scholarly journals Identifying Individuals Using Eigenbeat Features of Electrocardiogram

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yogendra Narain Singh ◽  
Sanjay Kumar Singh
Keyword(s):  
Recognition Accuracy ◽  
Normal Subjects ◽  
Orthogonal Basis ◽  
New Method ◽  
Individual Identification ◽  
Lower Dimension ◽  
Biometric System ◽  
Electrocardiogram Ecg

The authors of this paper present a new method to characterize the electrocardiogram (ECG) for individual identification. We propose an ECG biometric system which is insensitive to noise signals and muscle flexure. The method utilizes the principal of linearly projecting the heartbeat features into a subspace of lower dimension using an orthogonal basis that represents the most significant features to distinguish the individuals. The performance of the proposed biometric system is evaluated on the subjects of both health statuses such as the ECG recordings of MIT-BIH Arrhythmia database and the ECG recordings of normal subjects prepared at IIT(BHU). The result demonstrates that the derived eigenbeat features from proposed ECG characterization perform better and achieve the recognition accuracy of 91.42% and 95.55% on the subjects of MIT-BIH Arrhythmia database and IIT(BHU) database, respectively.

scholarly journals Individual Identification Using Linear Projection of Heartbeat Features

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Yogendra Narain Singh
Keyword(s):  
Healthy Subjects ◽  
Orthogonal Basis ◽  
Individual Identification ◽  
Support Vector ◽  
Ecg Signal ◽  
Linear Projection ◽  
Novel Method ◽  
Electrocardiogram Ecg ◽  
Better Than

This paper presents a novel method to use the electrocardiogram (ECG) signal as biometrics for individual identification. The ECG characterization is performed using an automated approach consisting of analytical and appearance methods. The analytical method extracts the fiducial features from heartbeats while the appearance method extracts the morphological features from the ECG trace. We linearly project the extracted features into a subspace of lower dimension using an orthogonal basis that represent the most significant features for distinguishing heartbeats among the subjects. Result demonstrates that the proposed characterization of the ECG signal and subsequently derived eigenbeat features are insensitive to signal variations and nonsignal artifacts. The proposed system utilizing ECG biometric method achieves the best identification rates of 85.7% for the subjects of MIT-BIH arrhythmia database and 92.49% for the healthy subjects of our IIT (BHU) database. These results are significantly better than the classification accuracies of 79.55% and 84.9%, reported using support vector machine on the tested subjects of MIT-BIH arrhythmia database and our IIT (BHU) database, respectively.

Multimodal biometric system using deep learning based on face and finger vein fusion

2021 ◽  
pp. 1-13
Author(s):  
Shikhar Tyagi ◽  
Bhavya Chawla ◽  
Rupav Jain ◽  
Smriti Srivastava
Keyword(s):  
High Performance ◽  
Recognition Accuracy ◽  
Error Rates ◽  
Facial Features ◽  
Biometric System ◽  
Deep Convolutional Neural Networks ◽  
Finger Vein ◽  
Biometric Systems ◽  
Overall Performance ◽  
Recognition Systems

Single biometric modalities like facial features and vein patterns despite being reliable characteristics show limitations that restrict them from offering high performance and robustness. Multimodal biometric systems have gained interest due to their ability to overcome the inherent limitations of the underlying single biometric modalities and generally have been shown to improve the overall performance for identification and recognition purposes. This paper proposes highly accurate and robust multimodal biometric identification as well as recognition systems based on fusion of face and finger vein modalities. The feature extraction for both face and finger vein is carried out by exploiting deep convolutional neural networks. The fusion process involves combining the extracted relevant features from the two modalities at score level. The experimental results over all considered public databases show a significant improvement in terms of identification and recognition accuracy as well as equal error rates.

One Extended OTSU Flame Image Recognition Method Using RGBL and Stripe Segmentation

2011 ◽  
Vol 121-126 ◽  
pp. 2141-2145 ◽  
Author(s):  
Wei Gang Yan ◽  
Chang Jian Wang ◽  
Jin Guo
Keyword(s):  
Image Segmentation ◽  
Water Vapor ◽  
Image Recognition ◽  
Recognition Accuracy ◽  
New Method ◽  
Recognition Method ◽  
Otsu’S Method ◽  
Flame Shape ◽  
Image Segmentation Algorithm ◽  
Rgb Image

