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    Wyświetlanie 1-4 z 4
    Tytuł:
    A hybrid approach of a deep learning technique for real-time ECG beat detection
    Autorzy:
    Patro, Kiran Kumar
    Prakash, Allam Jaya
    Samantray, Saunak
    Pławiak, Joanna
    Tadeusiewicz, Ryszard
    Pławiak, Paweł
    Powiązania:
    https://bibliotekanauki.pl/articles/2172118.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    cardiac abnormalities
    CAD
    convolutional neural network
    CNN
    deep learning
    ECG
    electrocardiogram
    supra ventricular ectopic beats
    SVE
    nieprawidłowości kardiologiczne
    sieć neuronowa konwolucyjna
    uczenie głębokie
    EKG
    elektrokardiogram
    Opis:
    This paper presents a new customized hybrid approach for early detection of cardiac abnormalities using an electrocardiogram (ECG). The ECG is a bio-electrical signal that helps monitor the heart’s electrical activity. It can provide health information about the normal and abnormal physiology of the heart. Early diagnosis of cardiac abnormalities is critical for cardiac patients to avoid stroke or sudden cardiac death. The main aim of this paper is to detect crucial beats that can damage the functioning of the heart. Initially, a modified Pan–Tompkins algorithm identifies the characteristic points, followed by heartbeat segmentation. Subsequently, a different hybrid deep convolutional neural network (CNN) is proposed to experiment on standard and real-time long-term ECG databases. This work successfully classifies several cardiac beat abnormalities such as supra-ventricular ectopic beats (SVE), ventricular beats (VE), intra-ventricular conduction disturbances beats (IVCD), and normal beats (N). The obtained classification results show a better accuracy of 99.28% with an F1 score of 99.24% with the MIT–BIH database and a descent accuracy of 99.12% with the real-time acquired database.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2022, 32, 3; 455--465
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Constant Q-transform-based deep learning architecture for detection of obstructive sleep apnea
    Autorzy:
    Kandukuri, Usha Rani
    Prakash, Allam Jaya
    Patro, Kiran Kumar
    Neelapu, Bala Chakravarthy
    Tadeusiewicz, Ryszard
    Pławiak, Paweł
    Powiązania:
    https://bibliotekanauki.pl/articles/24200694.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sleep apnea
    convolutional neural network
    constant Q-transform
    deep learning
    single lead ECG signal
    non apnea
    obstructive sleep apnea
    bezdech senny
    sieć neuronowa konwolucyjna
    uczenie głębokie
    sygnał EKG
    obturacyjny bezdech senny
    Opis:
    Obstructive sleep apnea (OSA) is a long-term sleep disorder that causes temporary disruption in breathing while sleeping. Polysomnography (PSG) is the technique for monitoring different signals during the patient’s sleep cycle, including electroencephalogram (EEG), electromyography (EMG), electrocardiogram (ECG), and oxygen saturation (SpO2). Due to the high cost and inconvenience of polysomnography, the usefulness of ECG signals in detecting OSA is explored in this work, which proposes a two-dimensional convolutional neural network (2D-CNN) model for detecting OSA using ECG signals. A publicly available apnea ECG database from PhysioNet is used for experimentation. Further, a constant Q-transform (CQT) is applied for segmentation, filtering, and conversion of ECG beats into images. The proposed CNN model demonstrates an average accuracy, sensitivity and specificity of 91.34%, 90.68% and 90.70%, respectively. The findings obtained using the proposed approach are comparable to those of many other existing methods for automatic detection of OSA.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2023, 33, 3; 493--506
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A new method of cardiac sympathetic index estimation using a 1D-convolutional neural network
    Autorzy:
    Kołodziej, Marcin
    Majkowski, Andrzej
    Tarnowski, Paweł
    Rak, Remigiusz Jan
    Rysz, Andrzej
    Powiązania:
    https://bibliotekanauki.pl/articles/2090741.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    epilepsy
    seizure detection
    seizure prediction
    convolutional neural network
    deep learning
    ECG
    HRV
    cardiac sympathetic index
    padaczka
    wykrywanie napadu
    przewidywanie napadu
    splotowa sieć neuronowa
    głęboka nauka
    technika deep learning
    EKG
    wskaźnik współczulny serca
    Opis:
    Epilepsy is a neurological disorder that causes seizures of many different types. The article presents an analysis of heart rate variability (HRV) for epileptic seizure prediction. Considering that HRV is nonstationary, our research focused on the quantitative analysis of a Poincare plot feature, i.e. cardiac sympathetic index (CSI). It is reported that the CSI value increases before the epileptic seizure. An algorithm using a 1D-convolutional neural network (1D-CNN) was proposed for CSI estimation. The usability of this method was checked for 40 epilepsy patients. Our algorithm was compared with the method proposed by Toichi et al. The mean squared error (MSE) for testing data was 0.046 and the mean absolute percentage error (MAPE) amounted to 0.097. The 1D-CNN algorithm was also compared with regression methods. For this purpose, a classical type of neural network (MLP), as well as linear regression and SVM regression, were tested. In the study, typical artifacts occurring in ECG signals before and during an epileptic seizure were simulated. The proposed 1D-CNN algorithm estimates CSI well and is resistant to noise and artifacts in the ECG signal.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e136921, 1--9
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A new method of cardiac sympathetic index estimation using a 1D-convolutional neural network
    Autorzy:
    Kołodziej, Marcin
    Majkowski, Andrzej
    Tarnowski, Paweł
    Rak, Remigiusz Jan
    Rysz, Andrzej
    Powiązania:
    https://bibliotekanauki.pl/articles/2173565.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    epilepsy
    seizure detection
    seizure prediction
    convolutional neural network
    deep learning
    ECG
    HRV
    cardiac sympathetic index
    padaczka
    wykrywanie napadu
    przewidywanie napadu
    splotowa sieć neuronowa
    głęboka nauka
    technika deep learning
    EKG
    wskaźnik współczulny serca
    Opis:
    Epilepsy is a neurological disorder that causes seizures of many different types. The article presents an analysis of heart rate variability (HRV) for epileptic seizure prediction. Considering that HRV is nonstationary, our research focused on the quantitative analysis of a Poincare plot feature, i.e. cardiac sympathetic index (CSI). It is reported that the CSI value increases before the epileptic seizure. An algorithm using a 1D-convolutional neural network (1D-CNN) was proposed for CSI estimation. The usability of this method was checked for 40 epilepsy patients. Our algorithm was compared with the method proposed by Toichi et al. The mean squared error (MSE) for testing data was 0.046 and the mean absolute percentage error (MAPE) amounted to 0.097. The 1D-CNN algorithm was also compared with regression methods. For this purpose, a classical type of neural network (MLP), as well as linear regression and SVM regression, were tested. In the study, typical artifacts occurring in ECG signals before and during an epileptic seizure were simulated. The proposed 1D-CNN algorithm estimates CSI well and is resistant to noise and artifacts in the ECG signal.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e136921
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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