## 1 Introduction  
Epilepsy is one of the neurological disorders affecting many people at all levels of age. It may occur at any time, with an estimated population suffering from this disorder at more than 50 million, with most of the patients residing in developing countries [1]. An unprovoked seizure occurs due to a sudden change in the brain's cell electrical activity, which, if proper monitoring and diagnosing steps are not put in place, may lead to loss of consciousness, uncontrolled motions, jerking, and loss of memory [2, 3]. These inconvenient and undesirable signs seriously undermine the quality of life of epilepsy patients as it is difficult for patients and doctors to predict when and where these seizures will occur. Therefore, it is of high importance to develop an efficient detection scheme and automate the system for monitoring epileptic seizures and assist clinicians in the proper and efficient diagnosis of this disease [4, 5]. The development and integration of modern, smart, portable, and low-cost devices in our health care system known as the internet of medical things (IoMT) can monitor, track, and transmit data wirelessly with an online consultation in real-time scenarios is highly desirable [6; 7; 8]. An example of such a device was seen in the work presented by [PERSON] et al. [9]. They proposed a real-time and low-power seizure detection system using a headband; they developed an application-based system that can record and transmit the data via a cloud. The efficiency of these devices largely depends on the accurate classification of these physiological signals, which, in turn, depend on the quality of feature extraction methods employed to extract the efficient and relevant signal information that characterize different signal properties [10; 11]. This work proposed an efficient feature extraction technique suitable for smart IoMT devices due to its less computational complexity and optimized deep neural network detection pipeline. This work uses focal and non-focal EEG signals, which depict the brain's electrical activity and can be studied by the neurologist to identify different parts of the brain, its function, and activities, including the epileptogenic part. Focal EEG signals are localized (i.e., indicating that a specific part of the brain is affected), while non-focal EEG signals are generalized (i.e., several parts are involved). However, manual and traditional interpretation of these signals, such as using the time-domain in extracting features, misses some useful information about the signal and renders this method ineffective [12]. In this paper, a comprehensive analysis of feature extraction and classification techniques for focal and non-focal EEG were studied using various signal analysis domains. An effective feature extraction algorithm was then developed using the time-frequency domain, statistical parameters, and nonlinear methods, as well as a new transform for extracting time-frequency sub-bands signals that have the ability to sharpen the time-frequency resolution and minimize the uneven distribution of energy in the wavelet transform domain. A new model was developed based on these features, while a two-dimensional-deep convolutional neural network (2D-DCNN) was adopted to distinguish the signal characteristics that are embedded in the EEG signals. The proposed method has the following novelties:  
* Extraction of useful and relevant features using time-frequency domain and nonlinear approaches. * Applying statistical parameters to reduce the feature's dimension for efficient classification
* Classification of focal and non-focal EEG signals using conventional techniques such as SVM and also deep learning approaches such as CNN with optimized function. * Investigation of the most robust classifier by applying different SVM kernel functions and DNN architecture.