Efficient wavelet families for ECG classification using neural classifiers

Singh, Ritu; Mehta, Rajesh; Rajpal, Navin

doi:10.1016/j.procs.2018.05.054

Cited by 28 publications

(14 citation statements)

References 14 publications

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“…As already mentioned, Daubechies was our first approach since this is the common mother wavelet used for the analysis of the ECG signal [41,42,43]. Nevertheless, this paper explores how to extract randomness from ECG signals by multi-level wavelet decomposition, and to the best of our knowledge, this is the first time this approach has been studied.…”

Section: Results and Analysismentioning

confidence: 99%

ECG-RNG: A Random Number Generator Based on ECG Signals and Suitable for Securing Wireless Sensor Networks

Cámara

Peris-López

Martín

et al. 2018

Sensors

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Wireless Sensor Networks (WSNs) are a promising technology with applications in many areas such as environment monitoring, agriculture, the military field or health-care, to name but a few. Unfortunately, the wireless connectivity of the sensors opens doors to many security threats, and therefore, cryptographic solutions must be included on-board these devices and preferably in their design phase. In this vein, Random Number Generators (RNGs) play a critical role in security solutions such as authentication protocols or key-generation algorithms. In this article is proposed an avant-garde proposal based on the cardiac signal generator we carry with us (our heart), which can be recorded with medical or even low-cost sensors with wireless connectivity. In particular, for the extraction of random bits, a multi-level decomposition has been performed by wavelet analysis. The proposal has been tested with one of the largest and most publicly available datasets of electrocardiogram signals (202 subjects and 24 h of recording time). Regarding the assessment, the proposed True Random Number Generator (TRNG) has been tested with the most demanding batteries of statistical tests (ENT, DIEHARDERand NIST), and this has been completed with a bias, distinctiveness and performance analysis. From the analysis conducted, it can be concluded that the output stream of our proposed TRNG behaves as a random variable and is suitable for securing WSNs.

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Section: Results and Analysismentioning

confidence: 99%

ECG-RNG: A Random Number Generator Based on ECG Signals and Suitable for Securing Wireless Sensor Networks

Cámara

Peris-López

Martín

et al. 2018

Sensors

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“…Therefore, the selection of wavelet transform basis function is a vital part of denoising and feature extraction. The Daubechies (db6) WT function has similarities related to the ECG signal, and most of the information is contained at low frequencies [18], [19]. In the present study, the db6 wavelet function has been deployed for noise removal and feature extraction.…”

Section: Ecg Data Processing and Implemented Feature Extraction Technmentioning

confidence: 99%

“…R-peaks detected are stored in an array having an amplitude in Ramp array and positions in Rloc array. In a previous work [19], this algorithm is successfully implemented using the db4 wavelet. The R peak detection is followed by a single beat finding of mentioned five categories of cardiac beats.…”

Section: Ecg Data Processing and Implemented Feature Extraction Technmentioning

confidence: 99%

“…Abnormality detection [19] in ECG in the wavelet domain holds promising results leading to high achievements in accuracy and time. The multi-resolution wavelet analysis has played a vital role in biomedical applications such as cardiology, Fetal Echocardiography, and neurology.…”

Section: Introductionmentioning

confidence: 99%

“…The effectiveness of the current method accomplishes using neural networks (BPNN, FNN) and KNN classifiers, along with a comparison with recent existing techniques under a similar environment. In our past work [19], two-class classification, i.e., detection of normal and abnormal ECG, is successfully achieved by using different orthogonal wavelet families and neural classifiers. Training and testing performed using random data partitioning.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wavelet and kernel dimensional reduction on arrhythmia classification of ECG signals

Singh

Rajpal

Mehta

2018

ICST Transactions on Scalable Information Systems

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Electrocardiogram (ECG) monitoring is continuously required to detect cardiac ailments. At times it is challenging to interpret the differences in the P-QRS-T curve. The proposed approach aims to show the excellence of kernel capabilities of Kernel Principal Component Analysis (KPCA) and Kernel Independent Component Analysis (KICA) in the wavelet domain. In this work, experiments are performed using five different categories of cardiac beats. The supervised classifiers like feed-forward neural network (FNN), backpropagation neural network (BPNN), and K nearest neighbor (KNN) statistically evaluates the impact of discrete wavelet with KPCA and KICA on extracted beats. The performance evaluation also compares the outcomes with existing techniques. The obtained results justify the supremacy of the combination of wavelet, kernel, and KNN approach, yielding a 99.7 % classification success rate. The five-fold crossvalidation scheme is used for measuring the efficacy of classifiers.

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Dynamic ECG Classification Using Shift-Invariant DTCWT and Discriminant Analysis

Singh

Rajpal

Mehta

2019

Proceedings of ICETIT 2019

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Efficient wavelet families for ECG classification using neural classifiers

Cited by 28 publications

References 14 publications

ECG-RNG: A Random Number Generator Based on ECG Signals and Suitable for Securing Wireless Sensor Networks

ECG-RNG: A Random Number Generator Based on ECG Signals and Suitable for Securing Wireless Sensor Networks

Wavelet and kernel dimensional reduction on arrhythmia classification of ECG signals

Dynamic ECG Classification Using Shift-Invariant DTCWT and Discriminant Analysis

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