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De Fazio R, Spongano L, De Vittorio M, Patrono L, Visconti P. Machine Learning Algorithms for Processing and Classifying Unsegmented Phonocardiographic Signals: An Efficient Edge Computing Solution Suitable for Wearable Devices. SENSORS (BASEL, SWITZERLAND) 2024; 24:3853. [PMID: 38931636 PMCID: PMC11207414 DOI: 10.3390/s24123853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 06/04/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024]
Abstract
The phonocardiogram (PCG) can be used as an affordable way to monitor heart conditions. This study proposes the training and testing of several classifiers based on SVMs (support vector machines), k-NN (k-Nearest Neighbor), and NNs (neural networks) to perform binary ("Normal"/"Pathologic") and multiclass ("Normal", "CAD" (coronary artery disease), "MVP" (mitral valve prolapse), and "Benign" (benign murmurs)) classification of PCG signals, without heart sound segmentation algorithms. Two datasets of 482 and 826 PCG signals from the Physionet/CinC 2016 dataset are used to train the binary and multiclass classifiers, respectively. Each PCG signal is pre-processed, with spike removal, denoising, filtering, and normalization; afterward, it is divided into 5 s frames with a 1 s shift. Subsequently, a feature set is extracted from each frame to train and test the binary and multiclass classifiers. Concerning the binary classification, the trained classifiers yielded accuracies ranging from 92.4 to 98.7% on the test set, with memory occupations from 92.7 kB to 11.1 MB. Regarding the multiclass classification, the trained classifiers achieved accuracies spanning from 95.3 to 98.6% on the test set, occupying a memory portion from 233 kB to 14.1 MB. The NNs trained and tested in this work offer the best trade-off between performance and memory occupation, whereas the trained k-NN models obtained the best performance at the cost of large memory occupation (up to 14.1 MB). The classifiers' performance slightly depends on the signal quality, since a denoising step is performed during pre-processing. To this end, the signal-to-noise ratio (SNR) was acquired before and after the denoising, indicating an improvement between 15 and 30 dB. The trained and tested models occupy relatively little memory, enabling their implementation in resource-limited systems.
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Affiliation(s)
- Roberto De Fazio
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy; (R.D.F.); (L.S.); (M.D.V.); (L.P.)
| | - Lorenzo Spongano
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy; (R.D.F.); (L.S.); (M.D.V.); (L.P.)
- Center for Biomolecular Nanotechnologies, Italian Institute of Technology, 73010 Arnesano, Italy
| | - Massimo De Vittorio
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy; (R.D.F.); (L.S.); (M.D.V.); (L.P.)
- Center for Biomolecular Nanotechnologies, Italian Institute of Technology, 73010 Arnesano, Italy
| | - Luigi Patrono
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy; (R.D.F.); (L.S.); (M.D.V.); (L.P.)
| | - Paolo Visconti
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy; (R.D.F.); (L.S.); (M.D.V.); (L.P.)
- Center for Biomolecular Nanotechnologies, Italian Institute of Technology, 73010 Arnesano, Italy
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Singh Aditya M, Rasipuram S, Chattopadhyay S, Maitra A, Sanjoy P, Maziyar BP, Roy D, Dey S. Early-warning of Cardiac Condition through Detection of Murmur in Heart Sound - A Case Study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-6. [PMID: 38083243 DOI: 10.1109/embc40787.2023.10340924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Cardiovascular disease, particularly Rheumatic Heart Disease (RHD), is one of the leading causes of death in many developing countries. RHD is manageable and treatable with early detection. However, multiple countries across the globe suffer from a scarcity of experienced physicians who can perform screening at large scales. Advancements in machine learning and signal processing have paved way for Phonocardiogram (PCG)-based automatic heart sound classification. The direct implication of such methods is that it is possible to enable a person without specialized training to detect potential cardiac conditions with just a digital stethoscope. Hospitalization or life-threatening situations can be dramatically reduced via such early screenings. Towards this, we conducted a case study amongst a population from a particular geography using machine learning and deep learning methods for the detection of murmur in heart sounds. The methodology consists of first pre-processing and identifying normal vs. abnormal heart sound signals using 3 state-of-the-art methods. The second step further identifies the murmur to be systolic or diastolic by capturing the auscultation location. Abnormal findings are then sent for early attention of clinicians for proper diagnosis. The case study investigates the efficacy of the automated method employed for early screening of potential RHD and initial encouraging results of the study are presented.
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Azmeen A, Vakilzadian H, Haider H, Mathers DH, Zimmerman R, Bedi S, O'Leary EL. Heart sounds: Past, present, and future from a technological and clinical perspective - a systematic review. Proc Inst Mech Eng H 2023:9544119231172858. [PMID: 37139865 DOI: 10.1177/09544119231172858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
The high prevalence of cardiac diseases around the world has created a need for quick, easy and cost effective approaches to diagnose heart disease. The auscultation and interpretation of heart sounds using the stethoscope is relatively inexpensive, requires minimal to advanced training, and is widely available and easily carried by healthcare providers working in urban environments or medically underserved rural areas. Since René-Théophile-Hyacinthe Laennec's simple, monoaural design, the capabilities of modern-day, commercially available stethoscopes and stethoscope systems have radically advanced with the integration of electronic hardware and software tools, however these systems are largely confined to the metropolitan medical centers. The purpose of this paper is to review the history of stethoscopes, compare commercially available stethoscope products and analytical software, and discuss future directions. Our review includes a description of heart sounds and how modern software enables the measurement and analysis of time intervals, teaching auscultation, remote cardiac examination (telemedicine) and, more recently, spectrographic evaluation and electronic storage. The basic methodologies behind modern software algorithms and techniques for heart sound preprocessing, segmentation and classification are described to provide awareness.
