Wireless communication systems for continuous multiparameter health monitoring

Rousselot, Jérôme; Decotignie, Jean-Dominique

doi:10.1109/icuwb.2009.5288747

Cited by 14 publications

(4 citation statements)

References 6 publications

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“…The sepsis-3 criteria require monitoring patients' vital signs (VS), such as heart rate, body temperature, blood pressure, and laboratory test results, among other variables, and calculating a sepsis score based on these values. Therefore, monitoring a patients' variables is essential for the diagnosis and treatment as well as for medical analyses in studies that improve the medicine [7,8].…”

Section: Introductionmentioning

confidence: 99%

Kinematics approach with neural networks for early detection of sepsis (KANNEDS)

Cruz

Ono

Huang

et al. 2021

BMC Med Inform Decis Mak

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Background Sepsis is a severe illness that affects millions of people worldwide, and its early detection is critical for effective treatment outcomes. In recent years, researchers have used models to classify positive patients or identify the probability for sepsis using vital signs and other time-series variables as input. Methods In our study, we analyzed patients’ conditions by their kinematics position, velocity, and acceleration, in a six-dimensional space defined by six vital signs. The patient is affected by the disease after a period if the position gets “near” to a calculated sepsis position in space. We imputed these kinematics features as explanatory variables of long short-term memory (LSTM), convolutional neural network (CNN) and linear neural network (LNN) and compared the prediction accuracies with only the vital signs as input. The dataset used contained information of approximately 4800 patients, each with 48 hourly registers. Results We demonstrated that the kinematics features models had an improved performance compared with vital signs models. The kinematics features model of LSTM achieved the best accuracy, 0.803, which was nine points higher than the vital signs model. Although with lesser accuracies, the kinematics features models of the CNN and LNN showed better performances than vital signs models. Conclusion Applying our novel approach for early detection of sepsis using neural networks will prove to be an invaluable, more accurate method than considering only simple vital signs as input variables. We expect that other researchers with similar objectives can use the model presented in this innovative approach to improve their results.

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Section: Introductionmentioning

confidence: 99%

Kinematics approach with neural networks for early detection of sepsis (KANNEDS)

Cruz

Ono

Huang

et al. 2021

BMC Med Inform Decis Mak

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“…Monitoring of health is usually carried out through wearable sensors, wireless devices and a computer system. Wearable sensors receive the human body information, wireless devices provide communications and a computer system, or smart phone, provide data processing and storage [1]. Wearable antennas are needed for these systems [2] - [9].…”

Section: Introductionmentioning

confidence: 99%

UWB antenna on 3D printed flexible substrate and foot phantom

Jun

Sanz-Izquierdo

Summerfield³

2015

2015 Loughborough Antennas &Amp; Propagation Conference (LAPC)

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“…QoS is of particular importance in UWB‐based networks 15 as the high bit rates supported by UWB allows data of varying information quality and bandwidth or delay requirements to be collected from the nodes. One such application scenario is a continuous health monitoring system 16, whereby sensors of varying modalities may be deployed for data acquisition. Multimedia data, such as video and voice that are captured by cameras and acoustic arrays, can typically provide richer information content about the environment.…”

Section: Introductionmentioning

confidence: 99%

SAUCeR,: a QoS‐aware slotted‐aloha based UWB MAC with cooperative retransmissions

Tan¹,

Chan²,

Kong³

et al. 2011

Wireless Communications

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The inherent temporal connectivity and existence of impairments in wireless channels pose challenges to network performance. Cooperative communication has been proposed as an effective technique to mitigate the imperfections of the wireless medium by exploiting channel diversity and availability of neighboring nodes that can act as relays. Although numerous cooperative communication techniques have been proposed in the literature, most of them do not consider Quality of Service (QoS) issues in a wireless sensor network. In this work, we study how cooperative communication can be applied to achieve differentiated QoS in a sensor network that uses Ultra-Wideband (UWB) as its underlying PHY layer technology. SAUCeR is a slotted-aloha based ultra-wideband medium access control protocol with cooperative retransmissions that provides differentiated QoS in networks with varying traffic classes. Despite the high transmission rates provided by UWB, its impulse-based nature renders many conventional carrier sensing MAC protocols incompatible. Consequently, SAUCeR utilizes slotted-aloha to reduce packet collisions without the need for carrier sensing. Differentiated QoS is provided by allocating different resources (time slots) to varying traffic classes to segregate the contention between them. A QoS-aware cooperative retransmission technique and two distributed relay selection schemes are also introduced to improve overall traffic throughput and reduce end-to-end delay, while preventing the starvation of any traffic class.

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Wireless communication systems for continuous multiparameter health monitoring

Cited by 14 publications

References 6 publications

Kinematics approach with neural networks for early detection of sepsis (KANNEDS)

Kinematics approach with neural networks for early detection of sepsis (KANNEDS)

UWB antenna on 3D printed flexible substrate and foot phantom

SAUCeR,: a QoS‐aware slotted‐aloha based UWB MAC with cooperative retransmissions

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