Epileptic seizure prediction based on multiresolution convolutional neural networks

Epilepsy withholds patients’ control of their body or consciousness and puts them at risk in the course of their daily life. This article pursues the development of a smart neurocomputational technology to alert epileptic patients wearing EEG sensors of an impending seizure. An innovative approach for epileptic seizure prediction has been proposed to improve prediction accuracy and reduce the false alarm rate in comparison with state-of-the-art benchmarks. Maximal overlap discrete wavelet transform was used to decompose EEG signals into different frequency resolutions, and a multiresolution convolutional neural network is designed to extract discriminative features from each frequency band. The algorithm automatically generates patient-specific features to best classify preictal and interictal segments of the subject. The method can be applied to any patient case from any dataset without the need for a handcrafted feature extraction procedure. The proposed approach was tested with two popular epilepsy patient datasets. It achieved a sensitivity of 82% and a false prediction rate of 0.058 with the Children’s Hospital Boston-MIT scalp EEG dataset and a sensitivity of 85% and a false prediction rate of 0.19 with the American Epilepsy Society Seizure Prediction Challenge dataset. This technology provides a personalized solution for the patient that has improved sensitivity and specificity, yet because of the algorithm’s intrinsic ability for generalization, it emancipates from the reliance on epileptologists’ expertise to tune a wearable technological aid, which will ultimately help to deploy it broadly, including in medically underserved locations across the globe

Transcriptome analysis highlights the influence of temperature on hydrolase and traps in nematode-trapping fungi

Pine wilt disease caused by Bursaphelenchus xylophilus poses a serious threat to the economic and ecological value of forestry. Nematode trapping fungi trap and kill nematodes using specialized trapping devices, which are highly efficient and non-toxic to the environment, and are very promising for use as biological control agents. In this study, we isolated several nematode-trapping fungi from various regions and screened three for their high nematocidal efficiency. However, the effectiveness of these fungi as nematicides is notably influenced by temperature and exhibits different morphologies in response to temperature fluctuations, which are categorized as “NA,” “thin,” “dense,” and “sparse.” The trend of trap formation with temperature was consistent with the trend of nematocidal efficiency with temperature. Both of which initially increased and then decreased with increasing temperature. Among them, Arthrobotrys cladodes exhibited the highest level of nematocidal activity and trap formation among the tested species. Transcriptome data were collected from A. cladodes with various trap morphologies. Hydrolase activity was significantly enriched according to GO and KEGG enrichment analyses. Eight genes related to hydrolases were found to be consistent with the trend of trap morphology with temperature. Weighted gene co-expression analysis and the Cytoscape network revealed that these 8 genes are associated with either mitosis or autophagy. This suggests that they contribute to the formation of “dense” structures in nematode-trapping fungi. One of these genes is the serine protein hydrolase gene involved in autophagy. This study reveals a potentially critical role for hydrolases in trap formation and nematocidal efficiency. And presents a model where temperature affects trap formation and nematocidal efficiency by influencing the serine protease prb1 involved in the autophagy process

A New Identification Jacobian For Robotic Hand/Eye Calibration

Hand/eye calibration is the process of identifying the un- known position and orientation of the camera frame with respect to the robot hand frame, when the camera is rigidly mounted on the robot hand. While computationally slightly more involved, one-stage iterative algorithms have two distinguished advantages over traditional two-stage linear approaches: (a) They are less sensitive to noise, and (b) they can handle cases in which the camera orientation information is not available. A more compact and lower dimensional Identification Jacobian is derived in this correspondence. The Jacobian, which relates measurement residuals to pose error parameters of the unknown hand/eye transformation, is a crucial component of one-stage iterative algorithms. The derivation procedure for the new Jacobian is straightforward and simple, owing to an alternative mathematical formulation of the band/eye calibration problem. Observability conditions of the pose error parameters in the unknown hand/eye transformation are also provided based on this Identification Jacobian. © 1994 IEE

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A new approach to self-calibrate a camera-equipped robot manipulator is proposed in this paper. Self-calibration here means that the camera-robot system is capable of determining its geometric parameters without any external measurements and/or ground truth calibration data. With the proposed approach, one is able to identify all the rotational parameters and, up to a scale factor, all the translational parameters of a robotic system without any ground truth data. It is known from the computer vision literature that the extrinsic and intrinsic parameters of the camera can be obtained up to a scale factor by using the corresponding points of objects in a natural environment from an image sequence without knowing the positions of these object points. It is also well known that if the camera is treated as the tool of the robot, one is able to compute the corresponding robot pose directly from the camera-extrinsic parameters