Classification of heart sounds using an artificial neural network
TL;DR: It is observed that HSs of patients are successfully classified by the GAL network compared to the LVQ network.
About: This article is published in Pattern Recognition Letters.The article was published on 2003-01-01. It has received 155 citations till now. The article focuses on the topics: Learning vector quantization & Wavelet transform.
Citations
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01 Jan 2007
TL;DR: A novel method for segmentation of heart sounds into single cardiac cycle (S"1-Systole-S"2-Diastole) using homomorphic filtering and K-means clustering is presented and could be a potential solution for automatic analysis of HSs.
Abstract: A novel method for segmentation of heart sounds (HSs) into single cardiac cycle (S"1-Systole-S"2-Diastole) using homomorphic filtering and K-means clustering is presented. Feature vectors were formed after segmentation by using Daubechies-2 wavelet detail coefficients at the second decomposition level. These feature vectors were then used as input to the neural networks. Grow and Learn (GAL) and Multilayer perceptron-Backpropagation (MLP-BP) neural networks were used for classification of three different HSs (Normal, Systolic murmur and Diastolic murmur). It was observed that the classification performance of GAL was similar to MLP-BP. However, the training and testing times of GAL were lower as compared to MLP-BP. The proposed framework could be a potential solution for automatic analysis of HSs that may be implemented in real time for classification of HSs.
238 citations
Cites background or methods from "Classification of heart sounds usin..."
...The network has a dynamic structure; nodes and their connections (weights) are added during learning when necessary [16,20,21]....
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...GAL has advantages of fast training, implementation simplicity, and better performance over MLP-BP [16]....
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...The performance of GAL as observed in [16,20] has advantages of fast training and better performance over MLP-BP....
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...Wavelet based feature extraction was applied as in [16] to obtain the features of the segmented PCG signals....
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...The signal formed by wavelet detail coefficients at the second decomposition level obtained using Daubechies-2 wavelets as in [16] was split into 32 subwindows that each contains 128 discrete data....
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05 Sep 2012
TL;DR: SpiroSmart, a low-cost mobile phone application that performs spirometry sensing using the built-in microphone, is presented and it is shown that pulmonologists can use SpiroSmart to diagnose varying degrees of obstructive lung ailments.
Abstract: Home spirometry is gaining acceptance in the medical community because of its ability to detect pulmonary exacerbations and improve outcomes of chronic lung ailments. However, cost and usability are significant barriers to its widespread adoption. To this end, we present SpiroSmart, a low-cost mobile phone application that performs spirometry sensing using the built-in microphone. We evaluate SpiroSmart on 52 subjects, showing that the mean error when compared to a clinical spirometer is 5.1% for common measures of lung function. Finally, we show that pulmonologists can use SpiroSmart to diagnose varying degrees of obstructive lung ailments.
221 citations
Cites background from "Classification of heart sounds usin..."
...Many systems exist that extract heart rate using a mobile phone [24,40] and, with higher-end microphones, some systems can actually be used to detect certain audible manifestations of high blood pressure referred to as Korotkoff sounds [1]....
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TL;DR: The paper provides the technological and medical basis for the development and commercialization of a real-time integrated heart sound detection, acquisition and quantification system.
Abstract: Most heart diseases are associated with and reflected by the sounds that the heart produces. Heart auscultation, defined as
listening to the heart sound, has been a very important method for the early diagnosis of cardiac dysfunction. Traditional auscultation requires substantial clinical experience and good listening skills. The emergence of the electronic stethoscope has paved the way for a new field of computer-aided auscultation. This article provides an in-depth study of (1) the electronic stethoscope technology, and (2) the methodology for diagnosis of cardiac disorders based on computer-aided auscultation. The paper is based on a comprehensive review of (1) literature articles, (2) market (state-of-the-art) products, and (3) smartphone stethoscope apps. It covers in depth every key component of the computer-aided system with electronic stethoscope, from sensor design, front-end circuitry, denoising algorithm, heart sound segmentation, to the final machine learning techniques. Our intent is to provide an informative and illustrative presentation of the electronic stethoscope, which is valuable and beneficial to academics, researchers and engineers in the technical field, as well as to medical professionals to facilitate its use clinically. The paper provides the technological and medical basis for the development and commercialization of a real-time integrated heart sound detection, acquisition and quantification system.
