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\author[1]{\bfseries M. Elmouzoun Elidrissi}
\author[2]{\bfseries E. Essoukaki}
\author[1]{\bfseries L. Ben Taleb}
\author[1]{\bfseries Az. Mouhsen}
\author[1]{\bfseries M. Harmouchi}
%%author's affiliation
\affil[1]{Laboratory of Radiation-Matter and Instrumentation, Faculty of Science and
Technology, University Hassan First of Settat, Morocco}
\affil[2]{Higher Institute of Health Sciences, University Hassan First of Settat, Morocco}

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\title{A new hybrid and optimized algorithm for drivers’ drowsiness detection}
\shorttitle{A new hybrid and optimized algorithm for drivers’ ... (Mouad Elmouzoun Elidrissi)}

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\journalname{IAES International Journal of Artificial Intelligence (IJ-AI)}
\journalshortname{Int J Artif Intell}
\journalhomepage{http://ijai.iaescore.com}
\vol{x}
\no{x}
\months{December}
\years{2021}
\issn{2252-8938}
\DOI{10.11591}
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\begin{keyword} 
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Drivers' drowsiness automatic detection \sep EEG signals analysis \sep Road safety \sep Machine Learning Hybrid algorithm \sep Real-time analysis
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The human brain generates millions of signals as they translate all our movements and thoughts, our physical and psychological state. While driving, all these signals are generated simultaneously. Vigilance at the wheel is necessary. However, when the roads are monotonous, especially on the highways, this state of vigilance decreases and the state of drowsiness appears. In Morocco, 1/3 of fatal accidents on the highway are caused by drowsiness and sleepiness at the wheel. Therefore, we proposed the idea of developing an automatic system based on electroencephalogram (EEG) signals that can predict the state of drowsiness in real time using several features extracted from EEG recordings when this state occurs in drivers while driving. The proposed work is based on time-frequency analysis of EEG signals from a single channel (FP1-Ref), and drowsiness is predicted using a modified and optimized machine learning model (optimized decision tree classification method) under Python. The results are much significant and optimized, improving the accuracy from 95.7\% to 96.4\% and a time consuming from 0.065 to 0.053 seconds.
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}
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\emph{\textbf{Corresponding Author:}}
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Mouad  Elmouzoun Elidrissi,\\
Laboratory of Radiation-Matter and Instrumentation,\\
Faculty of Science and
Technology, University Hassan 1st, Settat, Morocco,\\
Email: m.elmouzounelidrissi@uhp.ac.ma
}
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\section{Introduction}
\label{}
The rate of deadly accidents on highway caused by drowsiness and falling asleep while driving based on the latest statistics of the Ministry of Equipment, Transport, Logistics and Water, Directorate of Roads in Morocco is 33.3\% as provided by (Des et al., 2016) and (Statistiques, 2017) \cite{R7} , \cite{R11}.
\\These statistics gave us the idea of developing an automatic model that can predict drowsiness when occurring and before the situation becomes worst leading to dangerous accidents.
Therefore, the idea of our system is not new, but it came to improve the performance and solve the limitations of the existing ones by using the latest processing software ‘Python’, also by providing the best processing techniques ‘time and frequency’ and machine learning algorithms to perform a better hybrid and automatic method of detecting drowsiness based on single-channel of EEG signals \cite{R14}.
As a result, our model based on an optimized Decision Tree classifier shows a higher performance compared to our previous one and to all the previous works, improving the accuracy and the time consuming.

