# Adapting Action Recognition Neural Networks for Automated Infantile Spasm Detection

Samuel Diop¹, Nouha Essid¹, François Jouen¹, Jean Bergounioux, and Imen Trabelsi¹

**Abstract**— Infantile spasms are a severe epileptic syndrome characterized by short muscular contractions lasting from 0.5 to 2 seconds. They are often misdiagnosed due to their atypical presentation, and treatment is frequently delayed, leading to stagnation or regression in psychomotor development and significant cognitive and motor sequelae. One promising approach to addressing this issue is the use of markerless computer vision techniques. In this paper, we introduce a novel approach for recognizing infantile spasms based exclusively on video data. We utilize an expanded 3D neural network pre-trained on an extensive human action recognition dataset called Kinetics. By employing this model, we extract features from short segments of varying sizes sampled from seizure videos, which allows us to effectively capture the spatio-temporal characteristics of infantile spasms. We then apply multiple classifiers to perform binary classification on these extracted features. The best system achieved an average area under the ROC curve of 0.813±0.058 for a 3-second window.

**Index Terms**— Infantile spasm, video-based monitoring, X3D-M, dimension reduction, action recognition.

## I. INTRODUCTION

In 2005, the International League Against Epilepsy (ILAE) introduced a definition of epilepsy that has since become

Received 19 March 2024; revised 2 July 2024 and 6 September 2024; accepted 28 September 2024. Date of publication 2 October 2024; date of current version 9 October 2024. This work was supported by AXA and Oso-Ai. The work of Samuel Diop was supported by Oso-Ai. The work of Imen Trabelsi was supported by AXA Research Fund. (Corresponding author: Samuel Diop.)

This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the Ethical Scientific Committee for Research, Studies, and Evaluation in the Health Domain (CESREES), under Protocol No. 20240119163135.

Samuel Diop, François Jouen, and Imen Trabelsi are with the Laboratoire Cognitions Humaine et Artificielle (CHArt), Ecole Pratique des Hautes Etudes, PSL Research University, 75006 Paris, France, and also with the Systèmes Intelligents en Neurologie et Rèanimation Pèdiatrique, R2P2, 92380 Garches, France (e-mail: samuel.diop@etu.ephe.fr; francois.jouen@ephe.psl.eu; imen.trabelsi@ephe.psl.eu).

Nouha Essid is with the Pediatric Neurology and Intensive Care, Raymond Poincaré University Hospital, APHP, 92380 Garches, France (e-mail: nouha.essid@aphp.fr).

Jean Bergounioux is with the Pediatric Neurology and Intensive Care, Raymond Poincaré University Hospital, APHP, 92380 Garches, France, also with the Infection et Inflammation Chronique, Laboratoire 2IC, University Versailles Saint-Quentin en Yvelines (UVSQ), 78035 Versailles, France, and also with the Systèmes Intelligents en Neurologie et Rèanimation Pèdiatrique, R2P2, 92380 Garches, France (e-mail: jean.bergounioux@aphp.fr).

Digital Object Identifier 10.1109/TNSRE.2024.3472088

integral to its clinical diagnosis [15]. According to this definition, epilepsy is a disorder marked by an enduring predisposition to generate epileptic seizures and includes a spectrum of neurobiological, cognitive, psychological, and social consequences [5]. From a clinical perspective, this disorder requires the occurrence of at least one epileptic seizure [7]. Epidemiologically, epilepsy is defined by the manifestation of two or more unprovoked seizures separated by at least 24 hours [4].

An epileptic seizure itself is characterized as a transient event involving abnormal, excessive, or synchronous neuronal activity in the brain, manifesting in various symptoms such as altered consciousness or involuntary motor actions [6].

Epilepsy affects approximately 50 million people worldwide, with a disproportionate impact on developing countries, where about 85% of individuals with epilepsy reside. Each year, the global incidence of epilepsy includes roughly two million new cases [30]. It is estimated that effective treatment could enable up to 70% of individuals with epilepsy to lead normal lives [8]. However, the global majority of patients lack access to such care, a phenomenon attributed to the distinctive nature of epilepsy among neurological disorders, as a significant number of patients do not manifest permanent physical signs. Consequently, its diagnosis often hinges on patient history or the incidental observation of a seizure. The diagnosis of epilepsy largely relies on discretionary judgment, and this can vary significantly based on the diagnostician's expertise and experience, as well as the quality of information provided by witnesses. As of 2000, epilepsy contributed over seven million disability-adjusted life years (DALYs), representing 0.5% of the global burden of disease [1]. The research conducted in this paper is focused on the study of epilepsy in pediatric patients. Pediatric epilepsy can manifest through different types of seizures, including absence seizures, tonic-clonic seizures, focal seizures, and infantile spasms [32]. Infantile spasms, also known as West syndrome, are defined by the occurrence of epileptic episodes, themselves characterized by the appearance of very particular motor seizures made of rapid and repeated contractions ranging from 0.5 to 3 seconds, qualified as spasms. Most often, the spasms are expressed by brutal contractions of the body. Infantile spasms can start from the age of 2 months to 1 year. These symptoms are often diagnosed as colic, reflux, or a startle reflex due to their atypical epileptic presentation [28]. However, a misdiagnosis can delay treatment and cause dramatic consequences for the child,
ranging from a halt or regression in psychomotor development to substantial cognitive and motor impairments. The exclusive means for a neuro-pediatrician to achieve precise diagnosis of infantile spasms is through observation facilitated by video recording synchronized with an Electroencephalogram (EEG). During each movement correlating with a spasm, the neuro-pediatrician systematically assesses the EEG to ascertain its alignment with epileptic manifestations. In instances of infantile spasms, the EEG of a child typically manifests a disorderly pattern recognized as hypsarrhythmia, characterized by a high-amplitude, disordered brain wave pattern [33].

