scholarly journals Sleep Apnea Detection Based on Multi-Scale Residual Network

Life ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 119
Author(s):  
Hengyang Fang ◽  
Changhua Lu ◽  
Feng Hong ◽  
Weiwei Jiang ◽  
Tao Wang
Keyword(s):  
Sleep Apnea ◽  
Loss Function ◽  
Physiological Mechanism ◽  
Class Imbalance ◽  
Residual Network ◽  
Residual Structure ◽  
Multi Scale ◽  
Signal Features ◽  
Low Sensitivity ◽  
The Impact

Aiming at the fact that traditional convolutional neural networks cannot effectively extract signal features in complex application scenarios, a sleep apnea (SA) detection method based on multi-scale residual networks is proposed. First, we analyze the physiological mechanism of SA, which uses the RR interval signals and R peak signals derived from the ECG signals as input. Then, a multi-scale residual network is used to extract the characteristics of the original signals in order to obtain sensitive characteristics from various angles. Because the residual structure is used in the model, the problem of model degradation can be avoided. Finally, a fully connected layer is introduced for SA detection. In order to overcome the impact of class imbalance, a focal loss function is introduced to replace the traditional cross-entropy loss function, which makes the model pay more attention to learning difficult samples in the training phase. Experimental results from the Apnea-ECG dataset show that the accuracy, sensitivity and specificity of the proposed multi-scale residual network are 86.0%, 84.1% and 87.1%, respectively. These results indicate that the proposed method not only achieves greater recognition accuracy than other methods, but it also effectively resolves the problem of low sensitivity caused by class imbalance.

scholarly journals An Intra-Pulse Modulation Type Recognition Algorithm for Radar Signals Based on the Improved Residual Network

2021 ◽  
Vol 2050 (1) ◽  
pp. 012009
Author(s):  
Fan Wang ◽  
Yifeng Huang ◽  
Ming Zhu ◽  
Jun Tang ◽  
Zhaohong Jia
Keyword(s):  
Loss Function ◽  
Pulse Signal ◽  
Recognition Algorithm ◽  
Radar Signal ◽  
Residual Network ◽  
Pulse Modulation ◽  
Time Frequency ◽  
Signal Features ◽  
Modulation Type ◽  
Radar Signals

Abstract For purpose of solve the problem of poor discrimination and robustness of intra-pulse signal features extracted by the traditional methods, this paper proposes a radar signal intra-pulse modulation type recognition algorithm based on the improved residual network. Firstly, one-dimensional time-domain radar signal is converted into two-dimensional time-frequency image by Smoothing Pseudo Wigner-Ville Distribution; Then the time-frequency image is preprocessed; ResNet-50 network is chosen as the framework. In order to retain the feature map information as much as possible, the convolution kernel is increased in the residual module. The cross entropy loss function and the center loss function are used as the loss function to speed up the convergence of the network. The improved residual network is used to realize the intra-pulse modulation type recognition of radar signal. The simulation experiments show that when the SNR is -14dB, the overall average recognition accuracy of the improved algorithm for eight kinds of radar signals (CM, LFM, NLFM, BLFM, BPSK, QPSK, OPSK, LFM+BPSK) can reach 97.29%, which shows the effectiveness.

Aerodynamics Analysis of the Impact of Nasal Surgery on Patients with Obstructive Sleep Apnea and Nasal Obstruction

ORL ◽  
2021 ◽  
pp. 1-8
Author(s):  
Lifeng Li ◽  
Demin Han ◽  
Hongrui Zang ◽  
Nyall R. London
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Nasal Obstruction ◽  
Upper Airway ◽  
Apnea Hypopnea Index ◽  
Nasal Surgery ◽  
Subjective Sleep Quality ◽  
Obstructive Sleep ◽  
Significant Difference ◽  
The Impact