\section{Related Works}
\label{}
Our previous study (conference paper publishing in progress) was to conceive an efficient model based on a heavy analysis, during that period a detailed study was carried on the existing systems and their limitations.
Therefore, the existing works like cited in our previous work were based on sensors only, based on physiological signals like electroencephalogram, electrocardiogram, electro-oculogram (EEG, ECG, EOG) \cite{R15} - \cite{R19} , or even a mix of these two techniques \cite{R25}. Chang et al, proposed at the year of 2018 a smart glasses system  that detects drowsiness using signals generated by accelerometers and gyroscopes, capturing the head 's micro-falls in addition to an infra-red transceiver for capturing the blinking frequency and the eyes-closure degree \cite{R6} .
Other works used algorithms that can detect drowsiness using a facial recognition or eyes regions detection like (Ouabida et al., 2020) \& (Dhanalakshmi et al., 2016) or also using a thermal imaging tehniques (Kiashari et al., 2020) \cite{R10} - \cite{R9} .
However, We did mention on our last work that using signals issued only from sensors and not physiological signals is not accurate nor evident to confirm the detection's efficiency, because a driver's blinking or eyes closure or even his head’s movement are a standard and spontaneous actions. So, the solution was to use a method and technique based on signals recorded from EEG, ECG, EOG and others, EEG signals in our work \cite{R20} - \cite{R24} .
To situate our work, the following works used a single channel study in addition to using the same dataset of EEG signals available at the Physionet database to compare our results and show the improvement added by our hybrid method. (Belakhdar et al., 2018) proposed a technique that analyses the spectral domain of the EEG signals using MATLAB, applying the Fourier transform and an artificial neural network classification. Their work reached an accuracy of 88.8\% \cite{R4}. (Bajaj et al., 2020) reached an accuracy of 91\% using tunable Q-factor wavelet transform (TQWT) algorithm appliyed on the EEG signals, and the extreme machine learning classifier (ELM)  \cite{R3}. The highest accuracy of 94.45\% is reached in the work of (B et al., 2021) using the wavelet packet transform (WPT) method and fed to the extra-trees classifier\cite{R2}.
The proposed work aims to improve our previous algorithm’s efficiency of detecting drowsiness of drivers in the terms of rapidity and accuracy, using a personalized and optimized Decision Tree classifier that we will explain next.

\section{Proposed Approach}
\label{}
Our method proposed in this paper aims to provide an optimized and new hybrid algorithm drivers' drowsiness detection based on the mixed temporal and frequential domains by processing a single channel of EEG records (FP1). Many researchers have confirmed that the most accurate position for detecting drowsiness is the FP1 position like published in (Strijkstra et al., 2003) work \cite{R12} . Our proposed method is shown in the figure \ref{fig1} .
\begin{figure}[!h]
\centering
\includegraphics[width=6.5cm,height=7cm,scale=0.33]{fig1.png}
%% make sure to add \vspace{.7em} before figure's caption
\vspace{.7em}
\caption{Flow chart of our proposed method}
\label{fig1}
\end{figure}

\subsection{Pre-processing Phase : }
The open Physionet database is the one we used in our works because it's the best to use for similar works.
All the EEG records were artefact-free and noise filtered right after the acquisition step using a 30 Hz low-pass filter and a 50 Hz notch.
The signals were extracted from the subjects under the 10-20 international system, they were males and females with different ages.  
\cite{R1} , \cite{R26} .
\subsection{Time Segmentation Phase : }
We applied a segmentation of 3 seconds of EEG signals to ensure stationarity of spectral analysis (FFT and PSD analysis).
\subsection{Features Extraction Phase : }
This step aims to extract the most significant features using a single-channel of EEG from three mixed domains (temporal, Fourier and spectral). 
We designed a function that can extract all the features one by one, and scales all of them in the right shape for the classification step.
\subsubsection{Temporal domain analysis: }
Eight parameters are calculated in the time domain in a way to distinguish the awake from the drowsy state. After that, we calculate the minima, the maxima, the amplitude peaks and our proper mean of amplitude peaks parameter, in addition to the following ones:
%eqautions
\\
The median:

\begin{equation}
P(y \leqslant x) = P(x \leqslant z )
%P(y ≤ x) = P(z ≥ x)	
\end{equation} 
The mean: 
\begin{equation}
\bar{X}=\sum\frac{xi}{n}
%The mean (X̅) = \frac {xi}{n}  
\end{equation} 
The variance: 
\begin{equation}
Var = \frac{\sum (xi-\bar{x})^2}{n}	
\end{equation} 
The standard deviation: 
\begin{equation}
Std =\sqrt{Var}
\end{equation}     
The root-mean-square: 
\begin{equation}
RMS =\sqrt {\frac {\sum xi^2}{n}}
\end{equation} 

\subsubsection{Fourier Domain Analysis : }
In this phase, we proposed a frequency analysis of the recorded EEG signals using the fast Fourier transform. After extracting the same previous feature, the modulus of these features is calculated to eliminate the imaginary part and have only the real significant part.
%eqaution
 \\For : \[ 0 \leqslant k \leqslant N-1 \] 
\begin{equation}\label{equation 1}
Xk=\sum_{n=0}^{n-1} xn e^{-2\pi i \frac{kn}{N}}
\end{equation}
\subsubsection{Power Spectral Domain Analysis : }
A comparison of the brain band's power is calculated using the burg algorithm (spectrum analysis) to allow a good discrimination between the awake and drowsy states.\\
%equation
\begin{equation}\label{equation 3}
PSD = \frac{1}{N}\sum_{k=0}^{N-1} Y(n) e^{-2 k   \frac{n}{k}} = \frac{1}{N} Xk
\end{equation}
\subsection{Features Selection \& Classification : }
A total of eight machine learning classification methods is tested in our study to compare the efficiency and keep the best model, and secondly, to select the most appropriate features. As a result, our optimized model showed the best of accuracies and time performance.
The classifiers we used to compare our model's efficiency are Gaussian Process (GP),  K-Nearest-Neighbors (KNN), Multilayer Perceptron (MLP), Support Vector Machine (with its four kernels), our previous Decision Tree classifier (DT), and finally the proposed optimized DT.

