Adaptive wavelet-transform-based ECG waveforms detection

Szilagyi, S.M.; Benyó, Zoltán; Szilágyi, László; Dávid, László

doi:10.1109/iembs.2003.1280402

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…This signal process is implemented using the wavelet transform. Details of the method are presented in [6][7][8]. The QRS detection rate, in case of our own measurements, is generally above 99%.…”

Section: Ecg Processingmentioning

confidence: 99%

A novel ECG telemetry and monitoring system based on Z-Wave communication

Csernáth

Szilágyi

Fordos

et al. 2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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This paper presents a novel ECG telemetry system based on Z-Wave communication protocol. The proposed system consists of small portable devices that acquire, compress and transmit the ECG to a RF-USB interface connected to a central monitoring computer. The received signals are filtered, QRS complexes and P and T waves are localized, and different waveforms are classified in order to be able to provide diagnosis tools like heart rate variability and turbulence analysis. Due to the limitation of communication bandwidth, the maximum number of measuring devices connected to a central monitor is four. The proposed system composed of inexpensive components can serve as flexible alternative to current ECG monitoring systems.

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Section: Ecg Processingmentioning

confidence: 99%

A novel ECG telemetry and monitoring system based on Z-Wave communication

Csernáth

Szilágyi

Fordos

et al. 2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…Solutions presented in the literature [7][8][9] utilises Adaptive Wavelet Transform to analyse ECG signals. Mostly the tool is used to increase QRS complex detection accuracy.…”

Section: Wavelet Transform Fundamentals and Wavelet Based Ecg Signal mentioning

confidence: 99%

“…The initial and crucial as well step in the ECG signal analysis is the QRS complex detection (detection of the dominant wave in the ECG signal). This task is intensively discussed in the literature [6][7][8][9]. The nature and properties of the WT, make it potentially useful when applied not only in QRS complex localisation but additionally in specification of QRS complex morphology and arrhythmia classification.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiographic signals Wavelet Transform - identification and classification properties

Jóśko

2016

ELECTROTECHNICAL REVIEW

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Praca niniejsza dotyczy badań przeprowadzonych w zakresie analizy sygnałów elektrokardiograficznych (EKG). Można by odnieść wrażenie, że natura WT prowadzi do redundantnego charakteru otrzymywanych wyników, okupionych relatywnie większą złożonością obliczeniową. Jednakże właśnie specyfika przekształceń falkowych pozwala na osiągnięcie bardzo dobrych wyników analiz zapisów EKG, co więcej nie tylko w zakresie zdolności lokalizacji, ale i identyfikacji. W pracy Autor przedstawia wyniki analizy falkowej sygnałów EKG, wraz z propozycją nieskomplikowanej metody do podstawowej klasyfikacji zespołów QRS (Transformacje falkowe sygnałów elektrokardiograficznych-własności identyfikacyjne i klasyfikacyjne).

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“…The new AED algorithms generally integrate multiple techniques. For example, Oliveira et al [6] integrates the Hilbert transform and wavelet transform, and Szilágyi et al [7] combines the neural network, wavelet transform and genetic algorithm techniques. Generally, these hybrid methods can improve the detection accuracy, but have huge computation overhead, more resource consumption and less operation efficiency.…”

Section: State-of-the-artmentioning

confidence: 99%

Real-Time Automatic ECG Diagnosis Method Dedicated to Pervasive Cardiac Care

Zhou¹,

Hou²,

Zuo³

2009

WSN

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Recent developments of the wireless sensor network will revolutionize the way of remote monitoring in dif-ferent domains such as smart home and smart care, particularly remote cardiac care. Thus, it is challenging to propose an energy efficient technique for automatic ECG diagnosis (AED) to be embedded into the wireless sensor. Due to the high resource requirements, classical AED methods are unsuitable for pervasive cardiac care (PCC) applications. This paper proposes an embedded real-time AED algorithm dedicated to PCC sys-tems. This AED algorithm consists of a QRS detector and a rhythm classifier. The QRS detector adopts the linear time-domain statistical and syntactic analysis method and the geometric feature extraction modeling technique. The rhythm classifier employs the self-learning expert system and the confidence interval method. Currently, this AED algorithm has been implemented and evaluated on the PCC system for 30 patients in the Gabriel Monpied hospital (CHRU of Clermont-Ferrand, France) and the MIT-BIH cardiac arrhythmias da-tabase. The overall results show that this energy efficient algorithm provides the same performance as the classical ones

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Adaptive wavelet-transform-based ECG waveforms detection

Cited by 10 publications

References 9 publications

A novel ECG telemetry and monitoring system based on Z-Wave communication

A novel ECG telemetry and monitoring system based on Z-Wave communication

Electrocardiographic signals Wavelet Transform - identification and classification properties

Real-Time Automatic ECG Diagnosis Method Dedicated to Pervasive Cardiac Care

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