Detection and Classification of Heart Sounds Using a Heart-Mobile Interface

CardioPHON: Quality assessment and self-supervised pretraining for screening of cardiac function based on phonocardiogram recordings

Analysis of PCG signals using quality assessment and homomorphic filters for localization and classification of heart sounds

Most of the causes of death are related to cardiovascular disease. Heart sound classification plays a key role in the early detection of cardiovascular disease. The distinction between normal and abnormal heart sounds is not obvious, making heart sound classification a challenging task. This paper proposes a method based on statistical analysis and feature engineering to analyze the features extracted from heart sound signals. The discriminative features are selected and input into the classifier to classify the heart sound. While ensuring accuracy, the method reduces the scale of the input data and computational time for classification. The selected distinguishable features achieved an accuracy of 96.5 % for the classification of abnormal and normal heart sounds. The experimental results show that the selected features can achieve a high accuracy rate in the classification of heart sound, which is suitable for real-time applications.

Heart sounds classification plays an important role in cardiovascular disease detection. Currently, deep learning methods for heart sound classification with heavy parameters consumption cannot be deployed in environments with limited memory and computational budgets. Besides, de-noising of heart sound signals (HSSs) can affect accuracy of heart sound classification, because erroneous removal of meaningful components may lead to heart sound distortion. In this paper, an automated heart sound classification method using adaptive wavelet threshold and 1D LDCNN (One-dimensional Lightweight Deep Convolutional Neural Network) is proposed. In this method, we exploit WT (Wavelet Transform) with an adaptive threshold to de-noise heart sound signals (HSSs). Furthermore, we utilize 1D LDCNN to realize automatic feature extraction and classification for de-noised heart sounds. Experiments on PhysioNet/CinC 2016 show that our proposed method achieves the superior classification results and excels in consumption of parameter comparing to state-of-the-art methods.

Heart sounds are critical for early detection of cardiovascular diseases, yet existing studies mostly focus on traditional signal segmentation, feature extraction, and shallow classifiers, which often fail to sufficiently capture the dynamic and nonlinear characteristics of heart sounds, limit recognition of complex heart sound patterns, and are sensitive to data imbalance, resulting in poor classification performance. To address these limitations, this study proposes a novel heart sound classification method that integrates improved Mel-frequency cepstral coefficients (MFCC) for feature extraction with a convolutional neural network (CNN) and a deep Transformer model. In the preprocessing stage, a Butterworth filter is applied for denoising, and continuous heart sound signals are directly processed without segmenting the cardiac cycles, allowing the improved MFCC features to better capture dynamic characteristics. These features are then fed into a CNN for feature learning, followed by global average pooling (GAP) to reduce model complexity and mitigate overfitting. Lastly, a deep Transformer module is employed to further extract and fuse features, completing the heart sound classification. To handle data imbalance, the model uses focal loss as the objective function. Experiments on two public datasets demonstrate that the proposed method performs effectively in both binary and multi-class classification tasks. This approach enables efficient classification of continuous heart sound signals, provides a reference methodology for future heart sound research for disease classification, and supports the development of wearable devices and home monitoring systems.

Cardiovascular diseases (CVD) are often identified by the audio characteristics of persistent heart and lung sounds of healthy and abnormal subjects. Quite often, these sounds recorded from various subjects are found mixed with each other which in turn leads to ambiguity in the diagnosis. Hence, accurate and precise separation of heart and lung sounds is the essential and foremost task in heart-lung sound analysis. Various signal processing techniques and algorithms have attempted to decompose the heart sound and lung sounds from mixed signals. However, many of them were implemented on bulky and standalone computerized systems and have limited exposure of low-power embedded implementation. In this work, we attempted to implement a novel framework for heart-lung sound separation on a low-power and resource-constrained embedded platform. As a part of the work, we have explored variational mode decomposition (VMD) algorithm to separate the heart and lung sounds from the mixed signals. The proposed framework is implemented on standalone Windows operating system and Raspberry PI-based embedded platform to emulate the offline and real-time scenarios, respectively. Further, the proposed framework is tested with most popular and publicly available heart and lung sound databases obtained from Michigan heart sound library and Littmann lung sound library. The obtained results on both stand alone and embedded platforms are compared with the state-of-the-art techniques. The obtained results on embedded platform are found promising as compared to that of standalone PC-based implementation. As the targeted embedded platform is a low-power, portable, and low-cost computational device, the precise implementation of this work can find fascinating applications in IoT-based heart sound analysis and handheld heart sound assessment devices.

The heart sound signal is one of the signals that reflect the health of the heart. Research on the heart sound signal contributes to the early diagnosis and prevention of cardiovascular diseases. As a commonly used deep learning network, convolutional neural network (CNN) has been widely used in images. In this paper, the method of analyzing heart sound through using CNN has been studied. Firstly, the original data set was preprocessed, and then the heart sounds were segmented on U-net, based on the deep CNN. Finally, the classification of heart sounds was completed through CNN. The data from 2016 PhysioNet/CinC Challenge was utilized for algorithm validation, and the following results were obtained. When the heart sound segmented, the overall accuracy rate was 0.991, the accuracy of the first heart sound was 0.991, the accuracy of the systolic period was 0.996, the accuracy of the second heart sound was 0.996, and the accuracy of the diastolic period was 0.997, and the average accuracy rate was 0.995; While in classification, the accuracy was 0.964, the sensitivity was 0.781, and the specificity was 0.873. These results show that deep learning based on CNN shows good performance in the segmentation and classification of the heart sound signal.

