scholarly journals Adaptive single-channel EEG artifact removal with applications to clinical monitoring

2021 ◽  
Author(s):  
Matteo Dora ◽  
David holcman
Keyword(s):  
Real Time ◽  
Single Channel ◽  
Low Cost ◽  
Computational Cost ◽  
Clinical Monitoring ◽  
Artifact Removal ◽  
Anesthesia Monitoring ◽  
Data Driven Approach ◽  
Movement Artifacts ◽  
Low Computational Cost

Objective: Electroencephalography (EEG) has become very common in clinical practice due to its relatively low cost, ease of installation, non-invasiveness, and good temporal resolution. Portable EEG devices are increasingly popular in clinical monitoring applications such as sleep scoring or anesthesia monitoring. In these situations, for reasons of speed and simplicity only few electrodes are used and contamination of the EEG signal by artifacts is inevitable. Visual inspection and manual removal of artifacts is often not possible, especially in real-time applications. Our goal is to develop a flexible technique to remove EEG artifacts in these contexts with minimal supervision. Methods: We propose here a new wavelet-based method which allows to remove artifacts from single-channel EEGs. The method is based on a datadriven renormalization of the wavelet components and is capable of adaptively attenuate artifacts of different nature. We benchmark our method against alternative artifact removal techniques. Results: We assessed the performance of the proposed method on publicly available datasets comprising ocular, muscular, and movement artifacts. The proposed method shows superior performances on different kinds of artifacts and signal-to-noise levels. Finally, we present an application of our method to the monitoring of general anesthesia. Conclusions: We show that our method can successfully attenuate various types of artifacts in single-channel EEG. Significance: Thanks to its data-driven approach and low computational cost, the proposed method provides a valuable tool to remove artifacts in real-time EEG applications with few electrodes, such as monitoring in special care units.

scholarly journals Status Estimation and In-Process Connection of Kanbans Using BLE Beacons and LPWA Network to Implement Intra-Traceability for the Kanban System

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5038
Author(s):  
Kosuke Shima ◽  
Masahiro Yamaguchi ◽  
Takumi Yoshida ◽  
Takanobu Otsuka
Keyword(s):  
Inventory Management ◽  
Low Cost ◽  
Computational Cost ◽  
Low Energy ◽  
Manufacturing Strategy ◽  
Implementation Cost ◽  
Measurement Systems ◽  
Process Route ◽  
Kanban System ◽  
Low Computational Cost

IoT-based measurement systems for manufacturing have been widely implemented. As components that can be implemented at low cost, BLE beacons have been used in several systems developed in previous research. In this work, we focus on the Kanban system, which is a measure used in manufacturing strategy. The Kanban system emphasizes inventory management and is used to produce only required amounts. In the Kanban system, the Kanban cards are rotated through the factory along with the products, and when the products change to a different process route, the Kanban card is removed from the products and the products are assigned to another Kanban. For this reason, a single Kanban cannot trace products from plan to completion. In this work, we propose a system that uses a Bluetooth low energy (BLE) beacon to connect Kanbans in different routes but assigned to the same products. The proposed method estimates the beacon status of whether the Kanban is inside or outside a postbox, which can then be computed by a micro controller at low computational cost. In addition, the system connects the Kanbans using the beacons as paired connection targets. In an experiment, we confirmed that the system connected 70% of the beacons accurately. We also confirmed that the system could connect the Kanbans at a small implementation cost.

scholarly journals Decomposition-Assisted Computational Technique Based on Surrogate Modeling for Real-Time Simulations

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Nariman Fouladinejad ◽  
Nima Fouladinejad ◽  
Mohamad Kasim Abdul Jalil ◽  
Jamaludin Mohd Taib
Keyword(s):  
Real Time ◽  
Driving Simulator ◽  
Low Cost ◽  
Computational Cost ◽  
Surrogate Modeling ◽  
Computational Technique ◽  
Model Decomposition ◽  
Real Time Simulation ◽  
Time Simulation ◽  
Simulation Systems

The development of complex simulation systems is extremely costly as it requires high computational capability and expensive hardware. As cost is one of the main issues in developing simulation components, achieving real-time simulation is challenging and it often leads to intensive computational burdens. Overcoming the computational burden in a multidisciplinary simulation system that has several subsystems is essential in producing inexpensive real-time simulation. In this paper, a surrogate-based computational framework was proposed to reduce the computational cost in a high-dimensional model while maintaining accurate simulation results. Several well-known metamodeling techniques were used in creating a global surrogate model. Decomposition approaches were also used to simplify the complexities of the system and to guide the surrogate modeling processes. In addition, a case study was provided to validate the proposed approach. A surrogate-based vehicle dynamic model (SBVDM) was developed to reduce computational delay in a real-time driving simulator. The results showed that the developed surrogate-based model was able to significantly reduce the computing costs, unlike the expensive computational model. The response time in surrogate-based simulation was considerably faster than the conventional model. Therefore, the proposed framework can be used in developing low-cost simulation systems while yielding high fidelity and fast computational output.

scholarly journals High Quality - Low Computational Cost Technique for Automated Principal Object Segmentation Applied in Solar and Medical Imaging

