Wavelet transform and neural-network-based adaptive filtering for QRS detection

Szilagyi, S.M.; Szilágyi, László

doi:10.1109/iembs.2000.897966

Cited by 25 publications

(14 citation statements)

References 6 publications

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“…Wavelet transform represents a function by scaled wavelets in the time domain. 22 After ECG signals were digitized at 1000 Hz, the digital signals were divided into 10 frequency components or scales by using the ''coif5'' wavelet, a 5th order coiflet (a discrete wavelet) function designed by Ingrid Daubechies. 5,6 Coif5 wavelet is symmetrical, useful in preventing de-phasing image processing.…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

Micro-Electrocardiograms to Study Post-Ventricular Amputation of Zebrafish Heart

Park

Zhang

et al. 2009

Ann Biomed Eng

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The zebrafish (Danio rerio) is an emerging model for cardiovascular research. The zebrafish heart regenerates after 20% ventricular amputation. However, assessment of the physiological responses during heart regeneration has been hampered by the small size of the heart and the necessity of conducting experiments in an aqueous environment. We developed a methodology to monitor a real-time surface electrocardiogram (ECG) by the use of micro-electrodes, signal amplification, and a low pass-filter at a sampling rate of 1 kHz. Wavelet transform was used to further remove ambient noises. Rather than paralyzing the fish, we performed mild sedation by placing the fish in a water bath mixed with MS-222 (tricane methanesulfonate). We recorded distinct P waves for atrial contraction, QRS complexes for ventricular depolarization, and QT intervals for ventricular repolarization prior to, and 2 and 4 days post-amputation (dpa). Sedation reduced the mean fish heart rate from 149 +/- 18 to 90 +/- 17 beats/min. The PR and QRS intervals remained unchanged in response to ventricular apical amputation (n = 6, p > 0.05). Corrected QT intervals (QTc) were shortened 4 dpa (n = 6, p < 0.05). In a parallel study, histology revealed that apical thrombi were replaced with fibrin clots and collagen fibers. Atrial arrhythmia was noted in response to prolonged sedation. Unlike the human counterpart, ventricular tachycardia or fibrillation was not observed in response to ventricular amputation 2 and 4 dpa. Taken together, we demonstrated a minimally invasive methodology to monitor zebrafish heart function, electrical activities, and regeneration in real-time.

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Section: Data Acquisition and Processingmentioning

confidence: 99%

Micro-Electrocardiograms to Study Post-Ventricular Amputation of Zebrafish Heart

Park

Zhang

et al. 2009

Ann Biomed Eng

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“…Di Virgilio et al [8] use time-scale diagrams to illustrate the detection capability of fiducial points on ECG signal (QRS complexes, P and T waves). Szilágyi et al [9] use an adaptive whitening filter for modeling the lower frequencies of the ECG. They claim that the estimation error translates into the QRS complex.…”

Section: Introductionmentioning

confidence: 99%

Wavelet packet based algorithm for QRS region detection and R/S wave identification

Vega-Martínez

Alvarado-Serrano

Salas

2015

2015 12th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

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This paper comprises the methodology followed to design and implement a wavelet packet based algorithm for QRS region detection and R/S wave identification. Validation is performed using electrocardiographic (ECG) records 100 to 109 of the MIT-BIH Arrhythmia database. The proposed algorithm reconstructs the ECG signal using two nodes from the wavelet packet decomposition. Sensitivity (Se) and positive predictivity (+P) are calculated and compared to other algorithms which use approaches based on derivative signals and digital filters (Hamilton & Tompkins) as well as wavelet-based QRS detection (Li et al.), obtaining the values Se = 99.87 % and +P = 99.85 %.Only 31 false negatives were found, from a total of 21,680 heartbeats, which shows the algorithms' contribution to region location improvement. Also, once the region is determined, a simple max/min threshold is used for R/S wave identification.

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“…Para la detección de la onda R se han propuesto diferentes estrategias para resaltar las componentes espectrales que se encuentran en un ancho de banda entre los 10 y 20 Hz [4]. También se han obtenido éxitos a través del uso de redes neuronales y transformada wavelet [5], implementación de primeras derivadas [6], transformada curvelet [7], interpolación basada en curvas splines [8] y descomposición empíri-ca [9]. Los procesos de detección de la onda R proporcionan como resultado el realce de esta componente del complejo cardiaco en el dominio del tiempo.…”

Section: Introductionunclassified

Cálculo del umbral para detección de la onda R del complejo cardiaco

González-Barajas

2014

TecnoL.

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La señal electrocardiográfica es una señal eléctrica con una amplitud de 1 mV aproximadamente y componentes espectrales entre 0,7 y 100 Hz. El electrocardiograma representa el comportamiento eléctrico del corazón humano y está conformado principalmente por un grupo de ondas denominado el complejo cardiaco. Las ondas que componen el complejo cardiaco son: P, Q, R, S y T. La onda R corresponde a la onda positiva de mayor amplitud de la señal electrocardiográfica y el tiempo entre cada onda permite el cálculo de la frecuencia cardiaca instantánea. Para el cálculo del tiempo entre cada onda R es necesario la implementación de un sistema de filtrado que permita una atenuación de las componentes espectrales que no pertenecen a esta forma de onda. Posteriormente se procede a un proceso de umbralización que consiste en generar una señal binaria que toma el valor de uno en la muestra que registra la existencia de una onda R y cero en las demás muestras. El objetivo de este trabajo es presentar los resultados obtenidos al implementar un algoritmo para el establecer del umbral basado en el cálculo del histograma de la señal electrocardiográfica que ha sido previamente tratada a través de un sistema basado en bancos de filtros.

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Wavelet transform and neural-network-based adaptive filtering for QRS detection

Cited by 25 publications

References 6 publications

Micro-Electrocardiograms to Study Post-Ventricular Amputation of Zebrafish Heart

Micro-Electrocardiograms to Study Post-Ventricular Amputation of Zebrafish Heart

Wavelet packet based algorithm for QRS region detection and R/S wave identification

Cálculo del umbral para detección de la onda R del complejo cardiaco

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