An Efficient R-peak Detection Based on New Nonlinear Transformation and First-Order Gaussian Differentiator

Kathirvel, P.; Manikandan, M. Sabarimalai; Prasanna, S. R. Mahadeva; Soman, K P

doi:10.1007/s13239-011-0065-3

Cited by 88 publications

(41 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where, (11) and C(AB|AA) = − log 2 1 + 0.01 2 + 3 2 × 0.01 = 1.049 (12) which means C(x||x) = 1.269 or, in other words, using a xaFCM to represent the sequence x it is possible to compress it relatively to itself using just 1.269 bits.…”

Section: Examplementioning

confidence: 99%

Extended-alphabet finite-context models

Carvalho

Brás

Pratas

et al. 2018

Pattern Recognition Letters

View full text Add to dashboard Cite

The Normalized Relative Compression (NRC) is a recent dissimilarity measure, related to the Kolmogorov Complexity. It has been successfully used in different applications, like DNA sequences, images or even ECG (electrocardiographic) signal. It uses a compressor that compresses a target string using exclusively the information contained in a reference string. One possible approach is to use finite-context models (FCMs) to represent the strings.A finite-context model calculates the probability distribution of the next symbol, given the previous k symbols. In this paper, we introduce a generalization of the FCMs, called extended-alphabet finite-context models (xaFCM), that calculates the probability of occurrence of the next d symbols, given the previous k symbols.We perform experiments on two different sample applications using the xaFCMs and the NRC measure: ECG biometric identification, using a publicly available database; estimation of the similarity between DNA sequences of two different, but related, species -chromosome by chromosome.In both applications, we compare the results against those obtained by the FCMs. The results show that the xaFCMs use less memory and computational time to achieve the same or, in some cases, even more accurate results.

show abstract

Section: Examplementioning

confidence: 99%

Extended-alphabet finite-context models

Carvalho

Brás

Pratas

et al. 2018

Pattern Recognition Letters

View full text Add to dashboard Cite

show abstract

“…Algorithm described in [5] was used. It consists of novel nonlinear transformation of ECG signal, based on Shannon energy tresholding, and peak-finding strategy, based on the first-order Gaussian differentiator.…”

Section: Preprocessing and Beat Detectionmentioning

confidence: 99%

Atrial Fibrillation Detection Using Boosting and Stacking Ensemble

Smolen¹

2017

Computing in Cardiology Conference (CinC)

View full text Add to dashboard Cite

IntroductionAtrial Fibrillation (AF) is one of the most common cardiac arrythmia. It occurs in 1-2% of the general population, and this number will likely triple in the next 30-50 years [1]. AF can be easily mistaken with other arrythmias, or omitted, because of its episodic occurance.Throughout the years, considerable progress has been made in the automatic detection of AF. However, current methods are not promising. Algorithms that can be found in literature are usually tested on clean data, not properly seperated from the training set, based on small amount of patients.Physionet Challenge 2017 [2] has given an opportunity for scientific community to improve AF detection, by publishing dataset of short one-lead records, containing of more than 8528 training examples. Such dataset can satisfy previous limitations. Presented work is an attempt of creating reliable, patient-independent, resistant for other arrythmias system. Related work introduced various algorithms for predicting disease and detecting different types of arrhythmia. AF symptoms and though analysis could be basically divided into two categories: based on atrial activity or ventricular response. AF detectors that could combine both features could provide an enhanced performance. Published methods include approaches based on machine learning [3,4].The paper is organized as follows: Section 2 describes feature extraction, Section 3 explains briefly the theory behind the clasifiers, Sections 4 is the explenation of trainig approach, cross-validation techniques and the configurations of proposed models. Section 5 presents the results and concludes the paper. Features extractionOne of the most important components of proposed solution are features designed by the author. Overall, 36 features were obtained. They can be split in 3 categories: the inter-beat timing ('RR intervals') features, statistical features, frequency features, morphological features, noise features. The number of designed features in each category is respectively 8, 3, 5, 4, 16, 2. Preprocessing and beat detectionTo remove baseline wandering and high-frequency noise, Butterworth 3rd-order filtering was performed, with bandpass frequencies between 1 and 25 Hz. The frequencies were chosen based on later cross-validation.After frequencies removal, it was necessary to detect R-peaks, in order to calculate ventricular response features. Algorithm described in [5] was used. It consists of novel nonlinear transformation of ECG signal, based on Shannon energy tresholding, and peak-finding strategy, based on the first-order Gaussian differentiator. On a popular benchmark, MIT-BIH arrhythmia database, it achieves an average sensitivity of 99.94% and a positive predictivity of 99.96%, which is a competetive score.

show abstract

“…In this work, QRS complexes are detected using an algorithm reported in our previous work [15]. This algorithm uses Shannon energy transformation to obtain the QRS complex envelope of the filtered ECG signal and the first-order Gaussian differentiator for determining location of candidate R-peaks in the envelope.…”

Section: Cardiac Event Change Detection Algorithm 31 Ecg Beat Extracmentioning

confidence: 99%

Automated cardiac event change detection for continuous remote patient monitoring devices

Baby

Manikandan

Soman

2011

Proceedings of the 1st International Conference on Wireless Technologies for Humanitarian Relief

Self Cite

View full text Add to dashboard Cite

Recently, wireless body area network (WBAN) plays an important role in remote cardiac patient monitoring, and mobile healthcare applications. Generally, the use of WBAN technology is restricted by size, power consumption, transmission capacity (bandwidth), and computational loads. In this paper, we therefore propose an automated cardiac event change detection for continuous remote patient monitoring devices. The proposed event change detection algorithm consists of two stages: i) ECG beat extraction; and ii) ECG beat similarity measure. In the first stage, the onset of each QRS complex is identified using the Gaussian derivative based QRS detector and the two heuristics rules. In the second stage, we employ the weighted wavelet distance (WWD) metric for finding the similarity between two ECG beats in wavelet domain. The WWD is the weighted normalized Euclidean wavelet distance between the wavelet subband coefficients vectors of the current and past ECG beats, where weights are equal to the relative wavelet subband energies of the corresponding subbands. The experimental results show that the weighted wavelet distance measure works substantially better than the conventional PRD and the wavelet based weighted PRD (WWPRD) measures under noisy environments. The proposed approach has been tested and yielded an accuracy of 99.76% on MIT-BIH Arrhythmia Database.

show abstract

An Efficient R-peak Detection Based on New Nonlinear Transformation and First-Order Gaussian Differentiator

Cited by 88 publications

References 33 publications

Extended-alphabet finite-context models

Extended-alphabet finite-context models

Atrial Fibrillation Detection Using Boosting and Stacking Ensemble

Automated cardiac event change detection for continuous remote patient monitoring devices

Contact Info

Product

Resources

About