ABSTRACT
This work investigates whether inclusion of the low-frequency components of heart sounds can increase the accuracy, sensitivity and specificity of diagnosis of cardiovascular disorders. We standardized the measurement method to minimize changes in signal characteristics. We used the Continuous Wavelet Transform to analyze changing frequency characteristics over time and to allocate frequencies appropriately between the low-frequency and audible frequency bands. We used a Convolutional Neural Network (CNN) and deep-learning (DL) for image classification, and a CNN equipped with long short-term memory to enable sequential feature extraction. The accuracy of the learning model was validated using the PhysioNet 2016 CinC dataset, then we used our collected dataset to show that incorporating low-frequency components in the dataset increased the DL model's accuracy by 2% and sensitivity by 4%. Furthermore, the LSTM layer was 0.8% more accurate than the dense layer.
Subject(s)
Heart Sounds , Neural Networks, Computer , Phonocardiography/methods , Humans , Heart Sounds/physiology , Deep Learning , Male , Wavelet Analysis , Female , Cardiovascular Diseases/diagnosis , Cardiovascular Diseases/physiopathology , Adult , Signal Processing, Computer-AssistedABSTRACT
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.
Subject(s)
Algorithms , Machine Learning , Neural Networks, Computer , Signal Processing, Computer-Assisted , Support Vector Machine , Wearable Electronic Devices , Humans , Phonocardiography/methodsABSTRACT
The timely psychological stress detection can improve the quality of human life by preventing stress-induced behavioral and pathological consequences. This paper presents a novel framework that eliminates the need of Electrocardiography (ECG) signals-based referencing of Phonocardiography (PCG) signals for psychological stress detection. This stand-alone PCG-based methodology uses wavelet scattering approach on the data acquired from twenty-eight healthy adult male and female subjects to detect psychological stress. The acquired PCG signals are asynchronously segmented for the analysis using wavelet scattering transform. After the noise bands removal, the optimized segmentation length (L), scattering network parameters namely-invariance scale (J) and quality factor (Q) are utilized for computation of scattering features. These scattering coefficients generated are fed to K-nearest neighbor (KNN) and Extreme Gradient Boosting (XGBoost) classifier and the ten-fold cross validation-based performance metrics obtained are-accuracy 94.30 %, sensitivity 97.96 %, specificity 88.01 % and area under the curve (AUC) 0.9298 using XGBoost classifier for detecting psychological stress. Most importantly, the framework also identified two frequency bands in PCG signals with high discriminatory power for psychological stress detection as 270-290 Hz and 380-390 Hz. The elimination of multi-modal data acquisition and analysis makes this approach cost-efficient and reduces computational complexity.
Subject(s)
Stress, Psychological , Humans , Phonocardiography/methods , Stress, Psychological/physiopathology , Male , Female , Adult , Signal Processing, Computer-Assisted , Wavelet AnalysisABSTRACT
Traditionally, heart murmurs are diagnosed through cardiac auscultation, which requires specialized training and experience. The purpose of this study is to predict patients' clinical outcomes (normal or abnormal) and identify the presence or absence of heart murmurs using phonocardiograms (PCGs) obtained at different auscultation points. A semi-supervised model tailored to PCG classification is introduced in this study, with the goal of improving performance using time-frequency deep features. The study begins by investigating the behavior of PCGs in the time-frequency domain, utilizing the Stockwell transform to convert the PCG signal into two-dimensional time-frequency maps (TFMs). A deep network named AlexNet is then used to derive deep feature sets from these TFMs. In feature reduction, redundancy is eliminated and the number of deep features is reduced to streamline the feature set. The effectiveness of the extracted features is evaluated using three different classifiers using the CinC/Physionet challenge 2022 dataset. For Task I, which focuses on heart murmur detection, the proposed approach achieved an average accuracy of 93%, sensitivity of 91%, and F1-score of 91%. According to Task II of the CinC/Physionet challenge 2022, the approach showed a clinical outcome cost of 5290, exceeding the benchmark set by leading methods in the challenge.
