scholarly journals A non-invasive multimodal foetal ECG–Doppler dataset for antenatal cardiology research

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Eleonora Sulas ◽  
Monica Urru ◽  
Roberto Tumbarello ◽  
Luigi Raffo ◽  
Reza Sameni ◽  
...  
Keyword(s):  
Ground Truth ◽  
Signal Acquisition ◽  
Heart Activity ◽  
Non Invasive ◽  
Electrophysiological Recordings ◽  
Pulsed Wave Doppler ◽  
Signal Processing Algorithms ◽  
Respiration Signal ◽  
Processing Algorithms ◽  
Foetal Ecg

AbstractNon-invasive foetal electrocardiography (fECG) continues to be an open topic for research. The development of standard algorithms for the extraction of the fECG from the maternal electrophysiological interference is limited by the lack of publicly available reference datasets that could be used to benchmark different algorithms while providing a ground truth for foetal heart activity when an invasive scalp lead is unavailable. In this work, we present the Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research (NInFEA), the first open-access multimodal early-pregnancy dataset in the field that features simultaneous non-invasive electrophysiological recordings and foetal pulsed-wave Doppler (PWD). The dataset is mainly conceived for researchers working on fECG signal processing algorithms. The dataset includes 60 entries from 39 pregnant women, between the 21st and 27th week of gestation. Each dataset entry comprises 27 electrophysiological channels (2048 Hz, 22 bits), a maternal respiration signal, synchronised foetal trans-abdominal PWD and clinical annotations provided by expert clinicians during signal acquisition. MATLAB snippets for data processing are also provided.

scholarly journals Automatic Detection of Noisy Signals in sEMG Grids Using Statistical Thresholding

2021 ◽  
Vol 58 (1) ◽  
Author(s):  
Khalil Ullah ◽  
Khalid Shah
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Visual Inspection ◽  
Automatic Detection ◽  
Ground Truth ◽  
Noisy Channels ◽  
Perfect Agreement ◽  
Emg Signal ◽  
Signal Processing Algorithms ◽  
Processing Algorithms

Electromyogram (EMG) signal is often processed offline, after its acquisition, using digital signal processing algorithms to extract muscle anatomical and physiological information. As most of the signal processing algorithms work on an adequate quality of the signals, thus quality checking of the EMG in real-time during its acquisition is of immense importance. In multi-channel sEMG signals, usually there are some noisy or bad channels. If the noise is of low level, it is of little concern but high level of noise can limit the usefulness of the EMG. To make sure acquisition of a good quality EMG signal in terms of SNR, one way to detect noisy channels is through visual inspection by an expert human operator, however visual inspection of multiple electrodes in real-time is not possible and is also expensive both in terms of time and cost. In this research study, we propose a novel method for automatic detection of noisy channels in multi-channel surface EMG signals based on statistical thresholding of several parameters. The results of the proposed method are in perfect agreement with the ground truth for simulated EMG signals, with an accuracy of 98.6%.

scholarly journals The Overview of Human Localization and Vital Sign Signal Measurement Using Handheld IR-UWB Through-Wall Radar

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 402
Author(s):  
Degui Yang ◽  
Zhengliang Zhu ◽  
Junchao Zhang ◽  
Buge Liang
Keyword(s):  
Remote Sensing ◽  
Signal Processing ◽  
Vital Sign ◽  
Human Detection ◽  
Research Progress ◽  
Non Invasive ◽  
Signal Measurement ◽  
Blind Area ◽  
Signal Processing Algorithms ◽  
Processing Algorithms

Obtaining information (e.g., position, respiration, and heartbeat rates) on humans located behind opaque and non-metallic obstacles (e.g., walls and wood) has prompted the development of non-invasive remote sensing technologies. Due to its excellent features like high penetration ability, short blind area, fine-range resolution, high environment adoption capabilities, low cost and power consumption, and simple hardware design, impulse radio ultra-wideband (IR-UWB) through-wall radar has become the mainstream primary application radar used for the non-invasive remote sensing. IR-UWB through-wall radar has been developed for nearly 40 years, and various hardware compositions, deployment methods, and signal processing algorithms have been introduced by many scholars. The purpose of these proposed approaches is to obtain human information more accurately and quickly. In this paper, we focus on IR-UWB through-wall radar and introduce the key advances in system design and deployment, human detection theory, and signal processing algorithms, such as human vital sign signal measurement methods and moving human localization. Meanwhile, we discuss the engineering pre-processing methods of IR-UWB through-wall radar. The lasts research progress in the field is also presented. Based on this progress, the conclusions and the development directions of the IR-UWB through-wall radar in the future are also preliminarily forecasted.

