Multiparameter physiological signal reconstruction using NARX Neural Networks

2011 ◽  
Author(s):  
R. Matthew Wham ◽  
Xiaopeng Zhao
Keyword(s):  
Neural Networks ◽  
Signal Reconstruction ◽  
Physiological Signal

Automated Recognition of Arrhythmia Using Deep Neural Networks for 12-Lead Electrocardiograms with Fractional Time–Frequency Domain Extension

2020 ◽  
Vol 10 (11) ◽  
pp. 2764-2767
Author(s):  
Chuanbin Ge ◽  
Di Liu ◽  
Juan Liu ◽  
Bingshuai Liu ◽  
Yi Xin
Keyword(s):  
Neural Networks ◽  
Frequency Domain ◽  
Deep Neural Networks ◽  
Fractional Fourier Transform ◽  
Training Dataset ◽  
Clinical Tool ◽  
Time Frequency ◽  
Ecg Signals ◽  
Physiological Signal ◽  
Ecg Data

Arrhythmia is a group of conditions in which the heartbeat is irregular. There are many types of arrhythmia. Some can be life-threatening. Electrocardiogram (ECG) is an effective clinical tool used to diagnosis arrhythmia. Automatic recognition of different arrhythmia types in ECG signals has become an important and challenging issue. In this article, we proposed an algorithm to detect arrhythmia in 12-lead ECG signals and classify signals into 9 categories. Two 19-layer deep neural networks combining convolutional neural network and gated recurrent unit were proposed to realize this work. The first one was trained directly with the raw 12-lead ECG data while the other one was trained with an 18-"lead" ECG data, where the six extra leads containing morphology information in fractional time–frequency domain were generated utilizing fractional Fourier transform (FRFT). Overall detection results were obtained by fusing the output of these two networks and the final classification results on the testing dataset reports our proposed algorithm obtained a F1 score of 0.855. Furthermore, with our proposed algorithm, a better F1 score 0.81 was attained using training dataset provided by the China Physiological Signal Challenge held in 2018.

scholarly journals Signal recognition and background suppression by matched filters and neural networks for Tunka-Rex

2019 ◽  
Vol 216 ◽  
pp. 02003
Author(s):  
D. Shipilov ◽  
P.A. Bezyazeekov ◽  
N.M. Budnev ◽  
D. Chernykh ◽  
O. Fedorov ◽  
...  
Keyword(s):  
Neural Networks ◽  
Antenna Arrays ◽  
Signal Reconstruction ◽  
Cosmic Ray ◽  
Matched Filtering ◽  
Background Suppression ◽  
Signal Recognition ◽  
Air Showers ◽  
Matched Filters ◽  
Prospects Of Application

The Tunka Radio Extension (Tunka-Rex) is a digital antenna array, which measures radio emission of the cosmic-ray air-showers in the frequency band of 30-80 MHz. Tunka-Rex is co-located with the TAIGA experiment in Siberia and consists of 63 antennas, 57 of them are in a densely instrumented area of about 1 km2. In the present workwe discuss the improvements of the signal reconstruction applied for Tunka-Rex. At the first stage we implemented matched filtering using averaged signals as template. The simulation study has shown that matched filtering allows one to decrease the threshold of signal detection and increase its purity. However, the maximum performanceof matched filtering is achievable only in case of white noise, while in reality the noise is not fully random due to different reasons. To recognize hidden features of the noise and treat them, we decided to use convolutional neural network with autoencoder architecture. Taking the recorded trace as an input, the autoencoder returns denoised traces, i.e. removes all signal-unrelated amplitudes. We present the comparison between the standard method of signal reconstruction, matched filtering and the autoencoder, and discuss the prospects of application of neural networks for lowering the threshold of digital antenna arrays for cosmic-ray detection.

scholarly journals Deep Neural Networks and Transfer Learning on a Multivariate Physiological Signal Dataset

2021 ◽  
Vol 8 (3) ◽  
pp. 35
Author(s):  
Andrea Bizzego ◽  
Giulio Gabrieli ◽  
Gianluca Esposito
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Deep Neural Networks ◽  
Electrodermal Activity ◽  
Physiological Signals ◽  
Support Vector ◽  
Specific Measure ◽  
Physiological Signal ◽  
Different Types ◽  
Acceleration Signals

While Deep Neural Networks (DNNs) and Transfer Learning (TL) have greatly contributed to several medical and clinical disciplines, the application to multivariate physiological datasets is still limited. Current examples mainly focus on one physiological signal and can only utilise applications that are customised for that specific measure, thus it limits the possibility of transferring the trained DNN to other domains. In this study, we composed a dataset (n=813) of six different types of physiological signals (Electrocardiogram, Electrodermal activity, Electromyogram, Photoplethysmogram, Respiration and Acceleration). Signals were collected from 232 subjects using four different acquisition devices. We used a DNN to classify the type of physiological signal and to demonstrate how the TL approach allows the exploitation of the efficiency of DNNs in other domains. After the DNN was trained to optimally classify the type of signal, the features that were automatically extracted by the DNN were used to classify the type of device used for the acquisition using a Support Vector Machine. The dataset, the code and the trained parameters of the DNN are made publicly available to encourage the adoption of DNN and TL in applications with multivariate physiological signals.

scholarly journals AUTOMATIC DETECTION OF EPILEPSY EEG USING NEURAL NETWORKS

2012 ◽  
pp. 241-245
Author(s):  
SATYANARAYANA VOLLALA ◽  
KARNAKAR GULLA
Keyword(s):  
Neural Networks ◽  
Detection System ◽  
Statistical Parameter ◽  
Approximate Entropy ◽  
Epileptic Activity ◽  
Diagnostic Systems ◽  
Physiological Signal ◽  
Eeg Data ◽  
Eeg Recordings ◽  
First Time

The electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a timeconsuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy has emerged in recent years.This paper proposes a neural-network-based automated epileptic EEG detection system that uses approximate entropy (ApEn) as the input feature. ApEn is a statistical parameter that measures the predictability of the current amplitude values of a physiological signal based on its previous amplitude values. It is known that the value of the ApEn drops sharply during an epileptic seizure and this fact is used in the proposed system.Two different types of neural networks, namely, Elman and probabilistic neural networks are considered. ApEn is used for the first time in the proposed system for the detection of epilepsy using neural networks. It is shown that the overall accuracy values as high as 100% can be achieved by using the proposed system.

scholarly journals Defect Quantification with Thermographic Signal Reconstruction and Artificial Neural Networks

2006 ◽  
Author(s):  
Hernan Benitez ◽  
Clemente Ibarra-Castanedo ◽  
Humberto Loaiza ◽  
Eduardo Caicedo ◽  
Abdelhakim Bendada ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Signal Reconstruction ◽  
Artificial Neural ◽  
Defect Quantification
Sign in / Sign up

Export Citation Format

Share Document