Subject(s)
Algorithms , Signal Processing, Computer-Assisted , Humans , Phonocardiography/methods , Heart Murmurs/diagnosis , Heart AuscultationABSTRACT
BACKGROUND: Cardiac diseases are highly detrimental illnesses, responsible for approximately 32% of global mortality [1]. Early diagnosis and prompt treatment can reduce deaths caused by cardiac diseases. In paediatric patients, it is challenging for paediatricians to identify functional murmurs and pathological murmurs from heart sounds. OBJECTIVE: The study intends to develop a novel blended ensemble model using hybrid deep learning models and softmax regression to classify adult, and paediatric heart sounds into five distinct classes, distinguishing itself as a groundbreaking work in this domain. Furthermore, the research aims to create a comprehensive 5-class paediatric phonocardiogram (PCG) dataset. The dataset includes two critical pathological classes, namely atrial septal defects and ventricular septal defects, along with functional murmurs, pathological and normal heart sounds. METHODS: The work proposes a blended ensemble model (HbNet-Heartbeat Network) comprising two hybrid models, CNN-BiLSTM and CNN-LSTM, as base models and Softmax regression as meta-learner. HbNet leverages the strengths of base models and improves the overall PCG classification accuracy. Mel Frequency Cepstral Coefficients (MFCC) capture the crucial audio signal characteristics relevant to the classification. The amalgamation of these two deep learning structures enhances the precision and reliability of PCG classification, leading to improved diagnostic results. RESULTS: The HbNet model exhibited excellent results with an average accuracy of 99.72% and sensitivity of 99.3% on an adult dataset, surpassing all the existing state-of-the-art works. The researchers have validated the reliability of the HbNet model by testing it on a real-time paediatric dataset. The paediatric model's accuracy is 86.5%. HbNet detected functional murmur with 100% precision. CONCLUSION: The results indicate that the HbNet model exhibits a high level of efficacy in the early detection of cardiac disorders. Results also imply that HbNet has the potential to serve as a valuable tool for the development of decision-support systems that aid medical practitioners in confirming their diagnoses. This method makes it easier for medical professionals to diagnose and initiate prompt treatment while performing preliminary auscultation and reduces unnecessary echocardiograms.
Subject(s)
Heart Sounds , Humans , Phonocardiography/methods , Child , Heart Sounds/physiology , Deep Learning , Neural Networks, Computer , Heart Murmurs/diagnosis , Child, PreschoolABSTRACT
BACKGROUND AND OBJECTIVE: Valvular heart disease (VHD) is associated with elevated mortality rates. Although transthoracic echocardiography (TTE) is the gold standard detection tool, phonocardiography (PCG) could be an alternative as it is a cost-effective and noninvasive method for cardiac auscultation. Many researchers have dedicated their efforts to improving the decision-making process and developing robust and precise approaches to assist physicians in providing reliable diagnoses of VHD. METHODS: This research proposes a novel approach for the detection of anomalous valvular heart sounds from PCG signals. The proposed approach combines orthogonal non-negative matrix factorization (ONMF) and convolutional neural network (CNN) architectures in a three-stage cascade. The aim of the proposal is to improve the learning process by identifying the optimal ONMF temporal or spectral patterns for accurate detection. In the first stage, the time-frequency representation of the input PCG signal is computed. Next, band-pass filtering is performed to locate the spectral range that is most relevant for the presence of such cardiac abnormalities. In the second stage, the temporal and spectral cardiac structures are extracted using the ONMF approach. These structures are utilized in the third stage and fed into the CNN architecture to detect abnormal heart sounds. RESULTS: Several state-of-the-art CNN architectures, such as LeNet5, AlexNet, ResNet50, VGG16 and GoogLeNet, have been evaluated to determine the effectiveness of using ONMF temporal features for VHD detection. The results reveal that the integration of ONMF temporal features with a CNN classifier significantly improve VHD detection. Specifically, the proposed approach achieves an accuracy improvement of approximately 45% when ONMF spectral features are used and 35% when time-frequency features from the short-time Fourier transform (STFT) spectrogram are used. Additionally, feeding ONMF temporal features into low-complexity CNN architectures yields competitive results comparable to those obtained with complex architectures. CONCLUSIONS: The temporal structure factorized by ONMF plays a critical role in distinguishing between normal heart sounds and abnormal heart sounds since the repeatability of normal heart cycles is disrupted by the presence of cardiac abnormalities. Consequently, the results highlight the importance of appropriate input data representation in the learning process of CNN models in the biomedical field of valvular heart sound detection.
