These authors contributed equally to this work.

[1,2]Shehroz S. Khan These authors contributed equally to this work.

[1]KITE, University Health Network, 550, University Avenue, Toronto, M5G 2A2,
Ontario, Canada

2]Institute of Biomedical Engineering, University of Toronto, 64 College St.,
Toronto, M5S 3G9, Ontario, Canada

# Supervised and Unsupervised Deep Learning Approaches for EEG Seizure
Prediction

Zakary Georgis-Yap<EMAIL_ADDRESS>Milos R. Popovic<EMAIL_ADDRESS><EMAIL_ADDRESS>*  [

###### Abstract

Epilepsy affects more than $50$ million people worldwide, making it one of the
world’s most prevalent neurological diseases. The main symptom of epilepsy is
seizures, which occur abruptly and can cause serious injury or death. The
ability to predict the occurrence of an epileptic seizure could alleviate many
risks and stresses people with epilepsy face. We formulate the problem of
detecting preictal (or pre-seizure) with reference to normal EEG as a
precursor to incoming seizure. To this end, we developed several supervised
deep learning approaches model to identify preictal EEG from normal EEG. We
further develop novel unsupervised deep learning approaches to train the
models on only normal EEG, and detecting pre-seizure EEG as an anomalous
event. These deep learning models were trained and evaluated on two large EEG
seizure datasets in a person-specific manner. We found that both supervised
and unsupervised approaches are feasible; however, their performance varies
depending on the patient, approach and architecture. This new line of research
has the potential to develop therapeutic interventions and save human lives.

###### keywords:

deep learning, intracranial EEG, seizure prediction, signal processing

##  1 Introduction

Epilepsy is one of the most prevalent neurological disorders in the world,
affecting approximately $1\%$ of the world’s population [1, 2, 3]. Epilepsy is
characterized by spontaneously occurring seizures, which could lead to bodily
injuries, fractures, burns [4], and death in many cases [5]. People with
epilepsy are mostly concerned with the fear of incoming seizures [6].
Therefore, there is a dire need to reduce the unpredictability of seizures to
reduce the risk of injuries and improve their quality of life.

Electroencephalography (EEG) is normally used to analyze brain activity
pertaining to seizures [7]. Brain activity in people with epilepsy can be
separated into four states: regular brain activity (interictal), brain
activity before the seizure (preictal), brain activity during the seizure
(ictal), and brain activity immediately after a seizure (postictal). The
preictal state can contain observable physiological changes prior to the onset
of a seizure [8] that can be used to predict an incoming seizure. The
capability to predict an epileptic seizure could alleviate the risks patients
face [9]; it would give patients the time to get help and greatly reduce the
risk of injury. However, the biggest challenge is designing seizure prediction
approaches is that there is no universally agreed upon preictal period length
(PPL). Bandarabadi et al. [10] investigated the optimal PPL for seizure
prediction using statistical analysis and found that the optimal PPL varies
for each patient and for seizure within each patient [10].

Most of the work in this area is around seizure detection [11], which involves
detecting a seizure after its occurrence. Although this is important,
contemporary work must aim to predict seizures before their onset, as it can
save patients’ lives and improve their quality of life. Our main hypothesis is
that the correct detection of preictal state against normal brain activity
(through supervised or unsupervised approaches) can be a strong indicator of
an incoming epileptic seizure. In the supervised setting, a binary classifier
can be trained between interictal and preictal periods. Whereas, in the
unsupervised setting, a classifier can be trained on only normal EEG
(interictal) and preictal state can be identified as an anomaly. Our main
contributions are:

  * •

Presented supervised and new unsupervised deep learning approaches to predict
epileptic seizures.

  * •

Experimentally determined the PPL and window size, as against heuristics or
domain knowledge.

  * •

Performed leave-one-seizure out cross validation for better generalization of
results.

  * •

All the experiments were performed in a patient-specific manner to avoid data
leakages, overestimation of results and emphasis on individualized outcomes.

Our results showed that the unsupervised approaches were able to obtain
comparable results to supervised seizure prediction in many patients. However,
across all implementations there was not one clear best-performing model. This
paper is an extension of our preliminary work [12] that introduced supervised
convolution neural network (CNN) on SWEC-ETHZ dataset [13]. In this paper, we
present two new supervised approaches: CNN-Long Short Term Memory (LSTM) and
Temporal Convolution Network (TCN), and three new unsupervised approaches
(CNN, CNN-LSTM, TCN autoencoders). We developed new seizure prediction
baselines for the SWEC-ETHZ dataset [13] and included a new CHB-MIT dataset
[14].

##  2 Related Work

Seizure prediction using supervised machine learning has been used to
distinguish the interictal and preictal states [15]. Typical supervised
machine learning seizure prediction approaches involve signal pre-processing,
extracting and selecting features, followed a classifier [15]. Common signal
processing techniques include high-pass, low-pass, or band-pass filtering, as
well as artifact removal techniques [15]. Feature extraction is typically done
by a bio-signals or epilepsy expert examining a patient’s EEG and deciding
appropriate features for separating the preictal and interictal states [15].
These features are often patient-specific and include statistical, non-linear,
frequency domain, and time-frequency domain features [15, 16]. Common
classifier choices include support vector machines (SVM), k-nearest neighbour
and random forest [15].