169 citations
TL;DR: A new method for diagnosis of CAD using tunable-Q wavelet transform (TQWT) based features extracted from heart rate signals is presented and a novel CAD Risk index is developed using significant features to discriminate the two classes using a single number.
Abstract: Coronary artery disease (CAD) is the narrowing of coronary arteries leading to inadequate supply of nutrients and oxygen to the heart muscles. Over time, the condition can weaken the heart muscles and may lead to heart failure, arrhythmias and even sudden cardiac death. Hence, the early diagnosis of CAD can save life and prevent the risk of stroke. Electrocardiogram (ECG) depicts the state of the heart and can be used to detect the CAD. Small changes in the ECG signal indicate a particular disease. It is very difficult to decipher these minute changes in the ECG signal, as it is prone to artifacts and noise. Hence, we detect the R peaks from the ECG and use heart rate signals for our analysis. The manual inspection of the heart rate signals is time consuming, taxing and prone to errors due to fatigue. Hence, a decision support system independent of human intervention can yield accurate repeatable results. In this paper, we present a new method for diagnosis of CAD using tunable-Q wavelet transform (TQWT) based features extracted from heart rate signals. The heart rate signals are decomposed into various sub-bands using TQWT for better diagnostic feature extraction. The nonlinear feature called centered correntropy ( CC ) is computed on decomposed detail sub-band. Then the principal component analysis (PCA) is performed on these CC to transform the number of features. These clinically significant features are subjected to least squares support vector machine (LS-SVM) with different kernel functions for automated diagnosis. The experimental results demonstrate better classification accuracy, sensitivity, specificity and Matthews correlation coefficient using Morlet wavelet kernel function with optimized kernel and regularization parameters. Also, we have developed a novel CAD Risk index using significant features to discriminate the two classes using a single number. Our proposed methodology is more suitable in classification of normal and CAD heart rate signals and can aid the clinicians while screening the CAD patients.
151 citations
TL;DR: This study focuses on the first (S1) and second (S2) heart sound recognition based only on acoustic characteristics; the assumptions of the individual durations of S1 and S2 and time intervals of S2-S1 are not involved in the recognition process.
Abstract: Objective : This study focuses on the first (S1) and second (S2) heart sound recognition based only on acoustic characteristics; the assumptions of the individual durations of S1 and S2 and time intervals of S1–S2 and S2–S1 are not involved in the recognition process. The main objective is to investigate whether reliable S1 and S2 recognition performance can still be attained under situations where the duration and interval information might not be accessible. Methods : A deep neural network (DNN) method is proposed for recognizing S1 and S2 heart sounds. In the proposed method, heart sound signals are first converted into a sequence of Mel-frequency cepstral coefficients (MFCCs). The K-means algorithm is applied to cluster MFCC features into two groups to refine their representation and discriminative capability. The refined features are then fed to a DNN classifier to perform S1 and S2 recognition. We conducted experiments using actual heart sound signals recorded using an electronic stethoscope. Precision, recall, F-measure, and accuracy are used as the evaluation metrics. Results : The proposed DNN-based method can achieve high precision, recall, and F-measure scores with more than 91% accuracy rate. Conclusion : The DNN classifier provides higher evaluation scores compared with other well-known pattern classification methods. Significance : The proposed DNN-based method can achieve reliable S1 and S2 recognition performance based on acoustic characteristics without using an ECG reference or incorporating the assumptions of the individual durations of S1 and S2 and time intervals of S1–S2 and S2–S1.
149 citations
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...classification performance [12], [13]....
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...heart sound classification system [12]....
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TL;DR: It is shown that the dynamics of the reference (weight) vectors during the input-driven adaptation procedure are determined by the gradient of an energy function whose shape can be modulated through a neighborhood determining parameter and resemble the dynamicsof Brownian particles moving in a potential determined by a data point density.
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