2016 ◽  
Vol 9 (2) ◽  
pp. 23 ◽  
Author(s):  
Sofyan M. A. Hayajneh ◽  
AbdulRahman Rashad ◽  
Omar A. Saraereh ◽  
Obaida Al hazaimeh
Keyword(s):  
Real Time ◽  
Computational Cost ◽  
Real Life ◽  
Morphological Operation ◽  
Detection Process ◽  
Good Potential ◽  
Bit Plane ◽  
Noisy Background ◽  
Low Computational Cost ◽  
Bit Plane Slicing

The objective of this paper is to introduce a fully computerized, simple and low-computational cost technique that can be used in the preprocessing stages of digital images. This technique is specially designed to detect the principal (largest) closed shape object that embody the useful information in certain image types and neglect and avoid other noisy objects and artifacts. The detection process starts by calculating certain statistics of the image to estimate the amount of bit-plane slicing required to exclude the non-informative and noisy background. A simple closing morphological operation is then applied and followed by circular filter applied only on the outer coarse edge to finalize the detection process.  The proposed technique takes its importance from the huge explosion of images that need accurate processing in real time speedy manner. The proposed technique is implemented using MATLAB and tested on many solar and medical images; it was shown by the quantitative evaluation that the proposed technique can handle real-life (e.g. solar, medical fundus) images and shows very good potential even under noisy and artifacts conditions. Compared to the publicly available datasets, 97% and 99% of similarity detection is achieved in medical and solar images, respectively. Although it is well-know, the morphological bit-plane slicing technique is hoped to be used in the preprocessing stages of different applications to ease the subsequent image processing stages especially in real time applications where the proposed technique showed dramatic (~100 times) saving in processing time.

scholarly journals A Time-Consistent Video Segmentation Algorithm Designed for Real-Time Implementation

VLSI Design ◽  
2008 ◽  
Vol 2008 ◽  
pp. 1-12 ◽  
Author(s):  
M. El Hassani ◽  
S. Jehan-Besson ◽  
L. Brun ◽  
M. Revenu ◽  
M. Duranton ◽  
...  
Keyword(s):  
Real Time ◽  
Video Segmentation ◽  
Computational Cost ◽  
Segmentation Algorithm ◽  
Motion Information ◽  
Temporal Consistency ◽  
Merging Process ◽  
Specific Order ◽  
Previous Frame ◽  
Low Computational Cost

We propose a time-consistent video segmentation algorithm designed for real-time implementation. Our algorithm is based on a region merging process that combines both spatial and motion information. The spatial segmentation takes benefit of an adaptive decision rule and a specific order of merging. Our method has proven to be efficient for the segmentation of natural images with few parameters to be set. Temporal consistency of the segmentation is ensured by incorporating motion information through the use of an improved change-detection mask. This mask is designed using both illumination differences between frames and region segmentation of the previous frame. By considering both pixel and region levels, we obtain a particularly efficient algorithm at a low computational cost, allowing its implementation in real-time on the TriMedia processor for CIF image sequences.

scholarly journals Whole-Body Joint Angle Estimation for Real-Time Humanoid Robot Imitation Based on Gaussian Process Dynamical Model and Particle Filter

2019 ◽  
Vol 10 (1) ◽  
pp. 5
Author(s):  
Jian Mi ◽  
Yasutake Takahashi
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Humanoid Robot ◽  
Joint Angle ◽  
Computational Cost ◽  
High Dimensional ◽  
Motion Model ◽  
Joint Angles ◽  
Compact State ◽  
Low Computational Cost

Real-time imitation enables a humanoid robot to mirror the behavior of humans, being important for applications of human–robot interaction. For imitation, the corresponding joint angles of the humanoid robot should be estimated. Generally, a humanoid robot comprises dozens of joints that construct a high-dimensional exploration space for estimating the joint angles. Although a particle filter can estimate the robot state and provides a solution for estimating joint angles, the computational cost becomes prohibitive given the high dimension of the exploration space. Furthermore, a particle filter can only estimate the joint angles accurately using a motion model. To realize accurate joint angle estimation at low computational cost, Gaussian process dynamical models (GPDMs) can be adopted. Specifically, a compact state space can be constructed through the GPDM learning of high-dimensional time-series motion data to obtain a suitable motion model. We propose a GPDM-based particle filter using a compact state space from the learned motion models to realize efficient estimation of joint angles for robot imitation. Simulations and real experiments demonstrate that the proposed method efficiently estimates humanoid robot joint angles at low computational cost, enabling real-time imitation.

scholarly journals An IoT Assisted Real-Time High CMRR Wireless Ambulatory ECG Monitoring System with Arrhythmia Detection

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1871
Author(s):  
Hassan Ali ◽  
Hein Htet Naing ◽  
Raziq Yaqub
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Monitoring System ◽  
Rural Areas ◽  
Single Channel ◽  
Low Cost ◽  
Notch Filter ◽  
Commercial Application ◽  
Low Latency ◽  
Ecg Monitoring