This method, although considered the gold standard [20], [29], has notable limitations: (i) it necessitates the hospitalization of the child without a guarantee of observing an infantile spasm, (ii) not all children tolerate well the electrodes used for the EEG, introducing the possibility of erroneous measurements due to the infant's movements and electrode disconnection, and (iii) the associated equipment can be expensive, posing challenges to accessibility even in developed countries. As is the case with all forms of epileptic seizures, the accuracy of the diagnosis relies predominantly on the thoroughness of the patient's history of previous epileptic episodes. Elger et al. [11] highlight that patients are seldom reliable sources of information. Implementing an automated video detection system, suitable for both home and hospital settings, could significantly enhance the diagnostic process and facilitate follow-up treatment. In this paper, we propose an automatic binary classification system for distinguishing infantile spasms from non-infantile spasms. This system relies exclusively on the motion characteristics of seizures extracted from the video component of our EEG-video dataset, collected at Raymond Poincaré Hospital Assistance Publique-Hôpitaux de Paris (APHP). The system employs transfer learning from a large dataset of action recognition Kinetics-400 [9] to extract spatio-temporal features from video sequences and classify them as infantile spasm or non-infantile spasm.

This paper initiates with an examination of the existing literature on the automated detection of epileptic seizures in Section II. Section III outlines our novel Action Recognition for Infantile Spasms Detection system, and Section IV is dedicated to presenting our findings and the methodological aspects of our evaluation. The discussion is elaborated in Section V. Finally, our conclusions are summarized in Section VI.

## II. RELATED WORKS

Deep learning has seen significant advancements in the automatic detection of various motion disorders beyond epilepsy. For instance, research on Parkinson's disease has leveraged pose-based tremor classification from video data to aid diagnosis. Zhang et al. [41] demonstrated a robust approach to analyze body poses and classify tremors associated with Parkinson's disease. In this work, the authors introduce SPAP-Net, a new interpretable method for diagnosing Parkinson's disease using consumer-grade RGB video recordings. The proposed SPAPNet begins by extracting 2D skeleton features from video sequences. The core of SPAPNet is a graph neural network integrated with a spatial attention mechanism.

Similarly, in the realm of neurodevelopmental disorders, deep learning has been employed to diagnose conditions such as Autism Spectrum Disorder (ASD). Cai et al. proposed an advanced deep learning framework for video-based diagnosis of ASD. Initially, the proposed system extracts head-related features from the videos. It then employs a head-related characteristic attention mechanism to selectively focus on the most relevant features. A CNN is then used to analyze the temporal information from these features. The framework's effectiveness is validated using a custom video dataset of 82 children, demonstrating superior performance in diagnosing ASD compared to existing methods. Transitioning from these broader applications, we focus on the specific domain of epileptic seizure detection. Studies have been conducted on the automatic detection of various epileptic manifestations in both adults and infants. The majority of these studies concentrate on myoclonic and focal seizures, which represent the most prevalent epileptic manifestations. Myoclonic episodes are characterized by more prolonged and intense motor manifestations, while focal seizures exhibit an extended temporal footprint.

For adult epileptic seizures, many studies have been dedicated to creating a standard system of data acquisition NeuroKinect 3.0 [31]. Additionally, there has been significant exploration into end-to-end trainable systems, encompassing Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The research conducted by [24] underscored that action recognition models, specifically utilizing infrared (IR) and depth (3D) - I3D videos [9], serve as a robust foundation for transfer learning. This network architecture has demonstrated proficiency in extracting spatio-temporal information from videos. Their method achieved a ROC-AUC of 0.90 ± 0.04 and an F1 score of 0.844 ± 0.042. The focus of this study was on the binary classification of Frontal Lobe Epilepsy (FLE) and Temporal Lobe Epilepsy (TLE). In [3], the authors introduce a multi-modal analysis approach for the quantitative classification of patients with mesial temporal lobe (MTLE) and extra-temporal lobe (ETLE) epilepsy. They focus on leveraging visual motion clues, such as facial expressions and pose dynamics. The proposed deep learning model combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to automatically extract spatio-temporal features from recorded videos. The dataset includes videos from 12 patients with MTLE and 6 patients with ETLE in an Australian hospital. Experimental results demonstrate the effective recognition and tracking of facial semiology and body movements, providing valuable evidence for epilepsy type identification. The fusion model achieves an average test accuracy of 92.10% in multi-fold cross-validation, and a leave-one-subject-out cross-validation.

Building upon this research, the authors introduced a novel approach leveraging the depth sensor [22] integrated into the NeuroKinect 3.0 system [31]. This new end-to-end pipeline, also based on I3D, incorporates a semi-automatic preprocessing step and achieved an F1 score of 0.833±0.061 in the two-class classification and a 0.763 ± 0.083 F1 score in the three-class classification (FLE, TLE, and non-epileptic). Unfortunately, the ROC-AUC score was not disclosed. In [40],
the authors assess the feasibility and efficacy of neu-
ral network-based video classification seizure detection for
patients with generalized tonic-clonic seizures (GTCS). The
assessment of seizure detection performance was conducted
based on individual video frames using a CNN and on video
sequences utilizing a combined CNN-LSTM network. The
findings indicate that the CNN-LSTM networks surpass the
performance of GTCS detection based on individual frames,
achieving a mean sensitivity of 88% and a mean specificity of
92% across the patient cohort.

