Phonocardiogram Data Research Articles

PCGmix: A Data-Augmentation Method for Heart-Sound Classification.

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, responsible for 32% of all deaths, with the annual death toll projected to reach 23.3 million by 2030. The early identification of individuals at high risk of CVD is crucial for the effectiveness of preventive strategies. In the field of deep learning, automated CVD-detection methods have gained traction, with phonocardiogram (PCG) data emerging as a valuable resource. However, deep-learning models rely on large datasets, which are often challenging to obtain. In recent years, data augmentation has become a viable solution to the problem of scarce data. In this paper, we propose a novel data-augmentation technique named PCGmix, specifically engineered for the augmentation of PCG data. The PCGmix algorithm employs a process of segmenting and reassembling PCG recordings, incorporating meticulous interpolation to ensure the preservation of the cardinal diagnostic features pertinent to CVD detection. The empirical assessment of the PCGmix method was utilized on a publicly available database of normal and abnormal heart-sound recordings. To evaluate the impact of data augmentation across a range of dataset sizes, we conducted experiments encompassing both limited and extensive amounts of training data. The experimental results demonstrate that the novel method is superior to the compared state-of-the-art, time-series augmentation. Notably, on limited data, our method achieves comparable accuracy to the no-augmentation approach when trained on 31% to 69% larger datasets. This study suggests that PCGmix can enhance the accuracy of deep-learning models for CVD detection, especially in data-constrained environments.

Foundation models for cardiovascular disease detection via biosignals from digital stethoscopes

Auscultation of the heart and the electrocardiogram (ECG) are two central components of the cardiac exam. Recent innovations of the stethoscope have enabled the simultaneous acquisition of a high-quality digital acoustic signal and ECG. We present foundation models trained on phonocardiogram (PCG) and ECG data collected from digital stethoscopes during routine clinical practice. We show that these foundation models that are pre-trained on large unlabeled datasets in a self-supervised manner can be fine-tuned for a variety of cardiovascular disease detection tasks. This is the first study that builds foundation models specifically for synchronously captured PCG and ECG data. Our approach is based on the recently developed masked autoencoder framework which we extend to handle multiple signals that are synchronously captured. This paradigm makes it possible to use large capacity models leading to superior performance even though the size of datasets with medical label annotations may be limited.

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.

A deep neural network approach to heart murmur detection using spectrogram and peak interval features

Congenital heart disease affects about 1% of newborns, posing risks like heart failure and mortality. Developing countries often lack resources for diagnosis and treatment. The George B. Moody PhysioNet Challenge 2022 aims to develop systems for detecting murmurs and clinical outcome events using phonocardiogram (PCG) data, offering a cost-effective method for diagnosing cardiac diseases without invasive procedures. We proposed a deep learning model for this task and achieved the 5th rank out of 40 teams in both Track 1 (murmur detection) and Track 2 (clinical outcome prediction). This paper describes our methods and additional experiments. To extract features from the PCG, we employed techniques including Constant Q Transform (CQT) and Mel-scaled spectrogram (Mel-spectrogram) to generate two-dimensional representations in the frequency–time domain. Additionally, we extracted the Peak Interval (PI) feature, which measures the distance between peaks in the PCG data. This feature is useful because the peak intervals should be shorter in PCG recordings with murmurs. We also considered the sequence and mean of PI as additional features. Our proposed system, titled ‘Phonocardiogram-based Heart murmur Detection using Spectrogram and PI features (SpectroHeart),’ employs the Mel-spectrogram and PI features to detect heart murmurs and assess clinical outcomes. We believe that our deep learning-based system has great potential for automatically detecting heart signals from PCG.