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Affiliation(s)
- Ayesha Azmeen
- University of Nebraska Medical Center, Omaha, NE, USA
| | | | - Hani Haider
- University of Nebraska Medical Center, Omaha, NE, USA
| | | | | | - Shine Bedi
- Univeristy of Nebraska-Lincoln, Lincoln, NE, USA
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Monitoring of Sleep Breathing States Based on Audio Sensor Utilizing Mel-Scale Features in Home Healthcare. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:6197564. [PMID: 36818388 PMCID: PMC9935909 DOI: 10.1155/2023/6197564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 07/03/2022] [Accepted: 11/24/2022] [Indexed: 02/11/2023]
Abstract
Sleep-related breathing disorders (SBDs) will lead to poor sleep quality and increase the risk of cardiovascular and cerebrovascular diseases which may cause death in serious cases. This paper aims to detect breathing states related to SBDs by breathing sound signals. A moment waveform analysis is applied to locate and segment the breathing cycles. As the core of our study, a set of useful features of breathing signal is proposed based on Mel frequency cepstrum analysis. Finally, the normal and abnormal sleep breathing states can be distinguished by the extracted Mel-scale indexes. Young healthy testers and patients who suffered from obstructive sleep apnea are tested utilizing the proposed method. The average accuracy for detecting abnormal breathing states can reach 93.1%. It will be helpful to prevent SBDs and improve the sleep quality of home healthcare.
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On the analysis of data augmentation methods for spectral imaged based heart sound classification using convolutional neural networks. BMC Med Inform Decis Mak 2022; 22:226. [PMID: 36038901 PMCID: PMC9421122 DOI: 10.1186/s12911-022-01942-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 05/10/2022] [Indexed: 11/10/2022] Open
Abstract
Background The application of machine learning to cardiac auscultation has the potential to improve the accuracy and efficiency of both routine and point-of-care screenings. The use of convolutional neural networks (CNN) on heart sound spectrograms in particular has defined state-of-the-art performance. However, the relative paucity of patient data remains a significant barrier to creating models that can adapt to a wide range of potential variability. To that end, we examined a CNN model’s performance on automated heart sound classification, before and after various forms of data augmentation, and aimed to identify the most optimal augmentation methods for cardiac spectrogram analysis. Results We built a standard CNN model to classify cardiac sound recordings as either normal or abnormal. The baseline control model achieved a PR AUC of 0.763 ± 0.047. Among the single data augmentation techniques explored, horizontal flipping of the spectrogram image improved the model performance the most, with a PR AUC of 0.819 ± 0.044. Principal component analysis color augmentation (PCA) and perturbations of saturation-value (SV) of the hue-saturation-value (HSV) color scale achieved a PR AUC of 0.779 ± 045 and 0.784 ± 0.037, respectively. Time and frequency masking resulted in a PR AUC of 0.772 ± 0.050. Pitch shifting, time stretching and compressing, noise injection, vertical flipping, and applying random color filters negatively impacted model performance. Concatenating the best performing data augmentation technique (horizontal flip) with PCA and SV perturbations improved model performance. Conclusion Data augmentation can improve classification accuracy by expanding and diversifying the dataset, which protects against overfitting to random variance. However, data augmentation is necessarily domain specific. For example, methods like noise injection have found success in other areas of automated sound classification, but in the context of cardiac sound analysis, noise injection can mimic the presence of murmurs and worsen model performance. Thus, care should be taken to ensure clinically appropriate forms of data augmentation to avoid negatively impacting model performance.
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Li Z, Chang Y, Schuller BW. CNN-Based Heart Sound Classification with an Imbalance-Compensating Weighted Loss Function. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4934-4937. [PMID: 36085939 DOI: 10.1109/embc48229.2022.9871904] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Heart sound auscultation is an effective method for early-stage diagnosis of heart disease. The application of deep neural networks is gaining increasing attention in automated heart sound classification. This paper proposes deep Convolutional Neural Networks (CNNs) to classify normal/abnormal heart sounds, which takes two-dimensional Mel-scale features as input, including Mel frequency cepstral coefficients (MFCCs) and the Log Mel spectrum. We employ two weighted loss functions during the training to mitigate the class imbalance issue. The model was developed on the public PhysioNet/Computing in Cardiology Challenge (CinC) 2016 heart sound database. On the considered test set, the proposed model with Log Mel spectrum as features achieves an Unweighted Average Recall (UAR) of 89.6%, with sensitivity and specificity being 89.5% and 89.7% respectively. This work proposes a CNN-based model to enable automated heart sound classification, which can provide auxiliary assistance for heart auscultation and has the potential to screen for heart pathologies in clinical applications at a relatively low cost.
