scholarly journals Unveiling the Biometric Potential of Finger-Based ECG Signals

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
André Lourenço ◽  
Hugo Silva ◽  
Ana Fred
Keyword(s):  
Signal Processing ◽  
Minimum Distance ◽  
Relevant Information ◽  
Ecg Signal ◽  
Ecg Signals ◽  
User Acceptability ◽  
Ecg Signal Processing ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Amplitude Normalization

The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications.

scholarly journals Efficient Real-Time R and QRS Detection Method Using a Pair of Derivative Filters and Max Filter for Portable ECG Device

2019 ◽  
Vol 9 (19) ◽  
pp. 4128
Author(s):  
Tae Wuk Bae ◽  
Kee Koo Kwon
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Detection Method ◽  
Sliding Window ◽  
Ecg Signal ◽  
Qrs Detection ◽  
Real Time Processing ◽  
Ecg Signals ◽  
Ecg Signal Processing ◽  
Processing Techniques

Recently, with the active development of wearable electrocardiogram (ECG) devices such as smart-bands or portable ECG devices, efficient ECG signal processing technology that can be applied in real-time has been actively studied. However, a wearable ECG device is exposed to various noise situations, thereby reducing the reliability of the detected R point or QRS interval. In addition, as early warning techniques in healthcare systems have been studied, real-time ECG signal processing techniques have become very important in wearable ECG devices. In this paper, we propose an efficient real-time R and QRS detection method using two kinds of first-order derivative filters and a max filter to analyze ECG signals measured from wearable ECG devices in real-time. The proposed method detects the R point and QRS interval in units of a sliding window for real-time processing and combines the detected R points in each sliding window. Also, the reliability of the detected R points and RR intervals is examined through noise region analysis using the histogram characteristic of a sample point. The performance of the proposed method was verified by the MIT-BIH database (DB), CYBHi DB and real ECG data measured from the developed wearable ECG patch. The proposed method achieves Se = 99.80%, +P = 99.80%, and DER = 0.36% against MIT-BIH DB. In addition, the proposed method enables accurate R point detection and heart rate variability (HRV) analysis even with noisy ECG signals.

A Non-Linear Approach to ECG Signal Processing using Morphological Filters

2013 ◽  
Vol 3 (3) ◽  
pp. 46-59 ◽  
Author(s):  
Vikrant Bhateja ◽  
Rishendra Verma ◽  
Rini Mehrotra ◽  
Shabana Urooj
Keyword(s):  
Signal Processing ◽  
Noise Removal ◽  
High Signal ◽  
Ecg Signal ◽  
Processing Scheme ◽  
Baseline Drift ◽  
Ecg Signals ◽  
Non Linear ◽  
Ecg Signal Processing ◽  
Electrocardiogram Ecg

Analysis of the Electrocardiogram (ECG) signals is the pre-requisite for the clinical diagnosis of cardiovascular diseases. ECG signal is degraded by artifacts such as baseline drift and noises which appear during the acquisition phase. The effect of impulse and Gaussian noises is randomly distributed whereas baseline drift generally affects the baseline of the ECG signal; these artifacts induce interference in the diagnosis of cardio diseases. The influence of these artifacts on the ECG signals needs to be removed by suitable ECG signal processing scheme. This paper proposes combination of non linear morphological operators for the noise and baseline drift removal. Non flat structuring elements of varying dimensions are employed with morphological filtering to achieve low distortion as well as good noise removal. Simulation outcomes illustrate noteworthy improvement in baseline drift yielding lower values of MSE and PRD; on the other hand high signal to noise ratios depicts suppression of impulse and Gaussian noises.

scholarly journals Techniques of Acquisition and Processing of Electrocardiographic Signals in the Detection of Cardiac Arrhythmias

Respuestas ◽  
2019 ◽  
Vol 24 (2) ◽  
pp. 91-100 ◽  
Author(s):  
Giovanny Barbosa Casanova ◽  
Darwin Orlando Cardozo Sarmiento ◽  
Mario Joaquin Illera Bustos ◽  
Andrés Orozco Duque ◽  
Henry Andrade Caicedo
Keyword(s):  
Signal Processing ◽  
Adaptive Filters ◽  
Current Trend ◽  
Computational Cost ◽  
Ambulatory Monitoring ◽  
Digital Signal ◽  
Discrete Wavelet ◽  
Ecg Signal Processing ◽  
Processing Techniques ◽  
Signal Processing Techniques

