Enabling a Smart and Distributed Communication Infrastructure in Healthcare

Fernandes, Chrystinne Oliveira; Lucena, Carlos José Pereira de; Lucena, Carlos; Azevedo, Bruno Alvares de

doi:10.1007/978-3-319-23024-5_40

Cited by 6 publications

(7 citation statements)

References 7 publications

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“…Table 4 presents the results obtained for the 42 registers recorded during the experimental stage. The true-positive (TP), truenegative (TN), false-positive (FP) and false-negative (FN) results were applied to calculate the positive predictivity (16), negative predictivity (17), sensitivity (18), specificity (19), positive F1 score (20) and negative F1 score (21). Positive and negative refer to the results obtained according to the predictions of stress and relaxation, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Due to its small size, low cost and potential for acquiring analog data in real time, the ArdM platform is considered an optimal solution for acquiring physiological data [14]- [16]. The programming has been carried out in the Arduino language, which consists of a set of C/C++ functions that can be called from the code.…”

Section: ) Acquisitionmentioning

confidence: 99%

“…The recent popularity of the IoT paradigm has caused an increase in the amount of information that is stored in medical databases [14]- [16]. This approach makes patient information available anywhere and anytime from either a computer or a mobile device (after the corresponding ethical and confidentiality approvals) [16].…”

Section: Introductionmentioning

confidence: 99%

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A Low-Cost, Portable Solution for Stress and Relaxation Estimation Based on a Real-Time Fuzzy Algorithm

et al. 2020

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This paper proposes a reliable stress and relaxation level estimation algorithm that is implemented in a portable, low-cost hardware device and executed in real time. The main objective of this work is to offer an affordable and ''ready-to-go'' solution for medical and personal environments, in which the detection of the arousal level of a person is crucial. Methods: To achieve meaningful identification of stress and relaxation, a fuzzy algorithm based on expert knowledge is built according to parameters extracted from physiological records. In addition to the heart rate, parameters extracted from the galvanic skin response and breath are employed to extend the results. Moreover, this algorithm achieves accurate results with a restricted computational load and can be implemented in a miniaturized low-cost prototype. The developed solution includes standard and actively shielded electrodes that are connected to an Arduino device for acquisition, while parameter extraction and fuzzy processing are conducted with a more powerful Raspberry Pi board. The proposed solution is validated using real physiological registers from 42 subjects collected using BIOPAC MP36 hardware. Additionally, a real-time acquisition, processing and remote cloud storage service is integrated via IoT wireless technology. Results: Robust identification of stress and relaxation is achieved, with F1 scores of 91.15% and 96.61%, respectively. Moreover, processing is performed using a 20-second sliding window; thus, only a small frame of context is required. Significance: This work presents a reliable solution for identifying stress and relaxation levels in real time, which can lead to the production of low-cost commercial devices for use in medical and personal environments.

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Section: Resultsmentioning

confidence: 99%

Section: ) Acquisitionmentioning

confidence: 99%

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A Low-Cost, Portable Solution for Stress and Relaxation Estimation Based on a Real-Time Fuzzy Algorithm

et al. 2020

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“…In our previous work [1], we developed a software framework for remote patient monitoring with notification capabilities that were handled by the use of software agents. In the systems built through our framework, the anomaly detection process worked by triggering an alarm every time an anomaly occurred, independent of the circumstances [2,3].…”

