OSACN-Net: Automated Classification of Sleep Apnea Using Deep Learning Model and Smoothed Gabor Spectrograms of ECG Signal

Gupta, Kapil; Bajaj, Varun; Ansari, Irshad Ahmad

doi:10.1109/tim.2021.3132072

Cited by 32 publications

(15 citation statements)

References 36 publications

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“…The performance comparison with the existing investigations using the Physionet Apnea-ECG dataset was presented in Table 5 . It can be observed that some related work outperformed the proposed approach, such as [ 10 , 12 , 15 , 19 ]. However, the proposed method used the complete ECG data segments to increase the overall accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…This method could obtain significant results when integrated with other techniques, such as the deep learning method, to increase its robustness [ 17 ]. Various investigations used deep learning methods such as CNN, RNN, LSTM, and GRU to find hidden patterns, improving the diagnostics and detection of OSA [ 9 , 18 , 19 , 20 ]. However, this model requires a large number of training samples to provide satisfactory results, which increases the computational cost of the training phase.…”

Section: Introductionmentioning

confidence: 99%

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Sleep Apnea Detection Using Wavelet Scattering Transformation and Random Forest Classifier

Sharaf

2023

Entropy

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Obstructive Sleep Apnea (OSA) is a common sleep-breathing disorder that highly reduces the quality of human life. The most powerful method for the detection and classification of sleep apnea is the Polysomnogram. However, this method is time-consuming and cost-inefficient. Therefore, several methods focus on using electrocardiogram (ECG) signals to detect sleep apnea. This paper proposed a novel automated approach to detect and classify apneic events from single-lead ECG signals. Wavelet Scattering Transformation (WST) was applied to the ECG signals to decompose the signal into smaller segments. Then, a set of features, including higher-order statistics and entropy-based features, was extracted from the WST coefficients to formulate a search space. The obtained features were fed to a random forest classifier to classify the ECG segments. The experiment was validated using the 10-fold and hold-out cross-validation methods, which resulted in an accuracy of 91.65% and 90.35%, respectively. The findings were compared with different classifiers to show the significance of the proposed approach. The proposed approach achieved better performance measures than most of the existing methodologies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sleep Apnea Detection Using Wavelet Scattering Transformation and Random Forest Classifier

Sharaf

2023

Entropy

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“…One may design their own DLM by combining the layers listed above. The number of Conv2DLs, PLs, and FCLs can be increased or decreased until the model achieves the required performance [40] . As a result of recent advancements in deep learning various pre-trained DLMs have been employed for different machine learning applications.…”

Section: Proposed Methodsmentioning

confidence: 99%

Deep learning models-based CT-scan image classification for automated screening of COVID-19

Gupta

Bajaj²

2023

Biomedical Signal Processing and Control

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“…Deep learning has permeated the study of ECG signals, including applications such as arrhythmias [27], QRS complex [28] and R peak detection [29], fetal signal separation [30], sleep apnea [31], person identification [32], and sex recognition [16]. This section provides an overview of research related to the recognition of sex through the ECG signals of a person.…”

Section: Related Workmentioning

confidence: 99%

A Fast Deep Learning ECG Sex Identifier Based on Wavelet RGB Image Classification

Lopez

Parra

Buitrago

2023

Data

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Human sex recognition with electrocardiogram signals is an emerging area in machine learning, mostly oriented toward neural network approaches. It might be the beginning of a field of heart behavior analysis focused on sex. However, a person’s heartbeat changes during daily activities, which could compromise the classification. In this paper, with the intention of capturing heartbeat dynamics, we divided the heart rate into different intervals, creating a specialized identification model for each interval. The sexual differentiation for each model was performed with a deep convolutional neural network from images that represented the RGB wavelet transformation of ECG pseudo-orthogonal X, Y, and Z signals, using sufficient samples to train the network. Our database included 202 people, with a female-to-male population ratio of 49.5–50.5% and an observation period of 24 h per person. As our main goal, we looked for periods of time during which the classification rate of sex recognition was higher and the process was faster; in fact, we identified intervals in which only one heartbeat was required. We found that for each heart rate interval, the best accuracy score varied depending on the number of heartbeats collected. Furthermore, our findings indicated that as the heart rate increased, fewer heartbeats were needed for analysis. On average, our proposed model reached an accuracy of 94.82% ± 1.96%. The findings of this investigation provide a heartbeat acquisition procedure for ECG sex recognition systems. In addition, our results encourage future research to include sex as a soft biometric characteristic in person identification scenarios and for cardiology studies, in which the detection of specific male or female anomalies could help autonomous learning machines move toward specialized health applications.

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OSACN-Net: Automated Classification of Sleep Apnea Using Deep Learning Model and Smoothed Gabor Spectrograms of ECG Signal

Cited by 32 publications

References 36 publications

Sleep Apnea Detection Using Wavelet Scattering Transformation and Random Forest Classifier

Sleep Apnea Detection Using Wavelet Scattering Transformation and Random Forest Classifier

Deep learning models-based CT-scan image classification for automated screening of COVID-19

A Fast Deep Learning ECG Sex Identifier Based on Wavelet RGB Image Classification

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