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Obstructive sleep apnea detection using ecg-sensor with convolutional neural networks

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Abstract

In recent years, AI(Artificial Intelligence) has achieved great development in modern society. More and more modern technologies are applied in surveillance and monitoring. Healthcare monitoring is growing ubiquitous in modern wearable devices, such as a smart watch, electrocardiogram (ECG) necklace, smart band. Many sensors are attached to these smart devices to record and monitor physiological signals caused by activities, and then propagated those recorded electrical data to be further processed to give health diagnosis, disease prevention or making a distress call automatically. Obstructive sleep apnea (OSA) is a sleep disorder with a high occurrence in adult people and observed as an autonomous risk factor for circulatory problems such as ischemic heart attacks and stroke. Numerous traditional neural network based methods have been developed to detect OSA, where these methods however could not provide the intended result because they rely on shallow network. In this paper, we propose an effective OSA detection based on Convolutional neural network. Our method first extracts features from Apnea-Electrocardiogram (ECG) recordings using RR-intervals (time interval from one R-wave to the next R-wave in an ECG signal) and then CNN model having three convolution layers and three fully connected layers is trained with extracted features and applied for OSA detection. The first two convolution layers are followed by batch normalization and pooling layer, and softmax is connected to the last fully connected layer to give final decision. Experimental results on extracted feature of Apnea-ECG signal reveal that our model have better results in terms of performance measure sensitivity, specificity and accuracy. It is expected that the related technology can be applied into smart sensors, especially wearable devices.

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Acknowledgements

This work is partially funded by the MOE–Microsoft Key Laboratory of Natural Language Processing and Speech, and the National Natural Science Foundation of China under Grant No. 61572155, 61672188 and 61272386. We would also like to acknowledge NVIDIA Corporation who kindly provided two sets of GPU. We would like to acknowledge the editors and the anonymous reviewers whose important comments and suggestions led to greatly improved the manuscript.

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Authors and Affiliations

  1. Command Automation College, PLA Army Engineering University, Nanjing, 210007, China

    Xiaowei Wang

  2. College of Command Information System, PLA Army Engineering University, Nanjing, 210007, China

    Maowei Cheng

  3. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Yefu Wang

  4. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Shaohui Liu & Feng Jiang

  5. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China

    Zhihong Tian

  6. PLA Army Engineering University, Nanjing, 210007, China

    Hongjun Zhang

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  1. Xiaowei Wang
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  2. Maowei Cheng
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  3. Yefu Wang
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  4. Shaohui Liu
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  5. Zhihong Tian
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  6. Feng Jiang
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  7. Hongjun Zhang
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Correspondence to Shaohui Liu.

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Wang, X., Cheng, M., Wang, Y. et al. Obstructive sleep apnea detection using ecg-sensor with convolutional neural networks. Multimed Tools Appl 79, 15813–15827 (2020). https://doi.org/10.1007/s11042-018-6161-8

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  • DOI: https://doi.org/10.1007/s11042-018-6161-8

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