Subject(s)
Heart Valve Diseases , Phonocardiography , Humans , Algorithms , Heart Valve Diseases/diagnostic imaging , Neural Networks, Computer , Phonocardiography/methodsABSTRACT
OBJECTIVE: Murmurs are abnormal heart sounds, identified by experts through cardiac auscultation. The murmur grade, a quantitative measure of the murmur intensity, is strongly correlated with the patient's clinical condition. This work aims to estimate each patient's murmur grade (i.e., absent, soft, loud) from multiple auscultation location phonocardiograms (PCGs) of a large population of pediatric patients from a low-resource rural area. METHODS: The Mel spectrogram representation of each PCG recording is given to an ensemble of 15 convolutional residual neural networks with channel-wise attention mechanisms to classify each PCG recording. The final murmur grade for each patient is derived based on the proposed decision rule and considering all estimated labels for available recordings. The proposed method is cross-validated on a dataset consisting of 3456 PCG recordings from 1007 patients using a stratified ten-fold cross-validation. Additionally, the method was tested on a hidden test set comprised of 1538 PCG recordings from 442 patients. RESULTS: The overall cross-validation performances for patient-level murmur gradings are 86.3% and 81.6% in terms of the unweighted average of sensitivities and F1-scores, respectively. The sensitivities (and F1-scores) for absent, soft, and loud murmurs are 90.7% (93.6%), 75.8% (66.8%), and 92.3% (84.2%), respectively. On the test set, the algorithm achieves an unweighted average of sensitivities of 80.4% and an F1-score of 75.8%. CONCLUSIONS: This study provides a potential approach for algorithmic pre-screening in low-resource settings with relatively high expert screening costs. SIGNIFICANCE: The proposed method represents a significant step beyond detection of murmurs, providing characterization of intensity, which may provide an enhanced classification of clinical outcomes.
Subject(s)
Heart Murmurs , Heart Sounds , Humans , Child , Phonocardiography/methods , Heart Murmurs/diagnosis , Heart Auscultation/methods , Algorithms , AuscultationABSTRACT
Fetal phonocardiography (fPCG) is receiving attention as it is a promising method for continuous fetal monitoring due to its non-invasive and passive nature. However, it suffers from the interference from various sources, overlapping the desired signal in the time and frequency domains. This paper introduces the state-of-the-art methods used for fPCG signal extraction and processing, as well as means of detection and classification of various features defining fetal health state. It also provides an extensive summary of remaining challenges, along with the practical insights and suggestions for the future research directions.
Subject(s)
Algorithms , Heart Rate, Fetal , Pregnancy , Female , Humans , Phonocardiography/methods , Fetal Monitoring/methods , Signal Processing, Computer-AssistedABSTRACT
Objective. Heart sound segmentation (HSS), which aims to identify the exact positions of the first heart sound(S1), second heart sound(S2), the duration of S1, systole, S2, and diastole within a cardiac cycle of phonocardiogram (PCG), is an indispensable step to find out heart health. Recently, some neural network-based methods for heart sound segmentation have shown good performance.Approach. In this paper, a novel method was proposed for HSS exactly using One-Dimensional Convolution and Bidirectional Long-Short Term Memory neural network with Attention mechanism (C-LSTM-A) by incorporating the 0.5-order smooth Shannon entropy envelope and its instantaneous phase waveform (IPW), and third intrinsic mode function (IMF-3) of PCG signal to reduce the difficulty of neural network learning features.Main results. An average F1-score of 96.85 was achieved in the clinical research dataset (Fuwai Yunnan Cardiovascular Hospital heart sound dataset) and an average F1-score of 95.68 was achieved in 2016 PhysioNet/CinC Challenge dataset using the novel method.Significance. The experimental results show that this method has advantages for normal PCG signals and common pathological PCG signals, and the segmented fundamental heart sound(S1, S2), systole, and diastole signal components are beneficial to the study of subsequent heart sound classification.