Section: Overviewmentioning

confidence: 99%

Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation

Fernandes¹,

Miles²,

Lucena³

2020

JMIR Med Inform

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Background Although alarm safety is a critical issue that needs to be addressed to improve patient care, hospitals have not given serious consideration about how their staff should be using, setting, and responding to clinical alarms. Studies have indicated that 80%-99% of alarms in hospital units are false or clinically insignificant and do not represent real danger for patients, leading caregivers to miss relevant alarms that might indicate significant harmful events. The lack of use of any intelligent filter to detect recurrent, irrelevant, and/or false alarms before alerting health providers can culminate in a complex and overwhelming scenario of sensory overload for the medical team, known as alarm fatigue. Objective This paper’s main goal is to propose a solution to mitigate alarm fatigue by using an automatic reasoning mechanism to decide how to calculate false alarm probability (FAP) for alarms and whether to include an indication of the FAP (ie, FAP_LABEL) with a notification to be visualized by health care team members designed to help them prioritize which alerts they should respond to next. Methods We present a new approach to cope with the alarm fatigue problem that uses an automatic reasoner to decide how to notify caregivers with an indication of FAP. Our reasoning algorithm calculates FAP for alerts triggered by sensors and multiparametric monitors based on statistical analysis of false alarm indicators (FAIs) in a simulated environment of an intensive care unit (ICU), where a large number of warnings can lead to alarm fatigue. Results The main contributions described are as follows: (1) a list of FAIs we defined that can be utilized and possibly extended by other researchers, (2) a novel approach to assess the probability of a false alarm using statistical analysis of multiple inputs representing alarm-context information, and (3) a reasoning algorithm that uses alarm-context information to detect false alarms in order to decide whether to notify caregivers with an indication of FAP (ie, FAP_LABEL) to avoid alarm fatigue. Conclusions Experiments were conducted to demonstrate that by providing an intelligent notification system, we could decide how to identify false alarms by analyzing alarm-context information. The reasoner entity we described in this paper was able to attribute FAP values to alarms based on FAIs and to notify caregivers with a FAP_LABEL indication without compromising patient safety.

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“…2. Hypothesis 2 (H2): Our reasoning algorithm should add an indication of an FAP to each alarm, upon which the reasoner should decide whether or not to notify caregivers with an indication of FAP (ie, FAP_LABEL) 3…”

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confidence: 99%

Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation (Preprint)

Fernandes¹,

Miles²,

Lucena³

2019

Preprint

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BACKGROUND Although alarm safety is a critical issue that needs to be addressed to improve patient care, hospitals have not given serious consideration about how their staff should be using, setting, and responding to clinical alarms. Studies have indicated that 80%-99% of alarms in hospital units are false or clinically insignificant and do not represent real danger for patients, leading caregivers to miss relevant alarms that might indicate significant harmful events. The lack of use of any intelligent filter to detect recurrent, irrelevant, and/or false alarms before alerting health providers can culminate in a complex and overwhelming scenario of sensory overload for the medical team, known as alarm fatigue. OBJECTIVE This paper’s main goal is to propose a solution to mitigate alarm fatigue by using an automatic reasoning mechanism to decide how to calculate false alarm probability (FAP) for alarms and whether to include an indication of the FAP (ie, FAP_LABEL) with a notification to be visualized by health care team members designed to help them prioritize which alerts they should respond to next. METHODS We present a new approach to cope with the alarm fatigue problem that uses an automatic reasoner to decide how to notify caregivers with an indication of FAP. Our reasoning algorithm calculates FAP for alerts triggered by sensors and multiparametric monitors based on statistical analysis of false alarm indicators (FAIs) in a simulated environment of an intensive care unit (ICU), where a large number of warnings can lead to alarm fatigue. RESULTS The main contributions described are as follows: (1) a list of FAIs we defined that can be utilized and possibly extended by other researchers, (2) a novel approach to assess the probability of a false alarm using statistical analysis of multiple inputs representing alarm-context information, and (3) a reasoning algorithm that uses alarm-context information to detect false alarms in order to decide whether to notify caregivers with an indication of FAP (ie, FAP_LABEL) to avoid alarm fatigue. CONCLUSIONS Experiments were conducted to demonstrate that by providing an intelligent notification system, we could decide how to identify false alarms by analyzing alarm-context information. The reasoner entity we described in this paper was able to attribute FAP values to alarms based on FAIs and to notify caregivers with a FAP_LABEL indication without compromising patient safety.

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Enabling a Smart and Distributed Communication Infrastructure in Healthcare

Cited by 6 publications

References 7 publications

A Low-Cost, Portable Solution for Stress and Relaxation Estimation Based on a Real-Time Fuzzy Algorithm

A Low-Cost, Portable Solution for Stress and Relaxation Estimation Based on a Real-Time Fuzzy Algorithm

Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation

Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation (Preprint)

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