Mr-ResNeXt: A Multi-resolution Network Architecture for Detection of Obstructive Sleep Apnea

Chen, Qiren; Yue, Huijun; Pang, Xiongwen; Lei, Wenbin; Zhao, Gansen; Liao, Enhong; Wang, Yongquan

doi:10.1007/978-981-15-7670-6_35

Cited by 12 publications

(8 citation statements)

References 19 publications

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“…e recent years have seen a progress in deep CNN especially the emergence of FCN in image segmentation, which substantially enhances the performance of salient target detection. Most SOD network designs share a common pattern, which is to focus on the application of deep features extracted from the present backbone networks, e.g., AlexNet [54,55], VGG [56], Res-Net [57], ResNeXt [39,58], and DenseNet [59]. But these backbone networks were proposed for image classification, which extract features that represent semantics instead of local details and global contrast information that are crucial for saliency detection.…”

Section: Nnu-netmentioning

confidence: 99%

U-Net-Based Medical Image Segmentation

Yin

Sun

et al. 2022

Journal of Healthcare Engineering

199

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Deep learning has been extensively applied to segmentation in medical imaging. U-Net proposed in 2015 shows the advantages of accurate segmentation of small targets and its scalable network architecture. With the increasing requirements for the performance of segmentation in medical imaging in recent years, U-Net has been cited academically more than 2500 times. Many scholars have been constantly developing the U-Net architecture. This paper summarizes the medical image segmentation technologies based on the U-Net structure variants concerning their structure, innovation, efficiency, etc.; reviews and categorizes the related methodology; and introduces the loss functions, evaluation parameters, and modules commonly applied to segmentation in medical imaging, which will provide a good reference for the future research.

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Section: Nnu-netmentioning

confidence: 99%

U-Net-Based Medical Image Segmentation

Yin

Sun

et al. 2022

Journal of Healthcare Engineering

199

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“…The network achieved the accuracy of 86.22% with %90 sensitivity in 60 second segment OSA classification. Chen et al proposed an upgraded version of pretrained network ResNet and achieved nearly 1% accuracy improvement compared to ResNet [23]. Li et al used support vector machine (SVM) and artificial neural network (ANN) based classifier to classify features derived from ECG signals using a sparse auto-encoder.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks based OSA Event Prediction from ECG Scalograms and Spectrograms

Nasıfoğlu

Eroğul

2021

Preprint

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In this study, we conducted a comparative analysis of deep convolutional neural network (CNN) models in predicting Obstructive Sleep Apnea (OSA) using electrocardiograms. Unlike other studies in the literature, this study automatically extracts time-frequency features by using CNNs instead of manual feature extraction from ECG recordings. For this purpose, the proposed model generates scalogram and spectrogram representations by transforming preprocessed 30-sec ECG segments from time domain to the frequency domain using Continuous Wavelet Transform (CWT) and Short Time Fourier transform (STFT), respectively. We examined AlexNet, GoogleNet and ResNet18 models in predicting OSA events. The effect of transfer learning on success is also investigated. Based on the observed results, we proposed a new model that is found more effective in estimation. In total, 152 ECG recordings were included in the study for training and evaluation of the models. The prediction using scalograms immediately 30 seconds before potential OSA onsets gave the best performance with 82.30% accuracy, 83.22% sensitivity, 82.27% specificity and 82.95% positive predictive value. On the other hand, the prediction using spectrograms also provided up to 80.13% accuracy and 81.99% sensitivity on prediction. The results show that the proposed CNN model can be used as a good indicator to accurately predict OSA events using ECG signals.

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“…Based on 60 second segment OSA classification, the network achieved 86.22% accuracy and 90% sensitivity. Compared to ResNet, Chen et al proposed an upgraded version of pretrained network ResNet that improved accuracy by nearly 1% [16].…”

Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning Approach for Obstructive Sleep Apnea Classification with Photoplethysmography Signal

Jothi

Anitha

Hemanth

2023

Frontiers in Artificial Intelligence and Applications

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Human health and quality of life are negatively impacted by apnea, an increasingly prevalent sleep disorder. For monitoring and managing sleep apnea’s side effects and consequences, accurate automatic algorithms for detecting sleep apnea are crucial. In this paper, deep transfer learning methods are employed for the detection of OSA events from Electrocardiograph (ECG) and Photoplethysmography (PPG) signals. ResNet34 is a deep learning model based on convolutional neural networks (CNNs). Transfer learning algorithms such as AlexNet, VGG16, VGG19 and ResNet50 are implemented. In order to train the ResNet34 model data augmentation, optimal learning rate finding, and fine-tuning are used. To obtain generalizable models, a training set of data is divided into three sets: a validation set for adjusting hyperparameters and improving generalizability, and a test set for evaluating generalizability on unknown data. Deep transfer learning models have the best accuracy, sensitivity, specificity, precision, and F1 score with 97.86±1.24%, 99.65%, 97.12%, 98.16% and 98.90% respectively. It can assist sleep lab technicians in screening patients for OSA events continuously through PPG and ECG signals.

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Mr-ResNeXt: A Multi-resolution Network Architecture for Detection of Obstructive Sleep Apnea

Cited by 12 publications

References 19 publications

U-Net-Based Medical Image Segmentation

U-Net-Based Medical Image Segmentation

Convolutional Neural Networks based OSA Event Prediction from ECG Scalograms and Spectrograms

Deep Transfer Learning Approach for Obstructive Sleep Apnea Classification with Photoplethysmography Signal

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