<b><i>Objective:</i></b> The purpose of this study was to evaluate the effects of nasal surgery on airflow characteristics in patients with obstructive sleep apnea (OSA) by comparing the alterations of airflow characteristics within the nasal and palatopharyngeal cavities. <b><i>Methods:</i></b> Thirty patients with OSA and nasal obstruction who underwent nasal surgery were enrolled. A pre- and postoperative 3-dimensional model was constructed, and alterations of airflow characteristics were assessed using the method of computational fluid dynamics. The other subjective and objective clinical indices were also assessed. <b><i>Results:</i></b> By comparison with the preoperative value, all postoperative subjective symptoms statistically improved (<i>p</i> &#x3c; 0.05), while the Apnea-Hypopnea Index (AHI) changed little (<i>p</i> = 0.492); the postoperative airflow velocity and pressure in both nasal and palatopharyngeal cavities, nasal and palatopharyngeal pressure differences, and total upper airway resistance statistically decreased (all <i>p</i> &#x3c; 0.01). A significant difference was derived for correlation between the alteration of simulation metrics with subjective improvements (<i>p</i> &#x3c; 0.05), except with the AHI (<i>p</i> &#x3e; 0.05). <b><i>Conclusion:</i></b> Nasal surgery can decrease the total resistance of the upper airway and increase the nasal airflow volume and subjective sleep quality in patients with OSA and nasal obstruction. The altered airflow characteristics might contribute to the postoperative reduction of pharyngeal collapse in a subset of OSA patients.

scholarly journals Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1906
Author(s):  
Jia-Zheng Jian ◽  
Tzong-Rong Ger ◽  
Han-Hua Lai ◽  
Chi-Ming Ku ◽  
Chiung-An Chen ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Network Structure ◽  
Class Imbalance ◽  
Class Imbalance Problem ◽  
Multi Scale ◽  
Imbalance Problem ◽  
Average Accuracy ◽  
Significant Difference ◽  
Electrocardiogram Ecg

Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, it is unclear whether the network structure is too simple or complex. Toward this end, the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally, multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result, the N-Net reached a 95.76% accuracy in the MI detection task, whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of p < 0.001 evaluated by the U test compared with the state-of-the-art. The models are also smaller in size thus are suitable to fit in wearable devices for offline monitoring. In conclusion, testing throughout the simple and complex network structure is indispensable. However, the way of dealing with the class imbalance problem and the quality of the extracted features are yet to be discussed.

scholarly journals A Novel Focal Phi Loss for Power Line Segmentation with Auxiliary Classifier U-Net

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2803
Author(s):  
Rabeea Jaffari ◽  
Manzoor Ahmed Hashmani ◽  
Constantino Carlos Reyes-Aldasoro
Keyword(s):  
Loss Function ◽  
Class Imbalance ◽  
Power Line ◽  
Aerial Images ◽  
Class Imbalance Problem ◽  
Trade Off ◽  
Urban Scenes ◽  
Imbalance Problem ◽  
A Minor ◽  
Evaluation Parameters

The segmentation of power lines (PLs) from aerial images is a crucial task for the safe navigation of unmanned aerial vehicles (UAVs) operating at low altitudes. Despite the advances in deep learning-based approaches for PL segmentation, these models are still vulnerable to the class imbalance present in the data. The PLs occupy only a minimal portion (1–5%) of the aerial images as compared to the background region (95–99%). Generally, this class imbalance problem is addressed via the use of PL-specific detectors in conjunction with the popular class balanced cross entropy (BBCE) loss function. However, these PL-specific detectors do not work outside their application areas and a BBCE loss requires hyperparameter tuning for class-wise weights, which is not trivial. Moreover, the BBCE loss results in low dice scores and precision values and thus, fails to achieve an optimal trade-off between dice scores, model accuracy, and precision–recall values. In this work, we propose a generalized focal loss function based on the Matthews correlation coefficient (MCC) or the Phi coefficient to address the class imbalance problem in PL segmentation while utilizing a generic deep segmentation architecture. We evaluate our loss function by improving the vanilla U-Net model with an additional convolutional auxiliary classifier head (ACU-Net) for better learning and faster model convergence. The evaluation of two PL datasets, namely the Mendeley Power Line Dataset and the Power Line Dataset of Urban Scenes (PLDU), where PLs occupy around 1% and 2% of the aerial images area, respectively, reveal that our proposed loss function outperforms the popular BBCE loss by 16% in PL dice scores on both the datasets, 19% in precision and false detection rate (FDR) values for the Mendeley PL dataset and 15% in precision and FDR values for the PLDU with a minor degradation in the accuracy and recall values. Moreover, our proposed ACU-Net outperforms the baseline vanilla U-Net for the characteristic evaluation parameters in the range of 1–10% for both the PL datasets. Thus, our proposed loss function with ACU-Net achieves an optimal trade-off for the characteristic evaluation parameters without any bells and whistles. Our code is available at Github.

