FAULT-TOLERANT MULTIMODAL SAFETY-RELATED MEDICAL SYSTEMS

Abstract

The safety improvements in the medical systems or devices, specifically non-invasive patient monitoring systems (PMS) and point-of-care (POC) devices of human health monitoring (HHMS) systems, are in great need to perform precise measurements of vital parameters with uninterrupted continuous monitoring for diagnosis of significant human health ailments. During critical or non-critical nursing times, frequent monitoring of short and long-duration measures of vital parameters (like ECG, EEG, Respiratory, SpO2, Blood-pressure, Temperature, etc.) and non-vital parameters (like Glucose levels, Hemoglobin, Urea in blood, etc.) will allow for better nursing and early detection of diseases like cardiovascular diseases, respiratory diseases, liver dysfunction, diabetes, renal diseases, and other psychological disorders. A non-invasive medical instrumentation system for accomplishing these tasks must meet several criteria like accuracy and precision of the measurement, reduction of false alarms, effective detection of faults, and fault tolerance to systemic or random failures with an uninterrupted performance during nursing times. Many research groups have made many efforts worldwide to make strategies and guidelines and standardize the procedures for medical systems based on safety design approaches to raise alerts for any deviance during non-invasive monitoring of health parameters. In addition, health monitoring instruments should be portable, low-cost, and reliable over time. Many modes of diagnosis and related instrument maintenance procedures satisfy these criteria. However, most of them are still in the improvement stage for developing resilient instruments for integrating with critical auto-robotic surgery instruments with minimal human intervention. Several approaches have been proposed, out of which safety-related design approaches like using 2oo2, 2oo3, 1oo2, etc., along with AI-based data analytics, have the advantage of meeting these rigorous demands in fundamental safety improvements of Medical Systems. Based on the safety-related design 2oo2 concept, a configurable system prototype of the cardiac health monitoring system (CHMS) is developed and evaluated to meet the set objectives, such as fault detection effectiveness and fault tolerance with improved safety configurability. This configurable system uses various sensors to collect the bio-medical data in parallel. Primarily, three diverse sensors are used non-invasively in sensing the bio-signals in different forms like electric potential, light, and sound signals for computations. These diverse sensors are used to detect biomedical signals to obtain data from electrocardiogram (ECG), Photo-plethysmogram (PPG), and Phonocardiogram (PCG). Therefore, the accuracy of the vital estimates and the fundamental safety improvements were evaluated using this multimodal system with AI-based fault detection and predictive maintenance techniques. Traditional statistical and AI-based techniques have acquired authentic measurements of human health parameters from diverse signals received simultaneously from various sensing circuits like PPG/ECG/EEG. Secondly, these bio-signals are calibrated independently with known algorithms in diverse. Finally, these obtained parameters, along with the built-in-test (BIT) system for health signals, are processed with implemented safety functions, and the algorithms to generate the correct human health parameters and prognosticate abnormalities of human health 200 patients were available for instrument evaluation trials for HR parameter monitoring and tested after taking informed consent. A MATLAB-based CHMS tool is developed for configurable and then implemented on a field-programmable gate array (FPGA) to minimize the designed circuitry with improved resiliency of the instrument system. The CHMS has been configured to 2oo2 with the selected HR parameter to estimate the system's availability and health. The collected HR output was subjected to data analytics against individually collected data. We found a significant reduction in the generation of unimportant alarms and increased uninterruptable System availability by 45% to 55%, along with normal and abnormal artifact data. The measured normal artifacts are more than 99% accurate and are used for prognosis. The abnormal data is used for edge-AI-based analytics to infer the system's health for prescriptive maintenance. An experimental study has been carried out to effectively segregate normal and abnormal signals in 2oo2 and 2oo3 configurations. A detailed analysis is carried out in various sensor configurations as proposed. Similarly, in the 2oo3 configuration, we significantly improved the system's availability from 55% to 95% by eliminating spurious alarms with reduced downtime and improved accurate data vital parameters. Further, a reliability assessment is performed on CHMS on identified parameters, such as measuring Availability, Mean-time-between-failure (MTBF), Mean-time-to-failure (MTTF) Repeatability and Reproducibility. The higher the MTBF number, the higher the product's reliability. Measured the MTBF, in terms of its recovery time when a failure does occur. The experimental assessments performed for the estimation of reliability improvements have been verified in comparison with the existing 1oo1 systems. Overall Availability of the system improved by 45% to 55% in the 2oo2 configuration, whereas 55% to 85% improved in -2oo3 configured systems with respect to 1oo1 systems. The assessment of MTBF for the 2oo3 system is 28.40 min, and for the 2oo2 system is approx. 24.60 min is recorded. Thus, a significant improvement was noticed in reliability when compared to 1oo1 systems with MTBF of average approx. 13 min. The repeatability and reproducibility parameters are measured for the CHMS; in the 2oo2 configuration, the repeatability is assessed as 0.79, whereas for 2oo3, it is 0.1414, which shows very less variance in the system repeatability and reproducibility parameters. In this thesis, an attempt has been made to use safety-related architectures to build CHMS and evaluated with implemented functions like fault detection and identification logic, correlation coefficient-based safety function, and fault-tolerant safe degradation switching mechanism for accurate measurements. Furthermore, different correlative safe functions and reliability assessments were performed, such as system-level MTBF, MTTF, Availability, repeatability and reproducibility. Moreover, predictive or prescriptive maintenance methods have been adopted and evaluated to identify a safe design approach appropriate for measuring authentic data and improving system health.