Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards

Xu, Haoran; Li, Peiyao; Yang, Zhicheng; Liu, Xiaoli; Wang, Zhao; Yan, Wei; He, Maoqing; Chu, Wenya; She, Yingjia; Li, Yuzhu; Cao, Desen; Yan, Muyang; Zhang, Zhengbo

doi:10.1007/s10916-020-01653-z

Cited by 29 publications

(19 citation statements)

References 40 publications

(40 reference statements)

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“…We leveraged a large data set from the Medical Information Mart for Intensive Care III (MIMIC-III) [ 24 ] and its matched physiological waveform database (recorded with monitors) [ 36 ] to develop the TOP-Net model (codes available [ 37 ]). The pretrained model was transferred to a relatively small data set, from patients who were continuously monitored with a medical-grade wearable embedded system (SensEcho, Beijing SensEcho Science & Technology Co Ltd) in a real clinical environment [ 38 ]. The process is presented in Figure 1 .…”

Section: Methodsmentioning

confidence: 99%

“…In the general ward, we utilized a SensEcho medical-grade monitoring system, which can monitor patients anytime and anywhere. SensEcho contains 3 parts ( Figure 4 ): a wearable multisensor system unit, a wireless network and data transmission unit, and a central monitoring system [ 35 , 38 ]. The multisensors include a single-lead ECG sensor (200 Hz), a sensor for respiratory inductive plethysmography (25 Hz), a noninvasive photoplethysmogram sensor for SpO 2 monitoring (1 Hz) based on near-infrared spectroscopy, and a posture recognition sensor using a 3-axis accelerometer.…”

Section: Methodsmentioning

confidence: 99%

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TOP-Net Prediction Model Using Bidirectional Long Short-term Memory and Medical-Grade Wearable Multisensor System for Tachycardia Onset: Algorithm Development Study

Liu

Zhang

et al. 2021

JMIR Med Inform

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Background Without timely diagnosis and treatment, tachycardia, also called tachyarrhythmia, can cause serious complications such as heart failure, cardiac arrest, and even death. The predictive performance of conventional clinical diagnostic procedures needs improvement in order to assist physicians in detecting risk early on. Objective We aimed to develop a deep tachycardia onset prediction (TOP-Net) model based on deep learning (ie, bidirectional long short-term memory) for early tachycardia diagnosis with easily accessible data. Methods TOP-Net leverages 2 easily accessible data sources: vital signs, including heart rate, respiratory rate, and blood oxygen saturation (SpO2) acquired continuously by wearable embedded systems, and electronic health records, containing age, gender, admission type, first care unit, and cardiovascular disease history. The model was trained with a large data set from an intensive care unit and then transferred to a real-world scenario in the general ward. In this study, 3 experiments incorporated merging patients’ personal information, temporal memory, and different feature combinations. Six metrics (area under the receiver operating characteristic curve [AUROC], sensitivity, specificity, accuracy, F1 score, and precision) were used to evaluate predictive performance. Results TOP-Net outperformed the baseline models on the large critical care data set (AUROC 0.796, 95% CI 0.768-0.824; sensitivity 0.753, 95% CI 0.663-0.793; specificity 0.720, 95% CI 0.645-0.758; accuracy 0.721; F1 score 0.718; precision 0.686) when predicting tachycardia onset 6 hours in advance. When predicting tachycardia onset 2 hours in advance with data acquired from our hospital using the transferred TOP-Net, the 6 metrics were 0.965, 0.955, 0.881, 0.937, 0.793, and 0.680, respectively. The best performance was achieved using comprehensive vital signs (heart rate, respiratory rate, and SpO2) statistical information. Conclusions TOP-Net is an early tachycardia prediction model that uses 8 types of data from wearable sensors and electronic health records. When validated in clinical scenarios, the model achieved a prediction performance that outperformed baseline models 0 to 6 hours before tachycardia onset in the intensive care unit and 2 hours before tachycardia onset in the general ward. Because of the model’s implementation and use of easily accessible data from wearable sensors, the model can assist physicians with early discovery of patients at risk in general wards and houses.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

TOP-Net Prediction Model Using Bidirectional Long Short-term Memory and Medical-Grade Wearable Multisensor System for Tachycardia Onset: Algorithm Development Study

Liu

Zhang

et al. 2021

JMIR Med Inform

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“…The main challenges facing the sensing layer include sensor technology, node security, embedded operating systems, and multi-protocol gateways. In this paper, we propose a three-layer architecture of health IoT with cloud convergence including health IoT sensing layer, health IoT transmission layer, and health cloud service layer [29]. The health cloud service layer is further divided into cloud service support sub-layer and cloud service application sublayer, as shown in Figure 2.…”