This paper proposes a new image segmentation algorithm to detect the flame image from video in enclosed compartment. In order to avoid the contamination of soot and water vapor, this method first employs the cubic root of four color channels to transform a RGB image to a pseudo-gray one. Then the latter is divided into many small stripes (child images) and OTSU is employed to perform child image segmentation. Lastly, these processed child images are reconstructed into a whole image. A computer program using OpenCV library is developed and the new method is compared with other commonly used methods such as edge detection and normal Otsu’s method. It is found that the new method has better performance in flame image recognition accuracy and can be used to obtain flame shape from experiment video with much noise.

scholarly journals A New Method to Detect Driver Fatigue Based on EMG and ECG Collected by Portable Non-Contact Sensors

2017 ◽  
Vol 29 (5) ◽  
pp. 479-488 ◽  
Author(s):  
Lin Wang ◽  
Hong Wang ◽  
Xin Jiang
Keyword(s):  
Principal Components ◽  
Physiological Signals ◽  
New Method ◽  
Driver Fatigue ◽  
Independent Variables ◽  
Mode Decomposition ◽  
Calculation Results ◽  
Components Analysis ◽  
Electrocardiogram Ecg ◽  
Contact Sensors

Recently, detection and prediction on driver fatigue have become interest of research worldwide. In the present work, a new method is built to effectively evaluate driver fatigue based on electromyography (EMG) and electrocardiogram (ECG) collected by portable real-time and non-contact sensors. First, under the non-disturbance condition for driver’s attention, mixed physiological signals (EMG, ECG and artefacts) are collected by non-contact sensors located in a cushion on the driver’s seat. EMG and ECG are effectively separated by FastICA, and de-noised by empirical mode decomposition (EMD). Then, three physiological features, complexity of EMG, complexity of ECG, and sample entropy (SampEn) of ECG, are extracted and analysed. Principal components are obtained by principal components analysis (PCA) and are used as independent variables. Finally, a mathematical model of driver fatigue is built, and the accuracy of the model is up to 91%. Moreover, based on the questionnaire, the calculation results of model are consistent with real fatigue felt by the participants. Therefore, this model can effectively detect driver fatigue.

scholarly journals Novel CineECG Derived From Standard 12-Lead ECG Enables Right Ventricle Outflow Tract Localization of Electrical Substrate in Patients With Brugada Syndrome

2020 ◽  
Vol 13 (9) ◽  
Author(s):  
Peter M. van Dam ◽  
Emanuela T. Locati ◽  
Giuseppe Ciconte ◽  
Valeria Borrelli ◽  
Francesca Heilbron ◽  
...  
Keyword(s):  
Right Ventricle ◽  
Brugada Syndrome ◽  
Predictive Value ◽  
Normal Subjects ◽  
Outflow Tract ◽  
Arrhythmogenic Substrate ◽  
The Right ◽  
Type 3 ◽  
Electrocardiogram Ecg

Background: In Brugada syndrome (BrS), diagnosed in presence of a spontaneous or ajmaline-induced type-1 pattern, ventricular arrhythmias originate from the right ventricle outflow tract (RVOT). We developed a novel CineECG method, obtained by inverse electrocardiogram (ECG) from standard 12-lead ECG, to localize the electrical activity pathway in patients with BrS. Methods: The CineECG enabled the temporospatial localization of the ECG waveforms, deriving the mean temporospatial isochrone from standard 12-lead ECG. The study sample included (1) 15 patients with spontaneous type-1 Brugada pattern, and (2) 18 patients with ajmaline-induced BrS (at baseline and after ajmaline), in whom epicardial potential duration maps were available; (3) 17 type-3 BrS pattern patients not showing type-1 BrS pattern after ajmaline (ajmaline-negative); (4) 47 normal subjects; (5) 18 patients with right bundle branch block (RBBB). According to CineECG algorithm, each ECG was classified as Normal, Brugada, RBBB, or Undetermined. Results: In patients with spontaneous or ajmaline-induced BrS, CineECG localized the terminal mean temporospatial isochrone forces in the RVOT, congruent with the arrhythmogenic substrate location detected by epicardial potential duration maps. The RVOT location was never observed in normal, RBBB, or ajmaline-negative patients. In most patients with ajmaline-induced BrS (78%), the RVOT location was already evident at baseline. The CineECG classified all normal subjects and ajmaline-negative patients at baseline as Normal or Undetermined, all patients with RBBB as RBBB, whereas all patients with spontaneous and ajmaline-induced BrS as Brugada. Compared with standard 12-lead ECG, CineECG at baseline had a 100% positive predictive value and 81% negative predictive value in predicting ajmaline test results. Conclusions: In patients with spontaneous and ajmaline-induced BrS, the CineECG localized the late QRS activity in the RVOT, a phenomenon never observed in normal, RBBB, or ajmaline-negative patients. The possibility to identify the RVOT as the location of the arrhythmogenic substrate by the noninvasive CineECG, based on the standard 12-lead ECG, opens new prospective for diagnosing patients with BrS.

