Development of a fuzzy rule-based QRS detection algorithm for fetal and maternal heart rate monitoring

Azad, Khurshidul; Darus, Z.M.; Ali, Mohd

doi:10.1109/iembs.1998.745865

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…Therefore, parameterization and detection of the ECG signal events using a reliable algorithm is the first stage in a computer analysis of the ECG signal. Numerous approaches have been developed with the aim of detecting ECG events including mathematical models [5], Hilbert transform and the first derivative [6][7][8], the second-order derivative [9], wavelet transform and the filter banks [10,11], soft computing (neuro-fuzzy, genetic algorithm) [12], the hidden Markov model (HMM) application [13], etc. The performance of QRS detector algorithms can easily be verified using standard databases such as the MIT-BIH arrhythmia database; however, validation of the proposed algorithms for the detection of P-and T-waves and the corresponding parameters has proved to be a difficult problem due to the lack of a universal reference [10].…”

Section: Introductionmentioning

confidence: 99%

Finding events of electrocardiogram and arterial blood pressure signals via discrete wavelet transform with modified scales

Ghaffari

Homaeinezhad

Akraminia

et al. 2009

Proc Inst Mech Eng H

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A robust electrocardiogram (ECG) wave detection-delineation algorithm that can be applied to all ECG leads is developed in this study on the basis of discrete wavelet transform (DWT). By applying a new simple approach to a selected scale obtained from DWT, this method is capable of detecting the QRS complex, P-wave, and T-wave as well as determining parameters such as start time, end time, and wave sign (upward or downward). In the proposed method, the selected scale is processed by a sliding rectangular window of length n and the curve length in each window is multiplied by the area under the absolute value of the curve. In the next step, an adaptive thresholding criterion is conducted on the resulted signal. The presented algorithm is applied to various databases including the MIT-BIH arrhythmia database, European ST-T database, QT database, CinC Challenge 2008 database as well as high-resolution Holter data gathered in the DAY Hospital. As a result, the average values of sensitivity and positive prediction Se = 99.84 per cent and P+ = 99.80 per cent were obtained for the detection of QRS complexes with an average maximum delineation error of 13.7, 11.3, and 14.0 ms for the P-wave, QRS complex, and T-wave respectively. The presented algorithm has considerable capability in cases of a low signal-to-noise ratio, high baseline wander, and in cases where QRS complexes and T-waves appear with abnormal morphologies. Especially, the high capability of the algorithm in the detection of the critical points of the ECG signal, i.e. the beginning and end of the T-wave and the end of the QRS complex was validated by the cardiologist and the maximum values of 16.4 and 15.9 ms were recognized as absolute offset error of localization respectively. Finally, in order to illustrate an alternative capability of the algorithm, it is applied to all 18 subjects of the MIT-BIH polysomnographic database and the end-systolic and end-diastolic points of the blood pressure waveform were extracted and values of sensitivity and positive prediction Se = 99.80 per cent and P+ = 99.86 per cent were obtained for the detection of end-systolic, end-diastolic pulses.

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Section: Introductionmentioning

confidence: 99%

Finding events of electrocardiogram and arterial blood pressure signals via discrete wavelet transform with modified scales

Ghaffari

Homaeinezhad

Akraminia

et al. 2009

Proc Inst Mech Eng H

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“…Mathematical models incorporate wavelet transform, time-frequency approaches, Fourier transform, statistical signal analysis and higher order statistics. AI approaches towards signal recognition include Artificial Neural Networks (ANN) [5], Self-Organizing Map (SOM) neural network [6], Finite Impulse Response (FIR) neural network [7] and fuzzy logic system [8] a new technique combining the adaptive noise canceller and adaptive signal enhancer in a single recurrent neural network has been anticipated for the processing of abdominal ECG signal [9].…”

Section: Introductionmentioning

confidence: 99%

Fetal ECG Extraction from Maternal Abdominal ECG Using Neural Network

Hasan¹,

Ibrahimy

Reaz³

2009

JSEA

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“…Some approaches like fuzzy logic and moving averaged have been proposed to extract fetal ECG from abdominal ECG of pregnant woman [3] [4]. Among different artificial intelligence tools, neural networks are increasingly applied to detect and extract fetal ECG [5].…”

Section: Introductionmentioning

confidence: 99%

An approach of neural network based fetal ECG extraction

Reaz¹,

Lee²

Proceedings. 6th International Workshop on Enterprise Networking and Computing in Healthcare Industry - Healthcom 2004 (IEEE Ca

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In this paper, we describe an adaptive method to separate fetal ECG from composite ECG that consists of both maternal and fetal ECGs by using ADALINE (Adaptive Linear Network). The input signal is the maternal signal and the target signal is the composite signal. The network emulate maternal signal as closely as possible to abdominal signal thus only predict the maternal ECG in the abdominal ECG. The network error equals abdominal ECG minus maternal ECG, which is the fetal ECG. The characteristic that enables fetal extraction is due to correlation between maternal ECG signals with the abdominal ECG signal of pregnant woman. A GUI program is written in Matlab to detect the changes in extracted fetal ECG by different values of momentum, learning rate and initial weights used in the network. However, the learning rate, momentum and initial weights are adjusted until the results are reasonably well. It is found that filtering performs best by high learning rate, low momentum, and small initial weights.

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Development of a fuzzy rule-based QRS detection algorithm for fetal and maternal heart rate monitoring

Cited by 8 publications

References 8 publications

Finding events of electrocardiogram and arterial blood pressure signals via discrete wavelet transform with modified scales

Finding events of electrocardiogram and arterial blood pressure signals via discrete wavelet transform with modified scales

Fetal ECG Extraction from Maternal Abdominal ECG Using Neural Network

An approach of neural network based fetal ECG extraction

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