Purpose:To design a non–patient-specific system to detect the electrical onset of seizures in patients with temporal lobe epilepsy.Methods:We used EEG data from 29 seizures of 18 temporal lobe epilepsy patients who underwent multiday video-scalp EEG monitoring as part of their presurgical evaluations. We segmented each data set into preictal and ictal phases, and identified spectral entropy, spectral energy, and signal energy as useful features for discriminating normal and seizure conditions. The performance of five different classifiers was analyzed using these features to design an automated detection system.Results:Among the five classifiers, decision tree, k-nearest neighbor, and support vector machine performed with sensitivity (specificity) of 79% (81%), 75% (85%), and 80% (86%), respectively. The other two, linear discriminant algorithm and Naive Bayes classifiers, performed with sensitivity (specificity) of 54% (94%), 47% (96%), respectively.Conclusions:The support vector machine–based seizure detection system showed better detection capability in terms of sensitivity and specificity measures as compared to linear discriminant algorithm, Naive Bayes, decision tree, and k-nearest neighbor classifiers.Conclusions:Our study shows that a generalized system to detect the electrical onset of seizures in temporal lobe epilepsy using scalp-recorded EEG is possible. If confirmed on a larger data set, our findings may have significant implications for the management of seizures, especially in patients with drug-resistant epilepsy.

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Indoor air quality (IAQ) is a key factor for ensuring the safety, health and comfort of the people. Since physical variables describing IAQ, such as the concentration of volatile organic compounds (VOCs), concentrations of gaseous air contaminants and that of other toxic gases need to be closely monitored, concept of distributed monitoring and control network needs to be implemented.In this paper, a framework of sensor network for monitoring IAQ is explained where sensors are physically distributed and the serial common bus communication network CAN is used to exchange system information. CAN (Controller Area Network) is a high integrity serial bus protocol that is designed to operate at high speeds ranging from 20kbit/s to 1Mbit/s which provide an efficient, reliable and very economical link between sensor nodes and display node. This paper proposes Atmel CANary based sensor nodes and display node for the monitoring of indoor air quality. The communication between the sensor nodes and the display node through the CAN bus is evaluated through hardware tests.

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