Machine learning approaches may have limitations in terms of extracting
handcrafted features, which could be suboptimal and time-consuming. Deep
learning approaches can overcome some of these challenges by able to learn
features from data with little to no pre-processing, generate high-level
representations of data, and learn complex functions [17]. However, deep
learning approaches need vast amount of data and computing resources to train
and deploy the models, which can be time-consuming and costly. Also, it
requires a careful tuning of the hyperparameters to avoid overfitting and
under fitting.An overview of preictal-interictal classification seizure
prediction methods (on human subjects) using deep learning is shown in Table
1.

Citation | Window Size | PPL | Pre-processing | DL Architecture
---|---|---|---|---
| (Seconds) | (Minutes) |  |
[18] | 5 | 10,20,30,40 | Various fixed features | MLP
[19] | 10 | Unknown | Fractional FT | MLP
[20] | 300 | 50 | Handcrafted | CNN
[21] | 1 | 10 | CWT | CNN
[22] | 30 | 60 | STFT | CNN
[23] | 15 | 60 | None | CNN
[24] | 5 | 30 | Common spatial pattern | CNN
[25] | 30 | 60 | Fast FT | Multi-view CNN
[26] | 10 | 60 | MFCC | CNN
[27] | 1,2,3,8 | 10 | None | CNN
[12] | 5,10,15,30,60 | 30,60,120 | STFT | CNN
[28] | 20 | 30,60 | None | CNN
[29] | 5 | 15 | Handcrafted | LSTM
[1] | 10 | 30 | STFT | CNN + LSTM
[30] | 4 | 60 | None | CNN-AE+BiLSTM
[31] | 5 | 60 | None | MLP,CNN,CNN+LSTM
[32] | 10 | 30 | Image conversion | CNN+LSTM
[33] | 30 | 60 | STFT | CNN+SVM
[34] | 30 | 30 | None | 1DCNN+GRU
[35] | Unknown | 60 | Image conversion | 3DCNN
[36] | 4 | 30 | Various fixed features | 3DCNN
[37] | 28 | 30 | STFT | Convolutional GAN
Table 1: Overview of deep learning EEG seizure prediction methods.

Many reviewed deep learning methods performed some type of pre-processing the
EEG data before passing it on to the classifier, typically through filtering
[1, 21], artifact removal [35], or time-frequency analysis [22, 21]. Common
deep learning architectures used for seizure prediction include CNN [1, 22],
LSTM network [29, 32], and feed-forward multilayer perceptron (MLP) [18]. We
observed that majority of the studies use CNNs, LSTMS and/or their
combinations to benefit from learning spatial and temporal features. The
window size (fixed duration of data to analyze) and PPL were kept fixed in
most of the studies, and they varied even when working on the same dataset and
patients. This is an issue in building classifiers to predict seizures because
the optimal PPL varies across patients (as concluded by Bandarabadi et al.
[10]). Only four of the studies reported experimenting with the PPL [18, 29,
21, 35], while others did not present any rationale for their choices. Some of
the studies (e.g., [14]) also found different PPLs sizes, showing that the
optimal PPL varies depending on the method’s implementation. These studies
show that it is better to determine the PPL empirically at a patient-specific
level, rather than using a generic or pre-determined average over a
population.

We extend the existing supervised methods by obtaining PPL and window size
using a leave-one-seizure-out (LOSO) evaluation and introduced a new
supervised TCN classifier for this task. Unsupervised deep learning approach
has been used by Daoud et al. [31]; in conjunction with classification
approach to predict epileptic seizures. They trained a deep autoencoder on
unlabelled EEG segments (balanced data of preictal and interictal segments)
and replace the trained autoencoder in their classification pipeline. Their
intent to use autoencoder is to leverage the transfer learning abilities of
the model by allowing the training process to have a good start point instead
of random initialization of the parameters, which reduces the training time
drastically. We introduced three different autoencoder models in a total
unsupervised manner and separate from the classification approaches and
studied their performance for this problem.

##  3 Supervised Seizure Prediction

Preictal-interictal classification for seizure prediction is performed with
three different architectures: convolutional neural networks (CNN) (used in
our previous work [12]), and two new architectures, i.e., CNN-LSTM), and TCN.
We briefly discuss them below.

###  3.1 CNN

The CNN model takes in EEG samples that have been time-frequency transformed
using a STFT [22] (see Section 5.3). This helps the model in extracting time
and frequency features and puts the data into a suitable format for $2$D
convolutions [22]. The CNN architecture takes advantage of spatial information
in data to learn relevant features. Each sample was converted into a $2$D
matrix $F\times T$ using a STFT, where $F$ was the number of sample
frequencies used and $T$ was the number of segment times used. The matrix was
then resized to a $128\times 128$ “image” using bilinear interpolation so that
image sizes were consistent regardless of the window size. The time-frequency
transform was done independently for each channel, resulting in each sample
being of dimensions $C\times 128\times 128$, where $C$ is the total number of
channels. The samples were then passed to the CNN model, which is made up of
three convolutional blocks (see Figures 1(a) and 1(b)), followed by three
fully connected layers with ReLU activation functions. Table 2 shows the model
hyperparameters used for the CNN.

