Detection and Classification of Cardiovascular Disease from Phonocardiogram using Deep Learning Models
04 Aug 2021-
TL;DR: In this article, the authors proposed deep learning architectures for anomaly detection from heart sounds and achieved an accuracy of 99.1%, 98.2%, 99.4% for CNN, LSTM and 1DCNN-LSTM respectively.
Abstract: Cardiovascular disease (CVD) is one of the prime reason for death in India and across the globe. Rural areas of India suffer from shortage of cardiologist and medical facilities. Hence there is a need for the development of an efficient, automated heart disease detection system that can analyse the phonocardiogram to detect the disease. The paper proposes deep learning architectures for anomaly detection from heart sounds. The work classifies the unsegmented phonocardiograms into five classes, four cardiovascular diseases and normal(N). The detected pathological conditions are mitral valve prolapse (MVP), mitral stenosis (MS), mitral regurgitation (MR) and aortic stenosis (AS). Features are extracted using Mel Frequency Cepstral Coefficient (MFCCs) and learning and classification are performed using deep learning methods such as Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and a combination of 1DCNN and LSTM. A total of 1960 phonocardiogram (PCG) segments are used to develop the models with 392 segments in each class. We have achieved an accuracy of 99.1%, 98.2%, 99.4% for CNN, LSTM and 1DCNN-LSTM respectively.
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TL;DR: In this paper , a hybrid classifier (CNN and SVM) complemented with a voting-based system is used for cycle classification of PCG signal. But the proposed method carries out analysis at cycle as well as signal level.
6 citations
TL;DR: In this article , the authors proposed a hybrid technique composed of high resolution spectrum generation, conversion of spectral contents to Spectrogram and multi-round training, which can be used for diagnosing cardiovascular diseases using Phonocardiography (PCG).
1 citations
29 Dec 2022
TL;DR: In this article , the authors developed a model with 9 years of rescue mission's real-time recorded data to recognize any cardiovascular situation in general, and compared different machine learning(ML) algorithms like Support Vector Machine(SVM), Random Forest(RF), K-nearest neighbor(KNN), Extreme Gradient Boosting(XGB), Logistic Regression(LR), Naive Bayes(NB), and Artificial Neural Network(ANN) were used.
Abstract: Cardiovascular complications are considered as one of the most common and fatal complications to the rescue personnel and require urgent medical intervention to save an emergency patient. Often due to the delay in detecting heart complication in urgent situation, necessary treatment paths cannot be followed which results in high mortality rate. Although the current researches show promising aspects in detecting cardiovascular diseases in early stage, they are focused on detecting only some of specific and common cardiovascular diseases from clinically recorded or long term historical patients’ data. The novel approach followed in this research is: instead of using traditional health data collected in clinical environment, we developed the model with 9 years of rescue mission’s real-time recorded data to recognise any cardiovascular situation in general. To find out the best model, different machine learning(ML) algorithms like Support Vector Machine(SVM), Random Forest(RF), K-nearest neighbour(KNN), Extreme Gradient Boosting(XGB), Logistic Regression(LR), Naive Bayes(NB) and Artificial Neural Network(ANN) were used. From the performance comparison, we concluded that extreme gradient boosting and neural network showed the best performance in terms of all evaluation parameters. Fast inference is the basic requirement for any rescue mission. So an inference time analysis of the ML models and Apache-TVM machine learning compiler was shown to understand their compatibility in real world applications.
1 citations
References
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TL;DR: The cardiovascular physical examination is used commonly as a basis for diagnosis and therapy in chronic heart failure, although the relationship between physical signs, increased ventricular filling pressure, and decreased cardiac output has not been established for this population.
Abstract: The cardiovascular physical examination is used commonly as a basis for diagnosis and therapy in chronic heart failure, although the relationship between physical signs, increased ventricular filling pressure, and decreased cardiac output has not been established for this population. We prospectively compared physical signs with hemodynamic measurements in 50 patients with known chronic heart failure (ejection fraction,.18±.06). Rales, edema, and elevated mean jugular venous pressure were absent in 18 of 43 patients with pulmonary capillary wedge pressures greater than or equal to 22 mm Hg, for which the combination of these signs had 58% sensitivity and 100% specificity. Proportional pulse pressure correlated well with cardiac index (r=.82), and when less than 25% pulse pressure had 91% sensitivity and 83% specificity for a cardiac index less than 2.2 L/min/m2. In chronic heart failure, reliance on physical signs for elevated ventricular filling pressure might result in inadequate therapy. Conversely, the adequacy of cardiac output is assessed reliably by pulse pressure. Our results facilitate decisions regarding treatment in chronic heart failure. (JAMA1989;261:884-888)
929 citations
14 Sep 2016
TL;DR: The authors' classifier ensemble approach obtained the highest score of the competition with a sensitivity, specificity, and overall score of 0.9424, 0.7781, and 0.8602, respectively.
