Heartbeat Signal Classification Using Wavelet Noise Removal
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Solution Overview
Problem
Current methods for analyzing heartbeat signals, such as those using stethoscopes, require skilled operation and optimal positioning, limiting their usability by untrained individuals and failing to provide accurate classification with low-quality audio signals.
Innovation Solution
A wearable device equipped with audio and bioelectric sensors, processing circuitry, and computer program code that identifies individual heartbeats, removes noise using wavelets, and classifies heartbeats as normal or abnormal, allowing operation by unskilled users and providing accurate results even with non-optimal sensor placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional stethoscope methods are used for heartbeat analysis, then measurement precision can be maintained with proper positioning, but ease of operation deteriorates due to requiring skilled operation and optimal positioning
Solution Approach 1:
The patent introduces a signal processing system that acts as an intermediary between the audio sensor and the analysis result. This system includes noise removal algorithms, heartbeat identification algorithms, and classification algorithms that automatically process the raw audio signals. The intermediary processing system compensates for suboptimal sensor placement and environmental noise, enabling untrained users to achieve reliable heartbeat analysis without requiring expert positioning skills.
Solution Approach 2:
The patent replaces the mechanical skill-based positioning requirement with an automated signal processing system. Instead of relying on the operator's mechanical skill to position the stethoscope optimally, the system uses digital signal processing techniques including noise removal, waveform analysis, and automated heartbeat detection to achieve accurate results regardless of initial positioning quality.
2Ease of operation
If audio sensing means is used to obtain heartbeat signals, then ease of operation improves, but reliability deteriorates due to noise interference and low-quality audio signals
Solution Approach 1:
The patent extracts the useful heartbeat signal from the noisy audio input by removing various types of noise including environmental noise, physiological noise from breathing and speech, and sensor artifacts. The noise removal module specifically targets and eliminates unwanted frequency components while preserving the characteristic heartbeat frequencies, thereby improving signal reliability without requiring more complex hardware.
Solution Approach 2:
The patent performs preliminary signal processing actions including noise removal and waveform enhancement before the actual heartbeat analysis. By pre-processing the audio signals to remove known types of interference and enhance heartbeat-related frequencies in advance, the system ensures that the subsequent analysis operates on cleaned, high-quality data, improving overall reliability of the classification results.
3Device complexity
If simple audio sensors are used for heartbeat detection, then device complexity is reduced, but measurement precision deteriorates due to inability to accurately identify individual heartbeats
Solution Approach 1:
The patent replaces complex hardware solutions with sophisticated software-based signal processing. Instead of using complex multi-sensor arrays or advanced transducers, the system employs algorithms that analyze audio waveforms, identify characteristic heartbeat patterns, detect R-peaks, and classify heartbeats based on extracted features. This software-intensive approach achieves high measurement precision while keeping the hardware simple and cost-effective.
Solution Approach 2:
The patent transforms the one-dimensional audio signal into a multi-dimensional analysis space by extracting multiple features from each heartbeat waveform including time-domain features, frequency-domain features, and temporal patterns. This dimensional expansion of the data allows simple sensors to achieve precise measurements by analyzing the signal from multiple computational perspectives rather than requiring complex physical sensors.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables untrained users to accurately analyze heartbeat signals, providing normal or abnormal classifications with reduced noise interference, facilitating self-auscultation and improving accessibility for health monitoring.
Implementation Method 1
obtain an audio signal from an audio sensing means wherein the audio signal comprises a subject's heartbeat
Implementation Method 2
obtain a further signal from a further sensing means wherein the further signal also comprises the subject's heartbeat
Implementation Method 3
use wavelets to remove noise from the audio signal
Data Source
AI summary
An apparatus, method and computer program, the apparatus comprising: processing circuitry and memory circuitry including computer program code, the memory circuitry and the computer program code arranged to, with the processing circuitry, cause the apparatus to: obtain an audio signal from an audio sensing means wherein the audio signal comprises a subject's heartbeat; obtain a further signal from a further sensing means wherein the further signal also comprises the subject's heartbeat; use the further signal to identify individual heart beats in the audio signal; and analyse the individual heartbeats of the audio signal to enable the audio signal to be classified.