(a)

(a)

(b)

(b)

Figure 1: (a) Convolutional block, (b) CNN architecture with convolution blocks and fully connected (FC) layers CNN kernel size | 5
---|---
CNN filter sizes | 8, 16, 32
Fully connected sizes | 128, 64
Dropout | 0.5
Table 2: CNN model hyperparameters.

###  3.2 CNN-LSTM

The CNN-LSTM architecture takes advantage of both the spatial feature
extraction of the CNN along with the LSTM’s propensity to work well with
temporal data. The CNN-LSTM model takes in STFT images similar to the CNN
model. The input is a consecutive series of images as one sample. The input
sequence is divided into smaller sub-sequences, which are independently time-
frequency transformed and resized into $64\times 64$ images, leading to
dimensions $C\times n\times 64\times 64$, where $n$ is the number of sub-
sequences in a sample and is equal to the sequence length divided by the sub-
sequence length. Each sub-window is passed into a CNN model with two
convolutional blocks that outputs a feature vector. Then, each feature vector
is concatenated into a sequence and passed into a $2$-layer LSTM, whose
outputs are passed to a fully connected layer that outputs the final scores.
An overview of the CNN-LSTM architecture and hyperparameters are shown in
Figure 2 and Table 3(a).We did experiments with LSTM on raw EEG data; however,
the results were not satisfactory and not discussed in the paper. Converting
the EEG to STFT highlighted the frequencies and enabled CNN to learn spatial
information, while the LSTM was able to model the temporal dependencies
between there. The RNNs were not used because LSTM supports longer memory and
better handling of vanishing gradient problem [38]. On the flip side, LSTMs
generally require more parameters to train, making it computationally
expensive and memory intensive. We resolved this issue by training LSTM models
on a high-performance computation GPU cluster (see Section 6).

Figure 2: CNN-LSTM architecture showing time-frequency (TF) transform, CNN
layers, LSTM and fully connected (FC) layer.

###  3.3 TCN

The TCN model takes in scaled sequences of size $C\times S/4$, where $S$ is
the sequence length and the sequences were down-sampled by a factor of $4$.
The TCN model [39] consisted of TCN blocks (see Figure 3). Each TCN block is
two consecutive sub-blocks that contain a causal $1$D convolution layer with a
dilation, a weight normalization layer, a ReLU activation function, and a
dropout layer [39]. The TCN blocks have skip connections, where the input to
the block is added to the output [39]. The model contained $6$ TCN blocks with
$32$ channels each, followed by a $1$D convolution layer, and a fully
connected layer. The dilation factor of each block was $2^{(n-1)}$, where $n$
is the layer number. Figure 3 and Table 3(b) shows the TCN architecture and
hyperparameters.

(a)

(b)

Figure 3: (a) TCN block description. TCN: temporal convolutional network.
ReLU: rectified linear unit. (b) Supervised TCN architecture overview. TCN:
temporal convolutional network. FC layer: fully connected layer.

CNN kernel size | 5
---|---
CNN filter sizes | 8, 16
LSTM feature vector size | 32
LSTM hidden size | 16
Fully connected size | 96
Dropout | 0.5
(a)

TCN kernel size |  5
---|---
TCN filter sizes |  32, 32, 32, 32, 32, 32
Fully connected size |  64
Dropout |  0.2
(b)

Table 3: Hyperparameters of (a) CNN-LSTM, and (b) TCN models

##  4 Unsupervised Seizure Prediction

The reliance on preictal data for supervised seizure prediction methods
remains a challenge. Preictal data is typically scarce, and deep learning
methods require a considerable amount of data from both classes to work well.
Preictal-interictal classification methods cannot be used effectively on
patients with little preictal data, and class imbalance still remains an
impending problem. An unsupervised approach (anomaly detection) to seizure
prediction could remedy these problems. Anomaly detection for seizure
prediction would require only interictal (and no preictal data) to train,
making it easier to be more accessible to a larger population. Autoencoders
(AEs) and its variants are apt to be used within this framework, with
reconstruction error used as an anomaly score [40]. To our knowledge, this is
one of the first seizure prediction work that uses unsupervised deep learning
approach for epileptic seizure prediction without utilizing preictal data. We
implemented the following autoencoder approaches for this task.

  * •

CNN autoencoder [41]. Similar to the supervised CNN, it takes STFT images as
input. The encoder is made up of three convolutional blocks followed by a
fully connected layer which generates an embedding state of size $64$. The
decoder is a mirrored version of the encoder (see Figure 4(a)).

  * •

CNN-LSTM autoencoder [42]. Similar to the supervised CNN-LSTM, the input
sequence was divided into smaller sub-sequences and then an STFT was performed
on each sub-sequence. The encoder consisted of an individual CNN encoder for
each sub-sequence, followed by an LSTM that generated an embedding state of
size $64$. The decoder has the reverse architecture to the encoder. (see
Figure 4(b)).

  * •

TCN autoencoder [43]. It takes in raw scaled sequences, as is the case with
the supervised TCN. The encoder was a TCN with three layers, each with $16$
channels followed by a $1$d convolution and a fully connected layer. The size
of the embedding state was $64$. The decoder was an exact mirror of the
encoder (see Figure 4(c)).