An open-access simultaneous electrocardiogram and phonocardiogram database

Objective. The EPHNOGRAM project aimed to develop a low-cost, low-power device for simultaneous electrocardiogram (ECG) and phonocardiogram (PCG) recording, with additional channels for environmental audio to enhance PCG through active noise cancellation. The objective was to study multimodal electro-mechanical activities of the heart, offering insights into the differences and synergies between these modalities during various cardiac activity levels. Approach. We developed and tested several hardware prototypes of a simultaneous ECG-PCG acquisition device. Using this technology, we collected simultaneous ECG and PCG data from 24 healthy adults during different physical activities, including resting, walking, running, and stationary biking, in an indoor fitness center. The data were annotated using a robust software that we developed for detecting ECG R-peaks and PCG S1 and S2 components, and overseen by a human expert. We also developed machine learning models using ECG-based, PCG-based, and joint ECG-PCG features, like R–R and S1–S2 intervals, to classify physical activities and analyze electro-mechanical dynamics. Main results. The results show a significant coupling between ECG and PCG components, especially during high-intensity exercise. Notable micro-variations in S2-based heart rate show differences in the heart’s electrical and mechanical functions. The Lomb-Scargle periodogram and approximate entropy analyses confirm the higher volatility of S2-based heart rate compared to ECG-based heart rate. Correlation analysis shows stronger coupling between R–R and R-S1 intervals during high-intensity activities. Hybrid ECG-PCG features, like the R-S2 interval, were identified as more informative for physical activity classification through mRMR feature selection and SHAP value analysis. Significance. The EPHNOGRAM database, is available on PhysioNet. The database enhances our understanding of cardiac function, enabling future studies on the heart’s mechanical and electrical interrelationships. The results of this study can contribute to improved cardiac condition diagnoses. Additionally, the designed hardware has the potential for integration into wearable devices and the development of multimodal stress test technologies.

A pooling convolution model for multi-classification of ECG and PCG signals

Electrocardiogram (ECG) and phonocardiogram (PCG) signals are physiological signals generated throughout the cardiac cycle. The application of deep learning techniques to recognize ECG and PCG signals can greatly enhance the efficiency of cardiovascular disease detection. Therefore, we propose a series of straightforward and effective pooling convolutional models for the multi-classification of ECG and PCG signals. Initially, these signals undergo preprocessing. Subsequently, we design various structural blocks, including a stacked block (MCM) comprising convolutional layer and max-pooling layers, along with its variations, as well as a residual block (REC). By adjusting the number of structural blocks, these models can handle ECG and PCG data with different sampling rates. In the final tests, the models utilizing the MCM structural block achieved accuracies of 98.70 and 92.58% on the ECG and PCG fusion datasets, respectively. These accuracies surpass those of all networks utilizing its variations. Moreover, compared to the models employing the REC structural block, the accuracies are improved by 0.02 and 4.30%, respectively. Furthermore, this research has been validated through tests conducted on multiple ECG and PCG datasets, along with comparisons to other published literature. To further validate the generalizability of the model, an additional experiment involving the classification of a synchronized ECG-PCG dataset was conducted. This dataset is divided into seven different levels of fatigue based on the amount of exercise performed by each healthy subject during the testing process. The results indicate that the model using the MCM block also achieved the highest accuracy.

Multi-Modal Feature Integration in Machine Learning Predictions for Cardiovascular Diseases

Early detection and prevention of cardiovascular illnesses rely heavily on phonocardiogram (PCG) and electrocardiogram (ECG). A novel multi-modal machine learning strategy based on ECG and PCG data is presented in this work for predicting cardiovascular diseases (CVD). ECG and PCG features are combined for optimal feature subset selection using a genetic algorithm (GA). Then, machine learning classifiers are implemented to do the classification of abnormal and normal signals

Multiclass Classification of Myocardial Infarction Based on Phonocardiogram Signals Using Ensemble Learning

Myocardial infarction (MI) is a serious cardiovascular disease with a high mortality rate worldwide. Early detection and consistent treatment can significantly reduce mortality from cardiovascular diseases. However, there is a need for efficient models that can enable the early detection of heart disease without relying on trained clinical experts. MI studies using phonocardiogram (PCG) signals and implementing ensemble learning models are still relatively scarce, often resulting in poor accuracy and low detection rates. This study aims to implement an ensemble learning model for the classification of MI using PCG signals into different classes. In this stage of research, several classification algorithms, including Random Forest and Logistic Regression, serve as basic models for ensemble learning, utilizing features extracted from audio signals. Evaluation of the model's performance reveals that the stacking model achieves an accuracy of 96%. These results demonstrate that our system can appropriately and accurately classify MI within PCG data. We believe that the findings of this study will enhance the diagnosis and treatment of heart attacks, making them more effective and accurate.