As previously mentioned, detecting seizures in infants
presents challenges, and researchers have explored numerous
approaches. However, currently, the quantifying movements
method yields the most consistent results. In this context, some
works are particularly noteworthy.

First, in the study by Karayiannis et al. [23], the primary
objective was to develop a seizure detection system by train-
ing different architectures of shallows neural network using
quantitative motion information extracted from short video
segments depicting neonatal seizures of the myoclonic and
focal clonic types, as well as random infant movements. The
proposed methods exhibit a sensitivity and specificity of > 0.9
but their applicability for real-time monitoring is limited due
to its computation-heavy feature engineering.

In [26], the authors identified the suitable regions of interest using the optical flow for real-time neonatal seizure detection. Their method comprised three steps: first, detecting the most intense vector field generated by the optical flow algorithm; second, tracking this region using the template matching technique; and finally, computing the 1D signal from the region to detect clonic movement. The periodicity of the signal was identified as indicative of a clonic seizure. For the detection of myoclonic seizures (known to be non-repetitive), a window average algorithm was employed. The proposed approach facilitates the differentiation of pathological movements from random movements.

In [10], the authors proposed a unified approach to motion analysis, for the automatic detection of neonatal diseases characterized by specific movement patterns. Their approach involves analyzing data from multiple video sensors placed around a patient, extracting relevant motion signals, and estimating their possible periodicity. Data fusion was considered to aggregate signals from multiple sensors. The performance of the proposed contactless detection algorithms is evaluated using real video recordings of newborns in terms of sensitivity, specificity, and ROC curves. The results demonstrate that a video processing-based system can effectively detect the specified patterns, with performance improving as the number of sensors increases. While our approach to epileptic seizure detection shares several characteristics with motion disorder analysis, the unique challenges of epilepsy necessitate distinct methodologies. For instance, feature engineering for epilepsy detection using video data is particularly challenging due to the complexity of visual information and the rapid, subtle nature of epileptic seizures, including spasms that occur very rapidly. Similarly, utilizing skeleton data extracted from video poses challenges that hinder its suitability for our task. Skeleton extraction itself is a complex process prone to

errors, which can compromise the accuracy of body movement
representation during seizures. This method's reliance on sim-
plified body posture and movement data may overlook subtle
nuances critical for spasms detection, potentially resulting in
missed diagnoses or false alarms. Furthermore, all experiments
were conducted in a controlled environment without other
individuals present during recordings, except for the patient
featured in the video. Consequently, these studies cannot lever-
age the routinely captured videos available in every epilepsy
unit. In summary, while our approach aligns with broader
principles of motion disorder analysis, addressing the specific
challenges of epilepsy requires careful consideration of these
factors to ensure accurate and reliable seizure detection.

### III. INFANTILE SPASMS DETECTION USING FINE-TUNED X3D-M

We propose a new approach for Infantile Spasms Detection. Our method is composed of four distinct steps, as shown in Figure. 1): (i) Medical Data acquisition. (ii) Feature Extraction using X3D-M. (iii) Dimension Reduction employing PCA, UMAP, or the Identity transformation. (iv) Classification utilizing KNN, RF, XGBOOST, SVM, and MLP.

#### A. Medical Data Acquisition Protocol

Medical data acquisition was a central point of the study
due to the lack of a common infant EEG video dataset.
For this, we collected in the neuro-pediatric database at
APHP Raymond Poincarè Hospital 136 minutes of video-EEG
exam annotated by neuro-pediatricians. This dataset comprises
7 patients from 10 weeks to 4 years and 9 months old,
a total of 96 independent videos took at patients' different
ages. The 96 videos allow us to observe and analyze a
wide variety of spasm occurrences, intensities, and durations
across different times and conditions. Informed consent was
obtained from each study participant's caregivers or from
capable patients aged over 18 years for the publication of
their data. Each patient's dataset includes multiple videos
captured at different ages, showcasing episodes of spasms and
other epileptic manifestations. All videos were initially taken
for clinician evaluation purposes, yielding real-world data
acquired in an uncontrolled environment. Each video follows
a similar setup: (i) The camera is fixed to the ceiling and
can be remotely adjusted by the technician for the clinician's
evaluation. (ii) The patient is on the bed, fully visible by the
camera. (iii) The patient can interact with medical personnel or
his parents during the video. (iv) Illumination varies during the
video, especially when the light is turned off and the camera
switches from RGB to IR (this often occurs during medical
interventions). The camera resolution and frames per second
(FPS) may vary across the videos due to changes in equipment
over the years and the use of different camera types. In our
dataset, we can note 4 resolutions (704 × 576, 800 × 400,
1280 × 720, 1920 × 1080) and two sample rates (25 FPS
or 50 FPS).