The absence of cardiovascular disease (CVD) diagnostic and management solutions cause significant morbidity among populations in rural areas and the coronavirus disease of 2019 (COVID-19) emergency. To tackle this problem, in this paper, the development of an Internet of things (IoT) assisted ambulatory electrocardiogram (ECG) monitoring system is presented. The system’s wearable single-channel data acquisition device supports 25 h of continuous operation. A right leg drive (RLD) circuit supported analog frontend (AFE) with a high common mode rejection ratio (CMRR) of 121 dB and a digitally implemented notch filter is used to suppress power-line frequency interference. The wearable device continuously sends the collected ECG data via Bluetooth to the user’s smartphone. An application on the user’s smartphone renders real-time ECG trace and heart rate and detects abnormal heart rhythms. This data are then shared in real-time with the user’s doctor via a real-time cloud database. An application on the doctor’s smartphone allows real-time visualization of this data and detection of arrhythmias. Simulations and experimental results demonstrate that reliable ECG signals can be captured with low latency and the heart rate computation is comparable to a commercial application. Low cost, scalability, low latency, real-time ECG monitoring, and improved performance of the system make the system highly suitable for the real-time remote identification and management of CVDs in users of rural areas and in the COVID-19 pandemic.

scholarly journals Software System Development to Diagnose Heart Diseases

2019 ◽  
Author(s):  
Renan Yuji Koga Ferreira ◽  
Guilherme Camargo Fabricio De Melloy ◽  
Fabio Sakurayz ◽  
Wesley Attrot
Keyword(s):  
Feature Extraction ◽  
Early Detection ◽  
Embedded System ◽  
Wavelet Transforms ◽  
System Development ◽  
Heart Diseases ◽  
Low Cost ◽  
Computational Cost ◽  
Extraction Algorithm ◽  
Low Computational Cost

Many deaths are caused from heart diseases and several of them could be prevented with early detection. Many people do not have conditions to seek for a doctor or sometimes there are not enough physicians to attend them. In order to detect heart diseases we are developing an electrocardiogram feature extraction algorithm using wavelet transforms prioritizing a low computational cost. This algorithm will be integrated in an embedded system that is under development. This system is going to be accessible, portable and have low cost, because we intend to assist people, mostly those who live in precarious regions, that do not have a physician to attend them. To execute tests on our algorithm we will use the ECG records from MITBIH database and after that we will classify the heartbeats in order to detect anomalies on them.

High Speed Method for CCT Estimation: Fundamental, Implemention and Application in Real-Time Simulation

2017 ◽  
Vol 18 (4) ◽  
Author(s):  
Mehdi Zareian Jahromi ◽  
Shahram Montaser Kouhsari
Keyword(s):  
Kinetic Energy ◽  
Power Systems ◽  
Real Time ◽  
High Speed ◽  
Transient Stability ◽  
Computational Cost ◽  
Time Simulation ◽  
Clearing Time ◽  
Low Computational Cost ◽  
Real Time Application

AbstractThis paper proposes a hybrid method based on corrected kinetic energy to determine the critical clearing time. The proposed method structure has been implemented utilizing network preserving model to take details of power systems into consideration. To implement proposed method, the initial critical point is estimated using new concept of equal area criterion. Critical corrected kinetic energy is obtained using method which determines the amount of severity of generator contribution in a fault scenario. Due to the latter, the behavior of AVR and governor are taken into account. From initial and corrected kinetic energy of generators and consequently system, high precision critical clearing time is calculated. In order to validate the proposed method, some comprehensive case studies have been conducted on the IEEE9-bus, IEEE39-bus and IEEE68-bus test systems. Some comprehensiveness in considering the details, simplicity in implementation and low computational cost are the outstanding features of the proposed approach. Also, simulation results approve that the proposed approach can be used in real-time application without loss of any detail in transient stability assessment.

Computational Acceleration of Real-Time Kernel-Based Tracking System

2016 ◽  
Vol 25 (04) ◽  
pp. 1650030 ◽  
Author(s):  
Manoj Pandey ◽  
J. S. Ubhi ◽  
Kota Solomon Raju
Keyword(s):  
Real Time ◽  
Video Processing ◽  
Tracking System ◽  
Low Cost ◽  
Computational Cost ◽  
Window Size ◽  
Image Resolution ◽  
Frame Rate ◽  
Color Distribution ◽  
Computational Speed

Object tracking in real-time is one of the applications of video processing, where the required computational cost is high due to intensive high data processing. In order to solve these problems, this paper presents an embedded solution, where the Hardware/Software (HW/SW) co-design architecture is used for the implementation of well-known kernel-based tracking system. In this algorithm, the target is searched in consecutive frame by maximizing the statistical match with similarity estimation of color distribution. The whole tracking system is implemented on low cost Field Programmable Gate Array (FPGA) device with image resolution of 1280[Formula: see text]720 pixels and target window size of 160[Formula: see text]80 pixels. The HW/SW co-design architecture is proposed to accelerate the computational speed of the system. The performance of the system is evaluated in terms of execution speed and frame rate compared with software based implementation. The hardware cost of design is also compared with other existing methods. The proposed design achieves 22 times computational speed and maximum 60 Frames Per Second (FPS) compared with software based design.

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