\section{RESULTS AND DISCUSSION }

After extracting the features, all the calculated parameters were scaled and processed using machine learning classifiers. These classifiers depend on four parameters:
\\ i- True positive (TP): Prediction is positive (Drowsy state is predicted) and X is Drowsy. 
\\ ii- True negative (TN): Prediction is negative (Awake state is predicted) and X is Awake.
\\ iii- False positive (FP): Prediction is positive (Drowsy state is predicted) and X is Awake.
\\ iv- False negative (FN): Prediction is negative (Awake state is predicted) and X is Drowsy.
\\Based on these parameters we could calculate our different scoring outputs: 
\\
\begin{equation}
Precision=\frac{TP}{TP+FP}
\end{equation}  
\\
\begin{equation}
Sensitivity (Recall) =\frac{TP}{TP+FN}
\end{equation} 
\\
\begin{equation}
Accuracy =\frac{TP+TN}{TP+TN+FP+FN}
\end{equation} 
\\
\begin{equation}
F1-score=2 * \frac{precision * recall}{precision+ recall}
\end{equation}
\\
%%%
\begin{table}[ht!]
\centering
\begin{tabular}{|p{4cm}|p{1.3cm}|p{1.3cm}|p{1.3cm}|p{1.5cm}|} 
\hline
\textbf Classifier & \textbf First method & \textbf 	Second method& \textbf 	Third method	& \textbf Hybrid method \\
\hline 
\textbf Optimized DT	&51.2\%	&94.7\%	&95.0\%	& 96.4\%	 \\
\hline 	
\textbf DT(previous work)	&49.3\%	&93.6\%	&94.3\%	& 95.7\%	 \\
\hline
\textbf SVM(Linear kernel) &	\textbf 49.7\% &	\textbf 49.9\% &	\textbf 49.4\%  &	\textbf 49.5\% \\
\hline
\textbf SVM(Plynomial kernel) &	\textbf 54.6\% &	\textbf 85.5\% &	\textbf 93.2\%  &	\textbf 83.6\% \\
\hline
\textbf SVM(Sigmoid kernel) &	\textbf 35.3\% &	\textbf 66.8\% &	\textbf 88.7\%  &	\textbf 66.0\% \\
\hline
\textbf SVM(RBF kernel) &	\textbf 71.9\% &	\textbf 86.5\% &	\textbf 93.3\%  &	\textbf 87.8\% \\
\hline
\textbf MLP	& 49.8\%	&74.1\%	&48.9\%	& 75.6\% \\
\hline 	
\textbf KNN	& 90.6\%	&92.9\%	&94.1\%	& 93.1\%	 \\
\hline 	
\textbf GP	&49.1\%	&86.9\%	&49.0\%	& 56\%	 \\
\hline 			
\end{tabular}
\caption{Performance comparison between different classifiers applied on our selected features }
\label{tab: Table1}
\end{table}
\\Achieving a higher accuracy of a model depends on two studies, either we use a large segment of data to give the classifier a higher margin for the training and testing, or you try to build the analysis on solid features, therefore the first method is based on only PSD features, the second on only FFT features, the third method used only time features and the last one is our method based on the mixed features.
\\As we can conclude, our hybrid model based on the mixed domains of features and our optimized Decision Tree classifier achieved the best accuracy compared to our previous work presented during an international conference (BML21 : publishing on progress) and all the other selection of features and classifiers (Table.\ref{tab: Table1} ).
\\We used a personalized SearchGrid algorithm to select the best hyperparameters values of the DT classifier to achieve the best of accuracies (Figure \ref{fig2} ).
\\
\begin{figure}[H]
\centering
\includegraphics[width=5cm,height=5cm,scale=0.33]{fig2.png}
%% make sure to add \vspace{.7em} before figure's caption
\vspace{.7em}
\caption{Search Grid output}
\label{fig2}
\end{figure}
A two-axes study was conducted to compare our method to the previous ones using the same dataset and the aspect of single-channel-based processing in order to situate our method, the results are shown in the table \ref{tab: Table2} . \\Right after we generated a comparison in terms of the executing time and accuracies, in addition to the confusion matrix result of our optimized machine learning model  (Figure \ref{fig3} ).
\begin{figure}[!h]
\centerline{\includegraphics[width=4cm,height=6cm]{fig3.png}}
\vspace{.7em}
\caption{Output of our optimized model (Confusion matrix)}