On the Impact of Synchronous Electrocardiogram Signals for Heart Sounds Segmentation.

In this paper we study the heart sound segmentation problem using Deep Neural Networks. The impact of available electrocardiogram (ECG) signals in addition to phonocardiogram (PCG) signals is evaluated. To incorporate ECG, two different models considered, which are built upon a 1D U-net - an early fusion one that fuses ECG in an early processing stage, and a late fusion one that averages the probabilities obtained by two networks applied independently on PCG and ECG data. Results show that, in contrast with traditional uses of ECG for PCG gating, early fusion of PCG and ECG information can provide more robust heart sound segmentation. As a proof of concept, we use the publicly available PhysioNet dataset. Validation results provide, on average, a sensitivity of 97.2%, 94.5%, and 95.6% and a Positive Predictive Value of 97.5%, 96.2%, and 96.1% for Early-fusion, Late-fusion, and unimodal (PCG only) models, respectively, showing the advantages of combining both signals at early stages to segment heart sounds.Clinical relevance- Cardiac auscultation is the first line of screening for cardiovascular diseases. Its low cost and simplicity are especially suitable for screening large populations in underprivileged countries. The proposed analysis and algorithm show the potential of effectively including electrocardiogram information to improve heart sound segmentation performance, thus enhancing the capacity of extracting useful information from heart sound recordings.

Recognition of Pediatric Congenital Heart Diseases by Using Phonocardiogram Signals and Transformer-Based Neural Networks.

The phonocardiogram (PCG) or heart sound auscultation is a low-cost and non-invasive method to diagnose Congenital Heart Disease (CHD). However, recognizing CHD in the pediatric population based on heart sounds is difficult because it requires high medical training and skills. Also, the dependency of PCG signal quality on sensor location and developing heart in children are challenging. This study proposed a deep learning model that classifies unprocessed or raw PCG signals to diagnose CHD using a one-dimensional Convolution Neural Network (1D-CNN) with an attention transformer. The model was built on the raw PCG data of 484 patients. The results showed that the attention transformer model had a good balance of accuracy of 0.923, a sensitivity of 0.973, and a specificity of 0.833. The Receiver Operating Characteristic (ROC) plot generated an Area Under Curve (AUC) value of 0.964, and the F1-score was 0.939. The suggested model could provide quick and appropriate real-time remote diagnosis application in classifying PCG of CHD from non-CHD subjects.Clinical Relevance- The suggested methodology can be utilized to analyze PCG signals more quickly and affordably for rural doctors as a first screening tool before sending the cases to experts.

Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers.

The major cause of death worldwide is due to cardiovascular disorders (CVDs). For a proper diagnosis of CVD disease, an inexpensive solution based on phonocardiogram (PCG) signals is proposed. (1) Background: Currently, a few deep learning (DL)-based CVD systems have been developed to recognize different stages of CVD. However, the accuracy of these systems is not up-to-the-mark, and the methods require high computational power and huge training datasets. (2) Methods: To address these issues, we developed a novel attention-based technique (CVT-Trans) on a convolutional vision transformer to recognize and categorize PCG signals into five classes. The continuous wavelet transform-based spectrogram (CWTS) strategy was used to extract representative features from PCG data. Following that, a new CVT-Trans architecture was created to categorize the CWTS signals into five groups. (3) Results: The dataset derived from our investigation indicated that the CVT-Trans system had an overall average accuracy ACC of 100%, SE of 99.00%, SP of 99.5%, and F1-score of 98%, based on 10-fold cross validation. (4) Conclusions: The CVD-Trans technique outperformed many state-of-the-art methods. The robustness of the constructed model was confirmed by 10-fold cross-validation. Cardiologists can use this CVT-Trans system to help patients with the diagnosis of heart valve problems.

Augmented detection of septal defects using advanced optical coherence tomography network-processed phonocardiogram.