Subject(s)
Heart Sounds , Phonocardiography/methods , Signal Processing, Computer-Assisted , China , Algorithms , Neural Networks, ComputerABSTRACT
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.
Subject(s)
Heart Sounds , Heart Auscultation , Humans , Neural Networks, Computer , Phonocardiography/methods , Signal Processing, Computer-Assisted , Weight LossABSTRACT
In view of using abdominal microphones for fetal heart rate (FHR) monitoring, the analysis of the obtained abdominal phonocardiogram (PCG) signals is complex due to many interferential noises including blood flow sounds. In order to improve the understanding of abdominal phonocardiography, a preliminary study was conducted in one healthy volunteer and designed to characterize the PCG signals all over the abdomen. Acquisitions of PCG signals in different abdominal areas were realized, synchronously with one thoracic PCG signal and one electrocardiogram signal. The analysis was carried out based on the temporal behavior, amplitude and mean pattern of each signal. The synchronized rhythmic signature of each signal confirms that the PCG signals obtained on the abdominal area are resulting from heart function. However, the abdominal PCG patterns are totally different from the thoracic PCG one, suggesting the recording of vascular blood flow sounds on the abdomen instead of cardiac valve sounds. Moreover, the abdominal signal magnitude depends on the sensor position and therefore to the size of the underlying vessel. The sounds characterization of abdominal PCG signals could help improving the processing of such signals in the purpose of FHR monitoring.
Subject(s)
Heart Sounds , Sound Recordings , Abdomen , Female , Heart/physiology , Heart Sounds/physiology , Humans , Phonocardiography/methods , PregnancyABSTRACT
Fetal phonocardiography is a non-invasive, completely passive and low-cost method based on sensing acoustic signals from the maternal abdomen. However, different types of interference are sensed along with the desired fetal phonocardiography. This study focuses on the comparison of fetal phonocardiography filtering using eight algorithms: Savitzky-Golay filter, finite impulse response filter, adaptive wavelet transform, maximal overlap discrete wavelet transform, variational mode decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise. The effectiveness of those methods was tested on four types of interference (maternal sounds, movement artifacts, Gaussian noise, and ambient noise) and eleven combinations of these disturbances. The dataset was created using two synthetic records r01 and r02, where the record r02 was loaded with higher levels of interference than the record r01. The evaluation was performed using the objective parameters such as accuracy of the detection of S1 and S2 sounds, signal-to-noise ratio improvement, and mean error of heart interval measurement. According to all parameters, the best results were achieved using the complete ensemble empirical mode decomposition with adaptive noise method with average values of accuracy = 91.53% in the detection of S1 and accuracy = 68.89% in the detection of S2. The average value of signal-to-noise ratio improvement achieved by complete ensemble empirical mode decomposition with adaptive noise method was 9.75 dB and the average value of the mean error of heart interval measurement was 3.27 ms.
Subject(s)
Signal Processing, Computer-Assisted , Wavelet Analysis , Algorithms , Phonocardiography/methods , Signal-To-Noise RatioABSTRACT
Incidence and exacerbation of some of the cardiovascular diseases in the presence of the coronavirus will lead to an increase in the mortality rate among patients. Therefore, early diagnosis of such diseases is critical, especially during the COVID-19 pandemic (mild COVID-19 infection). Thus, for diagnosing the heart diseases related to the COVID-19, an automatic, non-invasive, and inexpensive method based on the heart sound processing approach is proposed. In the present study, a set of features related to the nature of heart signals is defined and extracted. The investigated features included morphological and statistical features in the heart sound frequencies. By extracting and selecting a set of effective features related to the mentioned diseases, and avoiding to use different segmentation and filtering techniques, dependence on a limited dataset and specific sampling procedures has been eliminated. Different classifiers with various kernels are applied for diagnosis in data unbalanced and balanced conditions. The results showed 93.15% accuracy and 93.72% F1-score using 60 effective features in data balanced conditions. The identification system using the extracted features from Azad dataset is able to achieve the desired results in a generalized dataset. In this way, in the shortest possible sampling time, the present system provided an effective and generalizable method and a practical model for diagnosing important cardiovascular diseases in the presence of coronavirus in the COVID-19 pandemic.