\label{fig3}
\end{figure}
%figure of features distribution
%\begin{figure}[!h]
%\centerline{\includegraphics[width=12cm,height=8cm]{fig4.png}}
%\caption{Features distribution along the total rows of data extracted.}
%\label{fig4}
%\end{figure}
%table5
\begin{table}[h!]
\centering
\scriptsize{
\begin{tabular}{|p{1.5cm}|p{1.3cm}|p{1.3cm}|p{1.3cm}|p{1.5cm}|p{2cm}|p{1cm}|} 
\hline
\textbf Works & \textbf Platform used & \textbf Sampling frequency & \textbf size of segments& \textbf Processing method& \textbf Classification method & \textbf Accuracy\\
\hline
\textbf Proposed &	\textbf  Python &	\textbf 100Hz &	\textbf 3s  &	\textbf Hybrid & \textbf Optimized Decidion Tree & \textbf 96.4\% \\
\hline
\textbf Previous work &	\textbf  Python &	\textbf 100Hz &	\textbf 3s  &	\textbf Hybrid & \textbf Decidion Tree & \textbf 95.7\% \\
\hline
\textbf (B and Chinara, 2021) \cite{R2}	& \textbf MATLAB	&\textbf 100Hz &\textbf 5s  &	\textbf WPT & \textbf ET & \textbf 94.45\% \\
\hline  
\textbf (Bajaj et al., 2020) \cite{R3}	& --	& --	& --	& \textbf TQWT & \textbf ELM & 91.8\% \\
\hline 
\textbf (Budak et al., 2019) \cite{R5} & \textbf MATLAB	&\textbf 250Hz &\textbf 30s  &\textbf STFT,TQWT & \textbf LSTM & \textbf 94.31\% \\
\hline 	 
\textbf (Belakhdar et al., 2018) \cite{R4}& \textbf MATLAB	&\textbf 250Hz &\textbf 30s  &\textbf FFT & \textbf ANN & \textbf 88.8\% \\
\hline 	
\textbf (Ogino and Mitsukura, 2018)\cite{R13} & \textbf Ipad app &\textbf 512Hz &\textbf 10s  &\textbf PSD & \textbf SVM,SWLDA & \textbf 72.7\% \\
\hline 	 			
\end{tabular}
}
\caption{ Performance comparison between our proposed model and existing models using same Physionet EEG dataset.}
\label{tab: Table2}
\end{table} 
\begin{table}[!h]
\centering
\begin{tabular}{|c|c|c|}
\hline
\textbf Classifier & \textbf Accuracy & \textbf 	Time(s) \\
\hline
\textbf Proposed (Optimized DT)		&96.4\%	& 0.053	 \\
\hline 	
\textbf Previous Work (DT)		&95.7\%	& 0.062	 \\
\hline 	
\textbf SVM (Linear kernel) &		\textbf 87.8\% &	0.985 \\
\hline
\textbf Gaussian Process		&56\%	& 12.57\\
\hline  
\textbf Stochastic Gradient Descent		&65.5\% &	0.366 \\
\hline 
\textbf Multi-Layer Perceptron		&	75.6\% &	 5.144\\
\hline 	 
\textbf Nearest Centroid		&73.4\%	&	0.006 \\
\hline 	 		
\end{tabular}
\caption{Time comparison between the different classifiers used in our method }
\label{tab: Table3}
\end{table}
\\Comparing the results in the table \ref{tab: Table3} , we conclude that the execution time is different from one classifier to another. But in terms of both time and accuracy, our optimized Decision Tree classifier is the most efficient and effective (Accuracy of 96.4\% and execution time within 53 milliseconds).
\\The final phase was to save our model (trained) and using it to predict the state of new subjects in order to approve our work and calculate the prediction time. The state of these subjects used for the approval was known already and tested by our new hybrid model.
\section{Conclusion}
The present work proposed an optimized hybrid method of detecting drivers’ drowsiness based on time-frequency analysis of a single channel (FP1) of EEG signals.
We extracted a total of eight features from the three domains, the time, Fourier and PSD. After that, we trained eight machine learning models, Multi-layer Perceptron (MLP), Gaussian process (GP), K-Nearest-Neighbors (KNN), support vector machine (with its four kernels), Decision Tree (DT) and finally our optimized Decision Tree.
We compared our proposed work to our previous one and to the ones based on the same dataset and the use of a single channel of EEG records. The added value of our model is the improvement of the detection’s performance in the term of accuracy, which achieved 96.4\% and the processing time 0.053 seconds.   

