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.

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 Verification and comparison of MIT-BIH arrhythmia database based on number of beats

2021 ◽  
Vol 11 (6) ◽  
pp. 4950
Author(s):  
Akram Jaddoa Khalaf ◽  
Samir Jasim Mohammed
Keyword(s):  
Signal Processing ◽  
Ecg Signal ◽  
Annotation File ◽  
Qrs Detection ◽  
Detection Algorithms ◽  
Matlab Program ◽  
Ecg Signal Processing ◽  
The Difference ◽  
Signal Processing Methods ◽  
Ecg Database

<span lang="EN-US">The ECG signal processing methods are tested and evaluated based on many databases. The most ECG database used for many researchers is the MIT-BIH arrhythmia database. The QRS-detection algorithms are essential for ECG analyses to detect the beats for the ECG signal. There is no standard number of beats for this database that are used from numerous researches. Different beat numbers are calculated for the researchers depending on the difference in understanding the annotation file. In this paper, the beat numbers for existing methods are studied and compared to find the correct beat number that should be used. We propose a simple function to standardize the beats number for any ECG PhysioNet database to improve the waveform database toolbox (WFDB) for the MATLAB program. This function is based on the annotation's description from the databases and can be added to the Toolbox. The function is removed the non-beats annotation without any errors. The results show a high percentage of 71% from the reviewed methods used an incorrect number of beats for this database.</span>

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.

Cloud-based real-time heart monitoring and ECG signal processing

2016 ◽  
Author(s):  
Fatima Bamarouf ◽  
Claire Crandell ◽  
Shannon Tsuyuki ◽  
Jose Sanchez ◽  
Yufeng Lu
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Ecg Signal ◽  
Heart Monitoring ◽  
Ecg Signal Processing

Real-Time Filtering and ECG Signal Processing Based on Dual-Core Digital Signal Controller System

2020 ◽  
Vol 20 (12) ◽  
pp. 6492-6503 ◽  
Author(s):  
Ngoc Thang Bui ◽  
Duc Tri Phan ◽  
Thanh Phuoc Nguyen ◽  
Giang Hoang ◽  
Jaeyeop Choi ◽  
...  
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Digital Signal ◽  
Ecg Signal ◽  
Digital Signal Controller ◽  
Ecg Signal Processing ◽  
Dual Core

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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