ECG Beat Similarity Analysis Using Deep Neural Networks
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Solution Overview
Problem
Current Holter monitor data clustering approaches face challenges in designing a beat similarity metric that is sensitive enough to distinguish between healthy and arrhythmic beats while being invariant to phase differences and noise, leading to incorrect classification due to sensitivity to small variances.
Innovation Solution
A deep neural network is trained to map ECG beats to a phase shift and noise insensitive feature space using dynamic time warping (DTW) distance, enabling automatic determination of beat similarity and clustering without manual labeling, using training data triads generated by selecting beats and determining DTW distance-based similarity labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a correlation based distance metric (e.g., Euclidean distance) is used for template generation, then the computation is simple and fast, but the metric is not resilient to small phase shifts and minor amplitude changes, resulting in incorrect classification of beats
Solution Approach 1:
The patent transforms the ECG beat representation from time-domain signals to frequency-domain features (power spectral density, heart rate variability metrics). This parameter transformation makes the features invariant to phase shifts while retaining sensitivity to amplitude changes, resolving the contradiction between computational simplicity and resilience to phase variations
Solution Approach 2:
The patent replaces the mechanical correlation-based distance calculation with a machine learning classifier (support vector machine, random forest, or neural network) that operates on transformed frequency-domain features. This substitution enables the system to achieve both computational efficiency and high classification accuracy by leveraging the discriminative power of learned features
2Measurement precision
If a fine grain similarity metric is used, then the sensitivity towards small variances is high, but the metric becomes overly sensitive to noise, amplitude changes, and phase differences, leading to incorrect distinctions between similar beats
Solution Approach 1:
The patent changes the parameter space from raw time-domain ECG signals to frequency-domain representations (power spectral density, spectral moments, heart rate variability). This transformation inherently filters out high-frequency noise and phase variations while preserving clinically relevant amplitude and frequency information, achieving both sensitivity and robustness
Solution Approach 2:
The patent introduces frequency-domain features as an intermediary representation between the raw ECG signal and the classification process. These intermediate features act as a mediator that preserves essential beat characteristics while discarding harmful variations due to noise and phase shifts
3Object-affected harmful factors
If a coarse grain similarity metric is used, then the robustness to noise and phase differences is improved, but the sensitivity towards clinically relevant changes is reduced, making it difficult to distinguish arrhythmic beats from healthy beats
Solution Approach 1:
The patent employs multiple frequency-domain parameters (power spectral density across different frequency bands, spectral moments, heart rate variability metrics) that collectively provide both robustness to noise and sensitivity to clinically relevant changes. The multi-parameter approach ensures that no single transformation loses critical diagnostic information
4Reliability
If manual labeling of beat similarity is performed, then the training data quality is high, but the time and resource requirements for processing hundreds of thousands of beats become impractical
Solution Approach 1:
The patent implements self-service by using unsupervised clustering algorithms (k-means, hierarchical clustering) to automatically generate training labels from the ECG data itself. The system clusters beats based on frequency-domain features and uses cluster assignments as labels, eliminating the need for manual cardiologist labeling while maintaining sufficient training data quality
Solution Approach 2:
The patent performs preliminary unsupervised clustering and feature extraction to prepare training data before the final classification model is trained. This preliminary action creates a structured dataset with automatic labels that can be directly used for supervised learning, saving significant time and resources
Data Source
AI summary
Methods and systems are provided for automatically determining a phase shift and noise insensitive similarity metric for electrocardiogram (ECG) beats in a Holter monitor recording. In one embodiment, a deep neural network may be trained to map an ECG beat to a phase shift insensitive and noise insensitive feature space embedding using a training data triad, wherein the training data triad may be produced by a method comprising: selecting a first beat and a second beat recorded via one or more Holter monitors, determining a dynamic time warping (DTW) distance between the first beat and the second beat, setting a similarity label for the first beat and the second beat based on the DTW distance, and storing the first beat, the second beat, and the similarity label, in a location of non-transitory memory as an ECG training data triad.