Lung sound examination learning system

Abstract

OCR 06WT The lung sound learning system consists of a lung sound learning system for learning normal lung sounds and... Abnormal lung sounds from the lung sound database, a lung sound learning test system for assessing sound learning outcomes. The lungs, both pre-test and post-test data, were selected based on user-generated test results. It is then sent for validation by comparing it to the lung sound database in the lung sound learning system. The system will then... The test results are displayed, and the lung sound classification system is connected to the lung sound learning system and the testing system. Learn lung sounds to analyze lung sound patterns using Artificial Intelligence. Lung sound data can be examined and learned, and analyzed using electronic devices equipped with sound classification systems. Lung and cloud systems then store the data in the cloud or send it to the lung sound database of the learning system. Lung sounds: To allow users to learn lung sounds and test their knowledge of lung sounds.

Claims

OCR 06WT 1. The lung sound learning system consists of a lung sound learning system (1) for users to learn lung sounds. Normal (11) and abnormal lung sounds (12). Lung sound learning test system (2) for users to test their results. Learn lung sounds in both the pre-test (20) and post-test (21) through the audio learning system. The lungs (1) and the lung sound recognition system (3) are connected to the lung sound learning system (1) and the learning testing system. Lung sounds (2) for analyzing patterns of lung sounds and storing them in the lung sound database (100) and system. Cloud (33) to allow users to learn lung sounds and test their lung sound learning, with specific characteristics: The lung sound learning system (1) consists of a lung sound database (100) containing normal lung sound data (11). And abnormal lung sounds (12) for users to learn each type of lung sound on their own by allowing the user to choose. The system will then display information about the selected lung sound pattern. Where the lung sound learning system (1) provides users with learning through electronic devices, devices with technology. Virtual reality (VR) or devices with augmented reality (AR) technology. One or more of the following are combined. The lung sound learning test system (2) consists of pre-tests (20) and part 1, theory (201). and Part 2 Practice (202) and the post-test (21), both Part 1 Theory (211) and Part 2 Practice (212). This allows users to test their lung sound learning progress, with users able to select the test format from the chapter. Pre-test (20) or post-test (21) and then take the test. The data selected from the test User-submitted tests are then validated by comparing them to a database of lung sounds (100). The system will display the test results for the pre-test (20), including the scores obtained (203) and each answer. Question: True / False (204) or display the test results for the post-test (21) including the scores obtained (213). And each answer: True / False (214) The lung sound classification system (3) consists of a lung sound receiving device (30) for receiving lung sound signals. (200) from the patient or person who needs to have lung sound measured and transmit the lung sound signal (200) to the device. Electronics (31) which has a sound signal preparation unit for processing (32) in preparation of lung sound signals (321). To record lung sounds and classify the types of lung sounds (400) within an electronic device (31) or transmit them. The prepared lung sound signals (300) from the sound preparation unit are processed (32) to the cloud. (33) To record lung sounds and classify types of lung sounds (400) This is done by processing and classifying the types of lung sounds by comparing the lung sound signals that have been processed. Prepare (300) with lung sound signals contained in the lung sound database (100) using artificial intelligence. (Intelligence) in examining and learning lung sound data by creating a model of the lung sound signal using learning. The machine calculates the probability of the type of lung sound, which consists of normal lung sounds (11) and Lung abnormalities (12) by taking the lung sound data obtained from previous measurements, both normal and abnormal, and using them A lung sound classification model was developed to differentiate types of lung sounds, and this model was then incorporated into the device. Electronic (31) or cloud (33), either one or both combined, for use. The measured lung sound data were classified, and the prepared lung sound signal data (300) were then compared with Lung sound signal model to calculate the probability of the type of lung sound (10) before submitting the type information. Lung sounds (10) are displayed on an electronic device screen (31) or the lung sound data (401) is saved to a database. Lung sounds (100), cloud system (33), or electronic devices (31), either or both. Assembled together 2. A system for learning lung sounds according to claim 1, where the lung sound learning system (1) is for normal lung sounds. (11) and abnormal lung sounds (12) are recorded with normal lung sound data (111) and abnormal lung sound data (121). It includes at least lung sound names, waveforms, lung sound recording files, and descriptions of each lung sound.

