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

Prasadraju Lakkamraju,Shubhajit Roy Chowdhury,Madhubabu Anumukonda

doi:10.1109/access.2020.2965396

Prasadraju Lakkamraju, Shubhajit Roy Chowdhury + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.2965396

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Abstract

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.

Highlights

Human Health Monitoring Medical Systems (HHMMS) are advancing at a dramatic rate, bringing with safety improvements by aiming to deliver improved quality and accuracy in predicting the disease, faster diagnostics, and user-friendly interfaces [1]–[4]
The safety function processed data and processed vital signals captured for a one-hour duration from channels ECG, PPG, outputs presented in Fig. 5, Fig. 6, and Fig. 7 for a single subject
The processed data of a single subject and its performance values of the system function tabulated with measured pulses, Se, positive predictive values (PPV) is in Table 1 and observed there is signal drift issue during capture, and appropriate sync mechanism needs to improve between the signals and channels

Summary

Introduction

Human Health Monitoring Medical Systems (HHMMS) are advancing at a dramatic rate, bringing with safety improvements by aiming to deliver improved quality and accuracy in predicting the disease, faster diagnostics, and user-friendly interfaces [1]–[4]. System architectures, i.e., one sensor measures one or more vital health parameters and generates an alarm as per safety severity level if any disturbance occurs, and halts system functional operation if the severity level is high [11], [12] This type of non-invasive medical monitoring devices is often subject to an insignificant number of failures with potentially catastrophic impacts on patients. 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). The presented framework provides an approach, along with the implementation results using the configurable safety-related 2oo design architecture

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Conclusion