Final Report Summary - ANAEWARE (Monitoring awareness during anaesthesia – a multi-modal approach)
The AnaeWARE project (http://www.imperial.ac.uk/neural-interfaces/research/projects/anaeware/) set out to investigate and characterise the effect of anaesthetics on biological signals monitored during surgery, either routinely (e.g. cardiovascular signals) or as an additional modality (brain activity). The motivation behind this approach is that anaesthetics cause measurable and significant changes to the relationships between these signals, with the main goals of improving patient monitoring and understanding the general mechanisms of anaesthetic action. The project focused on the following objectives:
Objective 1: To explore the anaesthetic-induced changes in (and between) various biological signals during surgery (multi-modal signals); and
Objective 2: To link the observed signals and signal changes with underlying models of anaesthetic action.
Objective 1: The fulfilment of this objective focused on obtaining and analysing relevant data. After obtaining relevant regulatory and ethical approval data collection was initially conducted at a single hospital site, with a second site added at a later stage. Data from 25 patients were collected from both sites, and together with similar data from an additional 40 patients (obtained from a collaborator, Dr. Vizcaychipi) and brain activity data from 20 healthy participants (available online from Chennu et al.: https://www.repository.cam.ac.uk/handle/1810/252736) were analysed for the purposes of the project. Preliminary analysis of this multi-modal dataset indicates that anaesthetics induce: (a) significant changes in nonlinear relationships (phase-amplitude coupling) at specific frequency ranges between brain activity and electrocardiogram, electromyogram, heart rate and bispectral index; and (c) significant changes in nonlinear relationships (phase-amplitude coupling) at specific frequency ranges between different brain areas (preliminary results published in Abstract form and presented at the 10th FENS Forum of Neuroscience); and (c) changes in time-frequency measures in cardiovascular activity that are significantly different during wakefulness and anaesthesia. The findings suggest that a better description of the patient state of hypnosis can be obtained from a combined analysis of these signals, which supports the benefits of a multi-modal approach to patient monitoring during surgery. Analysis also inspired the development of a nonlinear, multivariate and nonparametric measure of causality, which can be applied to study the anaesthetic-induced causal interactions in the recorded multi-modal activity. The measure was published in a scientific article (frontiers in Neuroinformatics) and will be utilised in future work emerging from the current project.
Objective 2: For the purposes of this objective a model of thalamocortical activity was initially implemented in software (Python) and hardware (as part of a student project supervised by the Fellow). However, the model was inaccurate and there were numerous discrepancies between its theoretical description in a published article and the actual model parameters. As a result, the particular model was abandoned. Nonetheless, important skills were learnt during its implementation, such as programming in Python using the Brian module, and theoretical knowledge on neuronal network interactions. Another model of anaesthetic effects on the brain activity was then investigated. The particular model also describes burst-suppression, which is an endpoint of deep anaesthesia. After personal discussions with one of the model’s co-authors (Prof. Bojak), it was concluded that extending the model to include multi-modal interactions, even though useful and important, this was highly non-trivial and, thus, not feasible within the scope of the project. Instead, the data-driven modelling approach described above (phase-amplitude coupling) was followed. Discussions, however, led towards interesting future investigations in linking actual data with modelled data: validating the burst-suppression model with data recorded during the project, which has not been attempted previously. An additional component of the project was to investigate whether it was feasible to implement some of the analysis methods using off-the-shelf hardware solutions for future prototyping of a monitoring device. This task was undertaken as a final year student project supervised by the Fellow, which included the implementation of causality on a Field Programmable Gate Array, and was successfully completed.
The main project results indicate that anaesthetics cause measurable changes to the communication mechanism between different signals of the body, i.e. they affect the human body as a whole and, as such, a reliable and robust index for monitoring patients during surgery should combine information from the entire body (multi-modal). The project also generated new research directions and ideas, both as a continuation (multi-modal analysis) and as a new direction (e.g. creating models of multi-modal signal interactions for prediction of awareness, hardware implementation of an online multi-modal analysis prototype). The project findings support the development of a new generation of monitoring devices that are more reliable, but also more cost effective (utilisation of information that is already monitored routinely during surgery). Target groups for whom the research could be relevant are anaesthesiologists, surgical patients and biomedical engineers. Through the project the Fellow was able to share ideas and thoughts within a highly multi-disciplinary group of people, had numerous opportunities for integration and knowledge transfer, and was the recipient of unique training courses offered by the host organisation. All these contributed positively towards reinforcing the Fellow’s potential for reaching professional maturity.
The project results were disseminated to the scientific community (journal and conference publications, and organisation/chairing of a special session at IEEE BioCAS 2015) and the public (e.g. demonstration at the Science Museum, public talk at Pint of Science, talk targeting 15-17 year olds at Westminster School, and articles for the Host Organisation’s Annual Research Reports). The project has also led to the development of independent collaboration between the Fellow and Dr. Vizcaychipi (the lead PI of the data recordings at the second site, Chelsea & Westminster Hospital). Common interests in developing improved systems for perioperative patient monitoring has led to fruitful collaboration and a joint application for funding is planned in the near future.
