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.

A Novel Method for the Detection of Sleep Apnea Syndrome Based on Single-Lead ECG Signal

2012 ◽  
Vol 239-240 ◽  
pp. 1079-1083 ◽  
Author(s):  
Yue Wen Tu ◽  
Xiao Min Yu ◽  
Hang Chen ◽  
Shu Ming Ye
Keyword(s):  
Sleep Apnea ◽  
Sleep Apnea Syndrome ◽  
Support Vector ◽  
Ecg Signal ◽  
Respiratory Signal ◽  
Peak Points ◽  
Novel Method ◽  
Apnea Syndrome ◽  
Prevention Of Hypertension ◽  
Electrocardiogram Ecg

The diagnosis of sleep apnea syndrome (SAS) has important clinical significance for the prevention of hypertension, coronary heart disease, arrhythmias, stroke and other diseases. In this paper, a novel method for the detection of SAS based on single-lead Electrocardiogram (ECG) signal was proposed. Firstly, the R-peak points of ECG recordings were pre-detected to calculate RR interval series and ECG-derived respiratory signal (EDR). Then 40 time- and spectral-domain features were extracted and normalized. Finally, support vector machine (SVM) was employed to these features as a classifier to detect SAS events. The performance of the presented method was evaluated using the MIT-BIH Apnea-ECG database, results show that an accuracy of 95% in train sets and an accuracy of 88% in test sets are achievable.

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 Anticipating Atrial Fibrillation Signal Using Efficient Algorithm

2021 ◽  
Vol 17 (02) ◽  
pp. 106
Author(s):  
Mohand Lokman Ahmad Al-dabag ◽  
Haider Th. Salim ALRikabi ◽  
Raid Rafi Omar Al-Nima
Keyword(s):  
Atrial Fibrillation ◽  
Computational Cost ◽  
Extraction Methods ◽  
Support Vector ◽  
Ecg Signal ◽  
Heart Problem ◽  
Ecg Signals ◽  
The Right ◽  
Electrocardiogram Ecg ◽  
Low Computational Cost

One of the common types of arrhythmia is Atrial Fibrillation (AF), it may cause death to patients. Correct diagnosing of heart problem through examining the Electrocardiogram (ECG) signal will lead to prescribe the right treatment for a patient. This study proposes a system that distinguishes between the normal and AF ECG signals. First, this work provides a novel algorithm for segmenting the ECG signal for extracting a single heartbeat. The algorithm utilizes low computational cost techniques to segment the ECG signal. Then, useful pre-processing and feature extraction methods are suggested. Two classifiers, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), are separately used to evaluate the two proposed algorithms. The performance of the last proposed method with the two classifiers (SVM and MLP) show an improvement of about (19% and 17%, respectively) after using the proposed segmentation method so it became 96.2% and 97.5%, respectively.

scholarly journals Adaptive Electrocardiogram Steganography Based on 2D Approach with Predetermined Rules

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Ching Yu Yang ◽  
Chi-Ming Lai ◽  
Hung-Chang Lin ◽  
Ting-Ying Lin ◽  
Ruei-Long Lu
Keyword(s):  
Data Hiding ◽  
Gaussian Noise ◽  
Decision Rules ◽  
Ecg Signal ◽  
Two Dimensional ◽  
Hiding Capacity ◽  
Real Time Applications ◽  
Electrocardiogram Ecg ◽  
Ecg Steganography ◽  
Better Than

Based on two-dimensional (2D) bit-embedding/-extraction approach, we propose a simple data hiding for electrocardiogram (ECG) signal. The patient’s sensitive (diagnostic) data can be efficiently hidden into 2D ECG host via the proposed decision rules. The performance of the proposed method using various sizes of the host bundles was demonstrated. Simulations have confirmed that the average SNR of the proposed method with a host bundle of size 3 ´ 3 is superior to that of existing techniques, while our payload is competitive to theirs. In addition, our method with a host bundle of size 2 ´  2 generated the best SNR values, while that with a host bundle of size 4 ´  4 provided the largest payload among the compared methods. Moreover, the proposed method provides robustness performance better than existing ECG steganography. Namely, our method provides high hiding capacity and robust against the attacks such as cropping, inversion, scaling, translation, truncation, and Gaussian noise-addition attacks. Since the proposed method is simple, it can be employed in real-time applications such as portable biometric devices.

scholarly journals A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification

2019 ◽  
Vol 9 (1) ◽  
pp. 201 ◽  
Author(s):  
Di Wang ◽  
Yujuan Si ◽  
Weiyi Yang ◽  
Gong Zhang ◽  
Tong Liu
Keyword(s):  
Heart Rate ◽  
Principal Component ◽  
Identification Accuracy ◽  
Ecg Signal ◽  
Identification Task ◽  
Short Term ◽  
Ecg Signals ◽  
Subject Identification ◽  
Novel Method ◽  
Electrocardiogram Ecg

In the past decades, the electrocardiogram (ECG) has been investigated as a promising biometric by exploiting the subtle discrepancy of ECG signals between subjects. However, the heart rate (HR) for one subject may vary because of physical activities or strong emotions, leading to the problem of ECG signal variation. This variation will significantly decrease the performance of the identification task. Particularly for short-term ECG signal without many heartbeats, the hardly measured HR makes the identification task even more challenging. This study aims to propose a novel method suitable for short-term ECG signal identification. In particular, an improved HR-free resampling strategy is proposed to minimize the influence of HR variability during heartbeat processing. For feature extraction, the Principal Component Analysis Network (PCANet) is implemented to determine the potential difference between subjects. The proposed method is evaluated using a public ECG-ID database that contains various HR data for some subjects. Experimental results show that the proposed method is robust to HR change and can achieve high subject identification accuracy (94.4%) on ECG signals with only five heartbeats. Thus, the proposed method has the potential for application to systems that use short-term ECG signals for identification (e.g., wearable devices).

