Browsing by Author "Fahier, Nicolas"

Now showing 1 - 5 of 5

An Advanced Patient Bedside Monitoring System Design Based on WBAN and Telemedicine Technologies
(2015)
Fahier, Nicolas
;
Huang, Wei-Chin
;
Chou, Chia-Ching
;
Fang, Wai-Chi
;
電機學院
An advanced patient-centric bedside monitoring system design based on wireless body area sensor networks and telemedicine technologies is proposed in this paper where each patient uses sensors with wireless transmission capabilities and transmit the physiological signals within a larger advanced network, which is connected to a secured section of the public network. This work intends to reduce the number of connections to the patient and proposes a wireless intelligent Intensive Care Unit (ICU) data security system to provide a wireless remote access to a patient\'s information anytime, and increase the information security to protect the patient\'s privacy.
An Advanced Plug-and-Play Network Architecture for Wireless Body Area Network Using HBC, Zigbee and NFC
(2014-01-01)
Fahier, Nicolas
;
Fang, Wai-Chi
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
This paper presents an advanced network system for Wireless Body Area Network (WBAN) using different types of transmission media and wireless technologies to satisfy the needs of WBAN. Based on current research and surveys, this network architecture constitutes an innovative plug and play sensor network for WBAN by using Human Body Communication (HBC), Zigbee and Near Field Communication (NFC).
An HBC-Based Biosensor Transmitter for Time Division Multiple Access Network
(2017-01-01)
Fahier, Nicolas
;
Fang, Wai-Chi
;
電機學院
;
電子工程學系及電子研究所
;
College of Electrical and Computer Engineering
A wireless medical biosensor using Human Body Communication(HBC)for data transmission is presented in this paper. HBC is included in the IEEE Standard 802.15.6 for Local and Metropolitan Area Network and presents the characteristic of being a new non-radio frequency type of wireless communication as it uses the human body as a transmission medium. The multiple access technique should be Time Division Multiple Access(TDMA) and data are transmitted over HBC capacitive coupling aside from a bio-signal sensing system. The transmitter time characteristics shows the feasibility of implementing a TDMA body sensors star network centralized into a single hub for a single connection to Wide Area Networks. The proposed system is composed by a bio-sensor unit and an HBC transmitter.
Development and Validation of an EEG-Based Real-Time Emotion Recognition System Using Edge AI Computing Platform With Convolutional Neural Network System-on-Chip Design
(2019-12-01)
Fang, Wai-Chi
;
Wang, Kai-Yen
;
Fahier, Nicolas
;
Ho, Yun-Lung
;
Huang, Yu-De
This study proposed an electroencephalogram (EEG)-based real-time emotion recognition hardware system architecture based on multiphase convolutional neural network (CNN) algorithm implemented on a 28-nm technology chip and on field programmable gate array (FPGA) for binary and quaternary classification. Sample entropy, differential asymmetry, short-time Fourier transform, and a channel reconstruction method were used for emotion feature extraction. In this work, six EEG channels were selected (FP1, FP2, F3, F4, F7, and F8), and EEG images were generated from spectrogram fusions. The complete CNN architecture included training and acceleration for efficient artificial intelligence (AI) edge application, and we proposed a multiphase CNN execution method to accommodate hardware resource constraints. Datasets of 32 subjects from the DEAP database were used to validate the proposed design, exhibiting mean accuracies for valance binary classification and valance-arousal quaternary classification of 83.36% and 76.67%, respectively. The core area and total power consumption of the CNN chip were 1.83 x 1.83 mm(2), respectively, and 76.61 mW. The chip operation was validated using ADVANTEST V93000 PS1600, and the training process and real-time classification processing time took 0.12495 ms and 0.02634 ms for each EEG image, respectively. The proposed EEG-based realtime emotion recognition system included a dry electrode EEG headset, feature extraction processor, CNN chip platform, and graphical user interface, and the execution time costed 450 ms for each emotional state recognition.
Multi-Leads ECG Premature Ventricular Contraction Detection using Tensor Decomposition and Convolutional Neural Network
(2019-01-01)
Hoang, Tung
;
Fahier, Nicolas
;
Fang, Wai-Chi
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
Premature Ventricular Contraction refers to irregular heartbeat and is one common symptom to several heart diseases. Currently, physiological databases are not only large in volume but also complex in dimensional aspect, so that intelligent systems that can process multi-dimensional data to detect Premature Ventricular Contraction (PVC) are highly needed. In this paper, we propose novel models of combinations of multi-leads ECG from the 12 lead ECG St. Petersburg Arrhythmias database to detect PVCs and optimize the required data pre-processing resources for Convolutional Neural Network(CNN) implemented on wearable devices. Although exhibiting fewer performances than previous works, the proposed method is able to perform automatic features extraction, reduce the CNN complexity and is scalable to be applied to 3-Lead to 16-Lead ECG systems. The combination scenarios include Wavelet fusion method and Tucker-decomposition before CNN is deployed as a classifier. The achieved accuracy to detect PVC for tensor-based feature extraction, the most optimized processing technique, is 90.84% with a sensitivity of 78.60% and a specificity of 99.86%.