RR-Interval variance of electrocardiogram for atrial fibrillation detection

Nuryani, N; Solikhah, M.Pd Imroatus; Nugoho, A. S.; Afdala, Adfal; Anzihory, Eka

doi:10.1088/1742-6596/776/1/012105

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…𝑡 𝑘=1 (6) where 𝑊 𝑖 , 𝑊 𝑓 , 𝑊 𝑜 , 𝑊 𝑖𝑛 are weight matrices for the input gate, forget gate, output gate, and the input, respectively.…”

Section: Long Short-term Memorymentioning

confidence: 99%

“…The ECG form changes in ST-elevation, T-waveform, and the ST interval length. In other cases, the detection of QT and RR interval for calculating QT corrected (QTc) to inform Long QT Syndrome (LQTS), and P-wave abnormality to diagnose Atrial Fibrillation, are difficult due to the lack of symptoms [4][5] [6]. A cardiologist needs to analyze ECG recordings that acquired over several hours or even days, making the task very troublesome and time-consuming [1].…”

Section: Introductionmentioning

confidence: 99%

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Automated ECG Waveform Annotation Based on Stacked Long Short-Term Memory

Darmawahyuni¹,

Nurmaini²,

Rachmatullah³

2020

ComEngApp

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The classification of electrocardiogram (ECG) waveform segmentation techniques can be difficult due to physiological variation of heart rate and different characteristics of the different ECG waves in terms of shape, frequency, amplitude, and duration. The P-wave, PR-segment, QRS-complex, ST-segment, and T-wave are extracted as the feature for classification algorithm to diagnose specified cardiac disorders. This requires the implementation of algorithms that identify specific points within the ECG wave. Some previous computational algorithms for automatic classification of ECG segmentation are proposed to overcome limitations of manual inspection of the ECG. This study presents new insight into the ECG semantic segmentation problem is surmounted by a deep learning approach for automatic ECG wave-form. Long short-term memory (LSTM) is proposed for this task. This experimental study has been performed for six different waveforms of ECG signal that represents cardiac disorders obtained from the Physionet: QT database. Overall, LSTM performance achieved accuracy, sensitivity, specificity, precision, F1-score, is 93.36%, 86.85%, 95.78%, 81.79%, and 83.09%, respectively.

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“…𝑡 𝑘=1 (6) where 𝑊 𝑖 , 𝑊 𝑓 , 𝑊 𝑜 , 𝑊 𝑖𝑛 are weight matrices for the input gate, forget gate, output gate, and the input, respectively.…”

Section: Long Short-term Memorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated ECG Waveform Annotation Based on Stacked Long Short-Term Memory

Darmawahyuni¹,

Nurmaini²,

Rachmatullah³

2020

ComEngApp

View full text Add to dashboard Cite

show abstract

“…The ECG form changes in ST-elevation, T-waveform, and the ST interval length. In other cases, the detection of QT and RR-interval for calculating QT-corrected (QTc) to inform long QT syndrome (LQTS), and P-wave abnormality to diagnose Atrial Fibrillation, are difficult due to the lack of symptoms [4][5][6]. A cardiologist needs to analyze ECG recordings that are acquired over several hours or even days, making the task very troublesome and time-consuming [1,7,8].…”

Section: Introductionmentioning

confidence: 99%

Delineation of electrocardiogram morphologies by using discrete wavelet transforms

Darmawahyuni

Nurmaini

Supriansyah

et al. 2021

IJEECS

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<span>The accuracy of electrocardiogram (ECG) delineation can affect the precise diagnose for cardiac disorders interpretation. Some nonideal ECG presentation can make a false decision in precision medicine. Besides, the physiological variation of heart rate and different characteristics of the different ECG waves in terms of shape, frequency, amplitude, and duration is also affected. <span>This paper proposes a discrete wavelet transform (DWT), non-stationary signal analysis for noise removal, and onset-offset of PQRST feature extraction. A well-known database from Physionet: QT database (QTDB) is used to validate the DWT function for detecting the onset and offset of P-wave, QRS-complex, and T-wave localization. From the results, P-peak detection gets the highest result that achieves 2.19 and 13.62 milliseconds of mean error and standard deviation, respectively. In contrast, Toff has obtained the highest error value due to differences in the T-wave morphology. It can be affected by inverted o</span>r biphasic T-waves and others.</span>

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Automatic atrial fibrillation detection from short ECG signals: A hybrid deep learning approach

Sui

Chu

et al. 2021

IISE Transactions on Healthcare Systems Engineering

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RR-Interval variance of electrocardiogram for atrial fibrillation detection

Cited by 7 publications

References 7 publications

Automated ECG Waveform Annotation Based on Stacked Long Short-Term Memory

Automated ECG Waveform Annotation Based on Stacked Long Short-Term Memory

Delineation of electrocardiogram morphologies by using discrete wavelet transforms

Automatic atrial fibrillation detection from short ECG signals: A hybrid deep learning approach

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