High-Accuracy Characterization of Ambulatory Holter Electrocardiogram Events

2012 ◽  
Vol 1 (3) ◽  
pp. 40-71 ◽  
Author(s):  
Mohammad Reza Homaeinezhad ◽  
Seyyed Amir Hoseini Sabzevari ◽  
Ali Ghaffari ◽  
Mohammad Daevaeiha
Keyword(s):  
High Accuracy ◽  
P Wave ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Qrs Complex ◽  
First Derivative ◽  
Time Distance ◽  
Noise Robust ◽  
Electrocardiogram Ecg

In this paper, three noise-robust high-accuracy methods aiming at the detection and delineation of the electrocardiogram (ECG) events (QRS complex, P-wave, T-wave) were developed. The ECG signal was initially appropriately preprocessed by application of a bandpass FIR filter and Discrete Wavelet Transform (DWT). The first detection-delineation method was the Walsh-Hadamard Transform (WHT). The WHT coefficients were divided into two groups and the signal was reconstructed using the second group coefficients. By this reconstruction, the values of first derivative of events are made stronger rather than the values of other parts of signal. In the second method, a feed forward artificial neural network was implemented to detect all events of the ECG signal. In the third method, the first derivative of signal was computed using a new signal smoothing algorithm with corresponding statistical properties. For decreasing False Positive (FP) errors associated with P-wave detection, a discriminating border was introduced as the post processing stage specified by three QRS parameters: the duration of a QRS complex, the time distance from the former and latter QRS complexes, and the potential difference from former QRS complex J-location and the latter QRS complex fiducial location. The proposed methods were applied to DAY general hospital high resolution holter data.

scholarly journals Classification of Heart Rate Time Series Using Machine Learning Algorithms

2021 ◽  
Vol 2 (1) ◽  
Keyword(s):  
Detection System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Heart Patients ◽  
Extraction Step ◽  
Ecg Arrhythmia ◽  
Electrocardiogram Ecg ◽  
The Given

An important diagnostic method for diagnosing abnormalities in the human heart is the electrocardiogram (ECG). A large number of heart patients increase the assignment of physicians. To reduce their assignment, an automatic computer detection system is needed. In this study, a computer system for classifying ECG signals is presented. The MIT-BIH, ECG arrhythmia database is used for analysis. After the ECG signal is noisy in the preprocessing stage, the data feature is extracted. In the feature extraction step, the decision tree is used and the support vector machine (SVM) is constructed to classify the ECG signal into two categories. It is normal or abnormal. The results show that the system classifies the given ECG signal with 90% sensitivity.

scholarly journals Classification of Multi Heart Diseases With Android Based Monitoring System

2020 ◽  
pp. 14-22
Keyword(s):  
Feature Extraction ◽  
Heart Diseases ◽  
Confusion Matrix ◽  
Predictive Coding ◽  
Classification Performance ◽  
Support Vector ◽  
Ecg Signal ◽  
Linear Predictive Coding ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) examination via computer techniques that involve feature extraction, pre-processing and post-processing was implemented due to its significant advantages. Extracting ECG signal standard features that requires high processing operation level was the main focusing point for many studies. In this paper, up to 6 different ECG signal classes are accurately predicted in the absence of ECG feature extraction. The corner stone of the proposed technique in this paper is the Linear predictive coding (LPC) technique that regress and normalize the signal during the pre-processing phase. Prior to the feature extraction using Wavelet energy (WE), a direct Wavelet transform (DWT) is implemented that converted ECG signal to frequency domain. In addition, the dataset was divided into two parts , one for training and the other for testing purposes Which have been classified in this proposed algorithm using support vector machine (SVM). Moreover, using MIT AI2 Companion was developed by MIT Center for Mobile Learning, the classification result was shared to the patient mobile phone that can call the ambulance and send the location in case of serious emergency. Finally, the confusion matrix values are used to measure the proposed classification performance. For 6 different ECG classes, an accuracy ration of about 98.15% was recorded. This ratio became 100% for 3 ECG signal classes and decreases to 97.95% by increasing ECG signal to 7 classes.

CARDIAC ARRHYTHMIA DIAGNOSIS USING A NEURO-FUZZY APPROACH

2010 ◽  
Vol 10 (03) ◽  
pp. 417-429 ◽  
Author(s):  
R. BENALI ◽  
N. DIB ◽  
F. REGUIG BEREKSI
Keyword(s):  
Fuzzy Neural Network ◽  
Ecg Signal ◽  
Qrs Detection ◽  
Medical Database ◽  
Fuzzy Classifier ◽  
Signal Parameters ◽  
Neuro Fuzzy ◽  
Fuzzy Neural ◽  
Novel Method ◽  
Electrocardiogram Ecg

The ventricular premature contractions (VPC) are cardiac arrhythmias that are widely encountered in the cardiologic field. They can be detected using the electrocardiogram (ECG) signal parameters. A novel method for detecting VPC from the ECG signal is proposed using a new algorithm (Slope) combined with a fuzzy-neural network (FNN). To achieve this objective, an algorithm for QRS detection is first implemented, and then a neuro-fuzzy classifier is developed. Its performances are evaluated by computing the percentages of sensitivity (SE), specificity (SP), and correct classification (CC). This classifier allows extraction of rules (knowledge base) to clarify the obtained results. We use the medical database (MIT-BIH) to validate our results.

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