Heart Condition Sensor Device for Biosignal Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current patient monitoring systems in wards and intensive care units suffer from frequent false alarms due to low-quality biosignal data, leading to decreased efficiency for medical staff and poorer patient prognosis. Additionally, the rapid doubling of medical data exceeds the capacity for labor-intensive analysis, and there is a shortage of medical personnel to handle the increased data burden.
Innovation Solution
A heart condition monitoring system utilizing IoT and artificial intelligence that attaches a sensor device to patients to measure biosignals, which are then analyzed using a server-based artificial intelligence algorithm to determine cardiac abnormality type models. This system aims to prevent misdiagnosis and false alarms by improving biosignal analysis accuracy and reducing the workload for medical staff.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional patient monitoring systems are used in wards and intensive care units, then continuous monitoring of patients is provided, but false alarms occur frequently due to low-quality biosignal data
Solution Approach 1:
The patent replaces manual analysis of biosignals by medical staff with an automated deep learning-based analysis system. The system automatically processes ECG, PPG, and other biosignals to detect arrhythmias and generate alerts, eliminating the need for manual review and reducing false alarms caused by human error or fatigue.
Solution Approach 2:
The patent introduces a deep learning model as an intermediary between biosignal acquisition and clinical decision-making. This intermediary layer processes raw biosignals, filters noise, detects patterns, and generates structured outputs that are more reliable than direct human interpretation, thereby improving accuracy while reducing the time burden on medical staff.
2Measurement precision
If manual analysis of biosignals is performed by medical staff, then diagnostic decisions are made, but the workload increases significantly with the doubling of medical data
Solution Approach 1:
The patent replaces manual diagnostic analysis with an automated deep learning system that can process large volumes of medical data rapidly. The system handles ECG, PPG, and other biosignals simultaneously, performing comprehensive arrhythmia detection without increasing medical staff workload, thereby maintaining diagnostic accuracy while dramatically increasing processing capacity.
Solution Approach 2:
The patent segments the complex task of biosignal analysis into multiple specialized deep learning models, each trained to detect specific arrhythmia types or analyze specific signal characteristics. This segmentation allows parallel processing of different signal types and features, increasing overall processing capacity while maintaining or improving diagnostic accuracy through specialized analysis.
3Productivity
If more medical personnel are hired to analyze the increased data volume, then data analysis capacity increases, but medical costs increase and revenue problems persist
Solution Approach 1:
The patent replaces human labor with an automated deep learning system that processes biosignals at scale without incurring additional personnel costs. The system handles increasing data volumes through algorithmic processing rather than hiring more staff, thereby increasing productivity while controlling or reducing medical costs associated with data analysis.
Solution Approach 2:
The patent implements a self-service monitoring system where the deep learning model automatically analyzes biosignals, detects arrhythmias, and generates alerts without requiring manual intervention. This self-service capability allows the system to handle increasing data volumes independently, eliminating the need for additional human resources and associated costs.
4Measurement precision
If deep learning models are used for arrhythmia detection, then detection accuracy improves, but the complexity of the system increases
Solution Approach 1:
The patent develops a universal deep learning platform that handles multiple types of biosignals (ECG, PPG, and others) and detects various arrhythmia types using a unified architecture. This multi-functional system reduces overall complexity compared to having separate specialized systems for each signal type or arrhythmia, while maintaining high detection accuracy through versatile pattern recognition capabilities.
Data Source
AI summary
The present invention relates to a technical idea of monitoring a heart condition by analyzing biosignals measured using a sensor device for detecting a heart condition. In a method of monitoring a heart condition according to one embodiment of the present invention, electrocardiogram signals are measured from a user, feature information is extracted by performing machine learning of the time domain of the measured electrocardiogram signals, a plurality of cardiac abnormality type models are determined by performing machine learning of the extracted feature information, classification accuracy for the determined cardiac abnormality type models is calculated, and a cardiovascular disease of the user is determined using the determined cardiac abnormality type models and public cardiovascular disease data based on the calculated accuracy. That is, the present invention relates to a technique for assisting medical diagnosis.