4/6/2024 0 Comments Lung sounds audioThe lung sounds are classified by the Back Propagation (BP) neural network. Lung sounds are classified into six categories by using an artificial neural network in. Lung sound signals are decomposed into the frequency subbands using wavelet transform and a set of statistical features are extracted from the subbands to represent the distribution of wavelet coefficients. The performance of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers in the diagnosis of respiratory pathologies have been compared by using respiratory sounds from the R.A.L.E database in. Mel Frequency Cepstral Coefficients (MFCC) are extracted from the pre-processed pulmonary acoustic signals. It could effectively recognize the polyphonic lung sounds and the sharp lung sounds. Wavelet decomposition is applied to each t–f image representation of the EEG signals resulting in Diagonal (D), Vertical (V), and Horizontal (H) components which are stored as images and are employed for feature extraction in. Features are extracted using a discrete wavelet transform and a Decision Tree Classifier is used for early predictions on symptom-based COPD exacerbations using Artificial Intelligence. Moreover, it can predict lung diseases like asthma, Chronic Obstructive Pulmonary Disorder (COPD), and health status. Computerized recorded lung sounds can be analyzed using time series and may offer an approach to the diagnosis via the recognition model. Electronic stethoscopes are also used with computer-aided auscultation programs to analyze the recorded heart sounds pathological or innocent heart murmurs. Unlike acoustic stethoscopes, which are all based on the same physics, transducers in electronic stethoscopes vary widely. Electronic stethoscopes require the conversion of acoustic sound waves to electrical signals which can then be amplified and processed for optimal listening. Currently, several companies offer electronic stethoscopes. However, amplification of stethoscope contact artifacts and component cutoffs (frequency response thresholds of electronic stethoscope microphones, pre-amps, amps, and speakers) limit electronically amplified stethoscopes’ overall utility by amplifying mid-range sounds, while simultaneously attenuating high- and low- frequency range sounds. In recent years, an electronic stethoscope (or stethoscope) overcomes low sound levels by electronically amplifying body sounds. The problem with acoustic stethoscopes is that the sound level is extremely low. Acoustic stethoscopes operate on the transmission of sound from the chest piece, via air-filled hollow tubes, to the listener’s ears. Therefore, auscultation results are affected by the external environment as well as the doctor’s medical experience and hearing condition. To optimize the effectiveness of auscultation the surroundings should be Quiet, Warm, and with Appropriate lighting. Auscultation serves as a quick and inexpensive way for the modern-day physician to infer a variety of disease states about the cardiovascular, respiratory, and gastrointestinal systems, which allows for streamlined diagnoses and management. Using a stethoscope, the doctor may hear normal breathing sounds, decreased or absent breath sounds, and abnormal breath sounds. The lung sounds are best heard with a stethoscope. The experimental results show that the proposed algorithm improves the recognition accuracy of lung sounds and the recognition accuracy of respiratory diseases.īreath sounds are the noises produced by the structures of the lungs during breathing. While fine-tuning of the parameter of VGGish is frozen which successfully improves the model. The multi-layer BiGRU stack is used to enhance the feature value and retain the model. The target model is built with the same structure as the source model which is the VGGish model and parameter transfer is done from the source model to the target model. A lung Sound Recognition Algorithm Based on VGGish-Stacked BiGRU is used as a feature extractor which is a pre-trained model used for transfer learning. To solve the problem, a lung sounds recognition algorithm based on VGGish- stacked BiGRU is proposed which combines the VGGish network with the stacked bidirectional gated recurrent unit neural network. The traditional convolutional neural network cannot extract the temporal features of lung sounds. Through advances in Artificial Intelligence, it appears possible for the days of misdiagnosis and treatment of respiratory disease symptoms rather than their root cause to move behind us. Respiratory disease is one of the leading causes of death in the world.
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