DISCRETE WAVELET TRANSFORM APPLIED ON PERSONAL IDENTITY VERIFICATION WITH ECG SIGNAL

2009 ◽  
Vol 07 (03) ◽  
pp. 341-355 ◽  
Author(s):  
CHUANG-CHIEN CHIU ◽  
CHOU-MIN CHUANG ◽  
CHIH-YU HSU
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Personal Identity ◽  
Rejection Rate ◽  
Normal Subjects ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Identity Verification ◽  
Ecg Signals ◽  
Electrocardiogram Ecg

The main purpose of this study is to present a novel personal authentication approach with the electrocardiogram (ECG) signal. The electrocardiogram is a recording of the electrical activity of the heart and the recorded signals can be used for individual verification because ECG signals of one person are never the same as those of others. The discrete wavelet transform was applied for extracting features that are the wavelet coefficients derived from digitized signals sampled from one-lead ECG signal. By the proposed approach applied on 35 normal subjects and 10 arrhythmia patients, the verification rate was 100% for normal subjects and 81% for arrhythmia patients. Furthermore, the performance of the ECG verification system was evaluated by the false acceptance rate (FAR) and false rejection rate (FRR). The FAR was 0.83% and FRR was 0.86% for a database containing only 35 normal subjects. When 10 arrhythmia patients were added into the database, FAR was 12.50% and FRR was 5.11%. The experimental results demonstrated that the proposed approach worked well for normal subjects. For this reason, it can be concluded that ECG used as a biometric measure for personal identity verification is feasible.

Evident difference in the excitability of the motoneuron pool between normal subjects and patients with spasticity assessed by a new method using H-reflex and M-response

1996 ◽  
Vol 203 (2) ◽  
pp. 127-130 ◽  
Author(s):  
Kozo Funase ◽  
Toshio Higashi ◽  
Toshiro Yoshimura ◽  
Kuniyasu Imanaka ◽  
Yoshiaki Nishihira
Keyword(s):  
Normal Subjects ◽  
H Reflex ◽  
New Method ◽  
Motoneuron Pool ◽  
Evident Difference

scholarly journals METHOD OF MULTI-MODAL VIDEO ANALYSIS OF HAND MOVEMENTS FOR AUTOMATIC RECOGNITION OF ISOLATED SIGNS OF RUSSIAN SIGN LANGUAGE

2021 ◽  
Vol XLIV-2/W1-2021 ◽  
pp. 7-13
Author(s):  
А. Axyonov ◽  
D. Ryumin ◽  
I. Kagirov
Keyword(s):  
Sign Language ◽  
Video Sequence ◽  
Recognition Accuracy ◽  
Video Data ◽  
New Method ◽  
Visual Features ◽  
Temporal Data ◽  
Sign Recognition ◽  
Spatio Temporal ◽  
Extract Information

Abstract. This paper presents a new method for collecting multimodal sign language (SL) databases, which is distinguished by the use of multimodal video data. The paper also proposes a new method of multimodal sign recognition, which is distinguished by the analysis of spatio-temporal visual features of SL units (i.e. lexemes). Generally, gesture recognition is a processing of a video sequence, which helps to extract information on movements of any articulator (a part of the human body) in time and space. With this approach, the recognition accuracy of isolated signs was 88.92%. The proposed method, due to the extraction and analysis of spatio-temporal data, makes it possible to identify more informative features of signs, which leads to an increase in the accuracy of SL recognition.

BIOLOGICAL SIGNAL ANALYSIS BY INDEPENDENT COMPONENT ANALYSIS USING COMPLEX WAVELET TRANSFORM

2010 ◽  
Vol 08 (04) ◽  
pp. 595-608 ◽  
Author(s):  
SATOSHI HORIHATA ◽  
ZHONG ZHANG ◽  
TAKASHI IMAMURA ◽  
TETSUO MIYAKE ◽  
HIROSHI TODA ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Independent Component Analysis ◽  
Signal Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
New Method ◽  
Discrete Wavelet ◽  
Complex Wavelet Transform ◽  
Emg Signal ◽  
Electrocardiogram Ecg

Independent component analysis (ICA) is a useful method for blind source separation of two or more signals. We have previously proposed a new method combining ICA with the complex discrete wavelet transform (CDWT), in which voice and noise signals were separated using a new method. At that time, we used a simulated signal. In this study, we analyze measured biological signals by using a new method, and discuss its effectiveness. As an experiment, we try to separate an electromyogram (EMG) signal from an electrocardiogram (ECG) signal.

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