Acoustic Respiratory Sensor Compression for Wide Dynamic Range
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Solution Overview
Problem
Existing acoustic respiratory monitoring systems face challenges in accurately capturing a wide range of physiological sounds, including both quiet and loud sounds, due to limitations in dynamic range, leading to signal saturation and distortion.
Innovation Solution
The implementation of an acoustic sensor system with a compression module that modifies the signal output from the sensing element to prevent saturation in subsequent components, such as preamplifiers, and includes noise reduction techniques using multiple sensors to enhance the signal-to-noise ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a wide dynamic range is used to capture both quiet and loud physiological sounds, then the ability to detect both low and high intensity sounds is improved, but signal saturation and distortion occur in subsequent components
Solution Approach 1:
The system dynamically adjusts the gain of the preamplifier based on the input signal level. When loud sounds are detected, the gain is reduced to prevent saturation; when quiet sounds are detected, the gain is increased to maintain sensitivity. This dynamic adaptation allows the system to handle a wide range of sound intensities without saturation or distortion.
Solution Approach 2:
The system employs automatic gain control (AGC) feedback mechanisms where the output signal level is continuously monitored and fed back to adjust the preamplifier gain. This feedback loop ensures that the signal remains within the optimal dynamic range of subsequent components, preventing saturation while maintaining the ability to detect both quiet and loud physiological sounds.
2Measurement precision
If high gain is applied to amplify quiet physiological sounds, then the sensitivity to low intensity sounds is improved, but loud sounds cause saturation and distortion
Solution Approach 1:
The preamplifier gain is made dynamic rather than fixed. The system continuously monitors the input signal level and adjusts the gain accordingly - applying high gain when quiet sounds are present to maintain sensitivity, and reducing gain when loud sounds are detected to prevent saturation and distortion.
Solution Approach 2:
The system changes the gain parameter of the preamplifier based on the signal level. By automatically adjusting this critical parameter, the system maintains optimal sensitivity for quiet physiological sounds while preventing distortion from loud sounds, thus resolving the contradiction between sensitivity and signal integrity.
3Adaptability or versatility
If compression is applied to prevent saturation, then the dynamic range handling capability is improved, but signal distortion may occur
Solution Approach 1:
The system introduces compression as an intermediary processing stage between the sensor and the preamplifier. This compression module gently limits the dynamic range of the signal before it reaches the preamplifier, preventing saturation while using appropriate compression ratios and attack/release times to minimize audible distortion and preserve signal fidelity.
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
This approach allows for the accurate capture and processing of a broader range of physiological sounds, reducing distortion and improving the dynamic range of the sensor system, enabling effective monitoring and diagnosis.
Implementation Method 1
The piezoelectric effect is the appearance of an electric potential and current across certain faces of a crystal when it is subjected to mechanical stresses. Due to their capacity to convert mechanical deformation into an electric voltage, piezoelectric crystals have been broadly used in devices such as transducers, strain gauges and microphones.
Data Source
AI summary
An acoustic sensor is provided according to certain aspects for non-invasively detecting physiological acoustic vibrations indicative of one or more physiological parameters of a medical patient. The sensor can include an acoustic sensing element configured to generate a first signal in response to acoustic vibrations from a medical patient. The sensor can also include front-end circuitry configured to receive an input signal that is based at least in part on the first signal and to produce an amplified signal in response to the input signal. In some embodiments, the sensor further includes a compression module in communication with the front-end circuitry and configured to compress portions of at least one of the input signal and the amplified signal according to a first compression scheme, the compressed portions corresponding to portions of the first signal having a magnitude greater than a predetermined threshold level.