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Analysis of Two-Piano Teaching Assistant Training Based on Neural Network Model Sound Sequence Recognition. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5768291. [PMID: 35694593 PMCID: PMC9184186 DOI: 10.1155/2022/5768291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 05/13/2022] [Accepted: 05/20/2022] [Indexed: 11/21/2022]
Abstract
In today's society, with the gradual improve5ment of material living standards, people are also more in pursuit of their own spiritual enjoyment. The study of piano has gradually become a way for people to enrich their spiritual life, and more and more people attach importance to it. In the field of piano teaching, the two-piano method is a unique form of playing the piano. In order to solve the problem that the recognition accuracy of the sequence of two pianos is seriously reduced in the environment of noise and reverberation, this paper proposes an auxiliary training analysis system based on the neural network model. Firstly, in order to learn the nonlinear relationship between the sound order and the target task label from the massive data, a multitask preprocessing method combining speech enhancement and detection is used to supervise the deep neural network training. Then, convolutional neural network is used to construct the end-to-end recognition system, and the initial recognition results are checked and corrected by the phonological sequence model. Finally, the sequence recognition is carried out under the condition of noise, and the articulation is improved by speech enhancement front-end module, and then the sequence recognition model is used for recognition. Compared with traditional training methods, it is proved that our method is effective in improving the training efficiency and performance quality of players. At the same time, this method breaks through the limitation of traditional training method of double piano, creates a more scientific training means, and realizes the practice and application of artificial intelligence technology in the teaching of double piano.
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Ren Z, Qian K, Dong F, Dai Z, Nejdl W, Yamamoto Y, Schuller BW. Deep attention-based neural networks for explainable heart sound classification. MACHINE LEARNING WITH APPLICATIONS 2022. [DOI: 10.1016/j.mlwa.2022.100322] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
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The Effect of Signal Duration on the Classification of Heart Sounds: A Deep Learning Approach. SENSORS 2022; 22:s22062261. [PMID: 35336432 PMCID: PMC8951308 DOI: 10.3390/s22062261] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 02/26/2022] [Accepted: 03/12/2022] [Indexed: 02/01/2023]
Abstract
Deep learning techniques are the future trend for designing heart sound classification methods, making conventional heart sound segmentation dispensable. However, despite using fixed signal duration for training, no study has assessed its effect on the final performance in detail. Therefore, this study aims at analysing the duration effect on the commonly used deep learning methods to provide insight for future studies in data processing, classifier, and feature selection. The results of this study revealed that (1) very short heart sound signal duration (1 s) weakens the performance of Recurrent Neural Networks (RNNs), whereas no apparent decrease in the tested Convolutional Neural Network (CNN) model was found. (2) RNN outperformed CNN using Mel-frequency cepstrum coefficients (MFCCs) as features. There was no difference between RNN models (LSTM, BiLSTM, GRU, or BiGRU). (3) Adding dynamic information (∆ and ∆²MFCCs) of the heart sound as a feature did not improve the RNNs' performance, and the improvement on CNN was also minimal (≤2.5% in MAcc). The findings provided a theoretical basis for further heart sound classification using deep learning techniques when selecting the input length.
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Khan SI, Qaisar SM, Pachori RB. Automated classification of valvular heart diseases using FBSE-EWT and PSR based geometrical features. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103445] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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11
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Camara C, Peris-Lopez P, Safkhani M, Bagheri N. ECGsound for human identification. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103335] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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12
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Cardiovascular Disease Recognition Based on Heartbeat Segmentation and Selection Process. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182010952. [PMID: 34682696 PMCID: PMC8535944 DOI: 10.3390/ijerph182010952] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 09/04/2021] [Accepted: 09/29/2021] [Indexed: 12/01/2022]
Abstract
Assessment of heart sounds which are generated by the beating heart and the resultant blood flow through it provides a valuable tool for cardiovascular disease (CVD) diagnostics. The cardiac auscultation using the classical stethoscope phonological cardiogram is known as the most famous exam method to detect heart anomalies. This exam requires a qualified cardiologist, who relies on the cardiac cycle vibration sound (heart muscle contractions and valves closure) to detect abnormalities in the heart during the pumping action. Phonocardiogram (PCG) signal represents the recording of sounds and murmurs resulting from the heart auscultation, typically with a stethoscope, as a part of medical diagnosis. For the sake of helping physicians in a clinical environment, a range of artificial intelligence methods was proposed to automatically analyze PCG signal to help in the preliminary diagnosis of different heart diseases. The aim of this research paper is providing an accurate CVD recognition model based on unsupervised and supervised machine learning methods relayed on convolutional neural network (CNN). The proposed approach is evaluated on heart sound signals from the well-known, publicly available PASCAL and PhysioNet datasets. Experimental results show that the heart cycle segmentation and segment selection processes have a direct impact on the validation accuracy, sensitivity (TPR), precision (PPV), and specificity (TNR). Based on PASCAL dataset, we obtained encouraging classification results with overall accuracy 0.87, overall precision 0.81, and overall sensitivity 0.83. Concerning Micro classification results, we obtained Micro accuracy 0.91, Micro sensitivity 0.83, Micro precision 0.84, and Micro specificity 0.92. Using PhysioNet dataset, we achieved very good results: 0.97 accuracy, 0.946 sensitivity, 0.944 precision, and 0.946 specificity.
