Systems, devices, and methods for performing active auscultation and detecting sonic energy measurements
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Publication Number
US-11647919-B2
Publication Date
2023-05-16
Expiration Date
Abstract
Active auscultation may be used to determine organ (e.g., lung or heart) characteristics of users. An acoustic or piezo-electric signal (e.g., a pulse, a tone, and/or a broadband pulse) may be projected into an animal (typically human) body or thorax. The signal interacts with the body, or lungs, and in some cases may induce resonance within the body/lungs. A resultant signal may be emitted from the body which may be analyzed to determine, for example, a lung's resonant frequency or frequencies and/or how the sound is otherwise absorbed, reflected, or modified by the body. This information may be indicative of lung characteristics such as lung capacity, a volume of air trapped in the lungs, and/or the presence of COPD.
Core Innovation
The invention provides active auscultation by directing acoustic energy from an emitter into a user's body toward a user's lung, and receiving an acoustic energy response from a receiver coupled to a processor and positioned proximate to the user's body. The processor determines a spectral shape of the acoustic energy response, determines a signature for the user using the spectral shape, and stores the signature in a database.
The approach extracts lung resonance and spectral features from the acoustical response for diagnosis and prognosis. Resonance-based analysis targets resonant frequency and resonant frequency ranges and supports estimating the respiratory cycle and trapped-air, and the described signatures include lung resonance signatures, spectral tilt, spectral centroid, harmonic-based signatures, and harmonic changes related to the user's lung characteristics.
The described system architecture supports active auscultation hardware and data processing workflows, including a housing with an emitter and receiver, mechanical and/or electro-acoustic noise reduction, and multi-receiver arrays with beamforming to mitigate cross-channel leakage. The document further describes a cloud or server/database workflow aggregating acoustic data and ancillary data for modeling and diagnosis/prognosis, together with physical lung models used to predict expected resonant ranges and support frequency selection.
Claims Coverage
The document includes two independent claims: one directed to a method and one directed to a system. Across these claims, the core inventive coverage includes providing processor-controlled acoustic stimuli toward a lung, receiving an acoustic response, determining a spectral shape, using that spectral shape to determine a user-specific signature, and storing the signature in a database.
Spectral shape-driven signature generation for active auscultation
Providing, by a processor in communication with an emitter, a set of signal stimuli to the emitter so that the emitter produces a set of acoustic energy directed into a user's body toward a user's lung; receiving, by the processor, an acoustic energy response from a receiver communicatively coupled to the processor and proximate to the user's body, the acoustic energy response being responsive to the set of acoustic energy directed into the user's body; determining, by the processor, a spectral shape of the acoustic energy response; determining, by the processor, a signature for the user using the spectral shape; and storing, by the processor, the signature in a database.
Processor-based active auscultation system with spectral shape-driven signature storage
Providing a set of signal stimuli to the emitter so that the emitter produces a set of acoustic energy directed into a user's body toward a user's lung; receive an acoustic energy response from a receiver communicatively coupled to the processor and proximate to the user's body, the acoustic energy response being responsive to the set of acoustic energy directed into the user's body; determine a spectral shape of the acoustic energy response; determine a signature for the user using the spectral shape; and store the signature in a database.
Both independent claims cover the same core workflow: processor-controlled acoustic energy directed into a user's lung, proximate receipt of a responsive acoustic energy response, processor determination of a spectral shape from that response, determination of a user signature using the spectral shape, and storage of that signature in a database. Dependent claim refinements referenced in the provided content further focus on resonant frequency, trapped-air volume, and spectral shape determination using relationships across multiple frequencies.
Stated Advantages
Supports diagnosis/prognosis based on lung resonance signatures derived from acoustic energy response spectral features.
Enables estimation of respiratory cycle and trapped-air (air remaining after exhalation) from resonance-based spectral analysis.
Documented Applications
Diagnosis/prognosis workflows using acoustic data and ancillary data aggregated in a server/database environment.
COPD-relevant analysis using lung resonance and trapped-air estimation.
Active auscultation using wearable/patch or housing-based architectures for projecting acoustic energy and analyzing the returned signal.
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