1,986 research outputs found
Deep wavelets for heart sound classification
A paper in ISPACS 201
A Method for Detecting Murmurous Heart Sounds based on Self-similar Properties
A heart murmur is an atypical sound produced by the flow of blood through the
heart. It can be a sign of a serious heart condition, so detecting heart
murmurs is critical for identifying and managing cardiovascular diseases.
However, current methods for identifying murmurous heart sounds do not fully
utilize the valuable insights that can be gained by exploring intrinsic
properties of heart sound signals. To address this issue, this study proposes a
new discriminatory set of multiscale features based on the self-similarity and
complexity properties of heart sounds, as derived in the wavelet domain.
Self-similarity is characterized by assessing fractal behaviors, while
complexity is explored by calculating wavelet entropy. We evaluated the
diagnostic performance of these proposed features for detecting murmurs using a
set of standard classifiers. When applied to a publicly available heart sound
dataset, our proposed wavelet-based multiscale features achieved comparable
performance to existing methods with fewer features. This suggests that
self-similarity and complexity properties in heart sounds could be potential
biomarkers for improving the accuracy of murmur detection
Deep Neural Networks for the Recognition and Classification of Heart Murmurs Using Neuromorphic Auditory Sensors
Auscultation is one of the most used techniques for
detecting cardiovascular diseases, which is one of the main causes
of death in the world. Heart murmurs are the most common abnormal
finding when a patient visits the physician for auscultation.
These heart sounds can either be innocent, which are harmless, or
abnormal, which may be a sign of a more serious heart condition.
However, the accuracy rate of primary care physicians and expert
cardiologists when auscultating is not good enough to avoid most
of both type-I (healthy patients are sent for echocardiogram) and
type-II (pathological patients are sent home without medication or
treatment) errors made. In this paper, the authors present a novel
convolutional neural network based tool for classifying between
healthy people and pathological patients using a neuromorphic
auditory sensor for FPGA that is able to decompose the audio into
frequency bands in real time. For this purpose, different networks
have been trained with the heart murmur information contained in
heart sound recordings obtained from nine different heart sound
databases sourced from multiple research groups. These samples
are segmented and preprocessed using the neuromorphic auditory
sensor to decompose their audio information into frequency
bands and, after that, sonogram images with the same size are
generated. These images have been used to train and test different
convolutional neural network architectures. The best results
have been obtained with a modified version of the AlexNet model,
achieving 97% accuracy (specificity: 95.12%, sensitivity: 93.20%,
PhysioNet/CinC Challenge 2016 score: 0.9416). This tool could aid
cardiologists and primary care physicians in the auscultation process,
improving the decision making task and reducing type-I and
type-II errors.Ministerio de Economía y Competitividad TEC2016-77785-
Classification of heart disease based on PCG signal using CNN
Cardiovascular disease is the leading cause of death in the world, so early detection of heart conditions is very important. Detection related to cardiovascular disease can be conducted through the detection of heart signals interference, one of which is called phonocardiography. This study aims to classify heart disease based on phonocardiogram (PCG) signals using the convolutional neural networks (CNN). The study was initiated with signal preprocessing by cutting and normalizing the signal, followed by a continuous wavelet transformation process using a mother wavelet analytic morlet. The decomposition results are visualized using a scalogram, then the results are used as CNN input. In this study, the PCG signals used were classified into normal, angina pectoris (AP), congestive heart failure (CHF), and hypertensive heart disease (HHD). The total data used, classified into 80 training data and 20 testing data. The obtained model shows the level of accuracy, sensitivity, and diagnostic specificity of 100%, 100%, and 100% for training data, respectively, while the prediction results for testing data indicate the level of accuracy, sensitivity, and specificity of 85%, 80%, and 100%, respectively. This result proved to be better than the mother wavelet or other classifier methods, then the model was deployed into the graphical user interface (GUI)
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