An example of the video recording scene is given in
Figure 2.
<figure><img src="image_1.png" /><figcaption>Fig. 1. Action Recognition for Infantile Spasms Detection System: (a): Data acquisition. (b). Feature extraction using X3D-M. (c): Dimension Reduction. (d): Classification using several algorithms. (e): Spasms vs Non-Spasms.</figcaption></figure>

<figure><img src="image_2.png" /><figcaption>Fig. 2. Video acquisition system: on the left, the setup camera IR/RGB and the bed; on the right, example of a video recording of a 5-month-old male patient using this camera setup.</figcaption></figure>

## B. Feature Extraction

From the APHP Raymond Poincaré hospital dataset, we extracted all infantile spasm occurrences, totaling 96 occurrences, each lasting between 0.5 to 2 seconds. The remaining instances were labeled as non-spasms. This resulted in highly

imbbalanced classes, with spasms being the minority class. Addressing this class imbalance is necessary for a more robust and effective analysis. Firstly, we changed the video sample rate to 25 FPS, as 97% of the samples were originally recorded at this rate. Secondly, we split our dataset into different temporal resolutions or windows each lasting 1, 2, 3, 4, or 5 seconds. This approach allows us to explore different amounts of information surrounding infantile spasms within the dataset. Black frames completed every leftover from the slicing window to reach the desired length. This led to 5 unbalanced datasets. To remediate this issue, downsampling was used. From non-spasm videos, random sampling was done to reach an equivalent size with the number of spasm videos, as illustrated in Table I. These datasets are referenced as w1, w2, w3, w4, and w5. The entire process is illustrated in Figure 3.

Following the extraction of different datasets, each corresponding to various window lengths, feature engineering is performed on each dataset. Feature engineering often requires domain expertise to manually design relevant features for a specific task. However, in deep learning, models acquire hierarchical representations of raw input data through multiple layers of abstraction. Nevertheless, this process becomes
<figure><img src="image_3.png" /><figcaption>Fig. 3. Pre-processing steps for building the five different datasets, each one related to a specific temporal window.</figcaption></figure>

<table><caption>TABLE I
SPASM AND NON-SPASM NUMBER IN THE BALANCED DATASETS</caption><thead><tr><td>Temporal window (s)</td><td>Number of non-spasm</td><td>Number of spasm</td></tr></thead><tbody><tr><td>1</td><td>143</td><td>146</td></tr><tr><td>2</td><td>121</td><td>124</td></tr><tr><td>3</td><td>116</td><td>122</td></tr><tr><td>4</td><td>100</td><td>105</td></tr><tr><td>5</td><td>86</td><td>89</td></tr></tbody></table>

challenging with limited amounts of data. To address this challenge, we propose fine-tuning a pre-trained neural network model on our spasms detection-specific Raymond Poincarè dataset. For that, We use the X3D-M [13], pre-trained on the DeepMind Kinetics-400 dataset [25], a large-scale collection of approximately 300,000 video clips covering 400 human action classes, each lasting around 10 seconds. The X3D-M model, part of the X3D (Xception for Video Recognition) family, is designed for efficient video recognition. The resulting architecture is denoted as X3D (Expand3D), signifying its expansion from the 2D space into the 3D space-time domain. X3D-M follows a ResNet structure for basis network instantiation and the Fast pathway design of SlowFast networks [14]. To inflate the base X2D network into X3D-M, expansion factors were applied as follows: $γ_t = 5$, $γ_τ = 16$, $γ_s = 2$, $γ_w = 1$, $γ_b = 2.25$, $γ_d = 2.2$. This choice was made for multiple reasons.

The selection of X3D as the foundation for spasm recognition from video was driven by its demonstrated superiority in action recognition tasks, which closely align with our specific application. X3D outperforms other architectures in both the two-stream and 3D convolution network families in terms of robustness and efficiency, as shown by [13]. This advantage is particularly relevant for spasm recognition, where subtle and rapid movements must be accurately detected and classified. The efficacy of 3D convolutional networks in capturing spatiotemporal features has been well-established in the literature [9], [18], [37]. Performance metrics played a crucial role in our decision. The X3D-M variant achieves a top-5 accuracy of 91.7% on the Kinetics-400 dataset with a computational cost of 4.73 GFLOPs, while X3D-L reaches 92.5% top-5 accuracy at 18.37 GFLOPs [13]. This comparison illustrates the favorable trade-off between speed and reliability offered by the X3D architecture. The importance of such efficiency in medical applications has been emphasized by [12] and [38]. X3D's design, derived from MobileNets V1 and V2 [19], [34],

provides a significant speed advantage crucial for real-time or near-real-time spasm analysis. This efficiency is vital in clinical settings where prompt recognition of spasms can impact patient care, as highlighted by [21] in their work on epileptic seizure detection. Importantly, X3D achieves this speed without compromising performance, having outperformed all other networks in its family across various action recognition benchmarks [13]. A significant factor in our choice was the challenge presented by frequent transitions between RGB and IR imaging modalities in our application. The assumption of constant lighting, critical for optical flow techniques such as those used in I3D [9], is compromised in our setting due to these variations. This limitation of optical flow in variable lighting conditions has been noted by [16] and [36]. X3D, which does not rely on such assumptions, proves to be a more suitable option for our application. The robustness of X3D in capturing spatio-temporal information makes it particularly well-suited for detecting the nuanced movements associated with spasms. While other 3D convolutional networks like I3D [9], C3D [37], and S3D [39] have their merits, X3D's balanced approach to computational efficiency and recognition accuracy gives it a clear edge for our specific use case of spasm recognition from video. The importance of such balance in medical image analysis has been emphasized by [27] in their comprehensive review. In conclusion, X3D's combination of high accuracy, computational efficiency, and robustness in variable imaging conditions makes it the optimal choice for spasm recognition in clinical environments. Its ability to detect subtle, rapid movements without relying on constant lighting assumptions positions it as a superior solution for our research objectives. We conducted fine-tuning on X3D-M without making adjustments to its parameters, a process that typically involves utilizing a pre-trained neural network model and directly leveraging its learned feature representations for a new task without modifying the model's weights. Fine-tuning without parameter adjustments proves to be a valuable approach in transfer learning scenarios. This is particularly applicable when updating the pre-trained model's weights may not be necessary or desired, especially when dealing with constraints such as limited computational resources. Moreover, it is advantageous when the pre-trained model effectively captures pertinent features for the new task at hand. Furthermore, we find this approach especially relevant in our context as we consider that Kinetics, a dataset renowned for its portrayal of rapid actions, may align with the spasms observed in
our dataset. In accordance with the input requirements of X3D-M, we converted the segmented video to a resolution of 224 × 224 with a uniform temporal sub-sampling of 16. The last fully connected layers of X3D-M were omitted, and previous layers were frozen to extract the spatio-temporal features of a video segment, specifically for Kinetics video data. Subsequently, the video segment input was transformed into an embedding vector containing 2048 elements. Following this, the embedding vector was subjected to the dimension reduction step within our system.

