Telemetry of Hospitalized High-Risk Patients with Cardiovascular Diseases

Iliev, Ivo; Jekova, Irena; Tabakov, Serafim; Koshtikova, Krasimira Georgieva; Iovev, Svetoslav

doi:10.1109/et.2019.8878494

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Utilizing cutting-edge communication protocols, smart and advanced wearable monitoring systems gather and transmit biomedical signals over long distances. For using such devices properly, applicability, reliability, and accuracy issues should be taken into account [ 95 , 96 ]. The integration of wearable technology, wireless sensor networks, and artificial intelligence applications can lead to novel approaches able to enhance healthcare delivery.…”

Section: Wearables For Monitoring Individual Traitsmentioning

confidence: 99%

Wearable Systems for Unveiling Collective Intelligence in Clinical Settings

Pulcinelli,

Pinnelli,

Massaroni

et al. 2023

Sensors

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Nowadays, there is an ever-growing interest in assessing the collective intelligence (CI) of a team in a wide range of scenarios, thanks to its potential in enhancing teamwork and group performance. Recently, special attention has been devoted on the clinical setting, where breakdowns in teamwork, leadership, and communication can lead to adverse events, compromising patient safety. So far, researchers have mostly relied on surveys to study human behavior and group dynamics; however, this method is ineffective. In contrast, a promising solution to monitor behavioral and individual features that are reflective of CI is represented by wearable technologies. To date, the field of CI assessment still appears unstructured; therefore, the aim of this narrative review is to provide a detailed overview of the main group and individual parameters that can be monitored to evaluate CI in clinical settings, together with the wearables either already used to assess them or that have the potential to be applied in this scenario. The working principles, advantages, and disadvantages of each device are introduced in order to try to bring order in this field and provide a guide for future CI investigations in medical contexts.

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Section: Wearables For Monitoring Individual Traitsmentioning

confidence: 99%

Wearable Systems for Unveiling Collective Intelligence in Clinical Settings

Pulcinelli,

Pinnelli,

Massaroni

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…An alternative cloud computing approach involves sending data to an external, more powerful processor [13,14]. In spite of advances in data compression [15], continuous Figure 1: Possible integration of the proposed system into a wearable ECG monitor.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Ultra-Low Power ECG Anomaly Detection Using an Event-Driven Neuromorphic Processor

Bauer¹,

Muir²,

Indiveri

2019

IEEE Trans. Biomed. Circuits Syst.

103

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Accurate detection of pathological conditions in human subjects can be achieved through off-line analysis of recorded biological signals such as electrocardiograms (ECGs). However, human diagnosis is time-consuming and expensive, as it requires the time of medical professionals. This is especially inefficient when indicative patterns in the biological signals are infrequent. Moreover, patients with suspected pathologies are often monitored for extended periods, requiring the storage and examination of large amounts of non-pathological data, and entailing a difficult visual search task for diagnosing professionals.In this work we propose a compact and sub-mW low power neural processing system that can be used to perform on-line and real-time preliminary diagnosis of pathological conditions, to raise warnings for the existence of possible pathological conditions, or to trigger an off-line data recording system for further analysis by a medical professional. We apply the system to real-time classification of ECG data for distinguishing between healthy heartbeats and pathological rhythms.Multi-channel analog ECG traces are encoded as asynchronous streams of binary events and processed using a spiking recurrent neural network operated in a reservoir computing paradigm. An event-driven neuron output layer is then trained to recognize one of several pathologies. Finally, the filtered activity of this output layer is used to generate a binary trigger signal indicating the presence or absence of a pathological pattern. We validate the approach proposed using a Dynamic Neuromorphic Asynchronous Processor (DYNAP) chip, implemented using a standard 180 nm CMOS VLSI process, and present experimental results measured from the chip.

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Telemetry of Hospitalized High-Risk Patients with Cardiovascular Diseases

Cited by 2 publications

References 18 publications

Wearable Systems for Unveiling Collective Intelligence in Clinical Settings

Wearable Systems for Unveiling Collective Intelligence in Clinical Settings

Real-Time Ultra-Low Power ECG Anomaly Detection Using an Event-Driven Neuromorphic Processor

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