Marwa Qaraqe

Band-sensitive seizure onset detection via CSP-enhanced EEG features

This paper presents two novel epileptic seizure onset detectors. The detectors rely on a common spatial pattern (CSP)-based feature enhancement stage that increases the variance between seizure and nonseizure scalp electroencephalography (EEG). The proposed feature enhancement stage enables better discrimination between seizure and nonseizure features. The first detector adopts a conventional classification stage using a support vector machine (SVM) that feeds the energy features extracted from different subbands to an SVM for seizure onset detection. The second detector uses logical operators to pool SVM seizure onset detections made independently across different EEG spectral bands. The proposed detectors exhibit an improved performance, with respect to sensitivity and detection latency, compared with the state-of-the-art detectors. Experimental results have demonstrated that the first detector achieves a sensitivity of 95.2%, detection latency of 6.43s, and false alarm rate of 0.59perhour. The second detector achieves a sensitivity of 100%, detection latency of 7.28s, and false alarm rate of 1.2per hour for the MAJORITY fusion method.

Physical layer security for wireless implantable medical devices

Wireless communications are increasingly important in health-care applications, particularly in those that use implantable medical devices (IMDs). Such systems have many advantages in providing remote healthcare in terms of monitoring, treatment and prediction for critical cases. However, the existence of malicious adversaries, referred to as nodes, which attempt to control implanted devices, constitutes a critical risk for patients. Such adversaries may perform dangerous attacks by sending malicious commands to the IMD, and any weakness in the device authentication mechanism may result in serious problems including death. In this paper we present a physical layer (PHY) authentication technique for IMDs that does not use existing methods of cryptology. In addition to ensuring authentication, the proposed technique also provides advantages in terms of decreasing processing complexity of IMDs and enhances overall communications performance.

Experimental Evaluation of MIMO Capacity for Ultrawideband Body-Centric Wireless Propagation Channels

In this letter, the capacity of ultrawideband (UWB) multiple-input-multiple-output (MIMO) on-body channels is evaluated. Three on-body channels are investigated-namely, Chest-Waist, Wrist-Waist, and Ankle-Waist channels. The data transmission capacities of each channel are evaluated and presented using the two power allocations schemes - namely, equal power allocation and the waterfilling power allocation. Statistical analysis is performed on the MIMO channel data, and the Rician K-factors of the channels are analyzed. It is found that the waterfilling scheme outperforms the equal power scheme in terms of capacity for all three on-body channels, with an average improvement of 1.2 b/s/Hz for signal-to-noise ratio (SNR) less than 12 dB (percentage improvement of 187% at 0 dB and 100% at 8 dB). However, the performances of the two schemes become comparable as the SNR increases with less than 0.28 b/s/Hz difference (percentage improvement of 3.6%) at an SNR level of 20 dB.

Epileptic seizure onset detection based on EEG and ECG data fusion

This paper presents a novel method for seizure onset detection using fused information extracted from multichannel electroencephalogram (EEG) and single-channel electrocardiogram (ECG). In existing seizure detectors, the analysis of the nonlinear and nonstationary ECG signal is limited to the time-domain or frequency-domain. In this work, heart rate variability (HRV) extracted from ECG is analyzed using a Matching-Pursuit (MP) and Wigner-Ville Distribution (WVD) algorithm in order to effectively extract meaningful HRV features representative of seizure and nonseizure states. The EEG analysis relies on a common spatial pattern (CSP) based feature enhancement stage that enables better discrimination between seizure and nonseizure features. The EEG-based detector uses logical operators to pool SVM seizure onset detections made independently across different EEG spectral bands. Two fusion systems are adopted. In the first system, EEG-based and ECG-based decisions are directly fused to obtain a final decision. The second fusion system adopts an override option that allows for the EEG-based decision to override the fusion-based decision in the event that the detector observes a string of EEG-based seizure decisions. The proposed detectors exhibit an improved performance, with respect to sensitivity and detection latency, compared with the state-of-the-art detectors. Experimental results demonstrate that the second detector achieves a sensitivity of 100%, detection latency of 2.6s, and a specificity of 99.91% for the MAJ fusion case.