### C. Dimension Reduction

In this study, we incorporated a dimension reduction module into our system to mitigate the risk of overfitting arising from the high dimensionality of the data and the limited size of the dataset. This module can take three forms: a PCA module, a UMAP module, or an Identity module.

For the PCA module, after inspection of the variance of the data, the number of components was fixed to 3. This component number contains 95% variance of feature vectors. For the UMAP module, lacking a consensus method for hyperparameter tuning, we employed a grid search algorithm to explore the parameter space. This exploration encompassed the number of components, ranging from 3 to 103 with a step of 20, and the number of neighbors, varying from 15 to 150 with a step of 20. The Identity module aims to test our system without a dimension reduction module by replacing PCA or UMAP with an Identity transform. This enables the possibility of measuring the impact of the dimension reduction algorithm on this dataset.

### D. Classification

Several classifiers, namely k-Nearest Neighbors (KNN), Random Forest (RF), eXtreme Gradient Boosting (XGBOOST), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) were used. These models were chosen for multiple reasons. KNN, SVM, RF, MLP and XGBOOST are less data-intensive and are more suited for a small dataset such as the Raymond Poincarè Hospital dataset. These classifiers replaced the end of the standard fully connected layer of X3D-M, which consists of an input layer of 2048 neurons reduced to 400 neurons. The grid search algorithm was used for all presented models to select their hyperparameters.

In the KNN module, we tested different parameters, including the number of neighbors (5, 10, or 20), the weighting method (uniform or inverse distance weighting), and the power parameter for the Minkowski metric. When $p = 1$, it corresponds to the Manhattan distance (11), and when $p = 2$, it represents the Euclidean distance (12). In the RF module, we experimented with different parameters, including varying the number of estimators from 50 to 600 with a step of 100, testing the number of bins within the range of 10 to 50 with a step of 20, choosing the split criterion between gini and entropy, and employing two tree construction methods. The first method involved building each tree on a bootstrapped sample with replacement, while the second method used the

<table><caption>TABLE II
FLATTEN VECTORS SIZE FOR EACH DATASET</caption><thead><tr><td>Datasets</td><td>Vector size</td></tr></thead><tbody><tr><td>w1</td><td>3763200</td></tr><tr><td>w2</td><td>7526400</td></tr><tr><td>w3</td><td>11289600</td></tr><tr><td>w4</td><td>15052800</td></tr><tr><td>w5</td><td>18816000</td></tr></tbody></table>

entire dataset to construct each tree. In the XGBOOST module, we tested different parameters, including varying the number of estimators from 50 to 600 with a step of 100, and testing the number of bins within the range of 10 to 50 with a step of 20. In the SVM module, we tested different parameters, including the kernel type (sigmoid, radial basis function, or linear), and the regularization parameter ranging from 1 to 10 with a step of 1. For the MLP module, we opted for a shallow network comprising 2 fully connected layers: the first layer goes from 2048 to 400, while the second layer transitions from 400 to 2. This choice was made to imitate the fully connected layer structure of X3D-M.

During experimentation, we varied several parameters, such as the number of epochs (100, 200, or 400 epochs) and learning rates (0.1 or 0.01). We employed the Cross-Entropy loss function and utilized a batch size of 128 for training.

## IV. RESULTS

The Infantile Spasms Detection was trained and validated on 5 temporal windows. For cross-validation, a 5 K-fold approach was employed, resulting in a total of 23,490 tests, each with 5 K-fold iterations. The 80-20 ratio refers specifically to the distribution of spasm events. In our analysis, spasm and non-spasm events are treated as independent variables both within a single patient's video and across videos from different patients. This approach is justified because these events are temporally independent; for example, videos from the same patient may be captured at different ages, providing independent samples. For the purpose of our study, we divided the dataset such that videos from 5 patients were used for training, while videos from the remaining 2 patients were reserved for testing. This strategy was implemented to minimize any potential bias that could arise from having overlapping patient data in both training and testing sets. For all the experiments, the developed systems were evaluated based on the average AUC-ROC and their average standard deviation (std). The dataset was divided into an 80-20 ratio for the training set and test set. In each fold, a new 80-20 split was chosen for testing, and the results were either presented individually or averaged over the 5 folds. To validate our method, we undertake a comparative analysis of several conventional classifiers when applied directly to our dataset. To do that, we flattened our dataset from a ($w \times 25, 224, 224, 3$)-shaped tensor, with $w$ representing the window size in seconds, to a vector of size $w \times 25 \times 224 \times 224 \times 3$, as illustrated in Table II.