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Deep Layer Kernel Sparse Representation Network for the Detection of Heart Valve Ailments from the Time-Frequency Representation of PCG Recordings. BIOMED RESEARCH INTERNATIONAL 2020; 2020:8843963. [PMID: 33415163 PMCID: PMC7769642 DOI: 10.1155/2020/8843963] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 11/22/2020] [Accepted: 12/08/2020] [Indexed: 12/21/2022]
Abstract
The heart valve ailments (HVAs) are due to the defects in the valves of the heart and if untreated may cause heart failure, clots, and even sudden cardiac death. Automated early detection of HVAs is necessary in the hospitals for proper diagnosis of pathological cases, to provide timely treatment, and to reduce the mortality rate. The heart valve abnormalities will alter the heart sound and murmurs which can be faithfully captured by phonocardiogram (PCG) recordings. In this paper, a time-frequency based deep layer kernel sparse representation network (DLKSRN) is proposed for the detection of various HVAs using PCG signals. Spline kernel-based Chirplet transform (SCT) is used to evaluate the time-frequency representation of PCG recording, and the features like L1-norm (LN), sample entropy (SEN), and permutation entropy (PEN) are extracted from the different frequency components of the time-frequency representation of PCG recording. The DLKSRN formulated using the hidden layers of extreme learning machine- (ELM-) autoencoders and kernel sparse representation (KSR) is used for the classification of PCG recordings as normal, and pathology cases such as mitral valve prolapse (MVP), mitral regurgitation (MR), aortic stenosis (AS), and mitral stenosis (MS). The proposed approach has been evaluated using PCG recordings from both public and private databases, and the results demonstrated that an average sensitivity of 100%, 97.51%, 99.00%, 98.72%, and 99.13% are obtained for normal, MVP, MR, AS, and MS cases using the hold-out cross-validation (CV) method. The proposed approach is applicable for the Internet of Things- (IoT-) driven smart healthcare system for the accurate detection of HVAs.
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Heart sound classification based on improved MFCC features and convolutional recurrent neural networks. Neural Netw 2020; 130:22-32. [PMID: 32589588 DOI: 10.1016/j.neunet.2020.06.015] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 05/02/2020] [Accepted: 06/19/2020] [Indexed: 11/24/2022]
Abstract
Heart sound classification plays a vital role in the early detection of cardiovascular disorders, especially for small primary health care clinics. Despite that much progress has been made for heart sound classification in recent years, most of them are based on conventional segmented features and shallow structure based classifiers. These conventional acoustic representation and classification methods may be insufficient in characterizing heart sound, and generally suffer from a degraded performance due to the complicated and changeable cardiac acoustic environment. In this paper, we propose a new heart sound classification method based on improved Mel-frequency cepstrum coefficient (MFCC) features and convolutional recurrent neural networks. The Mel-frequency cepstrums are firstly calculated without dividing the heart sound signal. A new improved feature extraction scheme based on MFCC is proposed to elaborate the dynamic characteristics among consecutive heart sound signals. Finally, the MFCC-based features are fed to a deep convolutional and recurrent neural network (CRNN) for feature learning and later classification task. The proposed deep learning framework can take advantage of the encoded local characteristics extracted from the convolutional neural network (CNN) and the long-term dependencies captured by the recurrent neural network (RNN). Comprehensive studies on the performance of different network parameters and different network connection strategies are presented in this paper. Performance comparisons with state-of-the-art algorithms are given for discussions. Experiments show that, for the two-class classification problem (pathological or non-pathological), a classification accuracy of 98% has been achieved on the 2016 PhysioNet/CinC Challenge database.
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Stochastic Sequential Modeling: Toward Improved Prostate Cancer Diagnosis Through Temporal-Ultrasound. Ann Biomed Eng 2020; 49:573-584. [PMID: 32779056 PMCID: PMC7851024 DOI: 10.1007/s10439-020-02585-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 07/27/2020] [Indexed: 11/26/2022]
Abstract
Prostate cancer (PCa) is a common, serious form of cancer in men that is still prevalent despite ongoing developments in diagnostic oncology. Current detection methods lead to high rates of inaccurate diagnosis. We present a method to directly model and exploit temporal aspects of temporal enhanced ultrasound (TeUS) for tissue characterization, which improves malignancy prediction. We employ a probabilistic-temporal framework, namely, hidden Markov models (HMMs), for modeling TeUS data obtained from PCa patients. We distinguish malignant from benign tissue by comparing the respective log-likelihood estimates generated by the HMMs. We analyze 1100 TeUS signals acquired from 12 patients. Our results show improved malignancy identification compared to previous results, demonstrating over 85% accuracy and AUC of 0.95. Incorporating temporal information directly into the models leads to improved tissue differentiation in PCa. We expect our method to generalize and be applied to other types of cancer in which temporal-ultrasound can be recorded.
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Ozkan I, Yilmaz A, Celebi G. Improved Segmentation with Dynamic Threshold Adjustment for Phonocardiography Recordings. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:6681-6684. [PMID: 31947374 DOI: 10.1109/embc.2019.8856714] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this study with the intent to segment phonocardiography (PCG) recordings, an algorithm that processes a feature vector obtained by jointly using wavelet transform and mel scaled energy spectrum of the PCG signal is proposed. The feature vector is processed by a peak detection algorithm which results in a set of peaks that meet some certain criteria and will be exploited in the succeeding stages of the algorithm. Heart sounds are labeled by convolving circularly a template and a fragment of the feature vector which is picked up with the guidance of the peaks. At the final stage, the algorithm tries to detect and correct erroneous labels. The performance of the algorithm was tested on both normal heart sounds and abnormal heart sounds; 80 records in total. As a result of these tests, for normal heart sounds S1 and S2 sounds were detected with 99,51% recall and 97,28% precision, while with 97,59% recall and 92,53% precision for abnormal heart sounds.
