A novel wavelet-based algorithm for real-time detection of epileptic seizures using scalp EEG is proposed. In a moving-window analysis, the EEG from each channel is decomposed by wavelet packet transform. Using wavelet coefficients from seizure and nonseizure references, a patient-specific measure is developed to quantify the separation between seizure and nonseizure states for the frequency range of 1-30 Hz. Utilizing this measure, a frequency band representing the maximum separation between the two states is determined and employed to develop a normalized index, called combined seizure index (CSI). CSI is derived for each epoch of every EEG channel based on both rhythmicity and relative energy of that epoch as well as consistency among different channels. Increasing significantly during ictal states, CSI is inspected using one-sided cumulative sum test to generate proper channel alarms. Analyzing alarms from all channels, a seizure alarm is finally generated. The algorithm was tested on scalp EEG recordings from 14 patients, totaling approximately 75.8 h with 63 seizures. Results revealed a high sensitivity of 90.5%, a false detection rate of 0.51 h(-1) and a median detection delay of 7 s. The algorithm could also lateralize the focus side for patients with temporal lobe epilepsy.

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Sleep spindles are one of the most important short-lasting rhythmic events occurring in the EEG during Non-Rapid Eye Movement sleep. Their accurate identification in a polysomnographic signal is essential for sleep professionals to help them mark Stage 2 sleep. Visual spindle scoring however is a tedious workload, as there are often a thousand spindles in an all-night recording. In this paper a novel approach for the automatic detection of sleep spindles based upon the Teager Energy Operator and wavelet packets has been presented. The Teager operator was found to accurately enhance periodic activity in epochs of the EEG containing spindles. The wavelet packet transform proved effective in accurately locating spindles in the time-frequency domain. The autocorrelation function of the resultant Teager signal and the wavelet packet energy ratio were used to identify epochs with spindles. These two features were integrated into a spindle detection algorithm which achieved an accuracy of 93.7%.

A novel patient-specific seizure prediction method based on the analysis of positive zero-crossing intervals in scalp electroencephalogram (EEG) is proposed. In a moving-window analysis, the histogram of these intervals for the current EEG epoch is computed, and the values corresponding to specific bins are selected as an observation. Then, the set of observations from the last 5 min is compared with two reference sets of data points (preictal and interictal) through novel measures of similarity and dissimilarity based on a variational Bayesian Gaussian mixture model of the data. A combined index is then computed and compared with a patient-specific threshold, resulting in a cumulative measure which is utilized to form an alarm sequence for each channel. Finally, this channel-based information is used to generate a seizure prediction alarm. The proposed method was evaluated using ∼ 561 h of scalp EEG including a total of 86 seizures in 20 patients. A high sensitivity of 88.34 % was achieved with a false prediction rate of 0.155 h⁻¹ and an average prediction time of 22.5 min for the test dataset. The proposed method was also tested against a Poisson-based random predictor.

Synthetic diesel fuel produced from natural gas via gas-to-liquid (GTL) technology is referred to as ultraclean fuel but is still challenged for full certification as diesel fuel. GTL diesel lacks certain hydrocarbons and chemical constituents, which although are benign to the environment, result in a trade-off in performance when used in a diesel engine. To boost GTL diesel physicochemical properties and thereby enable its use in conventional diesel engines, GTL diesel needs improvement. This can be achieved by mixing suitable additives to the GTL diesel and through the development of surrogate fuels that have fewer components. Screening of thousands of additives is a tedious task and can be done efficiently via computer based modeling to quickly and reliably identify a small number of promising candidates. These models are used to guide the formulation of five surrogates and predict their physicochemical properties. These surrogates are further verified using rigorous mathematical tools as well as through advanced experimental techniques. An optimal surrogate MI-5 is identified, which closely mimics GTL diesel-conventional diesel blends in terms of its physicochemical properties. An engine study for the surrogate is also performed to understand the effect of physicochemical properties on combustion as well as the emission behavior of the fuel. MI-5 exhibited an optimal torque at higher load conditions. A reduction of 11.26% NOx emission for MI-5 is observed when compared to conventional fuel. At higher loads, diesel fuel surpasses the total hydrocarbon (THC) emissions for both the surrogate and the GTL fuel. No significant variation in CO and CO2 emissions for MI-5, GTL diesel and conventional diesel is observed. Analysis of combustion as well as emission behavior of the fuels helps to understand the role of physicochemical properties on the performance of the fuel.

This paper details the design process and features of a novel upper limb rehabilitation exoskeleton named CLEVER (Compact, Low-weight, Ergonomic, Virtual/Augmented Reality Enhanced Rehabilitation) ARM. The research effort is focused on designing a lightweight and ergonomic upper-limb rehabilitation exoskeleton capable of producing diverse and perceptually rich training scenarios. To this end, the knowledge available in the literature of rehabilitation robotics is used along with formal conceptual design techniques. This paper briefly reviews the systematic approach used for design of the exoskeleton, and elaborates on the specific details of the proposed design concept and its advantages over other design possibilities. The kinematic structure of CLEVER ARM has eight degrees of freedom supporting the motion of shoulder girdle, glenohumeral joint, elbow and wrist. Six degrees of freedom of the exoskeleton are active, and the two degrees of freedom supporting the wrist motion are passive. Kinematics of the proposed design is studied analytically and experimentally with the aid of a 3D printed prototype. The paper is concluded by some remarks on the optimization of the design, motorization of device, and the fabrication challenges.