Abstract: The goal of the 2016 PhysioNet/CinC Challenge is the development of an algorithm to classify normal/abnormal heart sounds. A total of 124 time-frequency features were extracted from the phonocardiogram (PCG) and input to a variant of the AdaBoost classifier. A second classifier using convolutional neural network (CNN) was trained using PCGs cardiac cycles decomposed into four frequency bands. The final decision rule to classify normal/abnormal heart sounds was based on an ensemble of classifiers combining the outputs of AdaBoost and the CNN. The algorithm was trained on a training dataset (normal= 2575, abnormal= 665) and evaluated on a blind test dataset. Our classifier ensemble approach obtained the highest score of the competition with a sensitivity, specificity, and overall score of 0.9424, 0.7781, and 0.8602, respectively.
230 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
TL;DR: An automated computer-aided system that effectively differentiates normal, ASD, and VSD categories using short term PCG time series is proposed and achieves a mean accuracy of 95.24% in classifying ASD, VSD, and normal subjects.
Abstract: Congenital heart disease (CHD) is a heart disorder associated with the devastating indications that result in increased mortality, increased morbidity, increased healthcare expenditure, and decreased quality of life. Ventricular Septal Defects (VSDs) and Arterial Septal Defects (ASDs) are the most common types of CHD. CHDs can be controlled before reaching a serious phase with an early diagnosis. The phonocardiogram (PCG) or heart sound auscultation is a simple and non-invasive technique that may reveal obvious variations of different CHDs. Diagnosis based on heart sounds is difficult and requires a high level of medical training and skills due to human hearing limitations and the non-stationary nature of PCGs. An automated computer-aided system may boost the diagnostic objectivity and consistency of PCG signals in the detection of CHDs. The objective of this research was to assess the effects of various pattern recognition modalities for the design of an automated system that effectively differentiates normal, ASD, and VSD categories using short term PCG time series. The proposed model in this study adopts three-stage processing: pre-processing, feature extraction, and classification. Empirical mode decomposition (EMD) was used to denoise the raw PCG signals acquired from subjects. One-dimensional local ternary patterns (1D-LTPs) and Mel-frequency cepstral coefficients (MFCCs) were extracted from the denoised PCG signal for precise representation of data from different classes. In the final stage, the fused feature vector of 1D-LTPs and MFCCs was fed to the support vector machine (SVM) classifier using 10-fold cross-validation. The PCG signals were acquired from the subjects admitted to local hospitals and classified by applying various experiments. The proposed methodology achieves a mean accuracy of 95.24% in classifying ASD, VSD, and normal subjects. The proposed model can be put into practice and serve as a second opinion for cardiologists by providing more objective and faster interpretations of PCG signals.
60 citations
TL;DR: The obtained results demonstrate that the proposed end-to-end architecture yields outstanding performance in all the evaluation metrics compared to the previous state-of-the-art methods with up to 99.60% accuracy, 98.56% precision, 99.52% recall and 99.68% F1- score on an average while being computationally comparable.
Abstract: The alarmingly high mortality rate and increasing global prevalence of cardiovascular diseases (CVDs) signify the crucial need for early detection schemes. Phonocardiogram (PCG) signals have been historically applied in this domain owing to its simplicity and cost-effectiveness. In this article, we propose CardioXNet, a novel lightweight end-to-end CRNN architecture for automatic detection of five classes of cardiac auscultation namely normal, aortic stenosis, mitral stenosis, mitral regurgitation and mitral valve prolapse using raw PCG signal. The process has been automated by the involvement of two learning phases namely, representation learning and sequence residual learning. Three parallel CNN pathways have been implemented in the representation learning phase to learn the coarse and fine-grained features from the PCG and to explore the salient features from variable receptive fields involving 2D-CNN based squeeze-expansion. Thus, in the representation learning phase, the network extracts efficient time-invariant features and converges with great rapidity. In the sequential residual learning phase, because of the bidirectional-LSTMs and the skip connection, the network can proficiently extract temporal features without performing any feature extraction on the signal. The obtained results demonstrate that the proposed end-to-end architecture yields outstanding performance in all the evaluation metrics compared to the previous state-of-the-art methods with up to 99.60% accuracy, 99.56% precision, 99.52% recall and 99.68% F1- score on an average while being computationally comparable. This model outperforms any previous works using the same database by a considerable margin. Moreover, the proposed model was tested on PhysioNet/CinC 2016 challenge dataset achieving an accuracy of 86.57%. Finally the model was evaluated on a merged dataset of Github PCG dataset and PhysioNet dataset achieving excellent accuracy of 88.09%. The high accuracy metrics on both primary and secondary dataset combined with a significantly low number of parameters and end-to-end prediction approach makes the proposed network especially suitable for point of care CVD screening in low resource setups using memory constraint mobile devices.
60 citations