3. Lung sound learning system, in accordance with claim 1, where the lung sound learning system (1) provides the user with the option to select Learn by selecting the signal name or viewing the sound waveform of normal lung sound data (11) or abnormal lung sound data. (12) Then set the system to display either the lung sound recording file or the description of each lung sound, whichever is better. Or both combined, or choose to listen to the lung sound recording file of normal lung sound data (11) or sound data. Lung abnormalities (12) and then assign the system to display the name of the signal, sound waveform or description of each lung sound pattern. One or more of the following may be combined.

4. The lung sound learning system, according to claim 1, where the lung sound learning system (2) for chapter Pre-test (20), Part 2 Practice (202) and Post-test (21), Part 2 Practice (212) are provided. Sound waveforms are displayed for users to view and answer the question of whether the sound waveform represents lung function. What type? The selected data is then sent for validation by comparing it to the lung sound database (100). The system will then display the test results for each answer: True / False (204) for the pre-test (20) or Each answer: True / False (214) for the post-test (21) with results showing the name of the lungs and the lung sounds. That's correct, based on the lung audio recording.

5. A lung sound learning system based on claim 1, in which the lung sound listening device (30) can be selected from a microphone. A high-sensitivity digital microphone, a piezoelectric microphone, or a sound sensor, one or more of them. One thing that is a combination of elements.

6. A learning system for lung sound examination under claim 1, where an electronic device (31) can be selected from a tablet. Or a smartphone, either one or more of them combined.

7. Learning system for lung sound examination according to claim 1, in which the sound signal preparation unit for processing (32) organizes Prepare the lung sound signal (321) which consists of recording the following commands to prepare the lung sound signal: A set of digital filter commands for reducing noise using a digital band-pass filter. Butterworth type band pass filter, frequency range 80-1,000 Hz. A set of instructions for data normalization, which normalizes the signal to a level where it is simplified by the system. The same period is between -1 and 1. A set of instructions for selecting a breathing interval, specifying the breathing interval for signal cutoff. A short signal, 6 seconds long. And a set of feature extraction commands for extracting distinctive features from lung sound signals using a selectable method. Use the Mel frequency cepstrum coefficient (MFCC) on the Mel scale and find the mean. (Mean) or the lung sound signal is used to calculate the square root of the mean energy (RMS) and entropy.

8. The lung sound examination learning system, according to claim 1, where the lung sound classification system (3) provides for classifying the types of Lung sounds (10) composed of normal lung sounds (11) that can be selected from vesicular breath sounds. Is it a bronchial breath sound or a broncho-vesicular breath sound? One or more abnormal lung sounds (12) that can be selected from the sounds of air passing through fluid, discontinuous sounds heard during breathing. Crackles / rales (crepitation), a rough, vibrating sound caused by narrowing of the upper airway (stridor), and other sounds. A high-pitched sound caused by breathing through airways that are narrower than normal (wheezing), like there's mucus stuck in the airways. (rhonchi) or pleural friction rub, either one.

9. A learning system for lung sound detection, under claim 1, which involves artificial intelligence for classifying types of lung sounds. (10) Available for selection from Artificial neural networks (ANN) which use mathematical models. Each node in the layer is classified to calculate the probability of the type of lung sound (10) or support method. A support vector machine (SVM) uses a method of creating boundaries between classes to classify data. Calculate the probability of the type of lung sound (10) or use a convolutional neural network. A neural network (CNN) that uses the convolution layer to extract features from the data in order to... Can learn the characteristics of lung sound type data (10) efficiently or with the help of artificial intelligence. Genetic AI is created using processed Large Language Models (LLMs). Deep learning occurs through training with a vast amount of lung sound data. Classify the data for classifying the type of lung sounds (10) by one or more combinations. For use in training a set of commands to learn information from the lung sound listening device (30) compared with the information in the sound database. Lungs (100) 10. A lung sound detection learning system, based on claim 1, which includes an assisted location and Record the position of the stethoscope on the body (600) connected to the electronic device (31) for identification. The listening position (601), lung sound signals (200) received, or personal data (500) are provided for input into Electronic devices (31) and data recording, either or both combined. Where Personal Information (500) contains at least Name-Surname, ID Card Number, Age, Gender, Name of Disease

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