Real-Time Alarm Pattern Classification Using Suffix Trees
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Solution Overview
Problem
Traditional electrophysiological monitoring systems generate too many non-critical alarms, leading to resource diversion and potential neglect of critical alarms due to faulty sensors or patient movement, and existing methods fail to detect extended or interdigitated alarm patterns effectively.
Innovation Solution
A method utilizing a suffix substring data structure and on-line monitoring module to identify and classify alarm patterns in real-time, generating an incidence vector and clustering algorithm to reduce non-critical alarms and alert medical staff to critical conditions before the alarm sequence is complete.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional alarm detection methods are used to monitor patient conditions, then all alarm events are detected and reported, but the number of non-critical alarms increases excessively, diverting medical staff resources and reducing monitoring efficiency
Solution Approach 1:
The patent extracts and removes non-critical alarms from the alarm sequence by identifying patterns associated with non-critical conditions (such as alarms caused by patient movement or faulty sensors) and filtering them out, keeping only the critical alarms that require medical attention
Solution Approach 2:
The patent segments the alarm sequence into distinct patterns by analyzing temporal relationships and groupings of alarms, identifying which segments represent critical conditions versus non-critical conditions, allowing selective reporting of only critical alarm patterns
2Adaptability or versatility
If traditional time series signal processing methods are used for alarm pattern detection, then simple alarm sequences are detected, but extended alarm patterns over long periods and interdigitated alarm patterns are not properly detected
Solution Approach 1:
The patent implements a dynamic pattern detection approach that adapts to varying alarm sequence characteristics, allowing the detection system to handle both short-term and long-term alarm patterns, as well as interdigitated patterns, by continuously analyzing the temporal relationships between alarms
3Loss of time
If traditional alarm detection methods are used, then alarm sequences are processed only after completion, but critical medical conditions are not identified in real-time, delaying medical response
Solution Approach 1:
The patent performs preliminary analysis of alarm sequences in real-time as they occur, identifying critical patterns during the alarm sequence rather than waiting for completion, enabling early warning and timely medical intervention while maintaining accurate pattern classification
Data Source
AI summary
A system and method for electrophysiological monitoring system including a plurality of sensors configured to detect one or more health parameters of a patient and a monitoring device configured to receive a plurality of sensing signals from the sensors and output a monitoring signal representative of an alarm sequence, wherein the alarm sequence comprises a set of alarm events identified in the sensing signals. The system also includes an on-line monitoring module configured to generate a suffix tree data structure in response to the monitoring signal to identify alarm patterns from the set of alarm events and classify the alarm sequence in response to the occurrences of alarm patterns in the alarm sequence. The on-line monitoring module is further configured to alert monitoring personnel of an alarm condition after processing the alarm sequence in real-time.