An approach for automatic identification of fundamental and additional sounds from cardiac sounds recordings.

This paper presents an approach for automatic segmentation of cardiac events from non-invasive sounds recordings, without the need of having an auxiliary signal reference. In addition, methods are proposed to subsequently differentiate cardiac events which correspond to normal cardiac cycles, from those which are due to abnormal activity of the heart. The detection of abnormal sounds is based on a model built with parameters which are obtained following feature extraction from those segments that were previously identified as normal fundamental heart sounds. The proposed algorithm achieved a sensitivity of 91.79% and 89.23% for the identification of normal fundamental, S1 and S2 sounds, and a true positive (TP) rate of 81.48% for abnormal additional sounds. These results were obtained using the PASCAL Classifying Heart Sounds challenge (CHSC) database

Automatic analysis and classification of cardiac acoustic signals for long term monitoring

Objective: Cardiovascular diseases are the leading cause of death worldwide resulting in over 17.9 million deaths each year. Most of these diseases are preventable and treatable, but their progression and outcomes are significantly more positive with early-stage diagnosis and proper disease management. Among the approaches available to assist with the task of early-stage diagnosis and management of cardiac conditions, automatic analysis of auscultatory recordings is one of the most promising ones, since it could be particularly suitable for ambulatory/wearable monitoring. Thus, proper investigation of abnormalities present in cardiac acoustic signals can provide vital clinical information to assist long term monitoring. Cardiac acoustic signals, however, are very susceptible to noise and artifacts, and their characteristics vary largely with the recording conditions which makes the analysis challenging. Additionally, there are challenges in the steps used for automatic analysis and classification of cardiac acoustic signals. Broadly, these steps are the segmentation, feature extraction and subsequent classification of recorded signals using selected features. This thesis presents approaches using novel features with the aim to assist the automatic early-stage detection of cardiovascular diseases with improved performance, using cardiac acoustic signals collected in real-world conditions. Methods: Cardiac auscultatory recordings were studied to identify potential features to help in the classification of recordings from subjects with and without cardiac diseases. The diseases considered in this study for the identification of the symptoms and characteristics are the valvular heart diseases due to stenosis and regurgitation, atrial fibrillation, and splitting of fundamental heart sounds leading to additional lub/dub sounds in the systole or diastole interval of a cardiac cycle. The localisation of cardiac sounds of interest was performed using an adaptive wavelet-based filtering in combination with the Shannon energy envelope and prior information of fundamental heart sounds. This is a prerequisite step for the feature extraction and subsequent classification of recordings, leading to a more precise diagnosis. Localised segments of S1 and S2 sounds, and artifacts, were used to extract a set of perceptual and statistical features using wavelet transform, homomorphic filtering, Hilbert transform and mel-scale filtering, which were then fed to train an ensemble classifier to interpret S1 and S2 sounds. Once sound peaks of interest were identified, features extracted from these peaks, together with the features used for the identification of S1 and S2 sounds, were used to develop an algorithm to classify recorded signals. Overall, 99 features were extracted and statistically analysed using neighborhood component analysis (NCA) to identify the features which showed the greatest ability in classifying recordings. Selected features were then fed to train an ensemble classifier to classify abnormal recordings, and hyperparameters were optimized to evaluate the performance of the trained classifier. Thus, a machine learning-based approach for the automatic identification and classification of S1 and S2, and normal and abnormal recordings, in real-world noisy recordings using a novel feature set is presented. The validity of the proposed algorithm was tested using acoustic signals recorded in real-world, non-controlled environments at four auscultation sites (aortic valve, tricuspid valve, mitral valve, and pulmonary valve), from the subjects with and without cardiac diseases; together with recordings from the three large public databases. The performance metrics of the methodology in relation to classification accuracy (CA), sensitivity (SE), precision (P+), and F1 score, were evaluated. Results: This thesis proposes four different algorithms to automatically classify fundamental heart sounds – S1 and S2; normal fundamental sounds and abnormal additional lub/dub sounds recordings; normal and abnormal recordings; and recordings with heart valve disorders, namely the mitral stenosis (MS), mitral regurgitation (MR), mitral valve prolapse (MVP), aortic stenosis (AS) and murmurs, using cardiac acoustic signals. The results obtained from these algorithms were as follows: • The algorithm to classify S1 and S2 sounds achieved an average SE of 91.59% and 89.78%, and F1 score of 90.65% and 89.42%, in classifying S1 and S2, respectively. 87 features were extracted and statistically studied to identify the top 14 features which showed the best capabilities in classifying S1 and S2, and artifacts. The analysis showed that the most relevant features were those extracted using Maximum Overlap Discrete Wavelet Transform (MODWT) and Hilbert transform. • The algorithm to classify normal fundamental heart sounds and abnormal additional lub/dub sounds in the systole or diastole intervals of a cardiac cycle, achieved an average SE of 89.15%, P+ of 89.71%, F1 of 89.41%, and CA of 95.11% using the test dataset from the PASCAL database. The top 10 features that achieved the highest weights in classifying these recordings were also identified. • Normal and abnormal classification of recordings using the proposed algorithm achieved a mean CA of 94.172%, and SE of 92.38%, in classifying recordings from the different databases. Among the top 10 acoustic features identified, the deterministic energy of the sound peaks of interest and the instantaneous frequency extracted using the Hilbert Huang-transform, achieved the highest weights. • The machine learning-based approach proposed to classify recordings of heart valve disorders (AS, MS, MR, and MVP) achieved an average CA of 98.26% and SE of 95.83%. 99 acoustic features were extracted and their abilities to differentiate these abnormalities were examined using weights obtained from the neighborhood component analysis (NCA). The top 10 features which showed the greatest abilities in classifying these abnormalities using recordings from the different databases were also identified. The achieved results demonstrate the ability of the algorithms to automatically identify and classify cardiac sounds. This work provides the basis for measurements of many useful clinical attributes of cardiac acoustic signals and can potentially help in monitoring the overall cardiac health for longer duration. The work presented in this thesis is the first-of-its-kind to validate the results using both, normal and pathological cardiac acoustic signals, recorded for a long continuous duration of 5 minutes at four different auscultation sites in non-controlled real-world conditions.Open Acces