Cardiac auscultation is a traditional method that is most frequently used for identifying congenital heart disease (CHD). Failure to diagnose CHD may occur in patients with faint murmurs or obesity. We aimed to develop an intelligent diagnostic method of detecting heart murmurs in patients with ventricular septal defects (VSDs) and atrial septal defects (ASDs). Digital recordings of heart sounds and phonocardiograms of 184 participants were obtained. All participants underwent echocardiography by pediatric cardiologists to determine the type of CHD. The phonocardiogram data were classified as normal, ASD, or VSD. Then, the phonocardiogram signal was used to extract features to construct diagnostic models for disease classification using an advanced optical coherence tomography network (AOCT-NET). Cardiologists were asked to distinguish normal heart sounds from ASD/VSD murmurs after listening to the electronic sound recordings. Comparisons of the cardiologists' assessment and AOCT-NET performance were performed. Echocardiography results revealed 88 healthy participants, 50 with ASDs, and 46 with VSDs. The AOCT-NET had no advantage in detecting VSD compared with cardiologist assessment. However, AOCT-NET performance was better than that of cardiologists in detecting ASD (sensitivity, 76.4 vs. 27.8%, respectively; specificity, 90 vs. 98.5%, respectively). The proposed method has the potential to improve the ASD detection rate and could be an important screening tool for patients without symptoms.

CARC3: Characterizing Audio Traits of Various Systemic to Pulmonary Connections in Ductal Dependent Infants

Background: Infants with congenital heart disease who have insufficient systemic or pulmonary blood flow require a connection between the systemic and pulmonary vasculature. There are four types of shunts: right ventricle to pulmonary artery (Sano), Blalock-TaussigThomas (BTTS), central, and ductal arterial stent (DAS). They have a high incidence of mortality and unplanned interventions. Machine learning algorithms evaluate a shunt murmur recorded on a digital stethoscope to differentiate the audio features of various shunts. Methods: A 3M Littman Electronic Stethoscope acquired phonocardiogram (PCG) data from infants with shunt placement on post-operative days 0, 1, 2, 7, 14, discharge and day of shunt take-down, as well as, when imaging (echo, CT, catheterization) was performed. Patient demographics and clinical outcomes noted. A wavelet decomposition algorithm denoised the PCG data with a bandpass filter ranging from 20 to 2000 Hz and then segmented into S1 and S2. One hundred and fifteen audio features, comprising temporal, spectral, statistical, and bandpower domains were extracted from the waveforms. A statistical algorithm including ANOVA, Kruskal Wallis and pairwise comparison tests detected 33 total features showing a statistically significant difference in means (alpha = 0.05). Results: Thirteen subjects (BTTS (n=3), DAS (n=2), and Sano (n=8)) who had second stage palliation were analyzed using the two most recent recordings prior to shunt takedown. Principal component analysis (Figure 1) and tdistributed stochastic neighbor embedding (Figure 2) visualization techniques indicate a separation among shunt types. Conclusion: Audio signatures could exist based on shunt type characteristics and may lead to a novel non-invasive tool to monitor shunt health. Figure 1. Principal Component Analysis (PCA) indicating a separation in BTTS, Sano, and PDA shunt types from selected recordings. Figure 2. t-distributed stochastic neighbor embedding (tSNE) indicating a separation in BTTS, Sano, and PDA shunt types from selected recordings.

Automatic Classification of Normal–Abnormal Heart Sounds Using Convolution Neural Network and Long-Short Term Memory

The phonocardiogram (PCG) is an important analysis method for the diagnosis of cardiovascular disease, which is usually performed by experienced medical experts. Due to the high ratio of patients to doctors, there is a pressing need for a real-time automated phonocardiogram classification system for the diagnosis of cardiovascular disease. This paper proposes a deep neural-network structure based on a one-dimensional convolutional neural network (1D-CNN) and a long short-term memory network (LSTM), which can directly classify unsegmented PCG to identify abnormal signal. The PCG data were filtered and put into the model for analysis. A total of 3099 pieces of heart-sound recordings were used, while another 100 patients’ heart-sound data collected by our group and diagnosed by doctors were used to test and verify the model. Results show that the CNN-LSTM model provided a good overall balanced accuracy of 0.86 ± 0.01 with a sensitivity of 0.87 ± 0.02, and specificity of 0.89 ± 0.02. The F1-score was 0.91 ± 0.01, and the receiver-operating characteristic (ROC) plot produced an area under the curve (AUC) value of 0.92 ± 0.01. The sensitivity, specificity and accuracy of the 100 patients’ data were 0.83 ± 0.02, 0.80 ± 0.02 and 0.85 ± 0.03, respectively. The proposed model does not require feature engineering and heart-sound segmentation, which possesses reliable performance in classification of abnormal PCG; and is fast and suitable for real-time diagnosis application.

