1,203 research outputs found
Computational approaches to alleviate alarm fatigue in intensive care medicine: A systematic literature review
Patient monitoring technology has been used to guide therapy and alert staff when a vital sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large amounts of technically false or clinically irrelevant alarms provoke alarm fatigue in staff leading to desensitisation towards critical alarms. With this systematic review, we are following the Preferred Reporting Items for Systematic Reviews (PRISMA) checklist in order to summarise scientific efforts that aimed to develop IT systems to reduce alarm fatigue in ICUs. 69 peer-reviewed publications were included. The majority of publications targeted the avoidance of technically false alarms, while the remainder focused on prediction of patient deterioration or alarm presentation. The investigated alarm types were mostly associated with heart rate or arrhythmia, followed by arterial blood pressure, oxygen saturation, and respiratory rate. Most publications focused on the development of software solutions, some on wearables, smartphones, or headmounted displays for delivering alarms to staff. The most commonly used statistical models were tree-based. In conclusion, we found strong evidence that alarm fatigue can be alleviated by IT-based solutions. However, future efforts should focus more on the avoidance of clinically non-actionable alarms which could be accelerated by improving the data availability
Integrating monitor alarms with laboratory test results to enhance patient deterioration prediction
AbstractPatient monitors in modern hospitals have become ubiquitous but they generate an excessive number of false alarms causing alarm fatigue. Our previous work showed that combinations of frequently co-occurring monitor alarms, called SuperAlarm patterns, were capable of predicting in-hospital code blue events at a lower alarm frequency. In the present study, we extend the conceptual domain of a SuperAlarm to incorporate laboratory test results along with monitor alarms so as to build an integrated data set to mine SuperAlarm patterns. We propose two approaches to integrate monitor alarms with laboratory test results and use a maximal frequent itemsets mining algorithm to find SuperAlarm patterns. Under an acceptable false positive rate FPRmax, optimal parameters including the minimum support threshold and the length of time window for the algorithm to find the combinations of monitor alarms and laboratory test results are determined based on a 10-fold cross-validation set. SuperAlarm candidates are generated under these optimal parameters. The final SuperAlarm patterns are obtained by further removing the candidates with false positive rate>FPRmax. The performance of SuperAlarm patterns are assessed using an independent test data set. First, we calculate the sensitivity with respect to prediction window and the sensitivity with respect to lead time. Second, we calculate the false SuperAlarm ratio (ratio of the hourly number of SuperAlarm triggers for control patients to that of the monitor alarms, or that of regular monitor alarms plus laboratory test results if the SuperAlarm patterns contain laboratory test results) and the work-up to detection ratio, WDR (ratio of the number of patients triggering any SuperAlarm patterns to that of code blue patients triggering any SuperAlarm patterns). The experiment results demonstrate that when varying FPRmax between 0.02 and 0.15, the SuperAlarm patterns composed of monitor alarms along with the last two laboratory test results are triggered at least once for [56.7–93.3%] of code blue patients within an 1-h prediction window before code blue events and for [43.3–90.0%] of code blue patients at least 1-h ahead of code blue events. However, the hourly number of these SuperAlarm patterns occurring in control patients is only [2.0–14.8%] of that of regular monitor alarms with WDR varying between 2.1 and 6.5 in a 12-h window. For a given FPRmax threshold, the SuperAlarm set generated from the integrated data set has higher sensitivity and lower WDR than the SuperAlarm set generated from the regular monitor alarm data set. In addition, the McNemar’s test also shows that the performance of the SuperAlarm set from the integrated data set is significantly different from that of the SuperAlarm set from the regular monitor alarm data set. We therefore conclude that the SuperAlarm patterns generated from the integrated data set are better at predicting code blue events
The Application of Computer Techniques to ECG Interpretation
This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field
Human-centred design of clinical auditory alarms
Auditory alarms are commonly badly designed, providing little to no information or
guidance. In the healthcare context, the poor acoustics of alarms is one contributor for the
noise problem. The goal of this thesis is to propose a human-centred methodology for the
design of clinical auditory alarms, by making them less disruptive and more informative,
thus improving the healthcare soundscape. It implements this methodology from concept
to evaluation and validation, combining psychoacoustics with usability and user
experience methods. Another aim of this research consisted in understanding the
limitations and possibilities offered by online tools for scientific studies. Thus, different
processes and methodologies were implemented, and corresponding results were
discussed.
