A simple real-time QRS detection algorithm utilizing curve-length concept with combined adaptive threshold for electrocardiogram signal classification

Lewandowski, Jerzy; Arochena, Hisbel; Naguib, R.N.G.; Chao, Kuo‐Ming

doi:10.1109/tencon.2012.6412176

Cited by 20 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…no. Method Sensitivity Predictivity 1 the proposed method 99.91 99.91 2 Pan–Tompkins [ 5 ] 99.75 99.54 3 Hilbert transform [ 11 ] 99.94 99.93 4 curve-length transform [ 23 ] 99.86 99.84 5 S-transform [ 3 ] 99.84 99.89 …”

Section: Resultsmentioning

confidence: 99%

“…Meyer et al [ 21 ] presented an approach to automatically combine different QRS complex detection algorithms, here the Pan–Tompkins and wavelet algorithms, to benefit from the strengths of both methods. Lewandowski et al [ 23 ] proposed a simple real-time QRS detection algorithm utilising curve-length concept with combined adaptive threshold for ECG signal classification.…”

Section: Related Workmentioning

confidence: 99%

“…Various QRS detection methods have been presented in literature [3–23]. Zidelmala et al [3] proposed an QRS detection method using S‐transform and Shannon energy.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

ECG signal analysis using modified S‐transform

Biswal¹

2017

Healthcare Technology Letters

View full text Add to dashboard Cite

Accurate detection of QRS complexes is essential for the investigation of heart rate variability. Several transform techniques have been proposed and extensively used for the detection and analysis of QRS complexes. In this proposed work, the de-noised ECG signal is subjected to a modified S-transform for QRS complex detection.The performance analysis of the proposed work is evaluated using parameters such as sensitivity, positive predictivity and accuracy. The algorithm delivers sensitivity, positive predictivity and overall accuracy of 99.91, 99.91 and 99.77%, respectively. Furthermore, a search back mechanism is employed, which specifies the filtered electrocardiogram (ECG) segment, which was traced for the true R-peak locations. The modified S-transform based QRS complex detection algorithm provides an excellent search back range of only ±2 samples in comparison with other earlier proposed algorithms.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

ECG signal analysis using modified S‐transform

Biswal¹

2017

Healthcare Technology Letters

View full text Add to dashboard Cite

show abstract

“…Numerous approaches proposed in the literature for QRS detection have been well reviewed and compared in [1]. Almost all the high performance algorithms involve two major steps: (i) transformation of the QRS complex into an impulse-like event through some linear or non-linear processing, (ii) R-peak detection by comparing the features of the transformed signal against adaptive [2], [3] or fixed thresholds or with the use of some heuristic detection logic [4]. Among these, methods based on the first derivative of the ECG signal are often used in real time applications because of their low computational load and lack of need for training and patient specific information [5].…”

Section: Introductionmentioning

confidence: 99%

Threshold-Independent QRS Detection Using the Dynamic Plosion Index

Ramakrishnan

Prathosh

Ananthapadmanabha

2014

IEEE Signal Process. Lett.

View full text Add to dashboard Cite

Detection of QRS serves as a first step in many automated ECG analysis techniques. Motivated by the strong similarities between the signal structures of an ECG signal and the integrated linear prediction residual (ILPR) of voiced speech, an algorithm proposed earlier for epoch detection from ILPR is extended to the problem of QRS detection. The ECG signal is pre-processed by high-pass filtering to remove the baseline wandering and by half-wave rectification to reduce the ambiguities. The initial estimates of the QRS are iteratively obtained using a non-linear temporal feature, named the dynamic plosion index suitable for detection of transients in a signal. These estimates are further refined to obtain a higher temporal accuracy. Unlike most of the high performance algorithms, this technique does not make use of any threshold or differencing operation. The proposed algorithm is validated on the MIT-BIH database using the standard metrics and its performance is found to be comparable to the state-of-the-art algorithms, despite its threshold independence and simple decision logic.Index Terms-DPI algorithm, dynamic plosion index, ECG signal, GCI detection, QRS detection, threshold free.

show abstract

“…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.

View full text Add to dashboard Cite

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.

show abstract

A simple real-time QRS detection algorithm utilizing curve-length concept with combined adaptive threshold for electrocardiogram signal classification

Cited by 20 publications

References 18 publications

ECG signal analysis using modified S‐transform

ECG signal analysis using modified S‐transform

Threshold-Independent QRS Detection Using the Dynamic Plosion Index

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

Contact Info

Product

Resources

About