(a)

(b)

(c)

Figure 4: Autoencoders comprising of (a) Convolution layers only, (b)CNN-LSTM,
and (c) TCN

##  5 Data Processing

###  5.1 Datasets

We used two EEG Epilepsy seizure datasets, the Sleep-Wake Epilepsy Centre ETH
Zurich (SWEC-ETHZ) dataset [13] and the Children’s Hospital Boston
Massachusetts Institute of Technology (CHB-MIT) dataset [14]. Both datasets
are publicly available, easy to access, and contain human raw EEG recordings,
where no seizure states have been pre-selected. This is important so we can
define and experiment with different preictal and interictal regions. The
SWEC-ETHZ dataset is an iEEG dataset containing over $2,500$ hours of
recordings across $18$ patients with a sampling rate of either $512$Hz or
$1024$Hz [13]. The CHB-MIT dataset contains scalp EEG recordings from $22$
patients sampled at $256$Hz with at least $22$ EEG electrodes [14]. Note that
one patient had their recordings taken on two separate occasions $1.5$ years
apart, and the two cases are treated as two separate patients for the rest of
this paper [14].

We define a “lead seizure“ as any seizure that occurs at least $30$ minutes
after a preceding seizure [22]. Only preictal periods from lead seizures were
considered because of the lack of interictal and preictal data to train
models. Patients that have less than three lead seizures were withheld from
the experiments because at least three lead seizures were required to perform
test partitioning combined with an internal leave-one-seizure-out (LOSO)
cross-validation step (see Figure 5(b)). Six out of the $18$ patients in the
SWEC-ETHZ dataset were not considered for this work due to this condition. All
patients in the CHB-MIT dataset had at least three lead seizures. A
description of dataset attributes for all patients used from both the SWEC-
ETHZ and CHB-MIT datasets is shown in Tables 4 and 5, respectively.

Patient ID | Hours of data | Seizures | Lead seizures | Electrodes
---|---|---|---|---
ID03 | 158 | 4 | 4 | 64
ID04 | 41 | 14 | 14 | 32
ID05 | 110 | 4 | 4 | 128
ID06 | 146 | 8 | 8 | 32
ID07 | 69 | 4 | 4 | 75
ID08 | 144 | 4 | 4 | 61
ID09 | 41 | 23 | 14 | 48
ID10 | 42 | 17 | 15 | 32
ID12 | 191 | 9 | 9 | 56
ID13 | 104 | 7 | 7 | 64
ID16 | 177 | 5 | 5 | 34
ID18 | 205 | 5 | 5 | 42
Table 4: SWEC-ETHZ dataset patient description [13] Patient ID | Hours of data | Seizures | Lead seizures | Electrodes
---|---|---|---|---
1 | 41 | 7 | 7 | 22
2 | 35 | 3 | 3 | 22
3 | 38 | 7 | 7 | 22
4 | 156 | 4 | 3 | 22
5 | 39 | 5 | 5 | 22
6 | 67 | 10 | 7 | 22
7 | 67 | 3 | 3 | 22
8 | 20 | 5 | 5 | 22
9 | 68 | 4 | 3 | 22
10 | 50 | 7 | 7 | 22
11 | 35 | 3 | 3 | 22
12 | 24 | 40 | 11 | 22
13 | 33 | 12 | 7 | 22
14 | 26 | 8 | 6 | 22
15 | 40 | 20 | 14 | 22
16 | 19 | 10 | 5 | 22
17 | 21 | 3 | 3 | 22
18 | 36 | 6 | 5 | 22
19 | 30 | 3 | 3 | 22
20 | 28 | 8 | 6 | 22
21 | 33 | 4 | 4 | 22
22 | 31 | 3 | 3 | 22
23 | 27 | 7 | 3 | 22
Table 5: CHB-MIT dataset patient description [14]

###  5.2 Data Preprocessing

The length and location of the preictal period is defined by the PPL and the
intervention time (IT). The IT is the time between the preictal state and the
seizure onset. Interictal data is defined as any data that is not preictal,
ictal, postictal, and is $d$ distance away from the preictal state, as shown
in Figure 5(a). The data was divided into samples of a fixed window size,
which were labelled as either interictal or preictal. We set $d=0$ to evaluate
the model’s ability to classify interictal and preictal samples in close
temporal proximity to actual seizures. The IT was set to $0$, increasing it
can be a future experiment after generating a baseline. In the SWEC-ETHZ
dataset, interictal samples were randomly selected with a down-sampling factor
of $8$ because interictal data were overly abundant, and the classes were
significantly imbalanced (patients ID04, ID09, and ID10 used a down-sampling
factor of $2$ instead because there was less interictal data). The number of
preictal samples were artificially increased by using $50\%$ overlapping
windows. The size of each sample was $sf\times C$ where $s$ was the window
size, $f$ was the sampling rate, and $C$ was the number of EEG electrodes.

The dataset was partitioned into a training set and a testing set using LOSO
partitioning. We used the last lead seizure’s preictal data as the test set,
while all other preictal data was part of the training set. As shown in Figure
5(b), LOSO partitioning is a better way to evaluate a model’s ability to
generalize to a new seizure’s preictal data. Standard test partitioning where
samples are randomly assigned to the training or test set may be an
overestimation of the actual performance of the classifier.

(a)

(b)

Figure 5: (a) Labelling of the preictal and interictal periods with
parameters. (b) Simplified visualization of LOSO test partitioning by
withholding the last seizure.