An open access database for the evaluation of heart sound algorithms

This is an author-created, un-copyedited version of an article published in Physiological Measurement. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/0967-3334/37/12/2181In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.This work was supported by the National Institutes of Health (NIH) grant R01-EB001659 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and R01GM104987 from the National Institute of General Medical Sciences.Liu, C.; Springer, DC.; Li, Q.; Moody, B.; Abad Juan, RC.; Li, Q.; Moody, B.... (2016). An open access database for the evaluation of heart sound algorithms. Physiological Measurement. 37(12):2181-2213. doi:10.1088/0967-3334/37/12/2181S21812213371

NRC-Net: Automated noise robust cardio net for detecting valvular cardiac diseases using optimum transformation method with heart sound signals

Cardiovascular diseases (CVDs) can be effectively treated when detected early, reducing mortality rates significantly. Traditionally, phonocardiogram (PCG) signals have been utilized for detecting cardiovascular disease due to their cost-effectiveness and simplicity. Nevertheless, various environmental and physiological noises frequently affect the PCG signals, compromising their essential distinctive characteristics. The prevalence of this issue in overcrowded and resource-constrained hospitals can compromise the accuracy of medical diagnoses. Therefore, this study aims to discover the optimal transformation method for detecting CVDs using noisy heart sound signals and propose a noise robust network to improve the CVDs classification performance.For the identification of the optimal transformation method for noisy heart sound data mel-frequency cepstral coefficients (MFCCs), short-time Fourier transform (STFT), constant-Q nonstationary Gabor transform (CQT) and continuous wavelet transform (CWT) has been used with VGG16. Furthermore, we propose a novel convolutional recurrent neural network (CRNN) architecture called noise robust cardio net (NRC-Net), which is a lightweight model to classify mitral regurgitation, aortic stenosis, mitral stenosis, mitral valve prolapse, and normal heart sounds using PCG signals contaminated with respiratory and random noises. An attention block is included to extract important temporal and spatial features from the noisy corrupted heart sound.The results of this study indicate that,CWT is the optimal transformation method for noisy heart sound signals. When evaluated on the GitHub heart sound dataset, CWT demonstrates an accuracy of 95.69% for VGG16, which is 1.95% better than the second-best CQT transformation technique. Moreover, our proposed NRC-Net with CWT obtained an accuracy of 97.4%, which is 1.71% higher than the VGG16

Acoustic sensing as a novel wearable approach for cardiac monitoring at the wrist