scholarly journals Validity and Cost-Effectiveness of Pediatric Home Respiratory Polygraphy for the Diagnosis of Obstructive Sleep Apnea in Children: Rationale, Study Design, and Methodology

2021 ◽  
Vol 4 (1) ◽  
pp. 9
Author(s):  
Esther Oceja ◽  
Paula Rodríguez ◽  
María Jurado ◽  
Maria Luz Alonso ◽  
Genoveva del Río ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Cost Effectiveness ◽  
Sleep Apnea ◽  
Therapeutic Interventions ◽  
Diagnostic Process ◽  
Urine Biomarkers ◽  
Obstructive Sleep ◽  
Respiratory Polygraphy ◽  
The Cost ◽  
The Impact

Obstructive sleep apnea (OSA) in children is a prevalent, albeit largely undiagnosed disease associated with a large spectrum of morbidities. Overnight in-lab polysomnography remains the gold standard diagnostic approach, but is time-consuming, inconvenient, and expensive, and not readily available in many places. Simplified Home Respiratory Polygraphy (HRP) approaches have been proposed to reduce costs and facilitate the diagnostic process. However, evidence supporting the validity of HRP is still scarce, hampering its implementation in routine clinical use. The objectives were: Primary; to establish the diagnostic and therapeutic decision validity of a simplified HRP approach compared to PSG among children at risk of OSA. Secondary: (a) Analyze the cost-effectiveness of the HRP versus in-lab PSG in evaluation and treatment of pediatric OSA; (b) Evaluate the impact of therapeutic interventions based on HRP versus PSG findings six months after treatment using sleep and health parameters and quality of life instruments; (c) Discovery and validity of the urine biomarkers to establish the diagnosis of OSA and changes after treatment.

OPTIMAL REINSURANCE FROM THE VIEWPOINTS OF BOTH AN INSURER AND A REINSURER UNDER THE CVAR RISK MEASURE AND VAJDA CONDITION

2021 ◽  
pp. 1-29
Author(s):  
Yanhong Chen
Keyword(s):  
Loss Function ◽  
Value At Risk ◽  
Numerical Study ◽  
Risk Measure ◽  
Convex Combination ◽  
Weighting Factor ◽  
Loss Functions ◽  
Conditional Value At Risk ◽  
Optimal Reinsurance ◽  
The Impact

ABSTRACT In this paper, we study the optimal reinsurance contracts that minimize the convex combination of the Conditional Value-at-Risk (CVaR) of the insurer’s loss and the reinsurer’s loss over the class of ceded loss functions such that the retained loss function is increasing and the ceded loss function satisfies Vajda condition. Among a general class of reinsurance premium principles that satisfy the properties of risk loading and convex order preserving, the optimal solutions are obtained. Our results show that the optimal ceded loss functions are in the form of five interconnected segments for general reinsurance premium principles, and they can be further simplified to four interconnected segments if more properties are added to reinsurance premium principles. Finally, we derive optimal parameters for the expected value premium principle and give a numerical study to analyze the impact of the weighting factor on the optimal reinsurance.

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