Deep learning based cardiovascular disease diagnosis system from heartbeat sound
TL;DR: In this article, the authors have classified PCG signals into five classes namely extra systole, extra heart sound, artifacts, normal heartbeat and murmur, and they have achieved an average accuracy of 94% while doing the classification of PCG sound.
Abstract: During each cardiac cycle of heart, vibrations creates sound and murmur. When these sound and murmur wave is represented graphically then it is called phonocardiogram (PCG). Digital stethoscope is used to record the audio wave signals generated due to heart vibration. Audio waves recorded through digital stethoscope can be used to fetch information like tone, quality, intensity, frequency, heart rate etc. Based on the heart condition, this information will be different for different people and can be used to predict the status of heart at early stage in non-invasive manner. In this research work, by using deep learning models, authors have classified PCG signals into 5 classes namely extra systole, extra heart sound, artifacts, normal heartbeat and murmur. Initially spectrograms in the form of images are extracted from PCG sound and feed into Regularized Convolutional Neural Network. From the simulation environment designed in python, it has found that proposed model has shown the average accuracy of 94% while doing the classification of PCG sound in five classes.
Citations
More filters
19 Oct 2022
TL;DR: In this article , a deep learning model is used to predict whether there is a likelihood of a potential condition in a user's medical data retrieved from the user to diagnose heart failure or pulmonary conditions.
Abstract: In the context of addressing the problem of people who do not undergo a diagnosis of heart failure due to pulmonary conditions on time, a solution to this problem would allow early preventive detection to avoid the development of severe disease efficiently. Our approach employs the use of medical data retrieved from the user to determine and predict whether there is a likelihood of a potential condition. To solve this problem, according to a users medical measurement history, a deep learning model can be implemented to determine a preventive diagnosis or otherwise to follow up on an already detected condition. By posing the problem as a classification task, it can be taken advantage of a deep learning model focused on heart failure or pulmonary conditions to make a preliminary diagnosis and determine if there are signs of any symptomatology.
References
More filters
TL;DR: The authors find that it can be regarded as the generalized frequency band energy (FBE) and is hence useful, which results in the FBE-MFCC, and propose a better analysis, namely the auto-regressive analysis, on the frame energy, which outperform its 1st and/or 2nd order differential derivatives.
Abstract: The performance of the Mel-Frequency Cepstrum Coefficients (MFCC) may be affected by (1) the number of filters, (2) the shape of filters, (3) the way in which filters are spaced, and (4) the way in which the power spectrum is warped. In this paper, several comparison experiments are done to find a best implementation. The traditional MFCC calculation excludes the 0th coefficient for the reason that it is regarded as somewhat unreliable. According to the analysis and experiments, the authors find that it can be regarded as the generalized frequency band energy (FBE) and is hence useful, which results in the FBE-MFCC. The authors also propose a better analysis, namely the auto-regressive analysis, on the frame energy, which outperform its 1st and/or 2nd order differential derivatives. Experiments with the “863” Speech Database show that, compared with the traditional MFCC with its corresponding auto-regressive analysis coefficients, the FBE-MFCC and the frame energy with their corresponding auto-regressive analysis coefficients form the best combination, reducing the Chinese syllable error rate (CSER) by about 10%, while the FBE-MFCC with the corresponding auto-regressive analysis coefficients reduces CSER by 2.5%. Comparison experiments are also done with a quite casual Chinese speech database, named Chinese Annotated Spontaneous Speech (CASS) corpus. The FBE-MFCC can reduce the error rate by about 2.9% on an average.
455 citations
TL;DR: The methodology discussed in this paper can be used to diagnose heart disorders in patients up to 97% accuracy and is improved, automatic classification algorithm for cardiac disorder by heart sound signal.