Section: A Artificial Intelligence-based Health Iot Architecture Designmentioning

confidence: 99%

Optimization of IoT-Based Artificial Intelligence Assisted Telemedicine Health Analysis System

Heng

Zhou

2021

IEEE Access

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This paper presents an in-depth study and exploration of the health IoT architecture and related implementation technologies from both theoretical and practical aspects, with important theoretical significance and practical application value. The research includes cloud fusion health IoT architecture, multimodal information acquisition in health IoT perception layer, multi-level service quality assurance of health IoT based on human LAN, and emotional perception and emotional interaction in health IoT. In terms of health IoT architecture, the cloud convergence health IoT architecture is proposed to deeply integrate the health cloud platform and perception layer by integrating multiple communication technologies to optimize the user experience and make health IoT applications more closely connected with people. This paper describes the basic concepts and main components of multimodal sensing information collection, the design and implementation of a health monitoring cloud robotics platform, robotics-based multimodal data sensing and aggregation, and high comfort sustainable physiological signal collection based on smart clothes. The feasibility and performance of the QoS framework proposed in this paper are verified by computer simulations. In this paper, migration learning is used to implement emotion data labeling, continuous conditional random fields to identify emotions based on data collected from smartphones and smart clothes, respectively, and finally decision layer fusion for emotion classification prediction.INDEX TERMS Internet of things, artificial intelligence, telemedicine, health monitoring, data analysis.

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“…Its battery supports continuous monitoring for a minimum of 24 hours. For detailed information about SensEcho and the monitoring system, please refer to [29]. At the time of writing, SensEcho has collected more than 1000 records from patients and healthy individuals.…”

Section: The Wearable Device and Data Sourcesmentioning

confidence: 99%

Assessing Electrocardiogram and Respiratory Signal Quality of a Wearable Device (SensEcho): Semisupervised Machine Learning-Based Validation Study

Xu¹,

Yan²,

Lan³

et al. 2021

JMIR Mhealth Uhealth

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Background With the development and promotion of wearable devices and their mobile health (mHealth) apps, physiological signals have become a research hotspot. However, noise is complex in signals obtained from daily lives, making it difficult to analyze the signals automatically and resulting in a high false alarm rate. At present, screening out the high-quality segments of the signals from huge-volume data with few labels remains a problem. Signal quality assessment (SQA) is essential and is able to advance the valuable information mining of signals. Objective The aims of this study were to design an SQA algorithm based on the unsupervised isolation forest model to classify the signal quality into 3 grades: good, acceptable, and unacceptable; validate the algorithm on labeled data sets; and apply the algorithm on real-world data to evaluate its efficacy. Methods Data used in this study were collected by a wearable device (SensEcho) from healthy individuals and patients. The observation windows for electrocardiogram (ECG) and respiratory signals were 10 and 30 seconds, respectively. In the experimental procedure, the unlabeled training set was used to train the models. The validation and test sets were labeled according to preset criteria and used to evaluate the classification performance quantitatively. The validation set consisted of 3460 and 2086 windows of ECG and respiratory signals, respectively, whereas the test set was made up of 4686 and 3341 windows of signals, respectively. The algorithm was also compared with self-organizing maps (SOMs) and 4 classic supervised models (logistic regression, random forest, support vector machine, and extreme gradient boosting). One case validation was illustrated to show the application effect. The algorithm was then applied to 1144 cases of ECG signals collected from patients and the detected arrhythmia false alarms were calculated. Results The quantitative results showed that the ECG SQA model achieved 94.97% and 95.58% accuracy on the validation and test sets, respectively, whereas the respiratory SQA model achieved 81.06% and 86.20% accuracy on the validation and test sets, respectively. The algorithm was superior to SOM and achieved moderate performance when compared with the supervised models. The example case showed that the algorithm was able to correctly classify the signal quality even when there were complex pathological changes in the signals. The algorithm application results indicated that some specific types of arrhythmia false alarms such as tachycardia, atrial premature beat, and ventricular premature beat could be significantly reduced with the help of the algorithm. Conclusions This study verified the feasibility of applying the anomaly detection unsupervised model to SQA. The application scenarios include reducing the false alarm rate of the device and selecting signal segments that can be used for further research.

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Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards

Cited by 29 publications

References 40 publications

TOP-Net Prediction Model Using Bidirectional Long Short-term Memory and Medical-Grade Wearable Multisensor System for Tachycardia Onset: Algorithm Development Study

TOP-Net Prediction Model Using Bidirectional Long Short-term Memory and Medical-Grade Wearable Multisensor System for Tachycardia Onset: Algorithm Development Study

Optimization of IoT-Based Artificial Intelligence Assisted Telemedicine Health Analysis System

Assessing Electrocardiogram and Respiratory Signal Quality of a Wearable Device (SensEcho): Semisupervised Machine Learning-Based Validation Study

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