A grid search algorithm was employed to select the hyperparameters for all the models. The best-performing
<table><caption>TABLE III
EXPERIMENTAL RESULTS COMPARING DIFFERENT CLASSIFIERS BASED ON AUC-ROC FOR DIFFERENT TEMPORAL WINDOWS. "N/A" INDICATES THAT NO RESULTS WERE OBTAINED. THE BEST RESULTS IN THE TABLE ARE HIGHLIGHTED IN BLUE COLOR</caption><thead><tr><th>Datasets</th><th>KNN</th><th>RF</th><th>SVM</th><th>MLP</th><th>XGBoost</th></tr></thead><tbody><tr><td>w1</td><td>N/A</td><td>0.681±0.047</td><td>0.593±0.051</td><td>0.5±0.0</td><td>N/A</td></tr><tr><td>w2</td><td>N/A</td><td>0.670±0.053</td><td>0.626±0.060</td><td>N/A</td><td>N/A</td></tr><tr><td>w3</td><td>N/A</td><td>0.711±0.042</td><td>N/A</td><td>N/A</td><td>N/A</td></tr><tr><td>w4</td><td>N/A</td><td>0.678±0.075</td><td>N/A</td><td>N/A</td><td>N/A</td></tr><tr><td>w5</td><td>N/A</td><td>0.696±0.074</td><td>N/A</td><td>N/A</td><td>N/A</td></tr></tbody></table>

hyperparameters were then chosen for each model. Table III gives a comparison between the different classifiers.

In Table III, it is evident that KNN and XGBOOST fail to compute any performance metrics for vectors of different sizes. Regarding MLP, results indicate that only w1 vectors could be computed, revealing the model's inability to establish an appropriate decision boundary to distinguish between spasm and non-spasm instances. For SVM, both w1 and w2 exhibit similar performance. Remarkably, RF stands out as the only model capable of defining a decision boundary between spasm and non-spasm instances, even with this brute-force approach.

Table IV describes the results obtained from our proposed method for building a spasm detection system. We can see two subgroups form. Firstly, we consider the combination of the UMAP algorithm with either SVM or MLP. It appears that this group greatly benefits from UMAP's capability for dimension reduction. These results can be explained in the case of SVM by the incapacity of SVM to learn decision boundaries on high dimensional space as ours (an embedding vector of 2048 dimension). In the case of MLP, the inability to learn a boundary decision is due to the number of spasm examples available at the moment, as deep learning methods are known to need a considerable amount of data for efficient results. At the top of the ranking, we found our second group, comprising the Identity module combined with either RF or XGBoost. The results imply that XGBoost and RF may use the plain capacity of the information in the X3D-M output embedding vector, as these two are consistently the most robust methods on the dataset. These classifiers obtain the best score on each of the datasets. More specifically, RF obtains the best score on four datasets, while XGBoost outperforms on one dataset. For the w3 dataset, RF is very close to XGBoost, but the average variance on the 5-fold is more significant compared to XGBoost. If we compare this result to the Table III, we can see the XGBoost and RF using X3D-M and identity consistently outperform any brute force approach explored in Table III. The best model is obtained using no dimension reduction and XGBoost with an AUC-ROC score of 0.813 ± 0.058.

The best model, which does not use dimensionality reduction and employs XGBoost, achieved an AUC-ROC score of 0.813 ± 0.058. The parameters for XGBoost are ten bins and 450 estimators. For our results, we focus the evaluation on the specificity and sensitivity of the model, as these metrics are crucial in medical applications. Using the ROC-AUC is indicated to obtain a balanced view of the different comparisons.

This approach will also aid future researchers, as in the field of ML for epilepsy detection, the ROC-AUC is the primary metric used.

## V. DISCUSSION

In epilepsy monitoring units, neuro-pediatricians depend on the subjective evaluation of EEG-video data. In order to support the diagnosis of infantile spasms and reduce the burden of data evaluation, it is beneficial to utilize Machine Learning (ML) for automatic spasm detection and classification. Employing ML may not only enhance diagnostic accuracy but also facilitate early intervention, ultimately leading to improved patient care. The dataset collected at Hospital Raymond-Poincaré using the standard EEG-video exam system provides invaluable support for supervised machine learning, thanks to its labeled clinical data. This is particularly advantageous as there is no need to acquire new equipment as the NeuroKinect 3.0 [31] system. Instead, existing EEG-Video databases can be readily utilized. In our paper, by employing a pre-trained X3D-M and an XGBoost classifier, the system reached an AUC-ROC of 0.813 ± 0.058. This represents the first proof of concept for automatically detecting infantile spasms using only video data.

Several studies have explored epilepsy detection from video data: In [2], researchers present a computer vision approach for identifying abnormal seizures in natural clinical settings. Additionally, the authors in [22] introduce a deep learning method for near real-time classification of epileptic seizures using 3D video action recognition. While other works have focused on epilepsy detection in children, they primarily utilize EEG data, such as in [17], where authors develop a comprehensive framework for EEG-based seizure detection using deep learning techniques on neonatal EEG signals. Similarly, the authors in [35] employ several classifiers for the classification of raw EEG signals into seizure and non-seizure categories.

To our knowledge, the use of video data alone for epilepsy investigation, particularly in detecting spasms in children, remains unexplored. Our study represents the first attempt to detect spasms through fine-tuning a pre-trained model. Given the absence of prior results, our work serves as a foundation for establishing benchmarks for future comparisons.

