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Publication Number
US-11083403-B1
Publication Date
2021-08-10
Expiration Date
Abstract
A pulmonary health assessment system for use with a handheld electronic device (HED) that includes a casing having a shape adapted to secure the HED with the casing. The casing includes a plurality of electrodes and a circuit board. The electrodes capture data indicative of the pulmonary health of the user. The circuit board includes a microphonic sensor, a diaphragm, a Photoplethysmography (PPG) sensor, an Inertial Measurement Unit (IMU) sensor, and a microcontroller. The microphonic sensor captures pulmonary audio signals indicative of the pulmonary health of the user. The diaphragm enhances the pulmonary audio signals. The PPG sensor measures pulmonary capillary blood flow. The IMU sensor captures seismic and auscultation signals indicative of the pulmonary health of the user. The microcontroller transmits pulmonary health data to the handheld electronic device and a computing device.
Core Innovation
The pulmonary health assessment system is used with a handheld electronic device (HED) and includes a casing having a shape adapted to secure the HED. The casing includes a plurality of ECG electrodes, including a first ECG electrode on an outer surface and a second and a third electrode on sides of the casing to facilitate thumb and fingers placement. The plurality of ECG electrodes are configured to capture data indicative of the pulmonary health of the user.
A circuit board is configured within the casing and electrically connected with the plurality of ECG electrodes and at least one microcontroller. The circuit board includes a microphonic sensor for capturing pulmonary audio signals, a Photoplethysmography (PPG) sensor for measuring pulmonary capillary blood flow, and an Inertial Measurement Unit (IMU) sensor for capturing seismic and auscultation signals. A diaphragm enhances the pulmonary audio signals captured by the microphonic sensor, and the microcontroller transmits pulmonary health data received from the ECG electrodes, the microphonic sensor, the PPG sensor, and the IMU sensor to at least one of the HED and a computing device.
The computing device receives, in one or more temporal windows, representations of data from the IMU sensor, the ECG electrodes, the PPG sensor signals, and the microphonic sensor signals. The computing device detects features from portions of the received representations within each temporal window and identifies patterns in the detected features based on a classification model and a regression model. Using the identified patterns, the computing device calculates a probability of whether the identified patterns correspond to a problem with the pulmonary health of the user, and estimates a severity of a lung disease using the regression model based on indicators detected with the classification model.
Claims Coverage
The only independent claim is clm-00001, which covers an end-to-end pulmonary health assessment system integrating ECG, enhanced pulmonary audio sensing, PPG, and IMU sensing with temporal-window feature extraction and classification/regression models to compute pulmonary health abnormality probability and estimate lung disease presence and severity.
Casing-secured handheld electronic device with multi-electrode ECG placement
A casing having a shape adapted to secure the HED, including a plurality of ECG electrodes configured to capture data indicative of the pulmonary health of the user.
Circuit board with microphonic pulmonary audio sensing, PPG capillary blood flow, IMU seismic and auscultation signals, and diaphragm audio enhancement
A circuit board configured within the casing and electrically connected with the plurality of ECG electrodes and at least one microcontroller, where the circuit board comprises a microphonic sensor, a Photoplethysmography (PPG) sensor, an Inertial Measurement Unit (IMU) sensor, and a diaphragm configured to enhance the pulmonary audio signals.
Transmission of pulmonary health data to a computing device and temporal-window representation processing
The at least one microcontroller transmits pulmonary health data received from the ECG electrodes, the microphonic sensor, the PPG sensor, and the IMU sensor to at least one of the HED and a computing device, and the computing device receives representations of sensor data in one or more temporal windows.
Feature detection and dual-model pattern identification using classification and regression
The computing device detects features from portions of the representations that fall within each temporal window, identifies patterns in the detected features based on a classification model and a regression model, and uses the identified patterns to calculate a probability of whether the identified patterns correspond to a problem with the pulmonary health of the user.
Wireless second handheld electronic device and disease presence with severity estimation
A second handheld electronic device worn by the user is wirelessly connected with the HED, where the second handheld electronic device comprises one or more sensors and a HED wireless transceiver configured to establish communication between the HED and the computing device to transmit pulmonary health data, and the computing device detects indicators of a lung disease based on the classification model and estimates a severity of the lung disease using the regression model.
Independent claim clm-00001 broadly covers a casing-secured HED system that combines ECG electrodes with a circuit board including diaphragm-enhanced pulmonary audio sensing, PPG capillary blood flow sensing, and IMU seismic/auscultation sensing, and it uses temporal-window feature detection with a classification model and a regression model to compute a pulmonary health abnormality probability and estimate lung disease severity based on detected indicators.
Stated Advantages
Calculates a probability of whether identified patterns correspond to a problem with the pulmonary health of the user.
Estimates a severity of a lung disease using indicators detected with a classification model.
Documented Applications
Pulmonary health assessment for detecting indicators of a lung disease, including lung disease presence and lung disease severity estimation.
Disease-related examples include SARS and lung cancer, and severity estimation based on the regression model.
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