Objective 1: To explore the anaesthetic-induced changes in (and between) various biological signals during surgery (multi-modal signals); and
Objective 2: To link the observed signals and signal changes with underlying models of anaesthetic action.
Objective 1: The fulfilment of this objective focused on obtaining and analysing relevant data. After obtaining relevant regulatory and ethical approval data collection was initially conducted at a single hospital site, with a second site added at a later stage. Data from 25 patients were collected from both sites, and together with similar data from an additional 40 patients (obtained from a collaborator, Dr. Vizcaychipi) and brain activity data from 20 healthy participants (available online from Chennu et al.: https://www.repository.cam.ac.uk/handle/1810/252736) were analysed for the purposes of the project. Preliminary analysis of this multi-modal dataset indicates that anaesthetics induce: (a) significant changes in nonlinear relationships (phase-amplitude coupling) at specific frequency ranges between brain activity and electrocardiogram, electromyogram, heart rate and bispectral index; and (c) significant changes in nonlinear relationships (phase-amplitude coupling) at specific frequency ranges between different brain areas (preliminary results published in Abstract form and presented at the 10th FENS Forum of Neuroscience); and (c) changes in time-frequency measures in cardiovascular activity that are significantly different during wakefulness and anaesthesia. The findings suggest that a better description of the patient state of hypnosis can be obtained from a combined analysis of these signals, which supports the benefits of a multi-modal approach to patient monitoring during surgery. Analysis also inspired the development of a nonlinear, multivariate and nonparametric measure of causality, which can be applied to study the anaesthetic-induced causal interactions in the recorded multi-modal activity. The measure was published in a scientific article (frontiers in Neuroinformatics) and will be utilised in future work emerging from the current project.
Objective 2: For the purposes of this objective a model of thalamocortical activity was initially implemented in software (Python) and hardware (as part of a student project supervised by the Fellow). However, the model was inaccurate and there were numerous discrepancies between its theoretical description in a published article and the actual model parameters. As a result, the particular model was abandoned. Nonetheless, important skills were learnt during its implementation, such as programming in Python using the Brian module, and theoretical knowledge on neuronal network interactions. Another model of anaesthetic effects on the brain activity was then investigated. The particular model also describes burst-suppression, which is an endpoint of deep anaesthesia. After personal discussions with one of the model’s co-authors (Prof. Bojak), it was concluded that extending the model to include multi-modal interactions, even though useful and important, this was highly non-trivial and, thus, not feasible within the scope of the project. Instead, the data-driven modelling approach described above (phase-amplitude coupling) was followed. Discussions, however, led towards interesting future investigations in linking actual data with modelled data: validating the burst-suppression model with data recorded during the project, which has not been attempted previously. An additional component of the project was to investigate whether it was feasible to implement some of the analysis methods using off-the-shelf hardware solutions for future prototyping of a monitoring device. This task was undertaken as a final year student project supervised by the Fellow, which included the implementation of causality on a Field Programmable Gate Array, and was successfully completed.
The main project results indicate that anaesthetics cause measurable changes to the communication mechanism between different signals of the body, i.e. they affect the human body as a whole and, as such, a reliable and robust index for monitoring patients during surgery should combine information from the entire body (multi-modal). The project also generated new research directions and ideas, both as a continuation (multi-modal analysis) and as a new direction (e.g. creating models of multi-modal signal interactions for prediction of awareness, hardware implementation of an online multi-modal analysis prototype). The project findings support the development of a new generation of monitoring devices that are more reliable, but also more cost effective (utilisation of information that is already monitored routinely during surgery). Target groups for whom the research could be relevant are anaesthesiologists, surgical patients and biomedical engineers. Through the project the Fellow was able to share ideas and thoughts within a highly multi-disciplinary group of people, had numerous opportunities for integration and knowledge transfer, and was the recipient of unique training courses offered by the host organisation. All these contributed positively towards reinforcing the Fellow’s potential for reaching professional maturity.
The project results were disseminated to the scientific community (journal and conference publications, and organisation/chairing of a special session at IEEE BioCAS 2015) and the public (e.g. demonstration at the Science Museum, public talk at Pint of Science, talk targeting 15-17 year olds at Westminster School, and articles for the Host Organisation’s Annual Research Reports). The project has also led to the development of independent collaboration between the Fellow and Dr. Vizcaychipi (the lead PI of the data recordings at the second site, Chelsea & Westminster Hospital). Common interests in developing improved systems for perioperative patient monitoring has led to fruitful collaboration and a joint application for funding is planned in the near future.