Traditionally, abnormal heart sound classification is framed as a three-stage process. The first stage involves segmenting the phonocardiogram to detect fundamental heart sounds; after which features are extracted and classification is performed. Some researchers in the field argue the segmentation step is an unwanted computational burden, whereas others embrace it as a prior step to feature extraction. When comparing accuracies achieved by studies that have segmented heart sounds before analysis with those who have overlooked that step, the question of whether to segment heart sounds before feature extraction is still open. In this study, we explicitly examine the importance of heart sound segmentation as a prior step for heart sound classification, and then seek to apply the obtained insights to propose a robust classifier for abnormal heart sound detection. Furthermore, recognizing the pressing need for explainable Artificial Intelligence (AI) models in the medical domain, we also unveil hidden representations learned by the classifier using model interpretation techniques. Experimental results demonstrate that the segmentation which can be learned by the model plays an essential role in abnormal heart sound classification. Our new classifier is also shown to be robust, stable and most importantly, explainable, with an accuracy of almost 100% on the widely used PhysioNet dataset.

In this study, classification of Normal and Extra systolic heart sounds (HS) have been carried out using in PASCAL Heart Sounds (HS) data base. The extrasystole is the HS that is produced by performing an extra beat in each heart cycle, unlike the heartbeat normal cycle. It can be felt by people as palpitations. Occurrence of these sounds in certain age groups may be the indication of tachycardia. In this study, firstly HS have been normalized at first. Then an elliptic filter has used for noise reduction. HS features have been obtained using Mel-Frequency Cepstrum Coefficients. These features have classified using Artificial Neural Network. In this study, 45 extra systoles heart sounds have used. 30 of them have been used as training data for classification while remaining 15 ones have been used for the test. Certainty, sensitivity, accuracy values have been calculated using confusion matrix. Classification success has been calculated as 90%.

Changes and murmurs of heart sounds often provide the earliest information of abnormal heart status, which makes heart sound monitor and diagnose an important mean for cardiovascular disease. Compared to other heart monitoring approaches, such as EGG, Color Doppler and Magnetic Resonance Imaging Diagnosis, heart sound detection is more portable, much safer and cheaper, and needs lower professional requirements, thus has a broader applicability. This paper aims to design a system for monitoring and analyzing heart sounds in noisy environments, which can achieve signal acquisition, automatic analysis and classification. For the signal acquisition part, a novel equipment prototype is designed by separately using the two channels of a digital sound, one to record mixed signals of heart sounds and some environmental noises, and the other to record only environmental noises as a reference, then the environmental noise can be filtered out based on Least Mean Square (LMS) adaptive noise cancellation to get purified heart sound. By disassembling and assembling a stethoscope and a recorder, this design is realized and verified. For the signal analysis and classification part, with the features extracted by wavelet decomposition and Normalized Average Shannon Energy, a novel fuzzy neural network method with structure learning is used for the heart sound classification. Experiments with real and simulated data demonstrated that our approach can correctly classify all the tested heart sounds with noises even for the ones with previous unseen heart diseases.

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 model, which achieved a score of 88.06%.

Auscultation of heart sounds is very important to identify cardiovascular diseases (CVD). In this paper new approach for classification of heart sounds is introduced. This approach depends on getting features from heart sounds using statistical calculations of coefficients of new mother wavelet transform. Then, these features are classified using support vector machine (SVM). Number of features is 40 features. Number of heart sounds used for training is 90 heart sounds. Number of heart sounds used for tested data is 64 heart sounds. The obtained accuracy percent is 92.29%. The obtained specificity is 95.38%. The obtained sensitivity is 90%. So, this new tool can help physicians to diagnose patients of CVD easily.

This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

To develop a digital algorithm that detects first and second heart sounds, defines the systole and diastole, and characterises the systolic murmur. Heart sounds were recorded in 300 children with a cardiac murmur, using an electronic stethoscope. A Digital algorithm was developed for detection of first and second heart sounds. R-waves and T-waves in the electrocardiography were used as references for detection. The sound signal analysis was carried out using the short-time Fourier transform. The first heart sound detection rate, with reference to the R-wave, was 100% within 0.05-0.2R-R interval. The second heart sound detection rate between the end of the T-wave and the 0.6R-R interval was 97%. The systolic and diastolic phases of the cardiac cycle could be identified. Because of the overlap between heart sounds and murmur a systolic segment between the first and second heart sounds (20-70%) was selected for murmur analysis. The maximum intensity of the systolic murmur, its average frequency, and the mean spectral power were quantified. The frequency at the point with the highest sound intensity in the spectrum and its time from the first heart sound, the highest frequency, and frequency range were also determined. This method will serve as the foundation for computer-based detection of heart sounds and the characterisation of cardiac murmurs.

One-dimensional heart sounds signal is converted into two-dimensional phonocardiogram (2D-PCG), then extracts image feature of heart sounds based on image processing technology in a 2D-PCG. Firstly we realize the wavelet noise reduction and amplitude normalization of one-dimensional heart sounds by one-dimensional signal processing method, and then convert Heart sounds into 2D-PCG with uniformity and comparability, and pretreatment. And analyze the image features of 2D-PCG which is characterization of Heart sounds' physiological information combining with heart sounds' physiological significance and 2D-PCG's image features, focus on vertical and horizontal ratio of coordinate and sequence code of inflection point. In order to quickly classify the heart sound signal, the paper introduces the new concept: degree of heart sound signal certainty (HSSCD). Finally, efficiency and feasibility are verified through the heart sound acquisition, classification and identification experiments. At last, explore the feasibility of classification and identification of 2D-PCG using Euclidean distance and degree of heart sound signal certainty based on vertical and horizontal ratio of coordinate and sequence code of inflection point and wavelet coefficients. Experimental results show that the three features can achieve the classification and recognition of the 2D-PCG, and inflection point sequence code gets the highest recognition rate. The method of 2D-PCG classification and identification based on a two-image processing has the feasibility and practical applicability, and has broad application prospects.