Automated detection of abnormal heart sound signals using Fano-factor constrained tunable quality wavelet transform

Enhancing cross-domain robustness in phonocardiogram signal classification using domain-invariant preprocessing and transfer learning

BACKGROUND AND OBJECTIVE

Phonocardiogram (PCG) signal analysis is a non-invasive and cost-efficient approach for diagnosing cardiovascular diseases. Existing PCG-based approaches employ signal processing and machine learning (ML) for automatic disease detection. However, machine learning techniques are known to underperform in cross-corpora arrangements. A drastic effect on disease detection performance is observed when training and testing sets come from different PCG databases with varying data acquisition settings. This study investigates the impact of data acquisition parameter variations in the PCG data across different databases and develops methods to achieve robustness against these variations.

METHODS

To alleviate the effect of dataset-induced variations, it employs a combination of three strategies: domain-invariant preprocessing, transfer learning, and domain-balanced variable hop fragment selection (DBVHFS). The domain-invariant preprocessing normalizes the PCG to reduce the stethoscope and environment-induced variations. The transfer learning utilizes a pre-trained model trained on diverse audio data to reduce the impact of data variability by generalizing feature representations. DBVHFS facilitates unbiased fine-tuning of the pre-trained model by balancing the training fragments across all domains, ensuring equal distribution from each class.

RESULTS

The proposed method is evaluated on six independent PhysioNet/CinC Challenge 2016 PCG databases using leave-one-dataset-out cross-validation. Results indicate that our system outperforms the existing study with a relative improvement of 5.92% in unweighted average recall and 17.71% in sensitivity.

CONCLUSIONS

The methods proposed in this study address variations in PCG data originating from different sources, potentially enhancing the implementation possibility of automated cardiac screening systems in real-life scenarios.

Deep learning fusion framework for automated coronary artery disease detection using raw heart sound signals

One of the most common cardiovascular diseases is coronary artery disease (CAD). Thus, it is crucial for early CAD diagnosis to control disease progression. Computer-aided CAD detection often converts heart sounds into graphics for analysis. However, this method relies heavily on the subjective experience of experts. Therefore, in this study, we proposed a method for CAD detection using raw heart sound signals by constructing a fusion framework with two CAD detection models: a multidomain feature model and a medical multidomain feature fusion model. We collected heart sound signal datasets from 400 participants, extracting 206 multidomain features and 126 medical multidomain features. The designed framework fused the same one-dimensional deep learning features with different multidomain features for CAD detection. The experimental results showed that the multidomain feature model and the medical multidomain feature fusion model achieved areas under the curve (AUC) of 94.7 % and 92.7 %, respectively, demonstrating the effectiveness of the fusion framework in integrating one-dimensional and cross-domain heart sound features through deep learning algorithms, providing an effective solution for noninvasive CAD detection.

Review of Phonocardiogram Signal Analysis: Insights from the PhysioNet/CinC Challenge 2016 Database

The phonocardiogram (PCG) is a crucial tool for the early detection, continuous monitoring, accurate diagnosis, and efficient management of cardiovascular diseases. It has the potential to revolutionize cardiovascular care and improve patient outcomes. The PhysioNet/CinC Challenge 2016 database, a large and influential resource, encourages contributions to accurate heart sound state classification (normal versus abnormal), achieving promising benchmark performance (accuracy: 99.80%; sensitivity: 99.70%; specificity: 99.10%; and score: 99.40%). This study reviews recent advances in analytical techniques applied to this database, and 104 publications on PCG signal analysis are retrieved. These techniques encompass heart sound preprocessing, signal segmentation, feature extraction, and heart sound state classification. Specifically, this study summarizes methods such as signal filtering and denoising; heart sound segmentation using hidden Markov models and machine learning; feature extraction in the time, frequency, and time-frequency domains; and state-of-the-art heart sound state recognition techniques. Additionally, it discusses electrocardiogram (ECG) feature extraction and joint PCG and ECG heart sound state recognition. Despite significant technical progress, challenges remain in large-scale high-quality data collection, model interpretability, and generalizability. Future directions include multi-modal signal fusion, standardization and validation, automated interpretation for decision support, real-time monitoring, and longitudinal data analysis. Continued exploration and innovation in heart sound signal analysis are essential for advancing cardiac care, improving patient outcomes, and enhancing user trust and acceptance.

Murmur identification and outcome prediction in phonocardiograms using deep features based on Stockwell transform

Traditionally, heart murmurs are diagnosed through cardiac auscultation, which requires specialized training and experience. The purpose of this study is to predict patients' clinical outcomes (normal or abnormal) and identify the presence or absence of heart murmurs using phonocardiograms (PCGs) obtained at different auscultation points. A semi-supervised model tailored to PCG classification is introduced in this study, with the goal of improving performance using time-frequency deep features. The study begins by investigating the behavior of PCGs in the time-frequency domain, utilizing the Stockwell transform to convert the PCG signal into two-dimensional time-frequency maps (TFMs). A deep network named AlexNet is then used to derive deep feature sets from these TFMs. In feature reduction, redundancy is eliminated and the number of deep features is reduced to streamline the feature set. The effectiveness of the extracted features is evaluated using three different classifiers using the CinC/Physionet challenge 2022 dataset. For Task I, which focuses on heart murmur detection, the proposed approach achieved an average accuracy of 93%, sensitivity of 91%, and F1-score of 91%. According to Task II of the CinC/Physionet challenge 2022, the approach showed a clinical outcome cost of 5290, exceeding the benchmark set by leading methods in the challenge.