Subject(s)
COVID-19 , Cardiovascular Diseases , Heart Sounds , COVID-19/diagnosis , COVID-19 Testing , Cardiovascular Diseases/diagnosis , Humans , Pandemics , Phonocardiography/methods , Signal Processing, Computer-AssistedABSTRACT
Conventional phonocardiography is useful for objective assessment of cardiac auscultation, but its availability is limited. More recently, an ankle-brachial index (ABI) measurement system equipped with simple phonocardiography has become widely used for diagnosing peripheral artery disease, however, whether this simple phonocardiography can be an alternative to conventional phonocardiography remains unclear.This retrospective study consisted of 48 patients with hypertrophic cardiomyopathy (HCM) and 107 controls. The presence of the fourth sound (S4) was assessed by conventional phonocardiography, in addition to apexcardiography and auscultation, in all patients with HCM. S4 was also estimated by the ABI measurement system with the phonocardiographic microphone on the sternum (the standard method) or at the apex (the apex method) in HCM patients and controls.S4 on conventional phonocardiography was detected in 42 of 48 patients (88%) with HCM. Auscultation for the detection of S4 had a sensitivity of 0.78, specificity of 0.57, and accuracy of 0.75. These diagnostic values were generally superior to those of the standard method using the ABI measurement system, whereas the apex method using the ABI measurement system had better diagnostic values, with an excellent specificity of 1.0, sensitivity of 0.77, and accuracy of 0.80. No significant differences were observed in low ABI defined as < 0.9.Simple phonocardiography equipped with the ABI measurement system may be an alternative to conventional phonocardiography for the detection of S4 in patients with HCM when the phonocardiographic microphone is moved from the sternum to the apex.
Subject(s)
Ankle Brachial Index , Cardiomyopathy, Hypertrophic/diagnosis , Heart Sounds , Peripheral Arterial Disease/diagnosis , Phonocardiography/methods , Cardiomyopathy, Hypertrophic/physiopathology , Heart Auscultation/standards , Heart Sounds/physiology , Humans , Peripheral Arterial Disease/physiopathology , Retrospective Studies , Sensitivity and SpecificityABSTRACT
Objective.Fetal heart rate (FHR) is an important parameter for assessing fetal well-being and is usually measured by doppler ultrasound. Fetal phonocardiography can provide non-invasive, easy-to-use and passive alternative for fetal monitoring method if reliable FHR measurements can be made even in noisy clinical environments. Therefore, this work presents an automatic algorithm to determine FHR from the fetal heart sound recordings in a noisy clinical environment.Approach.Using an electronic stethoscope fetal heart sounds were recorded from the expecting mother's abdomen, during weeks 30-40 of their pregnancy. Of these, 60 recordings were analyzed manually by two observers to obtain reference heart rate measurement. An algorithm was developed to determine FHR using envelope detection and autocorrelation of the signals. Algorithm performance was improved by implementing peak validation algorithm utilizing knowledge of valid FHR from prior windows and power spectral density function. The improvements in accuracy and reliability of algorithm were measured by mean absolute error (MAE) and positive percent agreement.Main results.By including the validation step, the MAE reduced from 11.50 to 7.54 beats per minute and positive percent agreement improved from 81% to 87%.Significance.We classified the recordings into good, moderate and poor quality to understand how the algorithm works in each of the case. The proposed algorithms provide good accuracy overall but are sensitive to the noises in recording environment that influence the quality of the signals.