This paper introduces the concept of using acoustic sensing over the radial artery to extract cardiac parameters for continuous vital sign monitoring. It proposes a novel measurement principle that allows detection of the heart sounds together with the pulse wave, an attribute not possible with existing photoplethysmography (PPG)-based methods for monitoring at wrist. The validity of the proposed principle is demonstrated using a new miniature, battery-operated wearable device to sense the acoustic signals and a novel algorithm to extract the heart rate from these signals. The algorithm utilizes the power spectral analysis of the acoustic pulse signal to detect the S1 sounds and additionally, the K-means method to remove motion artifacts for an accurate heartbeat detection. It has been validated on a dataset consisting of 12 subjects with a data length of 6 hours. The results demonstrate an accuracy of 98.78\%, mean absolute error of 0.28 bpm, limits of agreement between -1.68 and 1.69 bpm, and a correlation coefficient of 0.998 with reference to a state-of-the-art PPG-based commercial device. The results in this proof of concept study demonstrate the potential of this new sensing modality to be used as an alternative, or to complement existing methods, for continuous monitoring of heart rate at wrist

Personal Heart Health Monitoring Based on 1D Convolutional Neural Network

The automated detection of suspicious anomalies in electrocardiogram (ECG) recordings allows frequent personal heart health monitoring and can drastically reduce the number of ECGs that need to be manually examined by the cardiologists, excluding those classified as normal, facilitating healthcare decision-making and reducing a considerable amount of time and money. In this paper, we present a system able to automatically detect the suspect of cardiac pathologies in ECG signals from personal monitoring devices, with the aim to alert the patient to send the ECG to the medical specialist for a correct diagnosis and a proper therapy. The main contributes of this work are: (a) the implementation of a binary classifier based on a 1D-CNN architecture for detecting the suspect of anomalies in ECGs, regardless of the kind of cardiac pathology; (b) the analysis was carried out on 21 classes of different cardiac pathologies classified as anomalous; and (c) the possibility to classify anomalies even in ECG segments containing, at the same time, more than one class of cardiac pathologies. Moreover, 1D-CNN based architectures can allow an implementation of the system on cheap smart devices with low computational complexity. The system was tested on the ECG signals from the MIT-BIH ECG Arrhythmia Database for the MLII derivation. Two different experiments were carried out, showing remarkable performance compared to other similar systems. The best result showed high accuracy and recall, computed in terms of ECG segments and even higher accuracy and recall in terms of patients alerted, therefore considering the detection of anomalies with respect to entire ECG recordings

