Analysis of encrypted ECG signal in steganography using wavelet transforms

Awasthi, Deepali; Madhe, Swati

doi:10.1109/ecs.2015.7125005

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…[11] An electrocardiogram (ECG) or ECG is a written record of the potentials generated by the heart that includes specified areas shown in figure 2. [12] In this study, the features of cardiac arrhythmia database were examined. As illustrated in table1, total number of features are 279.…”

Section: Obtaining Information About Heart Functionmentioning

confidence: 99%

Using the Neural Network to Diagnose the Severity of Heart Disease in Patients Using General Specifications and ECG Signals Received from the Patients

Jafari¹,

Rajebi²,

Haghipour³

2020

Adv. sci. technol. eng. syst. j.

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Nowadays, heart diseases cause the maximum death in the world. Also, due to the noticeable increase of heart diseases, studying this field is one of the important matters in medical community. Therefore, this study tries to benefit using information in data base of cardiac arrhythmia and employ arterial intelligent and neural network, in order to improve the speed in getting cardiac signals with minimum errors and maximum certainty. The dataset for the project is taken from the UCI machine learning repository https://archive.ics.uci.edu/m1/datasets/Arrhythmia. Used data base has 279 characteristics taken from 364 patients that includes general characteristics and ECG signals received from patients. In this study, firstly the primary classification has done with all characteristics, so some parts of information in data base of cardiac arrhythmia with values near zero has omitted. Considering the improvement of accuracy of the classification after omitting the characteristics near to zero, in next level the second series of data in neural networks that has negative effect on classification has omitted. In order to increase the accuracy of neural network and minimize the number of characteristics, the characteristics has classified in multiple classes and the obtained ratio has improved using genetic algorithm. In this level, the best accuracy of the neural classification has obtained but in order to get a network with minimum characteristics possible and preserve the 100% accuracy of the classification, ineffective characteristics has omitted using PCA algorithm.

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Section: Obtaining Information About Heart Functionmentioning

confidence: 99%

Using the Neural Network to Diagnose the Severity of Heart Disease in Patients Using General Specifications and ECG Signals Received from the Patients

Jafari¹,

Rajebi²,

Haghipour³

2020

Adv. sci. technol. eng. syst. j.

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“…Basically, the heart is depolarized when it is in systole state and repolarized when it is at diastole state. The depolarization case in normal ECG wave takes place from the beginning the P wave until the middle of ST segment as shown in Figure 1, while the repolarization case starts from the T wave [7], [8]. Broadly, ECG time-frequency features are classified as either waveform or characteristic based features.…”

Section: Ecg Signal Physiologymentioning

confidence: 99%

Utilizing ECG Waveform Features as New Biometric Authentication Method

Shdefat¹,

Joo²,

Kim³

2018

IJECE

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In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects" raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%.

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Pattern analysis based acoustic signal processing: a survey of the state-of-art

Chaki

2020

Int J Speech Technol

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Analysis of encrypted ECG signal in steganography using wavelet transforms

Cited by 6 publications

References 4 publications

Using the Neural Network to Diagnose the Severity of Heart Disease in Patients Using General Specifications and ECG Signals Received from the Patients

Using the Neural Network to Diagnose the Severity of Heart Disease in Patients Using General Specifications and ECG Signals Received from the Patients

Utilizing ECG Waveform Features as New Biometric Authentication Method

Pattern analysis based acoustic signal processing: a survey of the state-of-art

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