A NOVEL EXTENSIVE EX-VIVO OCT DATABASE FROM MURINE MODELS OF COLORECTAL CANCER

2021 ◽  
Vol 108 (Supplement_3) ◽  
Author(s):  
J Bote ◽  
J F Ortega-Morán ◽  
C L Saratxaga ◽  
B Pagador ◽  
A Picón ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Structural Information ◽  
Ex Vivo ◽  
Ground Truth ◽  
Colon Polyps ◽  
Learning Methods ◽  
Non Invasive ◽  
Machine Learning Methods ◽  
In Situ Methods

Abstract INTRODUCTION New non-invasive technologies for improving early diagnosis of colorectal cancer (CRC) are demanded by clinicians. Optical Coherence Tomography (OCT) provides sub-surface structural information and offers diagnosis capabilities of colon polyps, further improved by machine learning methods. Databases of OCT images are necessary to facilitate algorithms development and testing. MATERIALS AND METHODS A database has been acquired from rat colonic samples with a Thorlabs OCT system with 930nm centre wavelength that provides 1.2KHz A-scan rate, 7μm axial resolution in air, 4μm lateral resolution, 1.7mm imaging depth in air, 6mm x 6mm FOV, and 107dB sensitivity. The colon from anaesthetised animals has been excised and samples have been extracted and preserved for ex-vivo analysis with the OCT equipment. RESULTS This database consists of OCT 3D volumes (C-scans) and 2D images (B-scans) of murine samples from: 1) healthy tissue, for ground-truth comparison (18 samples; 66 C-scans; 17,478 B-scans); 2) hyperplastic polyps, obtained from an induced colorectal hyperplastic murine model (47 samples; 153 C-scans; 42,450 B-scans); 3) neoplastic polyps (adenomatous and adenocarcinomatous), obtained from clinically validated Pirc F344/NTac-Apcam1137 rat model (232 samples; 564 C-scans; 158,557 B-scans); and 4) unknown tissue (polyp adjacent, presumably healthy) (98 samples; 157 C-scans; 42,070 B-scans). CONCLUSIONS A novel extensive ex-vivo OCT database of murine CRC model has been obtained and will be openly published for the research community. It can be used for classification/segmentation machine learning methods, for correlation between OCT features and histopathological structures, and for developing new non-invasive in-situ methods of diagnosis of colorectal cancer.

scholarly journals A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Dheeraj Rathee ◽  
Haider Raza ◽  
Sujit Roy ◽  
Girijesh Prasad
Keyword(s):  
Scientific Community ◽  
Brain Computer Interface ◽  
Sampling Frequency ◽  
Computer Interface ◽  
Word Generation ◽  
Machine Learning Methods ◽  
Computer Interfaces ◽  
Signal Processing Algorithms ◽  
Processing Algorithms ◽  
Healthy Participants

AbstractRecent advancements in magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) have shown great potential. However, the performance of current MEG-BCI systems is still inadequate and one of the main reasons for this is the unavailability of open-source MEG-BCI datasets. MEG systems are expensive and hence MEG datasets are not readily available for researchers to develop effective and efficient BCI-related signal processing algorithms. In this work, we release a 306-channel MEG-BCI data recorded at 1KHz sampling frequency during four mental imagery tasks (i.e. hand imagery, feet imagery, subtraction imagery, and word generation imagery). The dataset contains two sessions of MEG recordings performed on separate days from 17 healthy participants using a typical BCI imagery paradigm. The current dataset will be the only publicly available MEG imagery BCI dataset as per our knowledge. The dataset can be used by the scientific community towards the development of novel pattern recognition machine learning methods to detect brain activities related to motor imagery and cognitive imagery tasks using MEG signals.

scholarly journals A Compact UWB Indoor and Through-Wall Radar with Precise Ranging and Tracking

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Yinan Yu ◽  
Jian Yang ◽  
Tomas McKelvey ◽  
Borys Stoew
Keyword(s):  
Signal Processing ◽  
Theoretical Analysis ◽  
Short Range ◽  
Indoor Environment ◽  
Low Cost ◽  
Moving Target ◽  
Radiation Characteristics ◽  
Uwb Radar ◽  
Signal Processing Algorithms ◽  
Processing Algorithms

Ultrawideband (UWB) technology has many advantages compared to its narrowband counterpart in many applications. We present a new compact low-cost UWB radar for indoor and through-wall scenario. The focus of the paper is on the development of the signal processing algorithms for ranging and tracking, taking into account the particular properties of the UWB CMOS transceiver and the radiation characteristics of the antennas. Theoretical analysis for the algorithms and their evaluations by measurements are presented in the paper. The ranging resolution of this UWB radar has achieved 1-2 mm RMS accuracy for a moving target in indoor environment over a short range, and Kalman tracking algorithm functions well for the through-wall detection.

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