A robust digital ECG signal watermarking and compression using biorthogonal wavelet transform

Kumar, Ashish; Ranganatham, Ramana; Singh, Samayveer; Komaragiri, Rama; Kumar, Manjeet

doi:10.1007/s42600-020-00108-1

Cited by 13 publications

(4 citation statements)

References 32 publications

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“…Digital watermarking based on the transform domain is the mainstream of the current digital watermark technology research. However, the wavelet transform is widely used in digital watermarking, such as digital audio watermarking 24 , digital ECG signal watermarking 25 , color image watermarking 26 , 27 and so on. A digital watermarking algorithm based on a discrete wavelet transform is briefly introduced in this section.…”

Section: Preliminarymentioning

confidence: 99%

Text information hiding and recovery via wavelet digital watermarking method

Zhou¹

2023

Sci Rep

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According to the wavelet digital watermarking method, wavelet text hiding algorithm is presented for hiding some text information in a signal with white noises and the corresponding recovery algorithm is also presented for obtaining text information from a synthesized signal. Firstly, wavelet text hiding algorithm is introduced and an example is given for demonstrating how to hide text information in a signal s with a white noise ε, where s = f(x) + ε and f(x) is a function such as sin x, cos x and so on. A synthesized signal $$\tilde{s}$$ s ~ can be obtained by wavelet text hiding algorithm. Then, the corresponding text recovery approach is also introduced and the text information is recovered from the synthesized signal $$\tilde{s}$$ s ~ by an example. Some figures of the example are shown that the wavelet text hiding algorithm and its recovery are feasible. Moreover, the roles of wavelet function, noise, embedding mode and embedding position are analyzed in the text information hiding and recovering, and it implicates its security. 1000 groups of English texts with different lengths are chosen for illustrating computational complexity and running time of the algorithms. The social application of this approach is explained by a system architecture figure. Finally, some future directions are discussed for our follow-up study.

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Section: Preliminarymentioning

confidence: 99%

Text information hiding and recovery via wavelet digital watermarking method

Zhou¹

2023

Sci Rep

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“…Several computational approaches have been applied, such as neural networks, digital signal analysis, statistical techniques and support vector machine. Detection of life-threatening ventricular arrhythmias in real-time was addressed by [30,31]. An algorithm named DIAGNOSIS was developed to classify ECG signals using four parameters regarding frequency domain.…”

Section: Ventricular Diseases Classification Approachesmentioning

confidence: 99%

Automatic Heart Disease Detection by Classification of Ventricular Arrhythmias on ECG Using Machine Learning

Alajlan¹

2022

Computers, Materials &Amp; Continua

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This paper focuses on detecting diseased signals and arrhythmias classification into two classes: ventricular tachycardia and premature ventricular contraction. The sole purpose of the signal detection is used to determine if a signal has been collected from a healthy or sick person. The proposed research approach presents a mathematical model for the signal detector based on calculating the instantaneous frequency (IF). Once a signal taken from a patient is detected, then the classifier takes that signal as input and classifies the target disease by predicting the class label. While applying the classifier, templates are designed separately for ventricular tachycardia and premature ventricular contraction. Similarities of a given signal with both the templates are computed in the spectral domain. The empirical analysis reveals precisions for the detector and the applied classifier are 100% and 77.27%, respectively. Moreover, instantaneous frequency analysis provides a benchmark that IF of a normal signal ranges from 0.8 to 1.1 Hz whereas IF range for ventricular tachycardia and premature ventricular contraction is 0.08-0.6 Hz. This indicates a serious loss of high-frequency contents in the spectrum, implying that the heart's overall activity is slowed down. This study may help medical practitioners in detecting the heart disease type based on signal analysis.

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“…Any program might be required to merge and recycle information as the result of a constraint. Because of its sensitivity to physical information, it is necessary must offer such connections proper protection of information accessibility [21]. Because an information plane has 2 routes of transferring information, one is sensing planes and another for the applications planes, all regulations on obtaining information across every platform must be distinct.…”

Section: Literature Surveymentioning

confidence: 99%

Implementation of QT Interval Measurement to Remove Errors in ECG

Chitra¹,

Jayalakshmi²

2022

IJACSA

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Wireless Body Sensor Network (WBSNs) are devices that can be ported with different detection, storage, computer, but also communication capabilities. Interfacing was beneficial whenever information was collected by many sources, which may lead to erroneous sensory information. During this paper, an information nuclear fission Ensembles technique for working raw healthcare information through WBSNs during ambient cloud computer settings as described. Monitoring data were collected through various instruments and combined to provide statistics on high movements. The simulation was conducted using the low-cost Internet of Things (IoT) surveillance system on chronic kidney disease (CKD). Biosensors have been used in healthcare surveillance systems to record health problems. Patients with CKD would benefit from the developed surveillance system, which will facilitate the early diagnosis of the predominant diseases. This merged information was then sent into using the Aggregation algorithm can forecast premature cardiac illness and CKD. These groups were housed within a Cloud processing context; therefore these forecasting calculations were distributed. Another lengthy practical investigation backs that system provides application, while those findings were encouraging, with 98 percent efficiency whenever the height of that tree was equivalent with 15, total amount if estimation methods are 40, while the overall predicting job was based upon 8 attributes. We compute a mean square ECG waveform from all available leads and use a new technique to measure the QT interval. We tested this algorithm using standard and unique ECG databases. Our real-time QT interval measurement algorithm was found to be stable, accurate, and capable of tracking changing QT values.

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A robust digital ECG signal watermarking and compression using biorthogonal wavelet transform

Cited by 13 publications

References 32 publications

Text information hiding and recovery via wavelet digital watermarking method

Text information hiding and recovery via wavelet digital watermarking method

Automatic Heart Disease Detection by Classification of Ventricular Arrhythmias on ECG Using Machine Learning

Implementation of QT Interval Measurement to Remove Errors in ECG

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