International Research Journal of Engineering and Technology (IRJET) Volume: 10 Issue: 08 | Aug 2023
e-ISSN: 2395-0056 p-ISSN: 2395-0072
www.irjet.net
PHONOCARDIOGRAM HEART SOUND SIGNAL CLASSIFICATION USING DEEP LEARNING TECHNIQUE Nishant Sanjay Indalkar 1, Shreyas Shrikant Harnale2, Prof. Namrata3 1Computer Engineering, Pimpri Chinchwad College of Engineering Pune, India 2 Computer Engineering, Pimpri Chinchwad College of Engineering Pune, India 3Computer Engineering, Pimpri Chinchwad College of Engineering Pune, India
--------------------------------------------------------------------------***--------------------------------------------------------------------heart disease. This research paper proposes heart signal Abstract— Most common reason for human mortality in today’s world that causes almost one-third of deaths is especially due to heart disease. It has become the most common disease where in every 5 people 4 of them are dealing with this disease. The common symptoms of heart diseases are breath shortness, loss of appetite, irregular heartbeat, chest pain. Identifying the disease at early stage increases the chances of survival of the patient and there are numerous ways of detecting heart disease at an early stage. For the sake of helping medical practitioners, a range of machine learning & deep learning techniques were proposed to automatically examine phonocardiogram signals to aid in the preliminary detection of several kinds of heart diseases. The purpose of this paper is to provide an accurate cardiovascular prediction model based on supervised machine learning technique relayed on recurrent neural network (RNN) and convolutional neural network (CNN). The model is evaluated on heart sound signal dataset, which has been gathered from two sources: 1. From general public via I Stethoscope pro iPhone app. 2. From clinical trials in the hospitals. Experimental results have shown that number of epochs and batch size of the training data for validation metrices have direct impact on the training and validation accuracies. With the proposed model we have achieved 91% accuracy.
Keywords— CNN, RNN, Epochs, Deep Learning I.
INTRODUCTION
Heart Disease is an illness that causes complications in human being such as heart failure, liver failure, stroke. Heart disease is mainly caused due to consumption of alcohol, depression, diabetes, hypertension [2]. Physical inactivity increase of cholesterol in body often causes heart to get weaken. There are several types of heart diseases such as Arrhythmia, congestive heart failure, stroke, coronary artery disease and many more. Identification of cardiovascular disease can be done by using the widely known auscultation techniques based on echocardiogram, phonocardiogram, or stethoscope. Machine learning and deep learning is a widely used method for processing huge data in the healthcare domain. Researchers apply several different deep learning and machine learning techniques to analyze huge complex medical data, to predict the abnormality in © 2023, IRJET
|
Impact Factor value: 8.226
|
analysis technique based on TFD (Time Frequency Distribution) analysis and MFCC (Mel Frequency Cestrum Coefficient). Time Frequency Distribution represents the heart sound signals in form of time vs frequency simultaneously and the MFCC determines a sound signal in the form of frequency coefficient corresponding to the Mel filter scale [3]. A quite Helpful method was used to improve the accuracy of heart disease model which is able to predict the chances of heart attack in any individual. Here, we present a Deep Learning technique based on Convolutional Auto-Encoder (CAE), to compress and reconstruct the vital signs in general and phonocardiogram (PCG) signals specifically with minimum distortion [4]. The results portray that the highest accuracy was achieved with convolution neural network with accuracy 90.60% with minimum loss and accuracy achieved through recurrent network was about 67% with minimum loss percentage.
II. LITERATURE REVIEW Ryu et al. [5] Studied about cardia diagnostic model using CNN. Phonocardiograms(PCG) were used in this model. It can predict whether a heart sound recording is normal or not. First CNN is trained to extract features and build a classification function. The CNN is trained by an algorithm called back propagation algorithm. The model then concludes between normal and abnormal labels. Tang et al. [6] Combined two methods i.e. deep learning and feature engineering algorithms for classification of heart sound into normal and abnormal. Then features were extracted form 8 domains. Then, these features were fed into convolution neural network(CNN) in such a way that the fully connected layers of neural network replaces the global average pooling layer to avoid over fitting and to obtain global information. The accuracy, sensitivity and specificity observed on the PhysioNet data set were 86.8%, 87%, 86.6% and 72.1% respectively. Jia Xin et al. [7] Proposed a system in which heart sounds are segmented and converted using two classification method: simple softmax regression network (SMR) and CNN. Features were determined automatically through training of the neural network model instead of using supervised machine learning features. After working on both Softmax regression and Convolutional neural network(CNN) they found out CNN gave the highest accuracy. The accuracy achieved through CNN model is 93%. ISO 9001:2008 Certified Journal
|
Page 842