###  5.3 Time-Frequency Transform

We transformed the EEG data from a time-series input into the time-frequency
domain [44, 45] using short-time Fourier transform (STFT). It converts a one-
dimensional time-series signal into a two-dimensional matrix of values with
axes of time and frequency [46]. The STFT splits the signal into a series of
smaller sequences and then performing Fourier transforms on each one
individually, providing a way to see changes in the frequency domain at
various points in time [47]. In this work, the sequence is split into segments
of 128 samples before performing Fourier transforms. In CNN based models used
in the work, an STFT was used to pre-process the input before passing samples
to the model. Other time-frequency analysis methods such as the continuous
wavelet transform [21] and phase-amplitude coupling [48] were experimented
with in our preliminary work but did not provide better results.

Figure 6: LOSO cross-validation example with four seizures. One seizure is
used for validation, while the others are used for model training.

##  6 Experimental Setting and Results

A grid search was performed to find the optimal window size and PPL for each
patient. We ran the model with varying window size ($5,10,15,30,60$ seconds)
and PPL ($30,60,120$ minutes) values. We used an internal LOSO cross-
validation to tune the parameters without looking at test data. This was done
by dividing the training set into folds, where each fold was a different
seizure’s preictal and interictal data. One fold was the validation set, while
the others were used for training. Each fold in the set was used as the
validation set once, and the performance across all runs in a patient was
averaged. An example of the cross-validation method used is shown in Figure 6.
The area under the Receiver Operating Characteristic curve (AUC ROC) [49] was
used as a performance metric for hyperparameter tuning. The test set was
completely withheld from this process. All the models were trained using an
NVIDIA V100S-PCIe GPU with $32$ GB memory. A class-weighted (class weights
vary per patient) cross-entropy loss function was used with the Adam optimizer
and was trained for $100$ epochs with a batch size of $128$ and a learning
rate of $0.0001$. All implementations were done in the PyTorch framework [50].
After the final parameters for a model were set, it was evaluated on the test
set using the AUC ROC and precision-recall curve (AUC PR). AUC PR is more
appropriate for imbalanced classification problems [51]. The reported
performance metrics are AUC ROC and AUC PR. The calculation of false positive,
false negative and derived metrics, such as accuracy, specificity, and
sensitivity depend on the choice of the threshold applied to the
reconstruction error or classification probabilities [31, 21]. ROC and PR
analysis is more thorough since it does not depend on the threshold choice,
and instead analyzes the specificities and sensitivities at all possible
thresholds and provides an overall summary metric in terms of AUC ROC/AUC PR.
For imbalanced datasets, AUC PR is a better metric as it takes care of
precision and recall at all thresholds and does not inflate the results.
Another important point to note is that in unsupervised deep learning models,
there is no validation set; therefore, calculating an operating threshold is
non-trivial.

###  6.1 Supervised Prediction

Hyperparameter tuning results using the supervised CNN are shown in Tables 6
and 7 for the SWEC-ETHZ and CHB-MIT datasets, respectively. The window size
and PPL obtained using cross-validations as well as AUC ROC vary considerably
across different patients in both datasets. More than half of the patients in
each dataset show AUC ROC values greater than $0.7$. In the SWEC-ETHZ dataset,
six of the patients had a test AUC ROC at least $0.1$ lower than their
validation AUC ROC, while in the CHB-MIT dataset it was eight patients. This
is consistent with Bandarabadi et al. [10] that the optimal preictal period
for seizure prediction varies even on seizures within the same patient. The
best way to account for this problem is to train and test on as many lead
seizures’ preictal data as possible.

Patient ID |  Window Size |  PPL (seconds) |  Validation AUC ROC |  Test AUC ROC |  Test AUC PR
---|---|---|---|---|---
ID03 |  30 |  1800 |  0.793 |  0.939 |  0.681
ID04 |  60 |  3600 |  0.708 |  0.509 |  0.610
ID05 |  60 |  7200 |  0.953 |  0.918 |  0.863
ID06 |  60 |  7200 |  0.704 |  0.948 |  0.842
ID07 |  30 |  7200 |  0.722 |  0.713 |  0.745
ID08 |  15 |  7200 |  0.722 |  0.454 |  0.452
ID09 |  30 |  7200 |  0.901 |  0.944 |  0.982
ID10 |  60 |  3600 |  0.807 |  0.574 |  0.790
ID12 |  60 |  1800 |  0.981 |  0.798 |  0.544
ID13 |  60 |  1800 |  0.721 |  0.499 |  0.394
ID16 |  10 |  1800 |  0.719 |  0.423 |  0.160
ID18 |  15 |  1800 |  0.832 |  0.850 |  0.401
Table 6: Validation and test results for preictal-interictal classification with optimized hyperparameters on the SWEC-ETHZ dataset. Patient ID |  Window Size |  PPL (seconds) |  Validation AUC ROC |  Test AUC ROC |  Test AUC PR
---|---|---|---|---|---
1 |  60 |  1800 |  0.987 |  0.997 |  0.991
2 |  5 |  7200 |  0.718 |  0.982 |  0.869
3 |  10 |  1800 |  0.853 |  1.000 |  1.000
4 |  5 |  7200 |  0.376 |  0.649 |  0.037
5 |  5 |  3600 |  0.821 |  0.828 |  0.555
6 |  10 |  3600 |  0.616 |  0.858 |  0.796
7 |  5 |  7200 |  0.744 |  0.133 |  0.113
8 |  10 |  1800 |  0.999 |  0.379 |  0.378
9 |  10 |  7200 |  0.789 |  0.788 |  0.471
10 |  5 |  1800 |  0.732 |  0.686 |  0.185
11 |  30 |  3600 |  0.890 |  0.978 |  0.803
12 |  60 |  7200 |  0.917 |  0.549 |  0.974
13 |  5 |  3600 |  0.973 |  0.898 |  0.394
14 |  5 |  1800 |  0.817 |  0.139 |  0.151
15 |  30 |  7200 |  0.824 |  0.470 |  0.806
16 |  60 |  1800 |  0.686 |  0.688 |  0.294
17 |  5 |  7200 |  0.933 |  0.565 |  0.221
18 |  60 |  1800 |  0.750 |  0.820 |  0.100
19 |  30 |  7200 |  1.000 |  0.966 |  0.930
20 |  5 |  1800 |  0.983 |  0.898 |  0.497
21 |  5 |  7200 |  0.807 |  0.721 |  0.200
22 |  5 |  7200 |  0.770 |  0.397 |  0.059
23 |  30 |  1800 |  1.000 |  0.614 |  0.662
Table 7: Validation and test results for preictal-interictal classification
with optimized hyperparameters on the CHB-MIT dataset.