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Zhang S, Zhang R, Chang S, Liu C, Sha X. A Low-Noise-Level Heart Sound System Based on Novel Thorax-Integration Head Design and Wavelet Denoising Algorithm. MICROMACHINES 2019; 10:mi10120885. [PMID: 31861068 PMCID: PMC6953004 DOI: 10.3390/mi10120885] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Revised: 12/08/2019] [Accepted: 12/13/2019] [Indexed: 11/17/2022]
Abstract
Along with the great performance in diagnosing cardiovascular diseases, current stethoscopes perform unsatisfactorily in controlling undesired noise caused by the surrounding environment and detector operation. In this case, a low-noise-level heart sound system was designed to inhibit noise by a novel thorax-integration head with a flexible electric film. A hardware filter bank and wavelet-based algorithm were employed to enhance the recorded heart sounds from the system. In the experiments, we used the new system and the 3M™ Littmann® Model 3200 Electronic Stethoscope separately to record heart sounds in different noisy environments. The results illustrated that the average estimated noise ratio represented 21.26% and the lowest represented only 12.47% compared to the 3M stethoscope, demonstrating the better performance in denoising ability of this system than state-of-the-art equipment. Furthermore, based on the heart sounds recorded with this system, some diagnosis results were achieved from an expert and compared to echocardiography reports. The diagnoses were correct except for two uncertain items, which greatly confirmed the fact that this system could reserve complete pathological information in the end.
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Affiliation(s)
- Shuo Zhang
- School of Fundamental Sciences, China Medical University, Shenyang 110122, China; (S.Z.)
| | - Ruiqing Zhang
- School of Fundamental Sciences, China Medical University, Shenyang 110122, China; (S.Z.)
| | - Shijie Chang
- School of Fundamental Sciences, China Medical University, Shenyang 110122, China; (S.Z.)
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
| | - Xianzheng Sha
- School of Fundamental Sciences, China Medical University, Shenyang 110122, China; (S.Z.)
- Correspondence:
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Dong F, Qian K, Ren Z, Baird A, Li X, Dai Z, Dong B, Metze F, Yamamoto Y, Schuller B. Machine Listening for Heart Status Monitoring: Introducing and Benchmarking HSS - the Heart Sounds Shenzhen Corpus. IEEE J Biomed Health Inform 2019; 24:2082-2092. [PMID: 31765322 DOI: 10.1109/jbhi.2019.2955281] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Auscultation of the heart is a widely studied technique, which requires precise hearing from practitioners as a means of distinguishing subtle differences in heart-beat rhythm. This technique is popular due to its non-invasive nature, and can be an early diagnosis aid for a range of cardiac conditions. Machine listening approaches can support this process, monitoring continuously and allowing for a representation of both mild and chronic heart conditions. Despite this potential, relevant databases and benchmark studies are scarce. In this paper, we introduce our publicly accessible database, the Heart Sounds Shenzhen Corpus (HSS), which was first released during the recent INTERSPEECH 2018 ComParE Heart Sound sub-challenge. Additionally, we provide a survey of machine learning work in the area of heart sound recognition, as well as a benchmark for HSS utilising standard acoustic features and machine learning models. At best our support vector machine with Log Mel features achieves 49.7% unweighted average recall on a three category task (normal, mild, moderate/severe).
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Gharehbaghi A, Lindén M, Babic A. An artificial intelligent-based model for detecting systolic pathological patterns of phonocardiogram based on time-growing neural network. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105615] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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20
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Vennemann B, Obrist D, Rösgen T. Automated diagnosis of heart valve degradation using novelty detection algorithms and machine learning. PLoS One 2019; 14:e0222983. [PMID: 31557196 PMCID: PMC6762068 DOI: 10.1371/journal.pone.0222983] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 09/11/2019] [Indexed: 11/28/2022] Open
Abstract
The blood flow through the major vessels holds great diagnostic potential for the identification of cardiovascular complications and is therefore routinely assessed with current diagnostic modalities. Heart valves are subject to high hydrodynamic loads which render them prone to premature degradation. Failing native aortic valves are routinely replaced with bioprosthetic heart valves. This type of prosthesis is limited by a durability that is often less than the patient's life expectancy. Frequent assessment of valvular function can therefore help to ensure good long-term outcomes and to plan reinterventions. In this article, we describe how unsupervised novelty detection algorithms can be used to automate the interpretation of blood flow data to improve outcomes through early detection of adverse cardiovascular events without requiring repeated check-ups in a clinical environment. The proposed method was tested in an in-vitro flow loop which allowed simulating a failing aortic valve in a laboratory setting. Aortic regurgitation of increasing severity was deliberately introduced with tube-shaped inserts, preventing complete valve closure during diastole. Blood flow recordings from a flow meter at the location of the ascending aorta were analyzed with the algorithms introduced in this article and a diagnostic index was defined that reflects the severity of valvular degradation. The results indicate that the proposed methodology offers a high sensitivity towards pathological changes of valvular function and that it is capable of automatically identifying valvular degradation. Such methods may be a step towards computer-assisted diagnostics and telemedicine that provide the clinician with novel tools to improve patient care.
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Affiliation(s)
- Bernhard Vennemann
- Institute of Fluid Dynamics, ETH Zürich, Zürich, Switzerland
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Dominik Obrist
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Thomas Rösgen
- Institute of Fluid Dynamics, ETH Zürich, Zürich, Switzerland
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Giordano N, Knaflitz M. A Novel Method for Measuring the Timing of Heart Sound Components through Digital Phonocardiography. SENSORS 2019; 19:s19081868. [PMID: 31010113 PMCID: PMC6515005 DOI: 10.3390/s19081868] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 03/31/2019] [Accepted: 04/16/2019] [Indexed: 11/29/2022]
Abstract
The auscultation of heart sounds has been for decades a fundamental diagnostic tool in clinical practice. Higher effectiveness can be achieved by recording the corresponding biomedical signal, namely the phonocardiographic signal, and processing it by means of traditional signal processing techniques. An unavoidable processing step is the heart sound segmentation, which is still a challenging task from a technical viewpoint—a limitation of state-of-the-art approaches is the unavailability of trustworthy techniques for the detection of heart sound components. The aim of this work is to design a reliable algorithm for the identification and the classification of heart sounds’ main components. The proposed methodology was tested on a sample population of 24 healthy subjects over 10-min-long simultaneous electrocardiographic and phonocardiographic recordings and it was found capable of correctly detecting and classifying an average of 99.2% of the heart sounds along with their components. Moreover, the delay of each component with respect to the corresponding R-wave peak and the delay among the components of the same heart sound were computed: the resulting experimental values are coherent with what is expected from the literature and what was obtained by other studies.
