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Constant Q-transform-based deep learning architecture for detection of obstructive sleep apnea

Kandukuri Usha Rani, Prakash Allam Jaya, Patro Kiran Kumar, Neelapu Bala Chakravarthy, Tadeusiewicz Ryszard, Pławiak Paweł

International Journal of Applied Mathematics and Computer Science

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2023

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Vol. 33, no. 3

493--506

EN

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.

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SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

Patro Kiran Kumar, Allam Jaya Prakash, Hammad Mohamed, Tadeusiewicz Ryszard, Pławiak Paweł

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 1

352--368

EN

Background and Objective: The global population has been heavily impacted by the COVID-19 pandemic of coronavirus. Infections are spreading quickly around the world, and new spikes (Delta, Delta Plus, and Omicron) are still being made. The real-time reverse transcription-polymerase chain reaction (RT-PCR) is the method most often used to find viral RNA in a nasopharyngeal swab. However, these diagnostic approaches require human involvement and consume more time per prediction. Moreover, the existing conventional test mainly suffers from false negatives, so there is a chance for the virus to spread quickly. Therefore, a rapid and early diagnosis of COVID-19 patients is needed to overcome these problems. Methods: Existing approaches based on deep learning for COVID detection are suffering from unbalanced datasets, poor performance, and gradient vanishing problems. A customized skip connection-based network with a feature union approach has been developed in this work to overcome some of the issues mentioned above. Gradient information from chest X-ray (CXR) images to subsequent layers is bypassed through skip connections. In the script’s title, ‘‘SCovNet” refers to a skip-connection-based feature union network for detecting COVID-19 in a short notation. The performance of the proposed model was tested with two publicly available CXR image databases, including balanced and unbalanced datasets. Results: A modified skip connection-based CNN model was suggested for a small unbalanced dataset (Kaggle) and achieved remarkable performance. In addition, the proposed model was also tested with a large GitHub database of CXR images and obtained an overall best accuracy of 98.67% with an impressive low false-negative rate of 0.0074. Conclusions: The results of the experiments show that the proposed method works better than current methods at finding early signs of COVID-19. As an additional point of interest, we must mention the innovative hierarchical classification strategy provided for this work, which considered both balanced and unbalanced datasets to get the best COVID-19 identification rate.

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A hybrid approach of a deep learning technique for real-time ECG beat detection

Patro Kiran Kumar, Prakash Allam Jaya, Samantray Saunak, Pławiak Joanna, Tadeusiewicz Ryszard, Pławiak Paweł

International Journal of Applied Mathematics and Computer Science

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2022

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Vol. 32, no. 3

455--465

EN

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.

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BAED: A secured biometric authentication system using ECG signal based on deep learning techniques

Jaya Prakash Allam, Patro Kiran Kumar, Hammad Mohamed, Tadeusiewicz Ryszard, Pławiak Paweł

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 4

1081--1093

EN

Biometric authentication technology has become increasingly common in our daily lives as information protection and control regulation requirements have grown worldwide. A biometric system must be simple, flexible, efficient, and secure from unauthorized access. The most suitable and flexible biometric traits are the face, fingerprint, palm print, voice, electrocardiogram (ECG), and iris. ECGs are difficult to falsify among these biometric traits and are less attack-prone. However, designing biometric systems based on ECG is very challenging. The major limitations of the existing techniques are that they require a large amount of training data and that they are trained and tested on an on-person database. To cope with these issues, this work proposes a novel biometric authentication scheme based on ECG detection called BAED. The system was developed based on deep learning algorithms, including a convolutional neural network (CNN) and a long-term memory (LSTM) network with a customized activation function. The authors evaluated the proposed model with on-and off-person databases including ECG-ID, Physikalisch-Technische Bundesanstalt (PTB), Check Your Bio-signals Here Initiative (CYBHi), and the University of Toronto Database (UofTDB). In addition to the standard performance parameters, certain key supportive identification parameters such as FMR, FNMR, FAR, and FRR were computed and compared to increase the model’s credibility.The proposed BAED system outperforms prior state-of-the-art approaches.