Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals. Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use. An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

This paper reports a novel self-detection method for tumor cells using living nano-robots. These living robots are a nonpathogenic strain of E. coli bacteria equipped with naturally synthesized bio-nano-sensory systems that have an affinity to VEGF, an angiogenic factor overly-expressed by cancer cells. The VEGF-affinity/chemotaxis was assessed using several assays including the capillary chemotaxis assay, chemotaxis assay on soft agar, and chemotaxis assay on solid agar. In addition, a microfluidic device was developed to possibly discover tumor cells through the overexpressed vascular endothelial growth factor (VEGF). Various experiments to study the sensing characteristic of the nano-robots presented a strong response toward the VEGF. Thus, a new paradigm of selective targeting therapies for cancer can be advanced using swimming E. coli as self-navigator miniaturized robots as well as drug-delivery vehicles.

Purpose: This paper aims to report a prototype of a reliable method for rapid, sensitive bacterial detection by using a low-cost zinc oxide nanorods (ZnONRs)-based electrochemical sensor. Design/methodology/approach: The ZnONRs have been grown on the surface of a disposable, miniaturized working electrode (WE) using the low-temperature hydrothermal technique. Scanning electron microscopy and energy dispersion spectroscopy have been performed to characterize the distribution as well as the chemical composition of the ZnONRs on the surface, respectively. Moreover, the cyclic voltammetry test has been implemented to assess the effect of the ZnONRs on the signal conductivity between −1 V and 1 V with a scan rate of 0.01 V/s. Likewise, the effect of using different bacterial concentrations in phosphate-buffered saline has been investigated. Findings: The morphological characterization has shown a highly distributed ZnONR on the WE with uneven alignment. Also, the achieved response time was about 12 minutes and the lower limit of detection was approximately 103 CFU abbreviation for Colony Forming Unit/mL. Originality/value: This paper illustrates an outcome of an experimental work on a ZnONRs-based electrochemical biosensor for direct detection of bacteria.

This paper presents a radio-telemetry system that provides the possibility of ECG signal transmission from a patient detection circuit via an RF data link. A PC then receives the signal through the National Instrument data acquisition card (NIDAQ). The PC is equipped with software allowing the received ECG signals to be saved, analysed, and sent by email to another part of the world. The proposed telemetry system consists of a patient unit and a PC unit. The amplified and filtered ECG signal is sampled 360 times per second, and the A/D conversion is performed by a PIC16f877 microcontroller. The major contribution of the final proposed system is that it detects, processes and sends patients ECG data over a wireless RF link to a maximum distance of 200 m. Transmitted ECG data with different numbers of samples were received, decoded by means of another PIC microcontroller, and displayed using MATLAB program. The designed software is presented in a graphical user interface utility.

The authors wish to correct the spelling of the third author’s name from Rami Owies to Rami Oweis in their paper published in Sensors [1], doi:10.3390/s17071580, http://www.mdpi.com/1424- 8220/17/7/1580.