A revised scheme for real time ECG Signal denoising based on recursive filtering

Cuomo, Salvatore; Pietro, Giuseppe De; Farina, Raffaele; Galletti, Ardelio; Sannino, Giovanna

doi:10.1016/j.bspc.2016.02.007

Cited by 60 publications

(17 citation statements)

References 24 publications

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“…Furthermore, a corrupt ECG signal can lead to the wrong diagnosis and it also has a major effect on the performance of algorithms during classification [37][38][39]. In the literature, there are many studies that are dedicated to innovating the algorithm for the filtering of noisy physiological signals for the highest performance of proposed models, for instance [38,39], this study applies the same filtering technique as used in [7,40], and these studies also addressed the similar aim of classification. The author believes that this is the major decision that helps to evaluate the performance of the current study with the existing methods.…”

Section: Ecg Signal Denoisingmentioning

confidence: 99%

“…After the application of median filters for the baseline wandering outlier's removal, this study applied 12th order finite impulse response (low pass filter) with the given cut-off frequency k = 35 Hz for removing the outliers which are related to power line interference by using the fir1 Matlab function [42]. The clear picture based on the results of these filters is reported in Figure 2a In the literature, there are many studies that are dedicated to innovating the algorithm for the filtering of noisy physiological signals for the highest performance of proposed models, for instance [39,40], this study applies the same filtering technique as used in [8,41], and these studies also addressed the similar aim of classification. The author believes that this is the major decision that helps to evaluate the performance of the current study with the existing methods.…”

Section: Ecg Signal Denoisingmentioning

confidence: 99%

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Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification

2019

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The abnormal conduction of cardiac activity in the lower chamber of the heart (ventricular) can cause cardiac diseases and sometimes leads to sudden death. In this paper, the author proposed the Reservoir Computing (RC) based Echo State Networks (ESNs) for ventricular heartbeat classification based on a single Electrocardiogram (ECG) lead. The Association for the Advancement of Medical Instrumentation (AAMI) standards were used to preprocesses the standardized diagnostic tool (ECG signals) based on the interpatient scheme. Despite the extensive efforts and notable experiments that have been done on machine learning techniques for heartbeat classification, ESNs are yet to be considered for heartbeat classification as a is fast, scalable, and reliable approach for real-time scenarios. Our proposed method was especially designed for Medical Internet of Things (MIoT) devices, for instance wearable wireless devices for ECG monitoring or ventricular heart beat detection systems and so on. The experiments were conducted on two public datasets, namely AHA and MIT-BIH-SVDM. The performance of the proposed model was evaluated using the MIT-BIH-AR dataset and it achieved remarkable results. The positive predictive value and sensitivity are 98.98% and 98.98%, respectively for the modified lead II (MLII) and 98.96% and 97.95 for the V1 lead, respectively. However, the experimental results of the state-of-the-art approaches, namely the patient-adaptable method, improved generalization, and the multiview learning approach obtained 92.8%, 87.0%, and 98.0% positive predictive values, respectively. These obtained results of the existing studies exemplify that the performance of this method achieved higher accuracy. We believe that the improved classification accuracy opens up the possibility for implementation of this methodology in Medical Internet of Things (MIoT) devices in order to bring improvements in e-health systems.

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Section: Ecg Signal Denoisingmentioning

confidence: 99%

Section: Ecg Signal Denoisingmentioning

confidence: 99%

Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification

2019

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“…For K = 1, the filter merely becomes an n-order Gaussian RF filter. Equations in (5) and (6) are conveniently referred to as the advancing and backing filters, respectively: when a Gaussian RF is implemented as an algorithm, the index j must be treated in increasing order in the former and decreasing in the latter [8]. The values α t and β are called smoothing coefficients and satisfy the constraint:…”

Section: Mathematical Backgroundmentioning

confidence: 99%

“…For example Gaussian RFs are usually involved in image processing [1], [2] and are also implemented for solving three-dimensional variational analysis schemes in data assimilation [3]. Moreover, they have been recently constructed specifically for the electrocardiogram denoising [4], [5], [6]. The idea of recursive filters is to approximate a given filter, or for example the convolution with the impulse response of such a filter, in a more efficient way.…”

Section: Introductionmentioning

confidence: 99%

Error analysis for the first-order Gaussian recursive filter operator

Galletti¹,

Giunta²

2016

Annals of Computer Science and Information Systems

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Abstract-Nowadays, recursive filters (RFs) are frequently used in several research fields. More in particular, Gaussian RFs offer a more efficient way for computing approximate Gaussian filters and Gaussian-based convolutions. The use of such recursive filters introduces many sources of errors. Among them, here we consider the filter truncation error, that is the error due to the transition from the starting filter operator to the RF approximating it. Since input and output signals have infinite dimensions, the analysis of the related filter operator involves infinite matrices. In this paper, starting from a summary of the comprehensive mathematical background, we consider the case of the first-order Gaussian recursive filter. Then, taking into account the matrix form of the related operator, we perform the error analysis and provide theoretical results that estimate the filter truncation error.

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“…For example, Gaussian RFs are usually involved in image processing [1], [2], in data assimilation for solving three-dimensional variational analysis schemes [3], [4] and in advanced signal processing such as other class of RFs has been recently constructed specifically for the electrocardiogram (ECG) denoising [6], [7], [8]. The idea of a recursive filter is to provide a more efficient approximation either to a given filter operator, or to the convolution with the impulse response of the filter.…”

Section: Introductionmentioning

confidence: 99%

An algorithm for Gaussian Recursive Filters in a Multicore Architecture

Galletti

Giunta

Marcellino

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-Recursive Filters (RFs) are a well-known way to approximate the Gaussian convolution and, due to their computational efficiency, are intensively used in several technical and scientific fields. The accuracy of the RFs can be improved by means of the repeated application of the filter, which gives rise to the so-called K-iterated Gaussian recursive filter. In this work we propose a parallel algorithm for the implementation of the Kiterated first-order Gaussian RF for multicore architectures. This algorithm is based on a domain decomposition with overlapping strategy. The presented implementation is tailored for multicore architectures and makes use of the Pthreads library. We will show through extensive numerical tests that our parallel implementation is very efficient for large one-dimensional signals and guarantees the same accuracy level of the sequential K-iterated first-order Gaussian RF.

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A revised scheme for real time ECG Signal denoising based on recursive filtering

Cited by 60 publications

References 24 publications

Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification

Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification

Error analysis for the first-order Gaussian recursive filter operator

An algorithm for Gaussian Recursive Filters in a Multicore Architecture

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