoring analysis using machine learning
techniques. No brain image test was evaluated, but it was compared to the
doctors’ final diagnosis, with very high confidence. It has been also
evaluated neurological complications and death risks, with also a very high
success rate.

In real clinical practice, it is uncommon to manage acute stroke treatment
without a brain image test (CT-scan or MRI). But it is still quite common in
some rural areas that early CT-scan access is not possible. This kind of
analysis could allow treating before CT-scan performance in case of a high
risk of losing the treatment opportunity.

Although in urban areas most patients have easy and fast access to medical
attention, depending on the local health organization system, stroke patients
not always arrive on time for treatment. Sometimes patients decide to seek
direct medical assistance instead of calling 112 (Europe emergency call
number), therefore the first medical exam is not performed by a neurologist
and a transfer to a stroke unit or stroke center is not possible. So, pre-
hospital stroke subtype diagnosis during transfer with routine vital signs
monitoring would allow direct transfer to the appropriate medical center in
order to receive the necessary treatment.

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## Glossary

CART

    Classification and Decision Tree
CHS

    Cardiovascular Health Study
CT

    Computed Tomography
DNN

    Deep Neural Networks
DTW

    Dynamic Time Warping
ECG

    Electrocardiogram
EEG

    Electroencephalography
FSVM

    Fuzzy Support Vector Machine
LSTM

    Long-Short Term Memory
ML

    Machine Leaning
MRI

    Magnetic Resonance Imaging
PCA

    Principal Component Analysis
PRC

    Precision Recall Curve
PVC

    Ppremature ventricular contraction
ROC

    Receiver Operating Characteristic
SICH

    Symptomatic Intracranial Haemorrhage
SVM

    Support Vector Machine

##  7 Biography

Luis García-Terriza received the B.S. degree in Computer Engineering from
Complutense University, Madrid, Spain, in 2016 and the M.S. degree in Computer
Engineering from Complutense University, Madrid, Spain, in 2018. He is
currently pursuing the Ph.D. degree in Computer Engineering at Complutense
University, Computer Science Faculty, Madrid, Spain.
His research interest includes the development and optimization of Artificial
Intelligence models applied to biological/medical area, and parallel and
distributed computing. In the private sector, he has been working since 2016
as Data Scientist, Big Data engineer, DevOps Engineer and, lately, Cloud
Security Architect.

José L. Risco-Martín is an Associate Professor of the Computer Science Faculty
at Universidad Complutense de Madrid, Spain, and head of the Department of
Computer Architecture and Automation at the same University. He received his
M.Sc. and Ph.D. degrees in Physics from Universidad Complutense de Madrid,
Spain in 1998 and 2004, respectively. His research interests focus on Computer
Simulation and Optimization, with emphasis on Discrete Event Modeling and
Simulation, Parallel and Distributed Simulation, Artificial Intelligence in
Modeling and Optimization and Feature Engineering. In these fields, he has co-
authored more than 150 publications in prestigious journals and conferences,
several book chapters, and three Spanish patents. He has received the SCS
Outstanding Service Award in 2017, and the HiPEAC Technology Transfer Award in
2018. He is associate editor of SIMULATION: Trans. of Soc. Mod. and Sim. Int.,
and has organized several Modeling and Simulation conferences like DS-RT,
ANSS, SCSC, SummerSim or SpringSim. He is ACM Member and SCS Senior Member.
Prof. José Luis Risco Martín has participated in more than 15 research
projects and more than 10 contracts with industry. He has elaborated
simulation and optimization models for companies like Airbus, Repsol or
ENAGAS.

Gemma Reig Roselló received the MD degree in Medicine and Surgery from
University of Barcelona (UB), Spain, in 2002. Becoming a Neurologist in 2007,
after Neurology residency program in Hospital de La Princesa, in Madrid,
Spain. Became Stroke Fellow for two years after residency in Hospital de La
Princesa. Being able to join as a staff member in the Neurology department,
working as a Stroke neurologist since 2009. She is a clinical teaching
collaborator in the Neurology Department, teaching students and residents, in
theory classes and clinical practices. She is also an Associate Member of
Asociación Madrileña de Neurología – Madrid Neurology Association (AMN) and
Sociedad Española de Neurología- Spanish Neurology Society (SEN). She has
participated in a large number of international and national research projects
and pharmaceutical clinical trials, mainly in stroke field. Her research
interests include all field of neurology, but specially stroke, and also from
a computer and engineering point of view, having the chance to work with big
data tools and non-invasive monitoring studies. She is active in publishing
posters and oral communications in Neurology medical conferences.

Jose L. Ayala received the M.Sc. and Ph.D.degrees in telecommunication
engineering from the Technical University of Madrid (UPM), Spain, in 2001 and
2005, respectively. His PhD dissertation was awarded by the Council of
Telecommunication Engineers, and the ACM Special Interest Group on Design
Automation. He is currently an Associate Professor in the Department of
Computer Architecture and Automation, Universidad Complutense de Madrid,
Madrid, Spain. He is also an Associate Member of the Center for Computational
Simulation (UPM), and the IEEE Council of Electronic Design Automation. He
also participates actively in the IEEE IoT, Smart Cities, and Brain
initiatives. He has served as organizing committee of many international
conferences, including DATE, GLSVLSI, VLSI-SoC, ISC2, ISLPED. He has led a
large number of international research projects and bilateral projects with
industry, in the fields of power and energy optimization of embedded systems,
and noninvasive health monitoring. His research interests include automatic
diagnostic tools, predictive modeling in the biomedical field, and wearable
non-invasive monitoring. Prof. Ayala has received 4 best paper awards, and
more than 15 awards related to technology transfer. He is also CEO of the
high-tech start-up, BrainGuard, devoted to the development of predictive
models from ambulatory data in the neurological field.