The development of ambulatory monitoring systems and its electrocardiographic (ECG) signal processing techniques has become an important field of investigation, due to its relevance in the early detection of cardiovascular diseases such as the arrhythmias. The current trend of this technology is oriented to the use of portable equipment and mobile devices such as Smartphones, which have been widely accepted due to the technical characteristics and common integration in daily life. A fundamental characteristic of these systems is their ability to reduce the most common types of noise by means of digital signal processing techniques.  Among the most used techniques are the adaptive filters and the Discrete Wavelet Transform (DWT) which have been successfully implemented in several studies. There are systems that integrate classification stages based on artificial intelligence, which increases the performance in the process of arrhythmias detection. These techniques are not only evaluated for their functionality but for their computational cost, since they will be used in real-time applications, and implemented in embedded systems. This paper shows a review of each of the stages in the construction of a standard ambulatory monitoring system, for the contextualization of the reader in this type of technology.

scholarly journals A Feasibility Study of the Application of Signal Processing Techniques to Corona Discharge Characterization on HVDC Systems

2021 ◽  
Vol 10 (1) ◽  
pp. 28
Author(s):  
Gabriel Augusto David ◽  
Pedro Oliveira Conceição Junior ◽  
Fabio Romano Lofrano Dotto ◽  
Benedito Roberto Santos
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Corona Discharge ◽  
Feasibility Study ◽  
Digital Signal ◽  
Relevant Information ◽  
Research Field ◽  
Prototype System ◽  
Processing Techniques ◽  
Signal Processing Techniques

This paper presents an initial and feasibility study of corona discharge characterization on HVDC systems using an alternative strategy based on digital signal processing techniques. In the corona discharge test, high voltages varying from ±30 to ±80 kV were applied through a DC high-voltage generator. In addition, corona discharges around the conductor were measured by means of a data acquisition prototype system equipped with a metal electrode device for the purpose of corona current measurement. The signals collected were subjected to digital signal processing parameters to extract the most relevant information related to corona discharge occurrence in specific frequency bands. The results reveal that the proposed method was able to detect the corona discharge characteristics following the changes in the signal content, especially, for narrow frequency ranges. The results indicate the feasibility of the proposed method to detect and characterize the corona discharge in a simple way, which expands the research field in corona discharge characterization in HVDC systems by means of digital signal processing and feature extraction.

scholarly journals Secured Electrocardiograph (ECG) Signal Using Partially Homomorphic Encryption Technique–RSA Algorithm

2020 ◽  
Vol 28 (S2) ◽  
Author(s):  
Muhammad Umair Shaikh ◽  
Wan Azizun Wan Adnan ◽  
Siti Anom Ahmad
Keyword(s):  
Signal Processing ◽  
Homomorphic Encryption ◽  
Digital Signal ◽  
Ecg Signal ◽  
The Public ◽  
Ecg Signals ◽  
Rsa Algorithm ◽  
Before And After ◽  
Ecg Signal Processing ◽  
Electrocardiogram Ecg

ECG signal differs from individual to individual, making it hard to be emulated and copied. In recent times ECG is being used for identifying the person. Hence, there is a requirement for a system that involves digital signal processing and signal security so that the saved data are secured at one place and an authentic person can see and use the ECG signal for further diagnosis. The study presents a set of security solutions that can be deployed in a connected healthcare territory, which includes the partially homomorphic encryption (PHE) techniques used to secure the electrocardiogram (ECG) signals. This is to record confidentially and prevent the information from meddling, imitating and replicating. First, Pan and Tompkins’s algorithm was applied to perform the ECG signal processing. Then, partially homomorphic encryption (PHE) technique - Rivest-Shamir-Adleman (RSA) algorithm was used to encrypt the ECG signal by using the public key. The PHE constitutes a gathering of semantically secure encryption works that permits certain arithmetical tasks on the plaintext to be performed straightforwardly on the ciphertext. The study shows a faster and 90% accurate result before and after encryption that indicates the lightweight and accuracy of the RSA algorithm. Secure ECG signal provides innovation in multiple healthcare sectors such as medical research, patient care and hospital database.

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