####  6.1.1 Comparison with Fixed Parameters

We implemented a preictal-interictal classification model with a fixed window
size of $30$ seconds and PPL of $1$ hour to compare to our tuned
hyperparameter model. The CNN model architecture is identical to the optimized
parameter implementation. This was done to explore the benefits of optimizing
hyperparameters for seizure prediction. Figures 7 show the comparison of the
two methods on the SWEC-ETHZ and CHB-MIT dataset. In general, for the SWEC-
ETHZ dataset, the optimized hyperparameter implementation performed slightly
better than the fixed parameter. In patient ID09, the optimized hyperparameter
implementation performed much better than the fixed parameter implementation.
For patient ID09, the hyperparameter tuning found a window size of $30$
seconds and a PPL of $2$ hours. It is likely that there was additional
preictal information in the extra hour of data not used in the fixed parameter
implementation. For the CHB-MIT dataset, most patients had similar results for
both the fixed and optimized hyperparameter implementations. There were a few
patients (ID $5$, $16$, $17$, $18$) that had much better results with the
optimized model. However, there were also patients (ID $9$, $22$, $23$) who
performed better with a fixed hyperparameter implementation. For these
patients, the last seizure’s optimal hyperparameters were likely different
from the optimal hyperparameters for the preceding seizures in the patient’s
dataset. Figures 9 and 10 show the comparison between the optimized and fixed
hyperparameter implementations for the SWEC-ETHZ and CHB-MIT datasets
respectively using AUC PR instead. It can be observed that the optimized
implementation generally performs better on the SWEC-ETHZ dataset in both
metrics, and that the difference is marginal in the CHB-MIT dataset. These
experiments indicate that hyperparameter tuning can potentially improve the
performance in comparison to fixed parameters.

Figure 7: AUC ROC Comparison of CNN models using optimized hyperparameters vs
fixed hyperparameters on the SWEC-ETHZ dataset.
Figure 8: AUC ROC Comparison of CNN models using optimized hyperparameters vs
fixed hyperparameters on the CHB-MIT dataset.
Figure 9: AUC PR Comparison of CNN models using optimized hyperparameters vs
fixed hyperparameters on the SWEC-ETHZ dataset.
Figure 10: AUC PR Comparison of CNN models using optimized hyperparameters vs
fixed hyperparameters on the CHB-MIT dataset.

####  6.1.2 Comparison with Other Architectures

Using a fixed hyperparameter implementation, CNN-LSTM and TCN models were
trained using a window size of $30$ seconds and a PPL of $1$ hour. A
comparison of the AUC PR for all models is shown in Figures 11 and 12. In the
SWEC-ETHZ dataset, the CNN, CNN-LSTM and TCN were the best performing model
for $3$, $5$ and $4$ patients. The CNN and CNN-LSTM performed comparably well,
and in each patient, the results were fairly similar. The TCN results were
more variable, performing well on some patients, while the other models
performed poorly. In the CHB-MIT dataset, the CNN, CNN-LSTM and TCN were the
best performing model for $7$, $6$ and $10$ patients. The TCN model performed
much better in the CHB-MIT dataset compared to the SWEC-ETHZ dataset. Overall,
the CHB-MIT results were very variable with AUC PR values, varying
considerably even within the same patient.

Figure 11: Comparison of CNN, CNN-LSTM, and TCN implementations for preictal-
interictal classification on the SWEC-ETHZ dataset.
Figure 12: Comparison of CNN, CNN-LSTM, and TCN implementations for preictal-
interictal classification on the CHB-MIT dataset.

###  6.2 Unsupervised Prediction

In the unsupervised approach, the training set only contained interictal data.
For these experiments, the hyperparameters were fixed, with a window size of
$30$ seconds and a PPL of $60$ minutes. The models were trained for $500$
epochs with a batch size of $128$ and a learning rate of $0.0005$. After
training, the models were evaluated on the test set that contained both
interictal and preictal samples. Both AUC ROC and AUC PR were used to evaluate
performance.