Unsupervised Phonocardiogram Analysis With Distribution Density Based Variational Auto-Encoders.

This paper proposes an unsupervised way for Phonocardiogram (PCG) analysis, which uses a revised auto encoder based on distribution density estimation in the latent space. Auto encoders especially Variational Auto-Encoders (VAEs) and its variant β−VAE are considered as one of the state-of-the-art methodologies for PCG analysis. VAE based models for PCG analysis assume that normal PCG signals can be represented by latent vectors that obey a normal Gaussian Model, which may not be necessary true in PCG analysis. This paper proposes two methods DBVAE and DBAE that are based on estimating the density of latent vectors in latent space to improve the performance of VAE based PCG analysis systems. Examining the system performance with PCG data from the a single domain and multiple domains, the proposed systems outperform the VAE based methods. The representation of normal PCG signals in the latent space is also investigated by calculating the kurtosis and skewness where DBAE introduces normal PCG representation following Gaussian-like models but DBVAE does not introduce normal PCG representation following Gaussian-like models.

A two-stage classification model integrating feature fusion for coronary artery disease detection and classification

According to the World Health Organization, Coronary Artery Disease (CAD) is a leading cause of death globally. CAD is categorized into three types, namely Single Vessel Coronary Artery Disease (SVCAD), Double Vessel Coronary Artery Disease (DVCAD), and Triple Vessel Coronary Artery Disease (TVCAD). At present, angiography is the most popular technique to detect CAD that is quite expensive and invasive. Phonocardiogram (PCG), being economical and non-invasive, is a crucial modality towards the detection of cardiac disorders, but only trained medical professionals can interpret heart auscultations in clinical environments. This research aims to detect CAD and its types from PCG signatures through feature fusion and a two-stage classification strategy. The self-developed low-cost stethoscope was used to collect PCG data from a local hospital. The PCG signals were preprocessed through an iterative signal decomposition method known as Empirical Mode Decomposition (EMD). EMD decomposes the raw PCG signal into its constituent components called Intrinsic Mode Functions (IMFs). Preprocessed PCG signal was generated exclusively through combining those signal components that contain high discriminative characteristics and less redundancy. Next, Mel Frequency Cepstral Coefficients (MFCCs), spectral and statistical features were extracted. A two-stage classification framework was devised to identify healthy and CAD types. The first stage framework relies on the fusion of MFCC and statistical features with the K-nearest neighbor classifier to predict normal and CAD cases. The second stage is activated only when the first stage detects CAD. The fusion of spectral, statistical, and MFCC features was employed with Support Vector Machines classifier to categorize PCG signatures into DVCAD, SVCAD, and TVCAD classes in the second stage. The proposed method yields mean accuracy values of 88.0%, 89.2%, 91.1%, and 85.3% for normal, DVCAD, SVCAD, and TVCAD, respectively, through 10-fold cross-validation. Comparative analysis with existing approaches confirmed the reliability of the proposed method for categorizing CAD in general clinical environments. The proposed model enhances the diagnosis performance by providing a second opinion during the medical examination.

Acoustic feature based unsupervised approach of heart sound event detection

This paper represents an unsupervised approach to detect the positions of S1, S2 heart sound events in a Phonocardiogram (PCG) recording. Insufficiency of correctly annotated heart sound database drives us to investigate unsupervised techniques. Gammatone filter bank features are used to characterize the spectral pattern of fundamental heart sound events from noise contaminated PCG data. An unsupervised spectral clustering technique is employed for segmentation of S1/S2 and non-S1/S2 heart sound events. A Feature winning score is computed to identify the S1/S2 and non-S1/S2 frames. Finally, time based threshold is applied to detect the accurate positions of S1 and S2 heart sounds. The performance of spectral clustering is compared with other clustering methods. The proposed method offers a maximum F1-score of 98% and 92.5% for normal and abnormal PCG data respectively on 2016 PhysioNet/CinC challenge dataset. The heart sound annotation algorithm provided by PhysioNet has been used as the ground truth after hand correction.

