Towards Brain First-Aid: A Diagnostic Device for Conscious Awareness

D’Arcy, Ryan C.N.; Hajra, Sujoy Ghosh; Liu, C; Sculthorpe, Lauren D.; Weaver, Donald F.

doi:10.1109/tbme.2010.2090880

Cited by 23 publications

(25 citation statements)

References 23 publications

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“…Effect on the perception and satisfaction of the patient: a. Patient feedback on usefulness of system [21,27] Many of the studies reported that the MVI approach led to a positive effect on the utility of the medical devices [11,12,13,14,15,16,19,21,23,27,28,29,32,33,34,35,36]. Miniaturization was mentioned in these studies as the most important motivation for using MVIs.…”

Section: Outcomes/resultsmentioning

confidence: 99%

“…This can reduce the usability and adoption level of the systems. The approach in one of the studies involved the employment of an MVI that reduced the need for system training to the barest minimum [34]. Such an approach can aid in the evolution and acceptance of MVIs.…”

Section: Discussionmentioning

confidence: 99%

“…The wireless protocol was the most common for local-MVI communication and was used in 73.9% of the cases (Table 3). This included 26.1% for Bluetooth [11,21,30,31,33,34], 8.7% for ZigBee [22,24], and 39.1% for unspecified wireless protocols [12,19,20,23,25,28,29,32,33]. RS-232 protocols were used in 8.7% of the cases [14,37] and another 17.4% used other methods [15,16,20,36].…”

Section: Network Models and Communication Systemmentioning

confidence: 99%

“…The details are shown in Table 3. Some articles used multiple techniques for remote-MVI communication while others failed to specify the local-MVI communication protocol [17,25,26,27,34,35,37].…”

Section: Network Models and Communication Systemmentioning

confidence: 99%

“…The high level of flexibility in MVIs makes them a ready fit for such a requirement. A total of 11 (42.3%) of the reviewed articles supported some level of adaptation [12,13,21,23,27,28,29,31,33,34,36], most of which were adaptation at the communication and architectural levels rather than at the patient level. Five of the six cases with adaptation at the subject level [12,28,29,33,34] were based on the subject's clinical profile.…”

Section: System Adaptabilitymentioning

confidence: 99%

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Medical Virtual Instrumentation for Personalized Health Monitoring: A Systematic Review

Adeluyi

Lee

2015

Journal of Healthcare Engineering

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The rising cost of healthcare and the increased senior population are some reasons for the growing adoption of the Personalized Health Monitoring (PHM) systems. Medical Virtual Instruments (MVIs) provide portable, flexible, and low-cost options for these systems. Our systematic literature search covered the Cochrane Library, Web of Science, and MEDLINE databases, resulting in 915 articles, and 25 of which were selected for inclusion after a detailed screening process that involved five stages. The review sought to understand the key aspects regarding the use of MVIs for PHM, and we identified the main disease domains, sensors, platforms, algorithms, and communication protocols for such systems. We also identified the key challenges affecting the level of integration of MVIs into the global healthcare framework. The review shows that MVIs provide a good opportunity for the development of low cost personalized health systems that meet the unique instrumentation requirements for a given medical domain.

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Section: Outcomes/resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Network Models and Communication Systemmentioning

confidence: 99%

Section: Network Models and Communication Systemmentioning

confidence: 99%

Section: System Adaptabilitymentioning

confidence: 99%

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Medical Virtual Instrumentation for Personalized Health Monitoring: A Systematic Review

Adeluyi

Lee

2015

Journal of Healthcare Engineering

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Detection of event-related potentials in individual subjects using support vector machines

et al. 2014

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Event-related potentials (ERPs) are tiny electrical brain responses in the human electroencephalogram that are typically not detectable until they are isolated by a process of signal averaging. Owing to the extremely smallsize of ERP components (ranging from less than 1 μV to tens of μV), compared to background brain rhythms, statistical analyses of ERPs are predominantly carried out in groups of subjects. This limitation is a barrier to the translation of ERP-based neuroscience to applications such as medical diagnostics. We show here that support vector machines (SVMs) are a useful method to detect ERP components in individual subjects with a small set of electrodes and a small number of trials for a mismatch negativity (MMN) ERP component. Such a reduced experiment setup is important for clinical applications. One hundred healthy individuals were presented with an auditory pattern containing pattern-violating deviants to evoke the MMN. Two-class SVMs were then trained to classify averaged ERP waveforms in response to the standard tone (tones that match the pattern) and deviant tone stimuli (tones that violate the pattern). The influence of kernel type, number of epochs, electrode selection, and temporal window size in the averaged waveform were explored. When using all electrodes, averages of all available epochs, and a temporal window from 0 to 900-ms post-stimulus, a linear SVM achieved 94.5 % accuracy. Further analyses using SVMs trained with narrower, sliding temporal windows confirmed the sensitivity of the SVM to data in the latency range associated with the MMN.

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