####  6.2.1 Comparison of Architectures

Figures 13, 14(a), and 14(b) show the anomaly detection based seizure
prediction AUC PR results for the CNN, CNN-LSTM, and TCN AEs on the SWEC-ETHZ
dataset and CHB-MIT dataset, respectively. We also show the supervised CNN
with fixed hyperparameters for comparison. It can be observed that the
performance varies significantly across different architectures and patients.
For the SWEC-ETHZ dataset, the CNN AE performed the worst across most patients
while the CNN-LSTM and TCN AEs performed relatively better, and even surpassed
the supervised implementation in some patients. In the CHB-MIT dataset, the
results vary even more, with no clear winner.

Figure 13: Comparison of unsupervised seizure prediction using different model
architectures on the SWEC-ETHZ dataset. U: unsupervised, S: supervised.

(a)

(b)

Figure 14: Comparison of unsupervised seizure prediction using different model
architectures on the CHB-MIT dataset (a) patients 1 to 11, (b) patients 12 to
23. U: unsupervised, S: supervised.

###  6.3 Best Implementations

Tables 8 and 9 show the best-performing implementation (from all experiments
with supervised and unsupervised approaches) for each patient in the SWEC-ETHZ
and CHB-MIT datasets and its corresponding AUC ROC and AUC PR. For SWEC-ETHZ
dataset, an unsupervised approach was the best-performing implementation for
$7$ out of $12$ patients. For the CHB-MIT dataset, for $16$ out of $23$
patients, supervised approaches performed better. In particular, the
supervised CNN performed the best for $8$ patients – the most of any model.
Figure 15 shows that using the CNN-LSTM was the most effective for the most
patients, with the best performance in $16$ of the $35$ patients.

Patient ID | Best Implementation | AUC PR | AUC ROC
---|---|---|---
ID03 | Supervised CNN | 0.735 | 0.930
ID04 | Unsupervised CNN-LSTM | 0.727 | 0.427
ID05 | Unsupervised CNN-LSTM | 0.991 | 0.995
ID06 | Supervised CNN-LSTM | 0.537 | 0.756
ID07 | Supervised CNN-LSTM | 0.726 | 0.787
ID08 | Unsupervised TCN | 0.501 | 0.778
ID09 | Unsupervised CNN-LSTM | 0.918 | 0.734
ID10 | Unsupervised TCN | 0.955 | 0.808
ID12 | Unsupervised CNN | 0.817 | 0.910
ID13 | Supervised TCN | 0.573 | 0.496
ID16 | Unsupervised CNN | 0.430 | 0.737
ID18 | Supervised CNN | 0.702 | 0.837
Table 8: Best-performing implementation for each patient in the SWEC-ETHZ dataset. Patient ID | Best Implementation | AUC PR | ROC AUC
---|---|---|---
1 | Supervised CNN | 0.966 | 0.988
2 | Supervised CNN | 0.737 | 0.968
3 | Supervised CNN | 1.000 | 1.000
4 | Supervised TCN | 0.064 | 0.751
5 | Unsupervised CNN-LSTM | 0.612 | 0.891
6 | Supervised CNN | 0.878 | 0.925
7 | Unsupervised CNN-LSTM | 0.115 | 0.526
8 | Supervised TCN | 0.704 | 0.773
9 | Supervised CNN | 0.660 | 0.947
10 | Supervised CNN-LSTM | 0.733 | 0.922
11 | Supervised CNN | 0.878 | 0.989
12 | Unsupervised TCN | 0.928 | 0.743
13 | Supervised CNN | 0.979 | 0.994
14 | Supervised TCN | 0.742 | 0.824
15 | Unsupervised CNN-LSTM | 0.720 | 0.526
16 | Unsupervised CNN-LSTM | 0.319 | 0.764
17 | Unsupervised CNN-LSTM | 0.414 | 0.808
18 | Supervised CNN-LSTM | 0.104 | 0.822
19 | Supervised CNN-LSTM | 0.733 | 0.964
20 | Supervised CNN-LSTM | 0.706 | 0.924
21 | Supervised CNN-LSTM | 0.477 | 0.868
22 | Unsupervised CNN-LSTM | 0.308 | 0.909
23 | Supervised TCN | 0.953 | 0.975
Table 9: Best-performing implementation for each patient in the CHB-MIT
dataset.
Figure 15: Number of times the best performing model was of each architecture
type (CNN, CNN-LSTM, TCN).

###  6.4 Discussion

We found that it is important to tune the window size and PPL to maximize
performance. Preictal-interictal classification performed slightly better in
both datasets when using an optimized hyperparameter implementation. However,
in the CHB-MIT dataset, this difference was marginal. This is likely because
of the size of the dataset. The CHB-MIT dataset has fewer data per patient
compared to the SWEC-ETHZ dataset, so it is harder to properly tune
hyperparameters that will generalize to new seizures. The CNN and CNN-LSTM
architectures performed similarly in most experiments. This is likely because
both use time-frequency transforms followed by two-dimensional convolutions
for spatial feature extraction. Even though the architectures are not exactly
the same, it is likely that both are capturing similar underlying patterns in
the data. The TCN performed fairly well and was able to get good results in
some patients when the other two models failed. Although there was not one
consistently high-performing model, it is encouraging that different
architectures were able to perform well for different patients in different
datasets.

