Related Experiment Video
Updated: Jan 20, 2026
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
Towards the classification of heart sounds based on convolutional deep neural network
Fatih Demir1, Abdulkadir Şengür1, Varun Bajaj2
11Electrical and Electronics Engineering, Technology Faculty, Firat University, Elazig, Turkey.
Background And Objective:
Heart sound contains various important quantities that help early detection of heart diseases. Many methods have been proposed so far where various signal-processing techniques have been used on heart sounds for heart disease detection.
Methods:
In this paper, a methodology is introduced for heart disease detection based on heart sounds. The proposed method employs three successive stages, such as spectrogram generation, deep feature extraction, and classification. In the spectrogram generation stage, the heart sounds are converted to spectrogram images by using time-frequency transformation.
Results:
The deep features are extracted from three different pre-trained convolutional neural network models such as AlexNet, VGG16, and VGG19. Support vector machine classifier is used in the third stage of the proposed method. The proposed method is evaluated on two datasets, which are taken from The Classifying Heart Sounds Challenge.
Conclusions:
The obtained results are compared with some of the existing methods. The comparisons show that the proposed method outperformed.
More Related Videos
03:31Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
Published on: December 15, 2023
06:22Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
Published on: September 19, 2025
Related Concept Videos
01:15Heart Sounds
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
01:17Convolution Properties II
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
01:30Heart Failure IV: Classification and Diagnostic Evaluation
01:20Convolution Properties I
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
01:17Soundness of Cement
01:12Korotkoff Sounds
During blood pressure assessment, inflating the cuff 30 millimeters of mercury above the patient's systolic blood pressure...