A Comparison of Artificial Neural Network(ANN) and Support Vector Machine(SVM) Classifiers for Neural Seizure Detection

Elgammal, Mohamed A.; Mostafa, Hassan; Salama, Khaled N.; Mohieldin, Ahmed N.

doi:10.1109/mwscas.2019.8884989

Cited by 18 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover, an FPGA-system level comparison between the proposed systems and other seizure detection systems is depicted in Table 8. The highest achieved sensitivity by the proposed designs is higher than that in [20], [21], [23], [24], and [25] and approximately equals to the sensitivity achieved in [21]. No DSPs are utilized in the proposed design in opposition to [21], and [23].…”

Section: Comparison With Prior Workmentioning

confidence: 91%

Hardware Acceleration of High Sensitivity Power-Aware Epileptic Seizure Detection System Using Dynamic Partial Reconfiguration

et al. 2021

Self Cite

View full text Add to dashboard Cite

In this paper, a high-sensitivity low-cost power-aware Support Vector Machine (SVM) training and classification based system, is hardware implemented for a neural seizure detection application. The training accelerator algorithm, adopted in this work, is the sequential minimal optimization (SMO). System blocks are implemented to achieve the best trade-off between sensitivity and the consumption of area and power. The proposed seizure detection system achieves 98.38% sensitivity when tested with the implemented linear kernel classifier. The system is implemented on different platforms: such as Field Programmable Gate Array (FPGA) Xilinx Virtex-7 board and Application Specific Integrated Circuit (ASIC) using hardwarecalibrated UMC 65nm CMOS technology. A power consumption evaluation is performed on both the ASIC and FPGA platforms showing that the ASIC power consumption is lower by at least 65% when compared with the FPGA counterpart. A power-aware system is implemented with FPGAs by the adoption of the Dynamic Partial Reconfiguration (DPR) technique that allows the dynamic operation of the system based on power level available to the system at the expense of degradation of the system accuracy. The proposed system exploits the advantages of DPR technology in FPGAs to switch between two proposed designs providing a decrease of 64% in power consumption.

show abstract

Section: Comparison With Prior Workmentioning

confidence: 91%

Hardware Acceleration of High Sensitivity Power-Aware Epileptic Seizure Detection System Using Dynamic Partial Reconfiguration

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…An SVM classifier was used, which along with deep learning techniques is wellestablished in conventional seizure detection using electroencephalography signals. [101][102] The authors investigated which signals were needed to achieve the highest accuracy, with temperature, EMG, PPG, and EDA providing the 98.2 % accuracy and 93.7 % sensitivity. Using only the temperature and EMG signals, however, achieved near-identical results of 98.1 % accuracy and 91.6 % sensitivity.…”

Section: Rehabilitation and Activity Assessmentmentioning

confidence: 99%

Advances in Biosignal Sensing and Signal Processing Methods with Wearable Devices

2022

View full text Add to dashboard Cite

Analysis & Sensing www.analysis-sensing.org Review doi.org/10.1002/anse.202200062Wearable devices have received significant attention recently for their ability to monitor critical physiological signals noninvasively, such as electrocardiography, electroencephalography, electromyography, and photoplethysmography. These biointegrated wearable systems can potentially fill gaps in conventional clinical practice by providing highly cost-effective health characterization and portable continuous health monitoring. Further, the physiological signals measured by wearables require post-processing to derive meaningful values, such as heart rate or blood oxygen saturation. This requirement, in conjunction with the smaller form factor and limited sensor count of the miniaturized systems, often necessitates robust signal processing and data analysis to approach the stringent performance specifications of conventional medical devices, and machine learning techniques have found success in filling this analytical role for their ability to learn complex functional relationships. Thus, this review outlines a systematic summary of the latest research on various wearable devices and their biosignal sensing and signal processing methods, emphasizing machine learning. We also discuss the developmental challenges and advantages of current machine-learning methods, while suggesting research directions for future studies.

show abstract

“…Dukungan mesin vektor (SVM) dianggap salah satu algoritma pembelajaran yang paling kuat dan digunakan untuk berbagai aplikasi dunia nyata. SVM algoritma pembelajaran yang diawasi yang memiliki banyak aplikasi di bidangnya biofotonik, pengenalan pola, dan klasifikasi paling populer (Elgammal et al, 2019). Awalnya, dikembangkan untuk dua klasifikasi kelas tetapi seseorang juga dapat menerapkannya pada masalah yang melibatkan banyak kelas dengan menggunakan strategi satu lawan satu dan satu lawan semua.…”

Section: Support Vector Machine (Svm)unclassified

“…Di mana w adalah normal untuk hyperplane, Φ (x) adalah pemetaan fungsi yang digunakan untuk memetakan setiap vektor masukan ke ruang fitur dan b adalah biasnya. Masalah optimasi menemukan hyperplane dengan margin terbesar dirumuskan sebagai berikut (Elgammal et al, 2019):…”

Section: Support Vector Machine (Svm)unclassified

Wayang Image Classification Using SVM Method and GLCM Feature Extraction

Muhathir

Santoso²,

Larasati³

2021

JITE

View full text Add to dashboard Cite

Wayang is a masterpiece of art that has been able to survive centuries of change and development as a reflection of life for the majority of society. Wayang has a high value because it does not only function as a "entertainment" spectacle, but also has many lessons and life values that can be learned from a wayang show. Puppet itself has various types and forms, and these forms have their own uniqueness, because of the many types of Puppet, many people do not know all the names and types of wayang. Therefore, in this research, we will discuss how to recognize wayang objects based on wayang images using the SVM and GLCM methods as feature extraction. The results showed that the classification of wayang using the SVM (Support Vector Machine) method and the GLCM (Gray Level Co-Occurrence Matrix) feature extraction can recognize wayang objects based on wayang images and classify them quite accurately and a maximum total accuracy of 83.2% is obtained.

show abstract

A Comparison of Artificial Neural Network(ANN) and Support Vector Machine(SVM) Classifiers for Neural Seizure Detection

Cited by 18 publications

References 14 publications

Hardware Acceleration of High Sensitivity Power-Aware Epileptic Seizure Detection System Using Dynamic Partial Reconfiguration

Hardware Acceleration of High Sensitivity Power-Aware Epileptic Seizure Detection System Using Dynamic Partial Reconfiguration

Advances in Biosignal Sensing and Signal Processing Methods with Wearable Devices

Wayang Image Classification Using SVM Method and GLCM Feature Extraction

Contact Info

Product

Resources

About