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Affiliation(s)
- Noemi Giordano
- Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, 10129 Torino, Italy.
| | - Marco Knaflitz
- Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, 10129 Torino, Italy.
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Gharehbaghi A, Linden M. A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018; 29:4102-4115. [PMID: 29035230 DOI: 10.1109/tnnls.2017.2754294] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
This paper presents a novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced performance. It is employed by a multiscale learning structure to classify cyclic time series (CTS), in which the dynamic contents of the time series are preserved in an efficient manner. This paper suggests a systematic procedure for finding the design parameter of the classification method for a one-versus-multiple class application. A novel validation method is also suggested for evaluating the structural risk, both in a quantitative and a qualitative manner. The effect of the DTGNN on the performance of the classifier is statistically validated through the repeated random subsampling using different sets of CTS, from different medical applications. The validation involves four medical databases, comprised of 108 recordings of the electroencephalogram signal, 90 recordings of the electromyogram signal, 130 recordings of the heart sound signal, and 50 recordings of the respiratory sound signal. Results of the statistical validations show that the DTGNN significantly improves the performance of the classification and also exhibits an optimal structural risk.
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23
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Liu Q, Wu X, Ma X. An automatic segmentation method for heart sounds. Biomed Eng Online 2018; 17:106. [PMID: 30081909 PMCID: PMC6080363 DOI: 10.1186/s12938-018-0538-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 07/30/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND There are two major challenges in automated heart sound analysis: segmentation and classification. An efficient segmentation is capable of providing valuable diagnostic information of patients. In addition, it is crucial for some feature-extraction based classification methods. Therefore, the segmentation of heart sound is of significant value. METHODS This paper presents an automatic heart sound segmentation method that combines the time-domain analysis, frequency-domain analysis and time-frequency-domain analysis. Employing this method, the boundaries of heart sound components are first located, and the components are then recognized. Finally, the heart sounds are divided into several segments on the basis of the results of boundary localization and component identification. RESULTS In order to evaluate the performance of the proposed method, quantitative experiments are performed on an authoritative heart sound database. The experimental results show that the boundary localization has a sensitivity (Se) of 100%, a positive predictive value (PPV) of 99.3% and an accuracy (Acc) of 99.93%. Moreover, the Se, PPV and Acc of component identification reach 98.63, 99.86 and 98.49%, respectively. CONCLUSION The proposed method shows reliable performance on the segmentation of heart sounds. Compared with previous works, this method can be applied to not only normal heart sounds, but also the sounds with S3, S4 and murmurs, thus greatly increasing the applied range.
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Affiliation(s)
- Qingshu Liu
- Department of Electronic Engineering, Fudan University, Room 522 B, Science Building, 220 Handan Rd., Shanghai, China
| | - Xiaomei Wu
- Department of Electronic Engineering, Fudan University, Room 522 B, Science Building, 220 Handan Rd., Shanghai, China
- Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, 138 Medical College Rd., Shanghai, China
| | - Xiaojing Ma
- Children’s Hospital of Fudan University, 399 Wanyuan Rd., Shanghai, China
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24
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A novel heart-mobile interface for detection and classification of heart sounds. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.05.008] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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25
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Liu C, Springer D, Clifford GD. Performance of an open-source heart sound segmentation algorithm on eight independent databases. Physiol Meas 2017; 38:1730-1745. [PMID: 28762336 DOI: 10.1088/1361-6579/aa6e9f] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Heart sound segmentation is a prerequisite step for the automatic analysis of heart sound signals, facilitating the subsequent identification and classification of pathological events. Recently, hidden Markov model-based algorithms have received increased interest due to their robustness in processing noisy recordings. In this study we aim to evaluate the performance of the recently published logistic regression based hidden semi-Markov model (HSMM) heart sound segmentation method, by using a wider variety of independently acquired data of varying quality. APPROACH Firstly, we constructed a systematic evaluation scheme based on a new collection of heart sound databases, which we assembled for the PhysioNet/CinC Challenge 2016. This collection includes a total of more than 120 000 s of heart sounds recorded from 1297 subjects (including both healthy subjects and cardiovascular patients) and comprises eight independent heart sound databases sourced from multiple independent research groups around the world. Then, the HSMM-based segmentation method was evaluated using the assembled eight databases. The common evaluation metrics of sensitivity, specificity, accuracy, as well as the [Formula: see text] measure were used. In addition, the effect of varying the tolerance window for determining a correct segmentation was evaluated. MAIN RESULTS The results confirm the high accuracy of the HSMM-based algorithm on a separate test dataset comprised of 102 306 heart sounds. An average [Formula: see text] score of 98.5% for segmenting S1 and systole intervals and 97.2% for segmenting S2 and diastole intervals were observed. The [Formula: see text] score was shown to increases with an increases in the tolerance window size, as expected. SIGNIFICANCE The high segmentation accuracy of the HSMM-based algorithm on a large database confirmed the algorithm's effectiveness. The described evaluation framework, combined with the largest collection of open access heart sound data, provides essential resources for evaluators who need to test their algorithms with realistic data and share reproducible results.
