Analysis of Abnormal Intra-QRS Potentials in Signal-Averaged Electrocardiograms Using a Radial Basis Function Neural Network

Lin, Chun-Cheng

doi:10.3390/s16101580

Cited by 3 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For that reason, there have been many attempts to eliminate noise using frequency-selective filters or wavelet de-noising. However, owing to the characteristics of the ECG signals in the presence of noise, detecting the P wave is still challenging; nevertheless, the QRS and T wave detection techniques have started to provide acceptable results in most cases [ 21 , 22 ]. In addition, heartbeats change, and the signals may fluctuate or be influenced by physical activities, drug consumption, and strong emotions; in this case, the identification process would become more difficult than that under static body conditions.…”

Section: Introductionmentioning

confidence: 99%

Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns

Lee

Kim

2018

Sensors

View full text Add to dashboard Cite

The electrocardiogram (ECG) waveform conveys information regarding the electrical property of the heart. The patterns vary depending on the individual heart characteristics. ECG features can be potentially used for biometric recognition. This study presents a new method using the entire ECG waveform pattern for matching and demonstrates that the approach can potentially be employed for individual biometric identification. Multi-cycle ECG signals were assessed using an ECG measuring circuit, and three electrodes can be patched on the wrists or fingers for considering various measurements. For biometric identification, our-fold cross validation was used in the experiments for assessing how the results of a statistical analysis will generalize to an independent data set. Four different pattern matching algorithms, i.e., cosine similarity, cross correlation, city block distance, and Euclidean distances, were tested to compare the individual identification performances with a single channel of ECG signal (3-wire ECG). To evaluate the pattern matching for biometric identification, the ECG recordings for each subject were partitioned into training and test set. The suggested method obtained a maximum performance of 89.9% accuracy with two heartbeats of ECG signals measured on the wrist and 93.3% accuracy with three heartbeats for 55 subjects. The performance rate with ECG signals measured on the fingers improved up to 99.3% with two heartbeats and 100% with three heartbeats of signals for 20 subjects.

show abstract

Section: Introductionmentioning

confidence: 99%

Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns

Lee

Kim

2018

Sensors

View full text Add to dashboard Cite

show abstract

“…They were described as the signals with sudden slope changes [40]. Extracting IQRSPs is challenging, considering that they are very weak signals, with abrupt changes in slope, approximation errors, and the diferences among patients with ventricular arrhythmias [41]. The root mean square values were highly correlated with the parameters of the abnormal intra-QRS potentials in healthy controls but not in patients with ventricular tachycardia [40].…”

Section: Intra-qrs Potentialsmentioning

confidence: 99%

Signal-Averaged ECG: Basics to Current Issues

Mozoş¹,

Stoian²

2017

Interpreting Cardiac Electrograms - From Skin to Endocardium

View full text Add to dashboard Cite

Signal-averaged ECG (SAECG) is a high-resolution, noninvasive electrocardiographic method enabling detection of late ventricular potentials (LVP), which are low-amplitude and high-frequency signals, predicting reentry ventricular arrhythmias, and sudden cardiac death (SCD). Three criteria are used to detect late ventricular potentials as follows: signal-average ECG QRS duration (SAECG-QRS), the duration of the terminal part of the QRS complex with an amplitude below 40 μV (LAS40) and the root mean square (RSM) signal amplitude of the last 40 ms of the signal < 20 μV (RMS40). Late ventricular potentials can be detected not only at the end of a QRS complex but also as intra-QRS (IQRS) potentials. Signal-averaged ECG was modiied to enable the analysis of the P-wave and to detect atrial late potentials (ALPs), low-amplitude potentials at the terminal part of the iltered P-wave, and predictors of atrial ibrillation (AF). Late atrial and ventricular potentials originate from areas of delayed, fragmented, and heterogenous conduction within atrial or ventricular myocardium. This chapter reviews the most important mechanisms explaining the occurrence of late ventricular, intra-QRS, and atrial potentials; their predictive value for arrhythmia, focusing on recent clinical data, long-term follow-up, and outcome; and analysis of SAECG variables in cardiac and noncardiac diseases.

show abstract

“…Classifiers based on RBF have been widely using in different fields for pattern recognition [48]. The RBF classifier includes three totally different layers of an input layer, a hidden layer and an output layer (see Figure 1).…”

Section: Radial Basis Function (Rbf) Classifiermentioning

confidence: 99%

Fault Detection for Vibration Signals on Rolling Bearings Based on the Symplectic Entropy Method

Lei

Meng

Dong

2017

Entropy

View full text Add to dashboard Cite

Abstract:Bearing vibration response studies are crucial for the condition monitoring of bearings and the quality inspection of rotating machinery systems. However, it is still very difficult to diagnose bearing faults, especially rolling element faults, due to the complex, high-dimensional and nonlinear characteristics of vibration signals as well as the strong background noise. A novel nonlinear analysis method-the symplectic entropy (SymEn) measure-is proposed to analyze the measured signals for fault monitoring of rolling bearings. The core technique of the SymEn approach is the entropy analysis based on the symplectic principal components. The dynamical characteristics of the rolling bearing data are analyzed using the SymEn method. Unlike other techniques consisting of high-dimensional features in the time-domain, frequency-domain and the empirical mode decomposition (EMD)/wavelet-domain, the SymEn approach constructs low-dimensional (i.e., two-dimensional) features based on the SymEn estimate. The vibration signals from our experiments and the Case Western Reserve University Bearing Data Center are applied to verify the effectiveness of the proposed method. Meanwhile, it is found that faulty bearings have a great influence on the other normal bearings. To sum up, the results indicate that the proposed method can be used to detect rolling bearing faults.

show abstract

Analysis of Abnormal Intra-QRS Potentials in Signal-Averaged Electrocardiograms Using a Radial Basis Function Neural Network

Cited by 3 publications

References 20 publications

Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns

Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns

Signal-Averaged ECG: Basics to Current Issues

Fault Detection for Vibration Signals on Rolling Bearings Based on the Symplectic Entropy Method

Contact Info

Product

Resources

About