Subject(s)
Fetal Monitoring , Heart Rate, Fetal , Algorithms , Female , Fetal Monitoring/methods , Heart Rate , Heart Rate, Fetal/physiology , Humans , Phonocardiography/methods , Pregnancy , Reproducibility of ResultsABSTRACT
BACKGROUND AND OBJECTIVE: Auscultation is the first technique applied to the early diagnose of any cardiovascular disease (CVD) in rural areas and poor-resources countries because of its low cost and non-invasiveness. However, it highly depends on the physician's expertise to recognize specific heart sounds heard through the stethoscope. The analysis of phonocardiogram (PCG) signals attempts to segment each cardiac cycle into the four cardiac states (S1, systole, S2 and diastole) in order to develop automatic systems applied to an efficient and reliable detection and classification of heartbeats. In this work, we propose an unsupervised approach, based on time-frequency characteristics shown by cardiac sounds, to detect and classify heartbeats S1 and S2. METHODS: The proposed system consists of a two-stage cascade. The first stage performs a rough heartbeat detection while the second stage refines the previous one, improving the temporal localization and also classifying the heartbeats into types S1 and S2. The first contribution is a novel approach that combines the dissimilarity matrix with the frame-level spectral divergence to locate heartbeats using the repetitiveness shown by the heart sounds and the temporal relationships between the intervals defined by the events S1/S2 and non-S1/S2 (systole and diastole). The second contribution is a verification-correction-classification process based on a sliding window that allows the preservation of the temporal structure of the cardiac cycle in order to be applied in the heart sound classification. The proposed method has been assessed using the open access databases PASCAL, CirCor DigiScope Phonocardiogram and an additional sound mixing procedure considering both Additive White Gaussian Noise (AWGN) and different kinds of clinical ambient noises from a commercial database. RESULTS: The proposed method outperforms the detection and classification performance of other recent state-of-the-art methods. Although our proposal achieves the best average accuracy for PCG signals without cardiac abnormalities, 99.4% in heartbeat detection and 97.2% in heartbeat classification, its worst average accuracy is always above 92% for PCG signals with cardiac abnormalities, signifying an improvement in heartbeat detection/classification above 10% compared to the other state-of-the-art methods evaluated. CONCLUSIONS: The proposed method provides the best detection/classification performance in realistic scenarios where the presence of cardiac anomalies as well as different types of clinical environmental noises are active in the PCG signal. Of note, the promising modelling of the temporal structures of the heart provided by the dissimilarity matrix together with the frame-level spectral divergence, as well as the removal of a significant number of spurious heart events and recovery of missing heart events, both corrected by the proposed verification-correction-classification algorithm, suggest that our proposal is a successful tool to be applied in heart segmentation.
Subject(s)
Heart Sounds , Signal Processing, Computer-Assisted , Algorithms , Heart , Heart Rate , Phonocardiography/methodsABSTRACT
OBJECTIVE: Cardiac auscultation is the most practiced non-invasive and cost-effective procedure for the early diagnosis of heart diseases. While machine learning based systems can aid in automatically screening patients, the robustness of these systems is affected by numerous factors including the stethoscope/sensor, environment, and data collection protocol. This article studies the adverse effect of domain variability on heart sound abnormality detection and develops strategies to address this problem. METHODS: We propose a novel Convolutional Neural Network (CNN) layer, consisting of time-convolutional (tConv) units, that emulate Finite Impulse Response (FIR) filters. The filter coefficients can be updated via backpropagation and be stacked in the front-end of the network as a learnable filterbank. RESULTS: On publicly available multi-domain datasets, the proposed method surpasses the top-scoring systems found in the literature for heart sound abnormality detection (a binary classification task). We utilized sensitivity, specificity, F-1 score and Macc (average of sensitivity and specificity) as performance metrics. Our systems achieved relative improvements of up to 11.84% in terms of MAcc, compared to state-of-the-art methods. CONCLUSION: The results demonstrate the effectiveness of the proposed learnable filterbank CNN architecture in achieving robustness towards sensor/domain variability in PCG signals. SIGNIFICANCE: The proposed methods pave the way for deploying automated cardiac screening systems in diversified and underserved communities.
