Cardiac Event Prediction via FFT Spectral Analysis
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Solution Overview
Problem
Conventional systems for detecting or predicting cardiac events are limited to specific types of events, rely on peak detection in biomedical signals, and fail to consider environmental noise or motion, making them ineffective in non-clinical noisy environments like in-vehicle settings.
Innovation Solution
The use of a machine learning algorithm that applies Markov transition matrices to real-time biomedical signals, trained on pre-event signal patterns, to predict cardiac events without relying on peak detection, and capable of identifying multiple types of medical conditions simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional peak detection methods are used to detect cardiac events, then the system can identify specific cardiac peaks, but it fails in noisy environments and cannot reliably detect multiple types of cardiac events simultaneously
Solution Approach 1:
The patent transforms the ECG signal from time-domain peak detection to frequency-domain analysis using Fast Fourier Transform (FFT). This parameter transformation allows the system to analyze spectral characteristics and identify multiple cardiac event types based on frequency patterns rather than relying on peak detection that is vulnerable to noise interference.
Solution Approach 2:
The patent introduces an intermediary classification system that processes FFT-derived features through trained classifiers (such as neural networks or support vector machines). This intermediary layer separates the raw signal processing from the final detection decision, enabling robust classification of multiple cardiac event types while filtering out noise interference.
2Adaptability or versatility
If the system is designed to detect specific cardiac events, then it can provide targeted detection, but it cannot identify other types of cardiac events or adapt to different conditions
Solution Approach 1:
The patent implements a universal detection framework where a single FFT-based processing pipeline can identify multiple types of cardiac events (arrhythmias, ischemia, heart failure) simultaneously. The trained classification system is configured to recognize various event types through their distinct spectral signatures, eliminating the need for separate detection algorithms for each cardiac condition.
Solution Approach 2:
The patent performs preliminary training of classification algorithms using labeled training data that represents multiple cardiac event types. This preliminary action creates pre-configured classification models that can automatically distinguish between different cardiac events during operation, enabling versatile detection without requiring complex real-time configuration changes.
3Reliability
If conventional systems ignore motion and environmental factors, then the processing is simpler, but the detection reliability in non-clinical environments deteriorates
Solution Approach 1:
The patent extracts relevant spectral features from the ECG signal using FFT, separating the essential cardiac event characteristics from noise and motion artifacts. By focusing on frequency-domain features rather than time-domain waveforms, the system isolates the diagnostically important information while discarding environmentally induced variations.
Solution Approach 2:
The patent incorporates feedback mechanisms where the classification system continuously monitors spectral patterns and adjusts detection thresholds based on learned characteristics from training data. This feedback loop enables the system to adapt to varying environmental conditions and maintain high detection reliability in non-clinical settings.
Data Source
AI summary
Systems and methods for predicting and/or detecting cardiac events based on real-time biomedical signals are discussed herein. In various embodiments, a machine learning algorithm may be utilized to predict and/or detect one or more medical conditions based on obtained biomedical signals. For example, the systems and methods described herein may utilize ECG signals to predict and detect cardiac events. In various embodiments, patterns identified within a signal may be assigned letters (i.e., encoded as distributions of letters). Based on the known morphology of a signal, states within the signal may be identified based on the distribution of letters in the signal. When applied in the in-vehicle environment, drivers or passengers within the vehicle may be alerted when an individual within the vehicle is, or is about to, experience a cardiac event.