Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms

Froese, Tom; Hadjiloucas, Sillas; Galvão, Roberto Kawakami Harrop; Becerra, Victor M.; Coelho, Clarimar José

doi:10.1016/j.patrec.2005.09.002

Cited by 38 publications

(19 citation statements)

References 22 publications

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“…Several papers on applications using this formulation before the extension for three vanishing moments have been published, some examples are, such as pattern recognition [4], linear estimation [5], and signal compression [10].…”

Section: Transient Detection In a Signalmentioning

confidence: 99%

“…There are several works using the formulation proposed in [11] for several applications without the extensions to ensure more than one vanishing moment, for example, [1,4,5,10]. However, after obtaining the constraints that ensure at least two vanishing moments, in [9], other papers have presented the use of that formulation and this restriction to various applications, some examples are, [3,8,6].…”

Section: Introductionmentioning

confidence: 99%

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Additional constraints to ensure three vanishing moments for orthonormal wavelet filter banks and transient detection

Uzinski

Paiva

Villareal

et al. 2015

Tend. Mat. Apl. Comput.

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Section: Transient Detection In a Signalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Additional constraints to ensure three vanishing moments for orthonormal wavelet filter banks and transient detection

Uzinski

Paiva

Villareal

et al. 2015

Tend. Mat. Apl. Comput.

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show abstract

“…denote polyphasic components of ( ) ( ). Further algorithmic details of the proposed filter banks and a discussion of adaptive filter banks for the purpose are discussed in [7]. Results from multi-level decompositions are shown in Figure 3.…”

Section: Ecg Signal Decomposition Using Discrete Wavelet Transforms (mentioning

confidence: 99%

“…Further parameterization of the signals using adaptive wavelets using various adaptive filter banks [7] from the wavelet transform literature are considered in this project.…”

Section: Ecg Signal Decomposition Using Discrete Wavelet Transforms (mentioning

confidence: 99%

Smart-phone based electrocardiogram wavelet decomposition and neural network classification

Jannah¹,

Hadjiloucas²,

Hwang³

et al. 2013

J. Phys.: Conf. Ser.

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Abstract. This paper discusses ECG classification after parametrizing the ECG waveforms in the wavelet domain. The aim of the work is to develop an accurate classification algorithm that can be used to diagnose cardiac beat abnormalities detected using a mobile platform such as smart-phones. Continuous time recurrent neural network classifiers are considered for this task. Records from the European ST-T Database are decomposed in the wavelet domain using discrete wavelet transform (DWT) filter banks and the resulting DWT coefficients are filtered and used as inputs for training the neural network classifier. Advantages of the proposed methodology are the reduced memory requirement for the signals which is of relevance to mobile applications as well as an improvement in the ability of the neural network in its generalization ability due to the more parsimonious representation of the signal to its inputs. IntroductionEarly diagnosis of heart diseases enables patients to improve their quality of life through more effective treatments [1]. Analysis and classification of ECG signals can be particularly helpful to identify the initiation of heart conditions such as atrial fibrillation or flutter, multifocal atrial tachycardia, palpitations, paroxysmal supraventricular tachycardia, reasons for frequent fainting, slow heart rate (bradycardia) or ventricular tachycardia. Normally patients will be given a Holter monitor and wear the monitoring electrodes over a period of 24-48 hours. The data-logged signals are postprocessed and examined for cardiac beat abnormalities by doctors over the following days. There are restrictions, however, to how often one should perform such measurements. It is not uncommon for patients to complain to their doctors that the arrhythmias they suffered prior to the examination period were not present during the monitoring process, making early diagnosis more difficult. If one is prepared to wear the appropriately placed electrodes more often, it would be possible to use mobile phones as data logging devices directly. Bluetooth emitters such as the RN-42 from Microchip can provide a direct input from a small footprint battery-operated mobile data acquisition card to which the electrodes are interfaced. With the ever-increasing capabilities of smart-phones, portable ECG telemonitoring is likely to become a common feature for these devices, performing data-logging functionality for the ageing population. Beyond their data-logging functionalities, smart -phones can also use Multimedia Messaging Services (MMS) to enable the recorded signals to be sent directly for diagnosis by experts through current mobile networks or perform directly classification tasks. With the number of patients increasing due to current population sedative lifestyles and unhealthy eating habits, an increased expectation by patients for personalized medical treatment, as well as the envisaged wider proliferation of ECG data-logging devices, it is widely anticipated that there will soon be an overwhelming requirement for expert...

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“…Over the last decade or so, the discrete wavelet transform (DWT) has been successfully adopted to various problems of signal and image processing, including data compression [20], image segmentation [17], and ECG signal classification [9]. The wavelet transform is fast, local in the time and the frequency domain, and provides multi-resolution analysis of real-world signals and images.…”

Section: Introductionmentioning

confidence: 99%

Similarity Matches of Gene Expression Data Based on Wavelet Transform

2009

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Abstract. This study presents a similarity-determining method for measuring regulatory relationships between pairs of genes from microarray time series data. The proposed similarity metrics are based on a new method to measure structural similarity to compare the quality of images. We make use of the Dual-Tree Wavelet Transform (DTWT) since it provides approximate shift invariance and maintain the structures between pairs of regulation related time series expression data. Despite the simplicity of the presented method, experimental results demonstrate that it enhances the similarity index when tested on known transcriptional regulatory genes.

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Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms

Cited by 38 publications

References 22 publications

Additional constraints to ensure three vanishing moments for orthonormal wavelet filter banks and transient detection

Additional constraints to ensure three vanishing moments for orthonormal wavelet filter banks and transient detection

Smart-phone based electrocardiogram wavelet decomposition and neural network classification

Similarity Matches of Gene Expression Data Based on Wavelet Transform

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