Abstract: Cardiac disorders are critical and must be diagnosed in the early stage using routine auscultation examination with high precision. Cardiac auscultation is a technique to analyze and listen to heart sound using electronic stethoscope, an electronic stethoscope is a device which provides the digital recording of the heart sound called phonocardiogram (PCG). This PCG signal carries useful information about the functionality and status of the heart and hence several signal processing and machine learning technique can be applied to study and diagnose heart disorders. Based on PCG signal, the heart sound signal can be classified to two main categories i.e., normal and abnormal categories. We have created database of 5 categories of heart sound signal (PCG signals) from various sources which contains one normal and 4 are abnormal categories. This study proposes an improved, automatic classification algorithm for cardiac disorder by heart sound signal. We extract features from phonocardiogram signal and then process those features using machine learning techniques for classification. In features extraction, we have used Mel Frequency Cepstral Coefficient (MFCCs) and Discrete Wavelets Transform (DWT) features from the heart sound signal, and for learning and classification we have used support vector machine (SVM), deep neural network (DNN) and centroid displacement based k nearest neighbor. To improve the results and classification accuracy, we have combined MFCCs and DWT features for training and classification using SVM and DWT. From our experiments it has been clear that results can be greatly improved when Mel Frequency Cepstral Coefficient and Discrete Wavelets Transform features are fused together and used for classification via support vector machine, deep neural network and k-neareast neighbor(KNN). The methodology discussed in this paper can be used to diagnose heart disorders in patients up to 97% accuracy. The code and dataset can be accessed at “https://github.com/yaseen21khan/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-/blob/master/README.md”.
177 citations
14 Sep 2016
TL;DR: The PhysioNet/Computing in Cardiology (CinC) Challenge 2016 addresses the issue of a large and open database of heart sound recordings by assembling the largest public heart sound database, aggregated from eight sources obtained by seven independent research groups around the world.
Abstract: In the past few decades heart sound signals (i.e., phono-cardiograms or PCGs) have been widely studied. Automated heart sound segmentation and classification techniques have the potential to screen for pathologies in a variety of clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of a large and open database of heart sound recordings. The PhysioNet/Computing in Cardiology (CinC) Challenge 2016 addresses this issue by assembling the largest public heart sound database, aggregated from eight sources obtained by seven independent research groups around the world. The database includes 4,430 recordings taken from 1,072 subjects, totalling 233,512 heart sounds collected from both healthy subjects and patients with a variety of conditions such as heart valve disease and coronary artery disease. These recordings were collected using heterogeneous equipment in both clinical and nonclinical (such as in-home visits). The length of recording varied from several seconds to several minutes. Additional data provided include subject demographics (age and gender), recording information (number per patient, body location, and length of recording), synchronously recorded signals (such as ECG), sampling frequency and sensor type used. Participants were asked to classify recordings as normal, abnormal, or not possible to evaluate (noisy/uncertain). The overall score for an entry was based on a weighted sensitivity and specificity score with respect to manual expert annotations. A brief description of a baseline classification method is provided, including a description of open source code, which has been provided in association with the Challenge. The open source code provided a score of 0.71 (Se=0.65 Sp=0.76). During the official phase of the competition, a total of 48 teams submitted 348 open source entries, with a highest score of 0.86 (Se=0.94 Sp=0.78).
162 citations
TL;DR: This paper proposes a new heart sound classification method based on improved Mel-frequency cepstrum coefficient (MFCC) features and convolutional recurrent neural networks and comprehensive studies on the performance of different network parameters and different network connection strategies are presented.
142 citations
TL;DR: It is shown how the ROC curve is an alternative way to present risk distributions of diseased and non-diseased individuals and how the shape of the R OC curve informs about the overlap of the risk distributions.
Abstract: The area under the receiver operating characteristic (ROC) curve (AUC) is commonly used for assessing the discriminative ability of prediction models even though the measure is criticized for being clinically irrelevant and lacking an intuitive interpretation. Every tutorial explains how the coordinates of the ROC curve are obtained from the risk distributions of diseased and non-diseased individuals, but it has not become common sense that therewith the ROC plot is just another way of presenting these risk distributions. We show how the ROC curve is an alternative way to present risk distributions of diseased and non-diseased individuals and how the shape of the ROC curve informs about the overlap of the risk distributions. For example, ROC curves are rounded when the prediction model included variables with similar effect on disease risk and have an angle when, for example, one binary risk factor has a stronger effect; and ROC curves are stepped rather than smooth when the sample size or incidence is low, when the prediction model is based on a relatively small set of categorical predictors. This alternative perspective on the ROC plot invalidates most purported limitations of the AUC and attributes others to the underlying risk distributions. AUC is a measure of the discriminative ability of prediction models. The assessment of prediction models should be supplemented with other metrics to assess their clinical utility.
130 citations