Lead I ECG Hyperkalemia Detection via ML Segmentation

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Solution Overview

Problem

Current methods for detecting hyperkalemia rely on blood potassium level measurements, which are invasive and not readily available in real-time. Additionally, they do not effectively utilize electrocardiogram (ECG) data for early detection of hyperkalemia.

Innovation Solution

The system employs Lead I ECG signals to detect hyperkalemia by segmenting and normalizing the ECG data. This involves breaking down the ECG signal into smaller segments, applying a consistent time-frame and amplitude scale, and using a machine learning model to identify patterns indicative of hyperkalemia.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If blood potassium level measurements are used for hyperkalemia detection, then measurement precision is improved, but ease of operation deteriorates due to invasive procedures and lack of real-time availability

Engineering Contradiction:

Improvehyperkalemia detection accuracyVSAvoiddetection convenience

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The patent replaces the mechanical/invasive blood sampling system with an electrical signal-based detection system. Specifically, it uses Lead I ECG signals and machine learning models to detect hyperkalemia, eliminating the need for invasive blood draws while maintaining detection capability through electrical waveform analysis

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces ECG signals as an intermediary medium to indirectly detect potassium levels. Instead of measuring potassium directly through blood sampling, the system uses ECG waveforms (which are influenced by potassium levels) as a non-invasive proxy indicator for hyperkalemia detection

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If traditional ECG data utilization methods are used, then device complexity is reduced, but measurement precision deteriorates due to ineffective utilization of ECG data for early detection

Engineering Contradiction:

Improvehyperkalemia detection accuracyVSAvoiddata processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the continuous ECG signal into discrete beats or waveforms for individual analysis. This segmentation allows the machine learning model to process and evaluate specific cardiac cycles independently, improving detection precision by focusing on relevant temporal patterns while managing data complexity through structured division

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies preliminary normalization and preprocessing to ECG data before analysis. By standardizing the ECG waveforms in advance (adjusting amplitude, baseline, and timing), the system improves measurement precision while reducing the complexity of subsequent analysis through pre-processed, standardized input data

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250134441A1Systems and processes for hyperkalemia detection using lead i ECG data

Publication Date: 2025.05.01 ACCURKARDIA INC

AI summary

The present disclosure provides systems and methods for detection of hyperkalemia from Lead I electrocardiogram (ECG) signals, particularly in patients with critically high potassium levels. The methods and systems of the disclosure are further demonstrated to identifying hyperkalemia across both acute kidney disease (AKD) and chronic kidney disease (CKD) patient groups, with performance varying according to serum potassium levels.