The prediction results vary considerably across datasets, patients, and
implementations. This demonstrates the variable nature of preictal and
interictal data. To account for this, it is important to have as many lead
seizures data in a patient as possible, since preictal data is typically
scarce. A limitation of our work is that a patient requires three lead
seizures in their data to work with this method. It may not always be feasible
for a patient’s data to have at least three lead seizures, especially
considering the difficulty of data acquisition.

Anomaly detection seizure prediction performance varied significantly across
different architectures. Although supervised preictal-interictal
classification performed better overall, there were many patients where an
unsupervised approach was the best implementation. Additionally, in the SWEC-
ETHZ dataset, an unsupervised approach was the best implementation for the
majority of patients. This is likely because the SWEC-ETHZ dataset had a much
larger recording duration and interictal-preictal ratio. In autoencoder based
unsupervised approaches, the model is trained on only interictal (or normal)
EEG data and reconstruction error is used to detect the onset of seizures (or
preictal events). If the interictal data is interfered with noise, it means
that the reconstruction error may be higher even for the interictal training
data, which could result in misidentifying pre-seizures (that may also have
higher reconstruction error because they were not seen before). Alternatively,
if the interictal data is not diverse enough, then a slight variation in test
interictal data could lead to misclassifying it as pre-seizure, which may lead
to higher false alarms rate. Nevertheless, anomaly detection seizure
prediction shows promise, and it implies that with improved signal processing
and predictive modelling it may not be necessary to collect substantial
preictal data to predict a seizure.

Figure 16: Average performance of each implementation. S: supervised. U:
unsupervised.

Figure 16 shows the average performance in terms of AUC PR across all
patients. It can be observed that the supervised CNN and the supervised CNN-
LSTM performed the best on average. However, the difference in performance
across models is not large, and with a large standard deviation, it is
impossible to make a statistical claim on the best performing model. In
general, it can be observed that the supervised approaches performed better
than the unsupervised approaches with results varying across individual
patients. Our results also showed the potential of using unsupervised
approaches for seizure prediction. A major advantage is that it only uses
unlabelled interictal EEG data, which is easier to acquire and is not
dependent on an expert to annotate.

##  7 Conclusions and Future Directions

We developed several supervised approaches and introduced new unsupervised
deep learning approaches for predicting epileptic seizures. In each approach,
the main goal was to identify a preictal state (either as a binary class or
anomaly) to predict the onset of an incoming seizure. We accounted for the
variability of EEG and the preictal period by tuning the window size and PPL
using a grid search. We trained personalized models and tuned hyperparameter
using LOSO approach for better generalization of results. This method has
achieved good results on more than half of the patients. We experimented with
different supervised and unsupervised deep learning architectures on two large
EEG datasets. Our results vary across different implementations depending on
the patient. The advantage of unsupervised methods is that they do not require
preictal data to train the models; thus, alleviating the challenges around
data acquisition, and effort and time spent in labelling. However, due to the
absence of validation set in unsupervised approaches (anomaly detection), it
is non-trivial to obtain a threshold to detect pre-seizure events. Previous
work on creating proxy outliers from the normal data can be extended to obtain
an operating threshold for predicting seizures [52, 53]. We found that in many
cases, an unsupervised approach was able to get similar or even better
performance than a supervised approach; however, there was no single best
performing model. Our extensive experiments show the feasibility of supervised
and unsupervised deep learning approaches for seizure prediction. However, the
amount of preictal data per patient appears to be a crucial factor in training
generalized models.

A future extension would be to experiment with a larger range for the
hyperparameters. These parameters can also vary across implementations, so
optimized hyperparameter implementations with the CNN-LSTM or TCN architecture
as the base could be valuable. Another direction is to obtain (person-
specific) operating threshold for deployment of these algorithms in a real-
world setting. However, there are several limiting factors, including data
imbalance, and unequal (and potentially unknown) costs of false positive and
false negative in this application. These costs must be informed by clinical
practices and guidelines as it pertains to human life. Generative adversarial
networks [54] and autoencoders trained in adversarial manner [55] can be
another potential unsupervised deep learning approach to predict epileptic
seizures. Another extension would be to try different signal processing
methods and advanced CNN and sequential models, including Resnet and
Transformers. A breakthrough in reducing intervention time before the onset of
seizure would lead to development of therapeutic interventions that can
empower epilepsy patients to live without the fear or adversarial outcomes.

## Declarations

### Ethics Approval

Two publicly available datasets were used in this work; therefore, ethics
approvals were not required to use them.

### Consent to Publish

All the co-authors give their consent to publish this paper.

### Competing interests

The authors declare no competing interests of a financial or personal nature

### Authors’ contributions

Zakary Georgis-Yap and Shehroz S. Khan contributed equally in terms of
developing algorithms, evaluation of results and manuscript preparation.
Shehroz S. Khan prepared subsequent revisions of the paper. Milos R. Popovic
provided academic support and consultation throughout the study.

### Funding

This work is supported by the Natural Sciences and Engineering Research
Council of Canada and Data Science Institute, University of Toronto.

### Availability of data and materials

The datasets used in the paper are publicly available.

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