QRS detection using a fuzzy neural network

Cohen, K.P.; Tompkins, W.J.; Djohan, A.; Webster, John G.; Hu, Yu

doi:10.1109/iembs.1995.575064

Cited by 5 publications

(1 citation statement)

References 5 publications

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“…A true beats can be missed when the threshold is too high. Similarly, if the threshold is too low, false detection can result during EMG artifact and external interference [5] . As the magnitude of the noise can become greater than the signal during these artifacts, based on amplitude thresholding alone is not satisfactory for the detection of R-peak in the ECG signal.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method for Fetal Electrocardiogram Extraction from the Abdominal Electrocardiogram Signal

Hasan¹,

Ibrahimy²,

Reaz³

2009

J. of Computer Science

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Problem statement: FECG (Fetal Electrocardiogram) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during pregnancy and labor. Approach: Conventional techniques were often unable to achieve the extraction of FECG from the Abdominal ECG (AECG) in satisfactorily level. A new methodology by combining the Artificial Neural Network (ANN) and Correlation (ANNC) approach had been proposed in this study. Results: The accuracy of the proposed method for FECG extraction from the AECG signal was about 100% and the performance of the method for FHR extraction is 93.75%. Conclusions/Recommendations: The proposed approach involved the FECG extraction even though the MECG and FECG are overlapped in the AECG signal so that the physician and clinician can make the correct decision for the well-being of the fetus and mother during the pregnancy period.

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Section: Introductionmentioning

confidence: 99%

An Efficient Method for Fetal Electrocardiogram Extraction from the Abdominal Electrocardiogram Signal

Hasan¹,

Ibrahimy²,

Reaz³

2009

J. of Computer Science

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NN-Based R-peak Detection in QRS Complex of ECG Signal

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-eural Network (NN) is designed to detect QRS complex from ECG signal. QRS complex detection is essential so that RR-interval can be measured for disease classification and can also be monitoring the heart rate. In this paper, a supervised Neural Network based algorithm has been used to detect R in QRS complex. It was tried to find out the R-peak in QRS complex with missing peak and false peak as well, so that the correct decision can be made by the physician and clinician. The accuracy of finding the R-peak by using the Neural Network was 99.09% averagely and the average percentage of missing and false peak was 00.09%. The technique appears to be exceedingly robust, correctly detects R-peaks even aberrant QRS complexes in noise-corrupted ECGs.

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

Azad

Darus²,

Ali³

Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedi

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Signal processing problems related to abdominal-lead fetal ECG include the cancellation of the maternal QRS complex, signal enhancement of the fetal QRS complex and detection of the presence of a fetal R-wave to compute the fetal heart rate. This paper describes an improved scheme for detecting the presence of the QRS complexes from the enhanced fetal ECG signal obtained by using a fuzzy decision algorithm. The decision method identifies maternal and fetal ECG from maternal abdominal recordings.

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QRS detection using a fuzzy neural network

Cited by 5 publications

References 5 publications

An Efficient Method for Fetal Electrocardiogram Extraction from the Abdominal Electrocardiogram Signal

An Efficient Method for Fetal Electrocardiogram Extraction from the Abdominal Electrocardiogram Signal

NN-Based R-peak Detection in QRS Complex of ECG Signal

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

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