However, some limitations should be noted. Up to this point, the Raymond-Poincaré Hospital dataset is still limited in infantile spasm data. However, it's important to note that this tool is not ready for real-world usage in hospitals as the system still needs to reach the 95% mark. Moreover, as seen in Figure 4, this visualization reveals an important limitation of our approach: some infantile spasms generate little to no visible movement, being detectable only through EEG. This highlights a key challenge in relying solely on video data for detection.

Other limitations underscore the challenges and considerations inherent in utilizing video for epilepsy detection in children. Firstly, the sensitivity of video-based detection algorithms may vary across different seizure types, potentially
<table><caption>TABLE IV
EXPERIMENTAL RESULTS COMPARING THE BEST CLASSIFIERS ACROSS VARIOUS TEMPORAL WINDOWS (W1, W2, W3, W4, W5) BASED ON AUC-ROC. THE BEST RESULTS IN THE TABLE ARE HIGHLIGHTED IN BLUE COLOR</caption><thead><tr><th>Datasets</th><th>Dimension reduction</th><th>Classifier</th><th>Average AUC-ROC</th><th>Average Accuracy</th><th>Average Recall</th><th>Average F1 score</th></tr></thead><tbody><tr><td rowspan="5">w1</td><td>Identity</td><td>KNN</td><td>0.665 ± 0.027</td><td>0.610 ± 0.045</td><td>0.652 ± 0.067</td><td>0.623 ± 0.050</td></tr><tr><td>Identity</td><td>RF</td><td>0.714 ± 0.029</td><td>0.637 ± 0.054</td><td>0.610 ± 0.072</td><td>0.627 ± 0.067</td></tr><tr><td>UMAP</td><td>SVM</td><td>0.646 ± 0.085</td><td>0.606 ± 0.040</td><td>0.636 ± 0.074</td><td>0.617 ± 0.060</td></tr><tr><td>Identity</td><td>XGBOOST</td><td>0.691 ± 0.026</td><td>0.619 ± 0.04</td><td>0.632 ± 0.094</td><td>0.622 ± 0.055</td></tr><tr><td>UMAP</td><td>Fine Tune X3D-M</td><td>0.681 ± 0.050</td><td>0.609 ± 0.082</td><td>0.661 ± 0.156</td><td>0.625 ± 0.073</td></tr><tr><td rowspan="5">w2</td><td>Identity</td><td>KNN</td><td>0.736 ± 0.044</td><td>0.649 ± 0.058</td><td>0.638 ± 0.105</td><td>0.643 ± 0.078</td></tr><tr><td>Identity</td><td>RF</td><td>0.798 ± 0.050</td><td>0.714 ± 0.061</td><td>0.778 ± 0.064</td><td>0.734 ± 0.045</td></tr><tr><td>UMAP</td><td>SVM</td><td>0.707 ± 0.036</td><td>0.637 ± 0.0374</td><td>0.734 ± 0.037</td><td>0.671 ± 0.040</td></tr><tr><td>Identity</td><td>XGBoost</td><td>0.793 ± 0.055</td><td>0.7061 ± 0.042</td><td>0.753 ± 0.052</td><td>0.722 ± 0.030</td></tr><tr><td>UMAP</td><td>Fine Tune X3D-M</td><td>0.707 ± 0.058</td><td>0.653 ± 0.029</td><td>0.857 ± 0.077</td><td>0.712 ± 0.035</td></tr><tr><td rowspan="5">w3</td><td>Identity</td><td>KNN</td><td>0.741 ± 0.075</td><td>0.648 ± 0.078</td><td>0.737 ± 0.055</td><td>0.681 ± 0.066</td></tr><tr><td>Identity</td><td>RF</td><td>0.817 ± 0.066</td><td>0.769 ± 0.053</td><td>0.820 ± 0.074</td><td>0.782 ± 0.056</td></tr><tr><td>UMAP</td><td>SVM</td><td>0.722 ± 0.073</td><td>0.656 ± 0.101</td><td>0.803 ± 0.092</td><td>0.705 ± 0.089</td></tr><tr><td>Identity</td><td>XGBoost</td><td>0.813 ± 0.058</td><td>0.732 ± 0.063</td><td>0.784 ± 0.057</td><td>0.748 ± 0.067</td></tr><tr><td>UMAP</td><td>Fine Tune X3D-M</td><td>0.737 ± 0.086</td><td>0.631 ± 0.060</td><td>0.847 ± 0.064</td><td>0.701 ± 0.048</td></tr><tr><td rowspan="5">w4</td><td>Identity</td><td>KNN</td><td>0.709 ± 0.058</td><td>0.650 ± 0.052</td><td>0.639 ± 0.077</td><td>0.686 ± 0.045</td></tr><tr><td>Identity</td><td>RF</td><td>0.761 ± 0.060</td><td>0.678 ± 0.060</td><td>0.748 ± 0.082</td><td>0.704 ± 0.048</td></tr><tr><td>PCA</td><td>SVM</td><td>0.712 ± 0.064</td><td>0.634 ± 0.031</td><td>0.819 ± 0.091</td><td>0.694 ± 0.043</td></tr><tr><td>Identity</td><td>XGBoost</td><td>0.760 ± 0.060</td><td>0.673 ± 0.078</td><td>0.730 ± 0.100</td><td>0.695 ± 0.066</td></tr><tr><td>UMAP</td><td>Fine Tune X3D-M</td><td>0.706 ± 0.043</td><td>0.615 ± 0.036</td><td>0.837 ± 0.091</td><td>0.687 ± 0.050</td></tr><tr><td rowspan="5">w5</td><td>Identity</td><td>KNN</td><td>0.716 ± 0.084</td><td>0.634 ± 0.075</td><td>0.715 ± 0.030</td><td>0.665 ± 0.077</td></tr><tr><td>Identity</td><td>RF</td><td>0.802 ± 0.068</td><td>0.726 ± 0.062</td><td>0.713 ± 0.073</td><td>0.722 ± 0.080</td></tr><tr><td>UMAP</td><td>SVM</td><td>0.722 ± 0.098</td><td>0.640 ± 0.01</td><td>0.681 ± 0.096</td><td>0.656 ± 0.108</td></tr><tr><td>Identity</td><td>XGBoost</td><td>0.788 ± 0.075</td><td>0.674 ± 0.0</td><td>0.675 ± 0.123</td><td>0.671 ± 0.104</td></tr><tr><td>UMAP</td><td>Fine Tune X3D-M</td><td>0.731 ± 0.050</td><td>0.611 ± 0.102</td><td>0.723 ± 0.218</td><td>0.641 ± 0.108</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>Fig. 4. The top graph shows the amount of movement, while the bottom graph indicates when spasms occurred, both plotted against frame numbers representing time. Spasms are mostly absent throughout the timeline, represented by red "non-spasm sections". However, there are three distinct spasm events shown as green "spasm sections". The movement generally remains low, with a few spikes, particularly around frame 300. The bottom graph indicates that spasms occurred three times—around frames 100, 300, and 400. Notably, the most significant spasm at frame 300 coincides with the highest spike in movement, suggesting a possible link between increased movement and spasm severity. However, the other two spasms generate little to no visible movement, indicating that not all spasms are accompanied by noticeable physical activity.</figcaption></figure>