Recent advances in heart sound analysis

"This is an author-created, un-copyedited versíon of an article published in Physiological Measurement. IOP Publishing Ltd is not responsíble for any errors or omissíons in this versíon of the manuscript or any versíon derived from it. The Versíon of Record is available online at https://doi.org/10.1088/1361-6579/aa7ec8".[EN] Objective: Auscultation of heart sound recordings or the phonocardiogram (PCG) has been shown to be valuable for the detection of disease and pathologies (Leatham 1975, Raghu et al 2015). The automated classification of pathology in heart sounds has been studied for over 50 years. Typical methods can be grouped into: artificial neural network-based approaches (Uguz 2012), support vector machines (Ari et al 2010), hidden Markov model-based approaches (Saracoglu 2012) and clustering-based approaches (Quiceno-Manrique et al 2010). However, accurate automated classification still remains a significant challenge due to the lack of highquality, rigorously validated, and standardized open databases of heart sound recordings. Approach: The 2016 PhysioNet/Computing in Cardiology (CinC) Challenge sought to create a large database to facilitate this, by assembling recordings from multiple research groups across the world, acquired in different real-world clinical and nonclinical environments (such as in-home visits), to encourage the development of algorithms to accurately identify, from a single short recording (10-60s), as normal, abnormal or poor signal quality, and thus to further identify whether the subject of the recording should be referred on for an expert diagnosis (Liu et al 2016). Until this Challenge, no significant open-access heart sound database was available for researchers to train and evaluate the automated diagnostics algorithms upon (Clifford et al 2016). Moreover, no open source heart sound segmentation and classification algorithms were available. The Challenge changed this situation significantly. Main results and Significance: This editorial reviews the follow-up research generated as a result of the Challenge, published in the concurrent special issue of Physiological Measurement. Additionally we make some recommendations for promising research avenues in the field of heart sound signal processing and classification as a result of the Challenge.This work was funded in part by the National Institutes of Health, grant R01-GM104987, the International Postdoctoral Exchange Programme of the National Postdoctoral Management Committee of China and Emory University. We are also grateful to Mathworks for providing free software licenses and sponsoring the Challenge prize money, and Computing in Cardiology for sponsoring the Challenge prize money and providing a forum to present the Challenge results. We would also like to thank the database contributors, and data annotators for their invaluable assistance. Finally, we would like to thank all the competitors and researchers themselves, without whom there would be no Challenge or special issue.Clifford, GD.; Liu, C.; Moody, B.; Millet Roig, J.; Schmidt, S.; Li, Q.; Silva, I.... (2017). Recent advances in heart sound analysis. Physiological Measurement. 38(8):10-25. https://doi.org/10.1088/1361-6579/aa7ec8S1025388Abdollahpur, M., Ghaffari, A., Ghiasi, S., & Mollakazemi, M. J. (2017). Detection of pathological heart sounds. Physiological Measurement, 38(8), 1616-1630. doi:10.1088/1361-6579/aa7840Ari, S., Hembram, K., & Saha, G. (2010). Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier. Expert Systems with Applications, 37(12), 8019-8026. doi:10.1016/j.eswa.2010.05.088Chauhan, S., Wang, P., Sing Lim, C., & Anantharaman, V. (2008). A computer-aided MFCC-based HMM system for automatic auscultation. Computers in Biology and Medicine, 38(2), 221-233. doi:10.1016/j.compbiomed.2007.10.006Nabhan Homsi, M., & Warrick, P. (2017). Ensemble methods with outliers for phonocardiogram classification. Physiological Measurement, 38(8), 1631-1644. doi:10.1088/1361-6579/aa7982Kay, E., & Agarwal, A. (2017). DropConnected neural networks trained on time-frequency and inter-beat features for classifying heart sounds. Physiological Measurement, 38(8), 1645-1657. doi:10.1088/1361-6579/aa6a3dLangley, P., & Murray, A. (2017). Heart sound classification from unsegmented phonocardiograms. Physiological Measurement, 38(8), 1658-1670. doi:10.1088/1361-6579/aa724cLiu, C., Springer, D., Li, Q., Moody, B., Juan, R. A., Chorro, F. J., … Clifford, G. D. (2016). An open access database for the evaluation of heart sound algorithms. Physiological Measurement, 37(12), 2181-2213. doi:10.1088/0967-3334/37/12/2181Maknickas, V., & Maknickas, A. (2017). Recognition of normal–abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients. Physiological Measurement, 38(8), 1671-1684. doi:10.1088/1361-6579/aa7841Plesinger, F., Viscor, I., Halamek, J., Jurco, J., & Jurak, P. (2017). Heart sounds analysis using probability assessment. Physiological Measurement, 38(8), 1685-1700. doi:10.1088/1361-6579/aa7620Da Poian, G., Liu, C., Bernardini, R., Rinaldo, R., & Clifford, G. D. (2017). Atrial fibrillation detection on compressed sensed ECG. Physiological Measurement, 38(7), 1405-1425. doi:10.1088/1361-6579/aa7652Quiceno-Manrique, A. F., Godino-Llorente, J. I., Blanco-Velasco, M., & Castellanos-Dominguez, G. (2009). Selection of Dynamic Features Based on Time–Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals. Annals of Biomedical Engineering, 38(1), 118-137. doi:10.1007/s10439-009-9838-3Jull, J., Giles, A., Boyer, Y., & Stacey, D. (2015). Cultural adaptation of a shared decision making tool with Aboriginal women: a qualitative study. BMC Medical Informatics and Decision Making, 15(1). doi:10.1186/s12911-015-0129-7Saraçoğlu, R. (2012). Hidden Markov model-based classification of heart valve disease with PCA for dimension reduction. Engineering Applications of Artificial Intelligence, 25(7), 1523-1528. doi:10.1016/j.engappai.2012.07.005Schmidt, S. E., Holst-Hansen, C., Graff, C., Toft, E., & Struijk, J. J. (2010). Segmentation of heart sound recordings by a duration-dependent hidden Markov model. Physiological Measurement, 31(4), 513-529. doi:10.1088/0967-3334/31/4/004Springer, D. B., Brennan, T., Ntusi, N., Abdelrahman, H. Y., Zühlke, L. J., Mayosi, B. M., … Clifford, G. D. (2016). Automated signal quality assessment of mobile phone-recorded heart sound signals. Journal of Medical Engineering & Technology, 40(7-8), 342-355. doi:10.1080/03091902.2016.1213902Springer, D., Tarassenko, L., & Clifford, G. (2015). Logistic Regression-HSMM-based Heart Sound Segmentation. IEEE Transactions on Biomedical Engineering, 1-1. doi:10.1109/tbme.2015.2475278Uğuz, H. (2010). A Biomedical System Based on Artificial Neural Network and Principal Component Analysis for Diagnosis of the Heart Valve Diseases. Journal of Medical Systems, 36(1), 61-72. doi:10.1007/s10916-010-9446-7Whitaker, B. M., Suresha, P. B., Liu, C., Clifford, G. D., & Anderson, D. V. (2017). Combining sparse coding and time-domain features for heart sound classification. Physiological Measurement, 38(8), 1701-1713. doi:10.1088/1361-6579/aa7623Zhu, T., Dunkley, N., Behar, J., Clifton, D. A., & Clifford, G. D. (2015). Fusing Continuous-Valued Medical Labels Using a Bayesian Model. Annals of Biomedical Engineering, 43(12), 2892-2902. doi:10.1007/s10439-015-1344-1Zhu, T., Johnson, A. E. W., Behar, J., & Clifford, G. D. (2013). Crowd-Sourced Annotation of ECG Signals Using Contextual Information. Annals of Biomedical Engineering, 42(4), 871-884. doi:10.1007/s10439-013-0964-

