IoT-Based Wireless Polysomnography Intelligent System for Sleep Monitoring

Lin, Chin‐Teng; Prasad, Mukesh; Chung, Chia-Hsin; Puthal, Deepak; El-Sayed, Hesham; Sankar, Sharmi; Wang, Yukai; Singh, Jagendra; Sangaiah, Arun Kumar

doi:10.1109/access.2017.2765702

Cited by 89 publications

(41 citation statements)

References 32 publications

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“…The specific calculation is illustrated in Ep. (4). KL divergence works for representing the similarity between I 1 − I 4 and corresponding raw data I 0 , and cross entropy loss is calculated to represent the similarity between the generated real and the predicted labels.…”

Section: ) Loss Functionmentioning

confidence: 99%

“…In this paper, two different types of pressure sensing mats are used to collect in-bed pose pressure data for sleep posture recognition. In sleep stage recognition, polysomnography (PSG) [4] is a sleep diagnostic tool which uses electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), electrocardiogram (ECG), and other physiological sensors to collect data and diagnose sleep disorders. However, it is not convenient to use many sensors during sleep.…”

Section: Introductionmentioning

confidence: 99%

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Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

2020

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Sleep recognition refers to detection or identification of sleep posture, state or stage, which can provide critical information for the diagnosis of sleep diseases. Most of sleep recognition methods are limited to single-task recognition, which only involves single-modal sleep data, and there is no generalized model for multi-task recognition on multi-sensor sleep data. Moreover, the shortage and imbalance of sleep samples also limits the expansion of the existing machine learning methods like support vector machine, decision tree and convolutional neural network, which lead to the decline of the learning ability and overfitting. Self-supervised learning technologies have shown their capabilities to learn significant feature representations. In this paper, a novel self-supervised learning model is proposed for sleep recognition, which is composed of an upstream self-supervised pre-training task and a downstream recognition task. The upstream task is conducted to increase the data capacity, and the information of frequency domain and the rotation view are used to learn the multi-dimensional sleep feature representations. The downstream task is undertaken to fuse bidirectional long-short term memory and conditional random field as the sequential data recognizer to produce the sleep labels. Our experiments shows that our proposed algorithm provide promising results in sleep identification and can further be applied in clinical and smart home environments as a diagnostic tool. The source code is provided at: ''https://github.com/zhaoaite/SSRM ''.

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Section: ) Loss Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

2020

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“…Most of the time, these studies are made through specialized wired instrumentation; however, at present (LTE systems) and future (considering the full potential of the Internet of Things {IoT} in 5G systems), wireless options are an accessible and fully functional resource [13]. For this reason, there are a wide variety of tools that can assist in the measurement of biopotential signals wirelessly [14].…”

Section: Related Workmentioning

confidence: 99%

Priority Schemes for Life Extension and Data Delivery in Body Area Wireless Sensor Networks with Cognitive Radio Capabilities

Chávez

Rivero-Ángeles

Jiménez

et al. 2019

Wireless Communications and Mobile Computing

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Wireless sensor networks have recently been used to monitor bioelectric signals generated by the human body. However, since the dimensions and capabilities of the network nodes are small and limited, a problem is generated inherently by the energy consumption and radioelectric interference. In view of this, we propose two priority schemes to improve the performance of the network. In the first one, we aim at increasing the system lifetime by reducing the number of transmissions of nodes with low energy levels. In the second scheme, we aim at conveying priority data to the sink node as fast and efficient as possible. In the former scheme, users can be monitored for long time periods considering a low data generation. On the other hand, the latter scheme allows expediting transmission of important data when energy efficiency is not relevant. In this article, we mathematically model, analyze, and study the performance of the system for both priority schemes considering cognitive radio capabilities to make efficient use of resources and limit the radioelectric interference when the network transmits both continuous monitoring and event detection information. Numerical results provided in this work allow a careful parameter selection for practical implementation of BANETs.

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“…Therefore, there have been researches on healthcare devices for self-monitoring sleep stage or quality [5]. Their common characteristic is that they are in the form of portable device type which is easy to carry and comfortable to be measured at home [6]. Besides, they have been designed to have wireless monitoring interfaces without storing massive data into optical or semiconductor storage devices [7].…”

Section: Introductionmentioning

confidence: 99%

Wearable Multi-Biosignal Analysis Integrated Interface With Direct Sleep-Stage Classification

et al. 2020

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This paper presents a wearable multi-biosignal wireless interface for sleep analysis. It enables comfortable sleep monitoring with direct sleep-stage classification capability while conventional analytic interfaces including the Polysomnography (PSG) require complex post-processing analyses based on heavy raw data, need expert supervision for measurements, or do not provide comfortable fit for long-time wearing. The proposed multi-biosignal interface consists of electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG). A readout integrated circuit (ROIC) is designed to collect three kinds of bio-potential signals through four internal readout channels, where their analog feature extraction circuits are included together to provide sleep-stage classification directly. The designed multi-biosignal sensing ROIC is fabricated in a 180-nm complementary metal-oxide-semiconductor (CMOS) process. For system-level verification, its low-power headband-style analytic device is implemented for wearable sleep monitoring, where the direct sleep-stage classification is performed based on a decision tree algorithm. It is functionally verified by comparison experiments with post-processing analysis results from the OpenBCI module, whose sleep-stage detection shows reasonable correlation of 74% for four sleep stages. INDEX TERMS Sleep-stage classification, multi-biosignal interface, rule-based decision tree, feature extraction stage, readout integrated circuit, wearable device.

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IoT-Based Wireless Polysomnography Intelligent System for Sleep Monitoring

Cited by 89 publications

References 32 publications

Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

Priority Schemes for Life Extension and Data Delivery in Body Area Wireless Sensor Networks with Cognitive Radio Capabilities

Wearable Multi-Biosignal Analysis Integrated Interface With Direct Sleep-Stage Classification

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