An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning

Heart-sound auscultation is one of the most widely used approaches for detecting cardiovascular disorders. Diagnosing abnormalities of heart sound using a stethoscope depends on the physician's skill and judgment. Several studies have shown promising results in automatically detecting cardiovascular disorders based on heart-sound signals. However, the accuracy performance needs to be enhanced as automated heart-sound classification aids in the early detection and prevention of the dangerous effects of cardiovascular problems. In this study, an optimal heart-sound classification method based on machine learning technologies for cardiovascular disease prediction is performed. It consists of three steps: pre-processing that sets the 5 s duration of the PhysioNet Challenge 2016 and 2022 datasets, feature extraction using Mel frequency cepstrum coefficients (MFCC), and classification using grid search for hyperparameter tuning of several classifier algorithms including k-nearest neighbor (K-NN), random forest (RF), artificial neural network (ANN), and support vector machine (SVM). The five-fold cross-validation was used to evaluate the performance of the proposed method. The best model obtained classification accuracy of 95.78% and 76.31%, which was assessed using PhysioNet Challenge 2016 and 2022, respectively. The findings demonstrate that the suggested approach obtained excellent classification results using PhysioNet Challenge 2016 and showed promising results using PhysioNet Challenge 2022. Therefore, the proposed method has been potentially developed as an additional tool to facilitate the medical practitioner in diagnosing the abnormality of the heart sound.

Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning

Heart sounds convey important information regarding potential heart diseases. Currently, heart sound classification attracts many researchers from the fields of telemedicine, digital signal processing, and machine learning-among others-mainly to identify cardiac pathology as quickly as possible. This article proposes chaogram as a new transform to convert heart sound signals to colour images. In the proposed approach, the output image is, therefore, the projection of the reconstructed phase space representation of the phonocardiogram (PCG) signal on three coordinate planes. This has two major benefits: (1) it makes possible to apply deep convolutional neural networks to heart sounds and (2) it is also possible to employ a transfer learning scheme by converting a heart sound signal to an image. The performance of the proposed approach was verified on the PhysioNet dataset. Due to the imbalanced data on this dataset, it is common to assess the results quality using the average of sensitivity and specificity, which is known as score, instead of accuracy. In this study, the best results were achieved using the InceptionV3 model, which achieved a score of 88.06%.

A novel intelligent system based on adjustable classifier models for diagnosing heart sounds

A novel intelligent diagnostic system is proposed to diagnose heart sounds (HSs). 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Rheumatic Heart Disease Screening Based on Phonocardiogram

Rheumatic heart disease (RHD) is one of the most common causes of cardiovascular complications in developing countries. It is a heart valve disease that typically affects children. Impaired heart valves stop functioning properly, resulting in a turbulent blood flow within the heart known as a murmur. This murmur can be detected by cardiac auscultation. However, the specificity and sensitivity of manual auscultation were reported to be low. The other alternative is echocardiography, which is costly and requires a highly qualified physician. Given the disease's current high prevalence rate (the latest reported rate in the study area (Ethiopia) was 5.65%), there is a pressing need for early detection of the disease through mass screening programs. This paper proposes an automated RHD screening approach using machine learning that can be used by non-medically trained persons outside of a clinical setting. Heart sound data was collected from 124 persons with RHD (PwRHD) and 46 healthy controls (HC) in Ethiopia with an additional 81 HC records from an open-access dataset. Thirty-one distinct features were extracted to correctly represent RHD. A support vector machine (SVM) classifier was evaluated using two nested cross-validation approaches to quantitatively assess the generalization of the system to previously unseen subjects. For regular nested 10-fold cross-validation, an f1-score of 96.0 ± 0.9%, recall 95.8 ± 1.5%, precision 96.2 ± 0.6% and a specificity of 96.0 ± 0.6% were achieved. In the imbalanced nested cross-validation at a prevalence rate of 5%, it achieved an f1-score of 72.2 ± 0.8%, recall 92.3 ± 0.4%, precision 59.2 ± 3.6%, and a specificity of 94.8 ± 0.6%. In screening tasks where the prevalence of the disease is small, recall is more important than precision. The findings are encouraging, and the proposed screening tool can be inexpensive, easy to deploy, and has an excellent detection rate. As a result, it has the potential for mass screening and early detection of RHD in developing countries.

Classification of heart sounds based on quality assessment and wavelet scattering transform

Automatic classification of heart sound plays an important role in the diagnosis of cardiovascular diseases. In this study, a heart sound sample classification method based on quality assessment and wavelet scattering transform was proposed. First, the ratio of zero crossings (RZC) and the root mean square of successive differences (RMSSD) were used for assessing the quality of heart sound signal. The first signal segment conforming to the threshold standard was selected as the current sample for the continuous heart sound signal. Using the wavelet scattering transform, the wavelet scattering coefficients were expanded according to the wavelet scale dimension, to obtain the features. Support vector machine (SVM) was used for classification, and the classification results for the samples were obtained using the wavelet scale dimension voting approach. The effects of RZC and RMSSD on the results are discussed in detail. On the database of PhysioNet Computing in Cardiology Challenge 2016 (CinC 2016), the proposed method yields 92.23% accuracy (Acc), 96.62% sensitivity (Se), 90.65% specificity (Sp), and 93.64% measure of accuracy (Macc). The results show that the proposed method can effectively classify normal and abnormal heart sound samples with high accuracy.