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Full-Text Articles in Engineering
Estimation Of The Depth Of Anesthesia By Using A Multioutput Least-Square Support Vector Regression, Mercedeh Jahanseir, Kamal Setarehdan, Sirous Momenzadeh
Estimation Of The Depth Of Anesthesia By Using A Multioutput Least-Square Support Vector Regression, Mercedeh Jahanseir, Kamal Setarehdan, Sirous Momenzadeh
Turkish Journal of Electrical Engineering and Computer Sciences
Today, most surgeries are performed under general anesthesia where one of the most growing methods for anesthesia depth monitoring is using electroencephalogram (EEG). The bispectral index (BIS) is the most commonly used parameter for anesthesia depth monitoring using EEG, the validity of which is still to be studied before being accepted as a routine method by clinicians. This paper proposes a new technique for detecting the depth of anesthesia by means of EEG, which is based on multioutput least-squares support vector regression (MLS-SVR), which provides the probability that the patient is in the four different possible anesthesia states. In this …
An Efficient Algorithm To Decompose A Compound Rectilinear Shape Into Simplerectilinear Shapes, Imran Sharif, Debasis Chaudhuri, Naveen Kushwaha, Ashok Samal, Brij Mohan Singh
An Efficient Algorithm To Decompose A Compound Rectilinear Shape Into Simplerectilinear Shapes, Imran Sharif, Debasis Chaudhuri, Naveen Kushwaha, Ashok Samal, Brij Mohan Singh
Turkish Journal of Electrical Engineering and Computer Sciences
Detection of a compound object is a critical problem in target recognition. For example, buildings form an important class of shapes whose recognition is important in many remote sensing based applications. Due to the coarse resolution of imaging sensors, adjacent buildings in the scenes appear as a single compound shape object. These compound objects can be represented as the union of a set of disjoint rectilinear shaped objects. Separating the individual buildings from the resulting compound objects in a segmented image is often difficult but important nevertheless. In this paper we propose a new and efficient technique to decompose a …
Topological Feature Extraction Of Nonlinear Signals And Trajectories And Its Application In Eeg Signals Classification, Saleh Lashkari, Ali Sheikhani, Mohammad Reza Hashemi Golpayegani, Ali Moghimi, Hamid Reza Kobravi
Topological Feature Extraction Of Nonlinear Signals And Trajectories And Its Application In Eeg Signals Classification, Saleh Lashkari, Ali Sheikhani, Mohammad Reza Hashemi Golpayegani, Ali Moghimi, Hamid Reza Kobravi
Turkish Journal of Electrical Engineering and Computer Sciences
This study introduces seven topological features that characterize attractor dynamic of nonlinear and chaotic trajectories in a phase space. These features quantify volume, occupied space, nonuniformity, and curvature of trajectory. The features are evaluated as initial point invariant measures by a practical approach, which means that a feature is only sensitive to dynamic changes. The Lorenz and Rossler system trajectories are employed in this evaluation. Moreover, the proposed features are used in a real world application, i.e. epileptic seizure electroencephalogram signal classification. As the result shows, these features are efficient in this task in comparison with others studies that used …