Journal ArticleDOI
Design of wavelet transform based electrocardiogram monitoring system.
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TLDR
It is found in this work that the usage of modified biorthogonal wavelet transform increases the detection accuracy and CR of the proposed design, and the Wi-Fi-based wireless protocol is used for compressed data transmission.Abstract:
The new age advancements in information technology due to materials and integrated circuit (IC) technologies and their applications in biomedical sciences have made the healthcare facilities more compact and affordable for the aging population. Market trends in healthcare and related devices indicate a sharp rise in their demand. Hence the researchers have converged the efforts on designing more smart and advanced medical devices using IC technology. Among these devices, cardiac pacemakers have become a recurrent biomedical device which is engrafted in the human body to detect and monitor a person's heart beating rate. The data thus generated is processed for various medical usages and devices via wireless methods. Cardiovascular diseases (CVDs) or diseases related to the heart are due to abnormalities or disorders of the heart and blood vessels. Till date, limited literature is available which focuses on a single technique that can perform all of the ECG signal denoising, ECG detection, lossless data compression and wireless transmission. In this work, a joint approach for denoising, detection, compression, and wireless transmission of ECG signal is proposed. The modified biorthogonal wavelet transform is used for denoising, detection and lossless compression of ECG signal. To reduce the circuit complexity, biorthogonal wavelet transform is realized using linear phase structure. Further, it is found in this work that the usage of modified biorthogonal wavelet transform increases the detection accuracy and CR of the proposed design. Also, in this work, the Wi-Fi-based wireless protocol is used for compressed data transmission. The proposed ECG detector achieves the highest sensitivity and positive predictivity of 99.95% and 99.92%, respectively, with the MIT-BIH arrhythmia database. The use of modified biorthogonal 3.1 wavelet transform and run-length encoding (RLE) for the compression of ECG data achieves a higher compression ratio (CR) of 6.271. To justify the effectiveness of the proposed algorithm, which uses modified biorthogonal wavelet 3.1transform, the results are compared with the existing methods, namely, Huffman coding/simple predictor, Huffman coding/adaptive, and slope predictor/fixed length packaging.read more
Citations
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Journal ArticleDOI
Review of noise removal techniques in ECG signals
Shubhojeet Chatterjee,Rini Smita Thakur,Ram Narayan Yadav,Lalita Gupta,Deepak Kumar Raghuvanshi +4 more
TL;DR: It is observed that Wavelet-VBE, EMD-MAF, GAN2, GSSSA, new MP-EKF, DLSR, and AKF are most suitable for additive white Gaussian noise removal and GAN1 is the best denoising option for composite noise removal.
Journal ArticleDOI
Stationary wavelet transform based ECG signal denoising method.
TL;DR: Signal-to-noise ratio, percentage root-mean-square difference, and root mean square error are used to compare the ECG signal denoising performance and the experimental result showed that the proposed stationary wavelet transform based ECGDenoising technique outperformed the other ECG Denoising techniques as more ECGs signal components are preserved than other denoised algorithms.
Journal ArticleDOI
Adaptive periodic mode decomposition and its application in rolling bearing fault diagnosis
TL;DR: The analysis results of rolling bearing signals show that APMD has excellent ability to identify and extract PCs and is a valid method for rolling bearing fault diagnosis.
Journal ArticleDOI
Design of efficient fractional operator for ECG signal detection in implantable cardiac pacemaker systems
TL;DR: It has been found that the proposed adaptive slope prediction threshold increases the QRS complex detection performance and the proposed fractional operator‐based digital ECG detector for modern pacemaker systems is proposed in this work.
Journal ArticleDOI
Adaptive Cuckoo Search based optimal bilateral filtering for denoising of satellite images.
Anju Asokan,J. Anitha +1 more
TL;DR: The proposed Adaptive Cuckoo Search based bilateral filter denoising gives better results in terms of Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Feature Similarity Index (FSIM), Entropy and CPU time in comparison to traditional methods such as Median filter and RGB spatial filter.
References
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Journal ArticleDOI
A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Journal ArticleDOI
PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.
Ary L. Goldberger,Luís A. Nunes Amaral,Leon Glass,Jeffrey M. Hausdorff,Plamen Ch. Ivanov,Roger G. Mark,Joseph E. Mietus,George B. Moody,Chung-Kang Peng,H. Eugene Stanley +9 more
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
Journal ArticleDOI
The impact of the MIT-BIH Arrhythmia Database
George B. Moody,Roger G. Mark +1 more
TL;DR: The history of the database, its contents, what is learned about database design and construction, and some of the later projects that have been stimulated by both the successes and the limitations of the MIT-BIH Arrhythmia Database are reviewed.
Journal ArticleDOI
A wavelet-based ECG delineator: evaluation on standard databases
TL;DR: A robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT), outperforming the results of other well known algorithms, especially in determining the end of T wave.
Journal ArticleDOI
The principles of software QRS detection
TL;DR: The authors provide an overview of these recent developments as well as of formerly proposed algorithms for QRS detection, which reflects the electrical activity within the heart during the ventricular contraction.