resulting in missed seizures. This variability is nearly unavoidable.

Additionally, video-based detection may be vulnerable to technical challenges, such as poor video quality and limited bandwidth, which can compromise the accuracy and reliability of seizure detection.

Furthermore, there are concerns regarding access and equity in utilizing this tool. Vulnerable populations, including those with low socioeconomic status, may face barriers to accessing and utilizing video-based seizure

detection technology, potentially leading to inequitable usage.

## VI. CONCLUSION

In this work, we demonstrate the usefulness of using X3D-M as the first step for binary classification of infantile spasm. These epileptic syndromes are often misdiagnosed due to their atypical presentations, leading to delayed treatment and resulting in regression of psychomotor acquisition and other motor sequelae. In our paper, we have introduced a novel
approach for infantile spasms recognition, based on video
data. We have used an expanded 3D neural network pre-
trained on Kinetics. By employing this model, we extracted
features from short segments of variable sizes sampled from
seizure video. We then employ multiple classifiers to perform
binary classification on these extracted feature. Our system
incorporates several design elements to address the challenges
of uncontrolled environments. The X3D-M model, with its
3D convolutional structure, is inherently robust to variations
in input data quality, lighting, and camera angles. Transfer
learning from the Kinetics-400 dataset, followed by fine-tuning
on our infantile spasm dataset, enhances the model's ability
to generalize while capturing the unique characteristics of
infantile spasms. We validated that action recognition for
detecting medical events can be achieved using videos cap-
tured during routine medical examinations, without specialized
equipment. This approach has the potential to improve early
detection and diagnosis of infantile spasms, potentially leading
to earlier treatment and better outcomes. To the best of
our knowledge, this system represents the first video-only
detection system for infantile spasms. However, our work
underscores the critical need for more comprehensive medical
data collection. An extensive data collection campaign would
allow the building of diverse, entirely automatic online moni-
toring applications for different diseases. Some focus could
be on active learning methods as the liberalization cost in
medical settings (expertise of qualified medical professionals)
is today the major obstacle to creating an automatic monitoring
system. For future studies, we will gather new datasets from
additional hospitals and focus on enhancing the explainability
and interpretability of our model. In the medical field, it is
crucial that any detected spasms are understandable to the
neuro-pediatrician, as their diagnosis will depend heavily on
the insights provided by the model. Additionally, we plan
to conduct a comparative analysis of X3D with other 3D
convolutional networks, including investigating multimodal
approaches. This will help provide a deeper understanding of
the relative advantages and limitations of different networks
in our specific application.

DATA AVAILABILITY STATEMENT

The data supporting the findings of this study are available
from the corresponding author upon reasonable request. Due
to the sensitive nature of the medical data, which includes
identifiable features such as children's faces, only the derived
embeddings (extracted features) will be shared. The original
raw data cannot be made publicly available due to licensing
agreements and privacy concerns. Researchers interested in
accessing the derived embeddings should submit a proposal
to the corresponding author, detailing the intended use of
the data. Access will be granted based on approval and
compliance with ethical and legal considerations. To ensure the
reproducibility of our results, we will provide detailed docu-
mentation, including the specific subject IDs used for training
and testing, and the procedures for data preprocessing and
analysis. This information will be made available alongside
the derived embeddings. Proposals should be directed to the
corresponding author.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

The collection, transfer, and storage of this data adhered to
stringent compliance with the General Data Protection Regula-
tion GDPR and the CNIL. Additionally, the Ethical Scientific
Committee for research, studies, and evaluation in the health
domain (CESREES) has approved the utilization of this data
for our research, under protocol number 20240119163135. All
identifiable patient information was removed from the video
data obtained prior to processing. Then, videos were ran-
domly separated into training and test datasets for comparison.
Informed consent was obtained from each study participant's
caregivers or from capable patients aged over 18 years for the
publication of their data.

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