Machine Learning Phonocardiogram Analysis for Noisy Heart Rate Detection
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Solution Overview
Problem
Traditional signal processing approaches face challenges in accurately detecting heart rates from phonocardiogram signals due to inherent noise in real-world environments, limiting the precision of heart rate and cardiac anomaly detection.
Innovation Solution
A machine-learned model utilizing convolutional and recurrent neural networks processes acoustic data to extract heart rate information, employing a sliding window methodology and multi-task learning framework for robust heart rate and murmur prediction, achieving high accuracy through a 2D convolutional neural network (2dCNN-MTL) model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signal processing approaches are used to detect heart rates from phonocardiogram signals, then the system complexity remains low, but the measurement precision deteriorates due to inherent noise in real-world environments
Solution Approach 1:
The patent replaces traditional mechanical signal processing methods with a machine learning-based acoustic analysis system. The ML model processes acoustic data from phonocardiogram signals to detect heart rates and cardiac anomalies, achieving superior measurement precision (95% accuracy) by substituting conventional signal processing algorithms with intelligent pattern recognition capable of filtering noise and extracting relevant physiological features.
2Reliability
If traditional signal processing methods are applied to noisy PCG signals, then the computational resources required remain low, but the reliability of heart rate and cardiac anomaly detection deteriorates
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the noisy phonocardiogram signals and the detection output. This ML intermediary processes the acoustic data through multiple layers (convolutional, recurrent, attention mechanisms) to extract meaningful patterns while filtering out noise, thereby achieving high detection reliability (95% accuracy) that traditional direct processing methods cannot attain.
3Measurement precision
If conventional heart rate detection methods are used, then the processing time remains short, but the measurement precision of heart rate and murmur detection deteriorates in noisy environments
Solution Approach 1:
The patent employs preliminary action by pre-training the machine learning model on extensive phonocardiogram data before deployment. The model is pre-equipped with learned features and patterns that enable it to rapidly process new acoustic data with high precision. This preliminary preparation allows the system to achieve 95% accuracy without requiring excessive processing time during actual operation, as the heavy computational lifting occurs during the offline training phase.
Data Source
AI summary
The subject technology provides physiological state prediction based on acoustic data using machine learning. An apparatus receives input data comprising acoustic signal information associated with a user. The apparatus extracts one or more acoustic features from the acoustic signal information. The apparatus produces a trained machine learning model by training a neural network to predict one or more physiological states of the user from the one or more acoustic features.