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Affiliation(s)
- Chengyu Liu
- Department of Biomedical Informatics, Emory University, Atlanta, United States of America
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26
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Maknickas V, Maknickas A. Recognition of normal–abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients. Physiol Meas 2017; 38:1671-1684. [DOI: 10.1088/1361-6579/aa7841] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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27
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Abdollahpur M, Ghaffari A, Ghiasi S, Mollakazemi MJ. Detection of pathological heart sounds. Physiol Meas 2017; 38:1616-1630. [PMID: 28594641 DOI: 10.1088/1361-6579/aa7840] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Heart sound analysis has been a major topic of research over the past few decades. However, the necessity for a large and reliable database has been a major concern in these studies. OBJECTIVE Noting that the current heart sound classification methods do not work properly for noisy signals, the PhysioNet/CinC Challenge 2016 aims to develop the heart sound classification algorithms by providing a global open database for challengers. This paper addresses the problem of heart sound classification methods within noisy real-world phonocardiogram recordings by implementing an innovative approach. SIGNIFICANCE After locating the fundamental heart sounds and the systolic and diastolic components, a novel method named cycle quality assessment is applied to each recording. The presented method detects those cycles which are less affected by noise and better segmented by the use of two criteria here proposed in this paper. The selected cycles are the inputs of a further feature extraction process. APPROACH Due to the variability of the heart sound signal induced by various cardiac arrhythmias, four sets of features from the time, time-frequency and perceptual domains are extracted. Before starting the main classification process, the obtained 90-dimensional feature vector is mapped to a new feature space to pre-detect normal recordings by applying a Fisher's discriminant analysis. The main classification procedure is then done based on three feed-forward neural networks and a voting system among classifiers. MAIN RESULTS The presented method is evaluated using the training and hidden test sets of the PhysioNet/CinC Challenge 2016. Also, the results are compared with the top five ranked submissions. The results indicate that the proposed method is effective in classifying heart sounds as normal versus abnormal recordings.
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Affiliation(s)
- Mostafa Abdollahpur
- CardioVascular Research Group (CVRG), Department of Mechanical Engineering at K. N., Toosi University of Technology, Tehran, Iran
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28
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Kay E, Agarwal A. DropConnected neural networks trained on time-frequency and inter-beat features for classifying heart sounds. Physiol Meas 2017; 38:1645-1657. [DOI: 10.1088/1361-6579/aa6a3d] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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29
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Liu C, Springer D, Li Q, Moody B, Juan RA, Chorro FJ, Castells F, Roig JM, Silva I, Johnson AE, Syed Z, Schmidt SE, Papadaniil CD, Hadjileontiadis L, Naseri H, Moukadem A, Dieterlen A, Brandt C, Tang H, Samieinasab M, Samieinasab MR, Sameni R, Mark RG, Clifford GD. An open access database for the evaluation of heart sound algorithms. Physiol Meas 2016; 37:2181-2213. [PMID: 27869105 PMCID: PMC7199391 DOI: 10.1088/0967-3334/37/12/2181] [Citation(s) in RCA: 213] [Impact Index Per Article: 26.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
In 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.
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Affiliation(s)
- Chengyu Liu
- Department of Biomedical Informatics, Emory University, USA
| | - David Springer
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
| | - Qiao Li
- Department of Biomedical Informatics, Emory University, USA
| | - Benjamin Moody
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, USA
| | - Ricardo Abad Juan
- Department of Biomedical Engineering, Georgia Institute of Technology, USA
- ITACA Institute, Universitat Politecnica de Valencia, Spain
| | - Francisco J Chorro
- Service of Cardiology, Valencia University Clinic Hospital, INCLIVA, Spain
| | | | | | - Ikaro Silva
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, USA
| | - Alistair E.W. Johnson
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, USA
| | - Zeeshan Syed
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Samuel E. Schmidt
- Department of Health Science and Technology, Aalborg University, Denmark
| | - Chrysa D. Papadaniil
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece
| | | | - Hosein Naseri
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Iran
| | - Ali Moukadem
- MIPS Laboratory, University of Haute Alsace, France
| | | | | | - Hong Tang
- Faculty of Electronic and Electrical Engineering, Dalian University of Technology, China
| | - Maryam Samieinasab
- School of Electrical & Computer Engineering, Shiraz University, Shiraz, Iran
| | | | - Reza Sameni
- School of Electrical & Computer Engineering, Shiraz University, Shiraz, Iran
| | - Roger G. Mark
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, USA
| | - Gari D. Clifford
- Department of Biomedical Informatics, Emory University, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, USA
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30
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Leng S, Tan RS, Chai KTC, Wang C, Ghista D, Zhong L. The electronic stethoscope. Biomed Eng Online 2015; 14:66. [PMID: 26159433 PMCID: PMC4496820 DOI: 10.1186/s12938-015-0056-y] [Citation(s) in RCA: 138] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Accepted: 06/11/2015] [Indexed: 11/13/2022] Open
Abstract
Most heart diseases are associated with and reflected by the sounds that the heart produces. Heart auscultation, defined as listening to the heart sound, has been a very important method for the early diagnosis of cardiac dysfunction. Traditional auscultation requires substantial clinical experience and good listening skills. The emergence of the electronic stethoscope has paved the way for a new field of computer-aided auscultation. This article provides an in-depth study of (1) the electronic stethoscope technology, and (2) the methodology for diagnosis of cardiac disorders based on computer-aided auscultation. The paper is based on a comprehensive review of (1) literature articles, (2) market (state-of-the-art) products, and (3) smartphone stethoscope apps. It covers in depth every key component of the computer-aided system with electronic stethoscope, from sensor design, front-end circuitry, denoising algorithm, heart sound segmentation, to the final machine learning techniques. Our intent is to provide an informative and illustrative presentation of the electronic stethoscope, which is valuable and beneficial to academics, researchers and engineers in the technical field, as well as to medical professionals to facilitate its use clinically. The paper provides the technological and medical basis for the development and commercialization of a real-time integrated heart sound detection, acquisition and quantification system.