Acoustic sensing as a novel approach for cardiovascular monitoring at the wrist

Cardiovascular diseases are the number one cause of deaths globally. An increased cardiovascular risk can be detected by a regular monitoring of the vital signs including the heart rate, the heart rate variability (HRV) and the blood pressure. For a user to undergo continuous vital sign monitoring, wearable systems prove to be very useful as the device can be integrated into the user's lifestyle without affecting the daily activities. However, the main challenge associated with the monitoring of these cardiovascular parameters is the requirement of different sensing mechanisms at different measurement sites. There is not a single wearable device that can provide sufficient physiological information to track the vital signs from a single site on the body. This thesis proposes a novel concept of using acoustic sensing over the radial artery to extract cardiac parameters for vital sign monitoring. A wearable system consisting of a microphone is designed to allow the detection of the heart sounds together with the pulse wave, an attribute not possible with existing wrist-based sensing methods. Methods: The acoustic signals recorded from the radial artery are a continuous reflection of the instantaneous cardiac activity. These signals are studied and characterised using different algorithms to extract cardiovascular parameters. The validity of the proposed principle is firstly demonstrated using a novel algorithm to extract the heart rate from these signals. The algorithm utilises the power spectral analysis of the acoustic pulse signal to detect the S1 sounds and additionally, the K-means method to remove motion artifacts for an accurate heartbeat detection. The HRV in the short-term acoustic recordings is found by extracting the S1 events using the relative information between the short- and long-term energies of the signal. The S1 events are localised using three different characteristic points and the best representation is found by comparing the instantaneous heart rate profiles. The possibility of measuring the blood pressure using the wearable device is shown by recording the acoustic signal under the influence of external pressure applied on the arterial branch. The temporal and spectral characteristics of the acoustic signal are utilised to extract the feature signals and obtain a relationship with the systolic blood pressure (SBP) and diastolic blood pressure (DBP) respectively. Results: This thesis proposes three different algorithms to find the heart rate, the HRV and the SBP/ DBP readings from the acoustic signals recorded at the wrist. The results obtained by each algorithm are as follows: 1. The heart rate algorithm is validated on a dataset consisting of 12 subjects with a data length of 6 hours. The results demonstrate an accuracy of 98.78%, mean absolute error of 0.28 bpm, limits of agreement between -1.68 and 1.69 bpm, and a correlation coefficient of 0.998 with reference to a state-of-the-art PPG-based commercial device. A high statistical agreement between the heart rate obtained from the acoustic signal and the photoplethysmography (PPG) signal is observed. 2. The HRV algorithm is validated on the short-term acoustic signals of 5-minutes duration recorded from each of the 12 subjects. A comparison is established with the simultaneously recorded electrocardiography (ECG) and PPG signals respectively. The instantaneous heart rate for all the subjects combined together achieves an accuracy of 98.50% and 98.96% with respect to the ECG and PPG signals respectively. The results for the time-domain and frequency-domain HRV parameters also demonstrate high statistical agreement with the ECG and PPG signals respectively. 3. The algorithm proposed for the SBP/ DBP determination is validated on 104 acoustic signals recorded from 40 adult subjects. The experimental outputs when compared with the reference arm- and wrist-based monitors produce a mean error of less than 2 mmHg and a standard deviation of error around 6 mmHg. Based on these results, this thesis shows the potential of this new sensing modality to be used as an alternative, or to complement existing methods, for the continuous monitoring of heart rate and HRV, and spot measurement of the blood pressure at the wrist.Open Acces