To understand the acoustic healthcare environment, field visits, interviews, and surveys
were performed with healthcare professionals. Additionally, sound pressure levels and
frequency analysis of several surgeries in different hospitals provided specific sound design
requirements, which were added to an existent body of knowledge on clinical alarm
design. A second stage consisted in prototyping very simple sounds to comprehend which
temporal and spectral parameters of sound could be manipulated to communicate clinical
information. Parameters such as frequency, speed, onset, and rhythm were studied, and
relations between subjective perception and physical parameters were established. In
parallel, and heavily influenced by the new IEC 60601-1-8 - General requirements, tests and
guidance for alarm systems in medical electrical equipment and medical electrical systems,
a design strategy with auditory icons was created. This strategy intended to provide as
much information as possible in an auditory alarm. To do so, it involved two main
components: a priority pointer indicating the priority of the alarm; an auditory icon
indicating the cause of the alarm. A third component indicating increasing or decreasing
tendency of the vital sign was designed, but not validated with users. After online
validation of the priority pointer and auditory icon for eight categories (cardiac, drug
administration, ventilation, blood pressure, perfusion, oxygen, temperature, and power
down), a new library of clinical auditory alarms is proposed.Os alarmes auditivos são habitualmente mal concebidos, dando poucas informações ou
orientações perante a situação que despoletou o aviso. No contexto da saúde, a má acústica
dos alarmes é um dos contribuidores para o problema do ruído. O objetivo desta tese é o
de melhorar a paisagem sonora em ambientes clínicos, propondo uma metodologia
centrada no Humano para o design de alarmes auditivos clínicos, tornando-os menos
disruptivos e mais informativos. Essa metodologia é implementada desde o conceito até a
avaliação e validação, combinando métodos da psicoacústica com métodos de usabilidade
e experiência do utilizador. Outro objetivo desta investigação é o de compreender as
limitações e possibilidades oferecidas pelas ferramentas online para estudos científicos.
Assim, diversos processos e metodologias foram implementados, e os respetivos resultados
são discutidos.
Para compreender o ambiente acústico clínico, foram realizadas visitas de campo,
entrevistas e inquéritos com profissionais de saúde. Além disso, avaliou-se o nível de
pressão sonora e frequências de várias cirurgias em diferentes hospitais. Esta atividade
forneceu requisitos específicos de design de som que foram adicionados a um corpo
existente de conhecimento sobre design de alarmes clínicos. Uma segunda etapa consistiu
na prototipagem de sons simples para compreender que parâmetros temporais e espectrais
do som poderiam ser manipulados para comunicar informações clínicas. Parâmetros como
frequência, velocidade, envelope e ritmo foram estudados, e as relações entre a perceção
subjetiva e os parâmetros físicos foram estabelecidas. Paralelamente, e fortemente
influenciado pela nova norma IEC 60601-1-8 - Requisitos gerais, testes e orientações para
sistemas de alarme em equipamentos médicos elétricos e sistemas médicos elétricos, foi
criada uma estratégia de design com ícones auditivos. Essa estratégia pretendia incorporar
o máximo de informações num alarme auditivo. Para isso, envolveu dois componentes
principais: um ponteiro de prioridade que indica a prioridade do alarme; e um ícone
auditivo que indica a causa do alarme. Um terceiro componente de tendência (aumento
ou diminuição do valor do sinal vital) foi criado, mas não validado com utilizadores. Após
a validação do ponteiro de prioridade e ícone auditivo para oito categorias (cardíaco,
administração de medicamentos, ventilação, pressão arterial, perfusão, oxigénio,
temperatura e falha de equipamento), propõe-se uma nova biblioteca de alarmes auditivos
clínicos
Intelligent Biosignal Processing in Wearable and Implantable Sensors
This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine
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