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Affiliation(s)
- Shuang Leng
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.
| | - Ru San Tan
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.
- Cardiovascular and Metabolic Disorders Program, Duke-NUS Graduate Medical School, 8 College Road, Singapore, 169857, Singapore.
| | - Kevin Tshun Chuan Chai
- Institute of Microelectronics, A*STAR, 11 Science Park Road, Singapore, 117685, Singapore.
| | - Chao Wang
- Institute of Microelectronics, A*STAR, 11 Science Park Road, Singapore, 117685, Singapore.
| | | | - Liang Zhong
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.
- Cardiovascular and Metabolic Disorders Program, Duke-NUS Graduate Medical School, 8 College Road, Singapore, 169857, Singapore.
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31
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Patidar S, Pachori RB. Segmentation of cardiac sound signals by removing murmurs using constrained tunable-Q wavelet transform. Biomed Signal Process Control 2013. [DOI: 10.1016/j.bspc.2013.05.004] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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32
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Altunkaya S, Kara S, Görmüş N, Herdem S. Comparison of first and second heart sounds after mechanical heart valve replacement. Comput Methods Biomech Biomed Engin 2013; 16:368-80. [PMID: 22263691 DOI: 10.1080/10255842.2011.623672] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
In this article, the spectral features of first heart sounds (S1) and second heart sounds (S2), which comprise the mechanical heart valve sounds obtained after aortic valve replacement (AVR) and mitral valve replacement (MVR), are compared to find out the effect of mechanical heart valve replacement and recording area on S1 and S2. For this aim, the Welch method and the autoregressive (AR) method are applied on the S1 and S2 taken from 66 recordings of 8 patients with AVR and 98 recordings from 11 patients with MVR, thereby yielding power spectrum of the heart sounds. Three features relating to frequency of heart sounds and three features relating to energy of heart sounds are obtained. Results show that in comparison to natural heart valves, mechanical heart valves contain higher frequency components and energy, and energy and frequency components do not show common behaviour for either AVR or MVR depending on the recording areas. Aside from the frequency content and energy of the sound generated by mechanical heart valves being affected by the structure of the lungs-thorax and the recording areas, the pressure across the valve incurred during AVR or MVR is a significant factor in determining the frequency and energy levels of the valve sound produced. Though studies on native heart sounds as a non-invasive diagnostic method has been done for many years, it is observed that studies on mechanical heart valves sounds are limited. The results of this paper will contribute to other studies on using a non-invasive method for assessing the mechanical heart valve sounds.
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Affiliation(s)
- Sabri Altunkaya
- Department of Electrical and Electronics Engineering, Selcuk University, Konya, 42075, Turkey.
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33
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Feature Extraction From Parametric Time–Frequency Representations for Heart Murmur Detection. Ann Biomed Eng 2010; 38:2716-32. [DOI: 10.1007/s10439-010-0077-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2009] [Accepted: 03/17/2010] [Indexed: 10/19/2022]
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34
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Reed NE, Nie Y, Mahnke CB. A portable graphical representation tool for phonocardiograms. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2009; 2009:3111-4. [PMID: 19963567 DOI: 10.1109/iembs.2009.5332544] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This paper describes a prototype software application to display graphical and editable representations of patient data for use in electronic medical records (EMRs). The application dynamically generates graphics of cardiac and other patient data, and displays or saves them both in graphic and in text formats. The presentation of heart and other data in a consistent, clinically familiar, graphical format is designed to reduce the time necessary for anyone to review and understand this important information. Results of preliminary testing on actual case data are encouraging.
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Affiliation(s)
- Nancy E Reed
- University of Hawaii, Department of Information and Computer Sciences, 1680 East-West Road, Honolulu, HI 96822, USA.
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35
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Selection of Dynamic Features Based on Time–Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals. Ann Biomed Eng 2009; 38:118-37. [DOI: 10.1007/s10439-009-9838-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2009] [Accepted: 11/06/2009] [Indexed: 10/20/2022]
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36
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Digital Auscultation Analysis for Heart Murmur Detection. Ann Biomed Eng 2008; 37:337-53. [DOI: 10.1007/s10439-008-9611-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2007] [Accepted: 11/20/2008] [Indexed: 10/21/2022]
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37
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Chauhan S, Wang P, Sing Lim C, Anantharaman V. A computer-aided MFCC-based HMM system for automatic auscultation. Comput Biol Med 2008; 38:221-33. [DOI: 10.1016/j.compbiomed.2007.10.006] [Citation(s) in RCA: 88] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2006] [Revised: 08/27/2007] [Accepted: 10/12/2007] [Indexed: 12/01/2022]
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