Smart-BIODEV 2016 Abstracts

Full Papers Short Papers
Full Papers
Paper Nr: 2
Title:

Mixed Hardware and Software Embedded Signal Processing Methods for in-situ Analysis of Cardiac Activity
Authors:

Bertrand Massot, Tanguy Risset, Gregory Michelet and Eric McAdams
Abstract: This paper presents the implementation of a combination of hardware and software signal processing methods on a wearable device for the continuous and long-term monitoring and analysis of cardiac activity during insitu experiments. Heart rate assessment and heart rate variability parameters are computed in real-time directly on the sensor, thus only few parameters are sent via wireless communication for power saving. Hardware method for heart rate measurement, and software methods for the calculation of time-domain and frequency-domain parameters of heart rate variability are described, and preliminary tests for the evaluation of the sensor are presented.
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Paper Nr: 4
Title:

A Novel Use of Hyperspectral Images for Human Brain Cancer Detection using in-Vivo Samples
Authors:

Himar Fabelo, Samuel Ortega, Raùl Guerra, Gustavo Callicó, Adam Szolna, Juan F. Piñeiro, Miguel Tejedor, Sebastián López and Roberto Sarmiento
Abstract: Hyperspectral Imaging is an emerging technology for medical diagnosis issues due to the fact that it is a non-contact, non-ionizing and non-invasive sensing technique. The work presented in this paper tries to establish a novel way in the use of hyperspectral images to help neurosurgeons to accurately determine the tumour boundaries in the process of brain tumour resection, avoiding excessive extraction of healthy tissue and the accidental leaving of un-resected small tumour tissues. So as to do that, a hyperspectral database of in-vivo human brain samples has been created and a procedure to label the pixels diagnosed by the pathologists has been described. A total of 24646 samples from normal and tumour tissues from 13 different patients have been obtained. A pre-processing chain to homogenize the spectral signatures has been developed, obtaining 3 types of datasets (using different pre-processing chain) in order to determine which one provides the best classification results using a Random Forest classifier. The experimental results of this supervised classification algorithm to distinguish between normal and tumour tissues have achieved more than 99% of accuracy.
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Paper Nr: 5
Title:

Accurate Level-crossing ADC Design for Biomedical Acquisition Board
Authors:

Mariam Tlili, Manel Ben-Romdhane, Asma Maalej, Mohamed Chaker Bali, François Rivet, Dominique Dallet and Chiheb Rebai
Abstract: The aim of this paper is to present a wireless biomedical system for the acquisition and transmission (Wibio’ACT) of biomedical signals. This work is a part of the Wibio’ACT project which main purpose is to ensure the minimum power consumption while diagnose patients continuously and in real time. For the Wibio’ACT system, the bottleneck is the analog-to-digital conversion (ADC) since it controls the power consumption of the digital signal processing step as well as the amount of the transmitted data. In fact, in this work case, the ADC continuously measures the electrical activity of the heart to deliver the electrocardiogram (ECG) signal. Hence, among conventional ADCs, level-crossing analog-to-digital converters (LC-ADCs) have been investigated for ECG signals processing. Authors propose some design consideration of the LC-ADC. This reduces the LC-ADC output samples by 13 % to help to save the power consumption of the wireless data transmitter. The samples with a small variation are reduced by at least 44%. The performance of the proposed design is measured in terms of percentage root mean square difference (PRD) applied to the reconstructed signal quality. A PRD of 1% is verified using behavioral simulations on ECG records extracted from different databases. A timer period TC of 0.14 ms ensures an effective number of bits of 10 bits and a signal to noise ratio of 62 dB.
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Paper Nr: 7
Title:

Improvement of a FPGA-based Detection of QRS Complexes in ECG Signal using an Adaptive Windowing Strategy
Authors:

Amina Habiboullah, Mehdi Terosiet, Aymeric Histace and Olivier Romain
Abstract: This paper presents an FPGA-based algorithm for automatic detection of QRS complexes in ECG signals, first step for the estimation of cardiac intervals. The proposed algorithm is divided into 3 parts : Filtering, Contrast Enhancement, and finally a Detection block based on an adaptive windowing and a thresholding of the enhanced data. The entire detection scheme was developed in accordance with embedding constraints and in the perspective of a real-time use. We evaluated the algorithm on manually annotated databases, such MIT-BIH Arrythmia and QT databases. The FPGA-based algorithm correctly detects 91,85 % percent of the QRS complexes, with a very limited ratio of false detection (only 5%) on standard databases, while for realtime records obtained from young subjects between 20 and 25 years, the sensitivity reaches 93,77 % with a false detection ratio of only 4 %. These results are in accordance with the most recent state-of-the-art off-line algorithms on the same database, and improves significantly FPGA-based ones that were tested on a limited number of ECG extracted from the MIT-BIH set of data only.
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Short Papers
Paper Nr: 3
Title:

Online Adaptive Filters to Classify Left and Right Hand Motor Imagery
Authors:

Kais Belwafi, Ridha Djemal, Fakhreddine Ghaffari, Olivier Romain, Bouraoui Ouni and Sofien Gannouni
Abstract: Sensorimotor rhythms (SMRs) caused by motor imagery are key issues for subject with severe disabilities when controlling home devices. However, the development of such EEG-based control system requires a great effort to reach a high accuracy in real-time. Furthermore, BCIs have to confront with inter-individual variability, imposing to the parameters of the methods to be adapted to each subjects. In this paper, we propose a novel EEG-based solution to classify right and left hands(RH and LH) thoughts. Our approach integrates adaptive filtering techniques customized for each subject during the training phase to increase the accuracy of the proposed system. The validation of the proposed architecture is conducted using existing data sets provided by BCI-competition and then using our own on-line validation platform experienced with four subjects. Common Spatial Pattern (CSP) is used for feature extraction to extract features vector from µ and β bands. These features are classified by the Linear Discriminant Analysis (LDA) algorithm. Our prototype integrates the Open-BCI acquisition system with 8 channels connected to Matlab environment in which we integrated all EEG signal processing including the adaptive filtering. The proposed system achieves 80.5% of classification accuracy, which makes approach a promising method to control an external devices based on the thought of LH and RH movement.
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Paper Nr: 6
Title:

Localisation of Epileptic Cerebral Activity Generators by 3D Spline Interpolation
Authors:

Ibtihel Nouira, Asma Ben Abdallah, Mohamed Hédi Bedoui and Mohamed Dogui
Abstract: The objective of this work is to manage the EEG signals for the localization of epileptic activity generators from brain measurements. Starting from 19 measured EEG signals, a 3D spline method was used to obtain 128 interpolated EEG signals. The evaluation of the 3D spline results was realized by computing the Root Mean Squared Error (RMSE). A Spectral variation mapping of brain waves with 128 channels was given by using the Fast Fourier Transform (FFT).