Modeling a safety-related system for continuous non-invasive blood pressure monitoring

Sheng, H.; Schwarz, Michael; Börcsök, Josef

doi:10.1109/biocas.2013.6679687

Cited by 5 publications

(8 citation statements)

References 12 publications

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“…The design of medical systems towards more accuracy and resiliency is quite challenging, and to mitigate, in the recent past decade, developed and used few techniques like data fusion techniques [1], [3], [12], [16], [21], Artificial Intelligence (AI) based techniques [17]- [23] and correlation techniques [7]- [9], [23], [24]. However, all these techniques will have its limitations and lack in mitigating the challenges, like accurate identification and separation of the apt signal data for data analytics to extract the artifacts, identification of inaccurate signal data, and finding the root cause of the faults like systemic or random failures towards determining the health of the medical systems.…”

Section: Background and Motivation A Related Work And Motivationmentioning

confidence: 99%

“…However, all these techniques will have its limitations and lack in mitigating the challenges, like accurate identification and separation of the apt signal data for data analytics to extract the artifacts, identification of inaccurate signal data, and finding the root cause of the faults like systemic or random failures towards determining the health of the medical systems. In-depth studies [5], [10]- [12], [16], [24] show that potential faults in sensors or related system design factors may cause the biosignal embedded with external environmental faults.…”

Section: Background and Motivation A Related Work And Motivationmentioning

confidence: 99%

“…In the recent past studies, reported that the same vital parametric data, like heart rate, can be realized with different mediums of the sensor [8], [9], [16], [20]. These studies motivate us to focus on to improve an additional safety feature in our fault-tolerant safety-related design research platform cardiac health monitoring system (CHMS).…”

Section: Background and Motivation A Related Work And Motivationmentioning

confidence: 99%

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Improvements in Accurate Detection of Cardiac Abnormalities and Prognostic Health Diagnosis Using Artificial Intelligence in Medical Systems

2020

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This paper presents an approach of apt prognostic diagnostics of cardiac health by using Artificial Intelligence (AI) in safety-related based non-invasive bio-medical systems. This approach addresses the existing challenge in identification of the actual abnormality of the vital cardiac signal from the various interrupting factors like bio-signal faulted due to high noise signal interference, electronic and software fault, mechanical fault like sensor contacts failures, wear and tear of equipment. Presently, most of the medical systems use a 1oo1(one-out-of-one) system architectures, and there exists a safety procedure to raise a particular defined type of standard alarm for a specific failure to detect an abnormality. These existing approaches may incur high maintenance costs and subject to random failures with long downtimes of the system and where it affects operational safety to a certain extent. However, there is a scope to improve in the segregation of the actual fault-free signal and extract the abnormality of the vital feature for prognostic diagnostics. With advancements in systems engineering and usage of safety-related design architectures in medical systems, we used an Artificial Intelligence (AI) based approach in performing the data analytics on the selected correct vital signal for prognostic analysis. As a case study, we evaluated by configuring the system with the 2oo2 fault-tolerant safety-related design architecture and implemented the diagnostic function using the AI-based method on the apt logged data during system operation. The results show a substantial improvement in the accuracy of the cardiac health findings.

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Section: Background and Motivation A Related Work And Motivationmentioning

confidence: 99%

Section: Background and Motivation A Related Work And Motivationmentioning

confidence: 99%

Section: Background and Motivation A Related Work And Motivationmentioning

confidence: 99%

See 1 more Smart Citation

Improvements in Accurate Detection of Cardiac Abnormalities and Prognostic Health Diagnosis Using Artificial Intelligence in Medical Systems

2020

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“…The signal has been used in the study of detecting and diagnosing HR and HR variability (HRV) , blood oxygen saturation (SpO 2 ) [6], blood pressure , respiratory rate , vascular assessment , cardiac output , and venous refilling time . Research has also shown that multisite PPG (ears, thumbs, and big toes) measurement is useful for vascular disease detection and measuring blood glucose levels . Multisite PPG compares two PPG signal sequences recorded at anatomically symmetric sites on the human body (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, researchers need methods to collect PPG information during long time periods and from multiple sites with high accuracy for the best diagnosis. Several PPG instruments have been developed, both custom‐made and application‐specific . Recently, communications and information technologies have aided significantly in the improvement and expansion of the PPG signal acquisition system.…”

Section: Introductionmentioning

confidence: 99%

Development of a real‐time adaptive delta compression algorithm for photoplethysmography system

Chong

Beng

Zahedi

2018

IEEJ Transactions Elec Engng

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Photoplethysmography (PPG) is a simple and low-cost optical technique that measures the blood volume change of the microvascular bed. The multichannel PPG recording system enables researchers to study the characteristics of the PPG signal from multiple body sites. Long-time, continuous PPG signal monitoring helps doctors in medical diagnosis and treatment. However, the development of the PPG signal acquisition system is limited by storage capacity and computational and hardware capability. Limitation in storage capacity and communication efficiency can be addressed by introducing data compression algorithms. In this paper, a single-channel, high-resolution (24-bit) wireless PPG data acquisition system using Bluetooth connectivity with the adaptive delta compression algorithm is presented. A graphical user interface was developed using MATLAB to interface with the system using a serial port profile. The PPG signals were decompressed and stored in the local hard drive for future analysis. The performance of adaptive data compression algorithm was evaluated with 12 healthy subjects. Results show that the compression ratio achieved is 41.5. The mean percentile root-mean-square (RMS) difference at the sampling frequency of 100 Hz is 0.13%. The mean normalized percentile RMS difference value is 9.78%. The system was successfully tested in a continuous acquisition mode for up to 60 min.

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A Literature Review on Current and Proposed Technologies of Noninvasive Blood Pressure Measurement

Mukherjee

Ghosh

Gupta

et al. 2018

Telemedicine and e-Health

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Modeling a safety-related system for continuous non-invasive blood pressure monitoring

Cited by 5 publications

References 12 publications

Improvements in Accurate Detection of Cardiac Abnormalities and Prognostic Health Diagnosis Using Artificial Intelligence in Medical Systems

Improvements in Accurate Detection of Cardiac Abnormalities and Prognostic Health Diagnosis Using Artificial Intelligence in Medical Systems

Development of a real‐time adaptive delta compression algorithm for photoplethysmography system

A Literature Review on Current and Proposed Technologies of Noninvasive Blood Pressure Measurement

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