Evaluation of Novel ECG Signal Processing on Quantification of Transient Ischemia and Baseline Wander Suppression

Kostic, Marko; Fakhar, S.; Foxall, T.; Drakulic, Budimir; Krucoff, Mitchell W.

doi:10.1109/iembs.2007.4352760

Cited by 3 publications

(4 citation statements)

References 3 publications

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“…However, larger studies that investigate patient populations with several different pathophysiologies are required to convince clinicians to adopt another patient monitoring technology in their workflows. Fidelity 100 is an FDA 510 (k) approved wireless ECG monitor developed by Signalife (Studio City, CA, USA), which was evaluated in real-time in 54 patients undergoing percutaneous coronary intervention [83]. Although these monitors come with different settings applicable for use in different types of CCUs, validation studies on population data from all application domains are not found.…”

Section: E Clinical Evaluationmentioning

confidence: 99%

“…Theoretically, a missed event or a false alarm are caused by artifacts of various types; for example, motion artifact and power line or optical noise induced in an attached or detached sensor. Studies that collected real-time annotated data or video monitored data, such as [27,31,36,[80][81][82][83][84][85][86] among others, can utilise the same data sets to develop and validate SQIs. Compatible SQIs can be used to compare performance of different AD algorithms and OEM monitors.…”

Section: E Clinical Evaluationmentioning

confidence: 99%

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Implementation of Artifact Detection in Critical Care: A Methodological Review

Nizami

Green

McGregor

2013

IEEE Rev. Biomed. Eng.

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Artifact detection (AD) techniques minimize the impact of artifacts on physiologic data acquired in critical care units (CCU) by assessing quality of data prior to clinical event detection (CED) and parameter derivation (PD). This methodological review introduces unique taxonomies to synthesize over 80 AD algorithms based on these six themes: 1) CCU; 2) physiologic data source; 3) harvested data; 4) data analysis; 5) clinical evaluation; and 6) clinical implementation. Review results show that most published algorithms: a) are designed for one specific type of CCU; b) are validated on data harvested only from one OEM monitor; c) generate signal quality indicators (SQI) that are not yet formalized for useful integration in clinical workflows; d) operate either in standalone mode or coupled with CED or PD applications; e) are rarely evaluated in real-time; and f) are not implemented in clinical practice. In conclusion, it is recommended that AD algorithms conform to generic input and output interfaces with commonly defined data: 1) type; 2) frequency; 3) length; and 4) SQIs. This shall promote: a) reusability of algorithms across different CCU domains; b) evaluation on different OEM monitor data; c) fair comparison through formalized SQIs; d) meaningful integration with other AD, CED and PD algorithms; and e) real-time implementation in clinical workflows.

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Section: E Clinical Evaluationmentioning

confidence: 99%

Section: E Clinical Evaluationmentioning

confidence: 99%

Implementation of Artifact Detection in Critical Care: A Methodological Review

Nizami

Green

McGregor

2013

IEEE Rev. Biomed. Eng.

View full text Add to dashboard Cite

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“…However, the same event could be very important clinically and crucial that it not be missed even once. Theoretically, a missed event or a false alarm may be caused by artifacts of various types; for example, motion artifact and power line or optical noise induced in an attached or detached sensor.Studies that collected real-time annotated data or video monitored data, such as[67,70,74,75,121,124,129,136,170,183] among others, can utilise the same data sets to develop and validate SQI. Compatible SQI can be used to compare performance of different AD algorithms and OEM monitors.…”

mentioning

confidence: 99%

“…Examples include work done by Portet et al[135] that contributed towards the BabyTalk project in[184], the author's own work in[132] that serves as preliminary research in Artemis[31], and work done by Seely et al[25,[185][186][187] that formed part of the CIMVA tool (this project is now closed; it was previously funded by TMS Inc., Ottawa, Canada). Research in which data were both annotated and evaluated in real-time include[126,129,170]; however, none of these algorithms were put into actual clinical practice. Correlating the results from Themes V and VI also identifies the research in which data were annotated and evaluated in real-time and the work was commercialized thus reaching clinical implementation.…”

mentioning

confidence: 99%

Integration of Artifact Detection in Clinical Decision Support Systems

Nizami¹

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Artifact detection (AD) is a critical component of Clinical Decision Support Systems (CDSS) to assess the quality of data and issue clinical alarms with high specificity and low false alarm rates. An in-depth review of the literature reveals that, although many advanced AD algorithms have been developed, very few have been evaluated in a realtime environment or reached actual clinical implementation. Furthermore, no single AD algorithm is necessarily best-suited to serve the varying needs of a CDSS. This identifies the need to develop and evaluate a framework that supports the interoperability of a range of configurable AD algorithms that could be integrated with CDSS. This thesis develops an AD framework to address six different shortcomings identified by the literature review. This framework supports the following six features: (f.1) Flexibility to serve the needs of patient populations from different types of Critical Care Units (CCU) through generalization and customizability; (f.2) Reusability across multiple types of physiologic data harvested by different Original Equipment Manufacturer (OEM) monitors; (f.3) Standardized definitions of Signal Quality Indicators (SQI) that promote interoperability and comparison between independently developed components; (f.4) Reusability and scalability by mixing and matching several AD, Parameter Derivation (PD), and Clinical Event Detection (CED) components in various combinations; (f.5) Customizability to evaluate and compare the performance of multiple combinations of independently developed components on offline and potentially real-time patient data when integrated with clinical workflows; and (f.6) Standardized component interfaces that can potentially support real-time clinical implementation of AD. The developed framework provides for a unique test bed with the ability to create new AD composition pipelines by mixing and matching independently developed I would like to express profound gratitude towards my thesis supervisors, Professor James R. Green and Professor Carolyn McGregor. They have both provided comprehensive guidance and tremendous support during my doctoral journey at Carleton.

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