Subject(s)
Heart Sounds/physiology , Neural Networks, Computer , Phonocardiography , Signal Processing, Computer-Assisted , Algorithms , Databases, Factual , Heart Diseases/diagnosis , Humans , Phonocardiography/classification , Phonocardiography/methods , Sensitivity and SpecificityABSTRACT
This paper proposes a robust and real-time capable algorithm for classification of the firstand second heart sounds. The classification algorithm is based on the evaluation of the envelope curveof the phonocardiogram. For the evaluation, in contrast to other studies, measurements on twelveprobands were conducted in different physiological conditions. Moreover, for each measurement theauscultation point, posture and physical stress were varied. The proposed envelope-based algorithmis tested with two different methods for envelope curve extraction: the Hilbert transform andthe short-time Fourier transform. The performance of the classification of the first heart soundsis evaluated by using a reference electrocardiogram. Overall, by using the Hilbert transform,the algorithm has a better performance regarding the F1-score and computational effort. Theproposed algorithm achieves for the S1 classification an F1-score up to 95.7% and in average 90.5 %.The algorithm is robust against the age, BMI, posture, heart rate and auscultation point (exceptmeasurements on the back) of the subjects. The ECG and PCG records are available from the authors.
Subject(s)
Algorithms , Electrocardiography/methods , Heart Sounds/physiology , Phonocardiography/methods , Adult , Aged , Fourier Analysis , Heart Rate/physiology , Humans , Male , Middle Aged , Signal Processing, Computer-Assisted , Young AdultABSTRACT
OBJECTIVE: Radar technology promises to be a touchless and thereby burden-free method for continuous heart sound monitoring, which can be used to detect cardiovascular diseases. However, the first and most crucial step is to differentiate between high- and low-quality segments in a recording to assess their suitability for a subsequent automated analysis. This paper gives a comprehensive study on this task and first addresses the specific characteristics of radar-recorded heart sound signals. METHODS: To gather heart sound signals recorded from radar, a bistatic radar system was built and installed at the university hospital. Under medical supervision, heart sound data were recorded from 30 healthy test subjects. The signals were segmented and labeled as high- or low-quality by a medical expert. Different state-of-the-art pattern classification algorithms were evaluated for the task of automated signal quality determination and the most promising one was optimized and evaluated using leave-one-subject-out cross validation. RESULTS: The proposed classifier is able to achieve an accuracy of up to 96.36% and demonstrates a superior classification performance compared with the state-of-the-art classifier with a maximum accuracy of 76.00%. CONCLUSION: This paper introduces an ensemble classifier that is able to perform automated signal quality determination of radar-recorded heart sound signals with a high accuracy. SIGNIFICANCE: Besides achieving a higher performance compared with state-of-the-art classifiers, this study is the first one to deal with the quality determination of heart sounds that are recorded by radar systems. The proposed method enables contactless and continuous heart sound monitoring for the detection of cardiovascular diseases.
Subject(s)
Heart Sounds/physiology , Monitoring, Physiologic/methods , Phonocardiography/methods , Radar/instrumentation , Signal Processing, Computer-Assisted , Adult , Algorithms , Electrocardiography , Equipment Design , Female , Humans , Male , Middle Aged , Phonocardiography/instrumentation , Young AdultABSTRACT
OBJECTIVE: This paper proposes a novel framework for the segmentation of phonocardiogram (PCG) signals into heart states, exploiting the temporal evolution of the PCG as well as considering the salient information that it provides for the detection of the heart state. METHODS: We propose the use of recurrent neural networks and exploit recent advancements in attention based learning to segment the PCG signal. This allows the network to identify the most salient aspects of the signal and disregard uninformative information. RESULTS: The proposed method attains state-of-the-art performance on multiple benchmarks including both human and animal heart recordings. Furthermore, we empirically analyse different feature combinations including envelop features, wavelet and Mel Frequency Cepstral Coefficients (MFCC), and provide quantitative measurements that explore the importance of different features in the proposed approach. CONCLUSION: We demonstrate that a recurrent neural network coupled with attention mechanisms can effectively learn from irregular and noisy PCG recordings. Our analysis of different feature combinations shows that MFCC features and their derivatives offer the best performance compared to classical wavelet and envelop features. SIGNIFICANCE: Heart sound segmentation is a crucial pre-processing step for many diagnostic applications. The proposed method provides a cost effective alternative to labour extensive manual segmentation, and provides a more accurate segmentation than existing methods. As such, it can improve the performance of further analysis including the detection of murmurs and ejection clicks. The proposed method is also applicable for detection and segmentation of other one dimensional biomedical signals.