An Overview of NCA-Based Algorithms for Transcriptional Regulatory Network Inference

In systems biology, the regulation of gene expressions involves a complex network of regulators. Transcription factors (TFs) represent an important component of this network: they are proteins that control which genes are turned on or off in the genome by binding to specific DNA sequences. Transcription regulatory networks (TRNs) describe gene expressions as a function of regulatory inputs specified by interactions between proteins and DNA. A complete understanding of TRNs helps to predict a variety of biological processes and to diagnose, characterize and eventually develop more efficient therapies. Recent advances in biological high-throughput technologies, such as DNA microarray data and next-generation sequence (NGS) data, have made the inference of transcription factor activities (TFAs) and TF-gene regulations possible. Network component analysis (NCA) represents an efficient computational framework for TRN inference from the information provided by microarrays, ChIP-on-chip and the prior information about TF-gene regulation. However, NCA suffers from several shortcomings. Recently, several algorithms based on the NCA framework have been proposed to overcome these shortcomings. This paper first overviews the computational principles behind NCA, and then, it surveys the state-of-the-art NCA-based algorithms proposed in the literature for TRN reconstruction.

Experimental Characterization of In Vivo Wireless Communication Channels

In this paper, we numerically investigated the in vivo wireless communication channel for human male torso at 915 MHz. Results show that in vivo channel is different than the classical communication channel and location dependency is very critical for link budget calculations. A statistical path loss model based on angle, depth and body region is introduced for near and far field regions. Furthermore, multipath characteristics are investigated using a power delay profile as well.

Numerical characterisation and modeling of in-vivo radio communication

In this paper, preliminary study on path loss characterization of in-vivo communication channel at 2.45 GHz using numerical modeling is presented. One of the antennas is implanted inside the chest and the second antenna is placed on the surface of body at certain distance. The on-body antenna is rotated at different angles with respect to the inbody antenna and path loss is calculated at certain fixed distance. The body-centric path loss is compared with the free space path loss as well. Results show that there is almost 10-15 dB variation in path loss in case of in-vivo channel as compared to free space. Also a degradation in path loss and channel response with respect to the orientation angle between implanted transmitter and on-body receiver is observed, which highlights the importance of carefully considering the location of on-body receiver for optimal system performance.

Channel selection and feature enhancement for improved epileptic seizure onset detector

This paper presents a novel architecture for a patient-specific epileptic seizure onset detector using scalp electroencephalography. The proposed architecture exploits the benefits of both channel selection and feature enhancement to improve the detector performance. The novel architecture results in higher energy difference between the pre-seizure and seizure states and hence performs better in terms of detection sensitivity and false alarm rate compared to benchmark detectors available in the literature. In detail, the proposed architecture achieves a 7% increase in sensitivity and a reduction of 9 false alarms per hour compared to the benchmark detector.

Ultra wideband in vivo radio channel characterisation and system modeling

In this paper radio channel characterisation and level system modeling for ultra wideband (UWB) in vivo communication is presented at different distances and angle between the the implant and the on-body node. Path loss is calculated for different scenarios and time delay analysis is performed. In addition, UWB-OFDM (orthogonal frequency division multiplexing) based system modeling is used to calculate the bit error rate (BER) performance. Result shows that BER remains less then 1e-3 for almost all cases up to 40 mm spacing between the implant and on-body node, when Eb/No is above 6 dB.

Joint multiuser switched diversity and adaptive modulation schemes for spectrum sharing systems

In this paper, we develop multiuser access schemes for spectrum sharing systems whereby secondary users are allowed to share the spectrum with primary users under the condition that the interference observed at the primary receiver is below a predetermined threshold. In particular, we devise two schemes for selecting a user among those that satisfy the interference constraint and achieve an acceptable signal-to-noise ratio level. The first scheme selects the user that reports the best channel quality. In order to alleviate the high feedback load associated with the first scheme, we develop a second scheme based on the concept of switched diversity where the base station scans the users in a sequential manner until an acceptable user is found. In addition to these two selection schemes, we consider two power adaptive settings at the secondary users based on the amount of interference available at the secondary transmitter. In the On/Off power setting, users are allowed to transmit based on whether the interference constraint is met or not, while in the full power adaptive setting, the users are allowed to vary their transmission power to satisfy the interference constraint. Finally, we present numerical results for our proposed algorithms where we show the trade-off between the average spectral efficiency and average feedback load for both schemes.