Cross-wavelet assisted convolution neural network (AlexNet) approach for phonocardiogram signals classification

The exponential growth of a multitude of cardiovascular diseases, leading to life frightening conditions, makes fast and accurate computer-aided techniques that are relevant and important to identify these lethally medical anomalies. The authors aim to build up an accurate scheme that analyzes the Phonocardiogram (PCG) signal and find out whether the patient’s heart works normally or required any special intervention for further diagnosis. This paper has a universal, object – to aid doctors and medical personnel, and an indispensable technique like a Cross-wavelet transform (XWT) assisted Convolution neural network (CNN) utilizing the AlexNet model to detect abnormal heart sounds which are the symbol of cardiovascular disease. A pre-trained AlexNet model has been used and fine-tuned to improve system performance. Convolution neural network (Alex Net architecture) utilizes the Cross-wavelet spectrum image as an input, to prevent and protect individuals from fatal medical conditions. The proposed method is applied both on the raw PCG data and on the PCG data after removing the noise. The results of the study show that our technique attained an accuracy of 98% and 97.89% when processed with the raw and the de-noised PCG dataset to distinguish abnormal heart sound from normal ones and outperformed all existing methods. The authors used the same dataset and evaluated three classic classifiers – LVQ, LS-SVM and PNN to compare the results with the proposed classifier. Also, the works which have been published by the previous researchers are sited and compared with our proposed approach.

Analysis of heart sound anomalies using ensemble learning

Phonocardiogram (PCG) signal analysis is a common method for evaluating the condition of the heart and detecting possible anomalies such as cardiovascular diseases. This work concentrates on the Physionet challenge database that stores PCG recordings for more than one thousand subjects, including healthy and pathological records. A complete methodology is provided to analyze and classify PCG data. The PCG signals are first filtered and segmented into different parts, then analyzed by applying a feature extraction process, followed by classifying the signal as that of a healthy or unhealthy person. The extracted optimal features subset includes statistical components, such as the mean and standard deviation from different parts of the signal, in addition to wavelet-based features. The classification mainly relies on bagging and boosting algorithms, as well as adequately preparing the data in order to yield an enhanced ensemble classifier. The work further provides the approach to combine multiple classification models to improve accuracy. The effect of segmenting the different beats of the PCG on classification scores is also addressed, and the results are shown to be of high precision with an accuracy score of 86.6% on the hidden test, in comparison with recent and best performing literature that achieved 86%. Also, the proposed methodology is extended to the Pascal heart sounds challenge with accurate results that exceed previous works confirming the performance and robustness of the work since it can be applied to multiple databases and sources. The work further provides important insights regarding analyzing PCG recordings and discusses future work possibilities.

Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features

Objective: Heart abnormality detection using heart sound signals (phonocardiogram (PCG)) has been an active research area for the last few decades. In this paper, automatic heart sound classification using segmented and unsegmented PCG signals is presented. Approach: In this paper: (i) we perform an in-depth analysis of various time and frequency domain features, followed by experimental determination of effective feature subsets for improved classification performance; (ii) both segmented and unsegmented PCG signals are studied and important results concerning the respective feature subsets and their classification performances are reported; and (iii) different classification algorithms, including the support vector machine, kth nearest neighbor, decision tree, ensemble classifier, artificial neural network and long short-term memory network (LSTMs), are employed to evaluate the performance of the proposed feature subsets and their comparison with other established features and methods is presented. Main results: It is observed that LSTM performs better on mel-frequency cepstral coefficient (MFCC) features extracted from unsegmented PCG data, with an area under curve (AUC) score of 91.39%, however, the MFCC features do not show a consistent performance with other classifiers (the second highest AUC score is 62.08% with the decision tree classifier). In contrast, in the case of time-frequency features from segmented data, the performance of all the classifiers is appreciable with AUC scores over 70%. In particular, the conventional machine learning techniques shows consistency in achieving over 80% in AUC scores. Significance: The results of this study highlight the importance of time and frequency domain features. Thus it is necessary to employ both the time and frequency features of segmented PCG signals to achieve improved classification.