QRS detection based on hidden Markov modeling

Cost, A.A.; Cano, G.G.

doi:10.1109/iembs.1989.95558

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…These metrics include false negative (art) which means not detecting a real beat, false positive (PHP) which represents the detection of true, false and positive strikes (TP) is the total number devoted to detection correctly by the technique. Using phenotype, sensitivity (C), positive prediction (+P) and detection error rate (DER) can be calculated using equations (11)(12)(13). Sensitivity (Sen) (C): The heart rate correctly determined by the algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…Although, neural networks technique is affected badly with noise, but it gives good results when it uses with wavelet transform [9], [10]. Different combination of successful techniques is used to enhance results such as Hidden Markov Model with bandpass filter [11]. These combination result can be sensitive to some artifacts such as baseline wander or from heart rate variability or from noise [12].…”

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confidence: 99%

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Detection of electrocardiogram QRS complex based on modified adaptive threshold

Hussein

Hassooni

Al-Libawy

2019

IJECE

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<p>It is essential for medical diagnoses to analyze Electrocardiogram (ECG signal). The core of this analysis is to detect the QRS complex. A modified approach is suggested in this work for QRS detection of ECG signals using existing database of arrhythmias. The proposed approach starts with the same steps of previous approaches by filtering the ECG. The filtered signal is then fed to a differentiator to enhance the signal. The modified adaptive threshold method which is suggested in this work, is used to detect QRS complex. This method uses a new approach for adapting threshold level, which is based on statistical analysis of the signal. Forty-eight records from an existing arrhythmia database have been tested using the modified method. The result of the proposed method shows the high performance metrics with sensitivity of 99.62% and a positive predictivity of 99.88% for QRS complex detection.</p>

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Detection of electrocardiogram QRS complex based on modified adaptive threshold

Hussein

Hassooni

Al-Libawy

2019

IJECE

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Background on ECG Processing

Habte

Saleh

Mohammad

et al. 2018

Analog Circuits and Signal Processing

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Development of robust, fast and efficient QRS complex detector: a methodological review

Raj

Ray

Shankar

2018

Australas Phys Eng Sci Med

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The basis and reliability for timely diagnosis of cardiovascular diseases depend on the robust and accurate detection of QRS complexes along with the fiducial points in the electrocardiogram (ECG) signal. Despite, the several QRS detection algorithms reported in the literature, the development of an efficient QRS detector remains a challenge in the clinical environment. Therefore, this article summarizes the performance analysis of various QRS detection techniques depending upon three assessment factors which include robustness to noise, computational load, and sensitivity validated on the benchmark MIT-BIH arrhythmia database. Moreover, the limitations of these algorithms are discussed and compared with the standard signal processing algorithms, followed by the future suggestions to develop a reliable and efficient QRS methodology. Further, the suggested method can be implemented on suitable hardware platforms to develop smart health monitoring systems for continuous and long-term ECG assessment for real-time applications.

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QRS detection based on hidden Markov modeling

Cited by 8 publications

References 5 publications

Detection of electrocardiogram QRS complex based on modified adaptive threshold

Detection of electrocardiogram QRS complex based on modified adaptive threshold

Background on ECG Processing

Development of robust, fast and efficient QRS complex detector: a methodological review

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