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 \end{thebibliography}


\section*{BIOGRAPHIES OF AUTHORS} 
%% make sure to add \vspace{-.7em} before \begin{biography}
\vspace{-.7em} 

\begin{biography}[{\includegraphics[width=2.5cm,height=4cm,clip,keepaspectratio]{my photo2}}]
\textbf{Mouad Elmouzoun Elidrissi} %% Affiliation and educational background.
Born in 10 September 1996 in Casablanca, Morocco. He is a PhD student, received his master degree in Biomedical Engineering: Instrumentation and Maintenance from faculty of science and technology Settat, in 2019. His  research areas  include EEG signals analysis for the use in drivers' drowsiness detection, Machine Learning models conception, Application of Artificial Intelligence for drivers and road safety. Laboratory of Radiation-Matter and  Instrumentation (RMI), Faculty of Sciences and Technology, Hassan 1st University, Morocco. BP : 577, route de Casablanca. Settat, Morocco.
%% Research interests, teaching, professional experiences, etc.
\\His researches are in fields of Machine Learning and Artificial Intelligence, EEG signal processing, and biomedical instrumentation.

\end{biography}

\begin{biography}[{\includegraphics[width=2.5cm,height=4cm,clip,keepaspectratio]{author1}}]
\textbf{Mohammed Harmouchi} %% Affiliation and educational background.
 was born in Sefrou, Morocco, in 1959. He is in charge of the master’s degree in biomedical engineering: instrumentation and maintenance. He is the Ex-Director of the Laboratory of Radiation-Matter and Instrumentation, Hassan First University, Settat, Morocco, where he is currently a Professor of Higher Education and the Ex-Head of the Department of Applied Physics, Faculty of Science and Technology. He holds several publications and innovation patents in the fields of biophysics and biomedical engineering.(Based on document published on 9 October 2020).

\end{biography}

\begin{biography}[{\includegraphics[width=2.5cm,height=4cm,clip,keepaspectratio]{author2}}]
\textbf{Azeddine Mouhsen} %% Affiliation and educational background.
Born in 10 July 1967, Azeddine MOUHSEN is Professor of Physics at Hassan First University, Morocco, since 1996. He holds a PhD from
Bordeaux I University (France) in 1995 and a thesis from Moulay Ismaïl University, Morocco, in 2001. He specializes in Instrumentation and measurements, sensors, applied optics, Energy transfer, radiation-matter Interactions. Pr. Azeddine Mouhsen has taught courses in physical sensors, chemical sensors, instrumentation, systems technology, digital electronics and industrial data processing. He has published over 30 papers and he is the co-inventor of one patent. Actually, he is the Director of Laboratory of radiation - matter and Instrumentation.


\end{biography}

\begin{biography}[{\includegraphics[width=2.5cm,height=4cm,clip,keepaspectratio]{Essoukaki}}]
\textbf{Elmaati Essoukaki } %% Affiliation and educational background.
born in 1 january 1990. He is currently a Professor of Instrumentation and Biomedical Engineering at Higher Institute of Health Sciences (ISSS), Hassan First University. His research interests include Instrumentation and Biomedical Engineering, biomedical imaging, and signal processing. He holds several publications and innovation patents in the fields of biophysics and biomedical engineering.


\end{biography}

\begin{biography}[{\includegraphics[width=2.5cm,height=4cm,clip,keepaspectratio]{bentaleb}}]
\textbf{Lhoucine Ben Taleb } %% Affiliation and educational background.
born in February 20, 1991. Ben Taleb Lhoucine is a professor of Electrical and Biomedical Engineering at Hassan First University (UHP), Morocco, since 2021. After a Master degree from UHP (2014), he obtained in 2020 a PhD from the same university Speciality Electrical and Biomedical Engineering (in the Laboratory of Radiation-Matter and Instrumentation).  Pr. Ben Taleb has taught courses in instrumentation for the functional exploration and therapeutic applications, medical imaging technologies as well as microcontrollers and programmable logic controllers. He has published 6 scientific papers, and he is an inventor of one patent and a co-inventor of another one.

\end{biography}
\end{document}

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