Objective: This paper considers challenges in developing algorithms for accurate segmentation and classification of heart sound (HS) signals. Methods: We propose an approach based on Markov switching autoregressive model (MSAR) to segmenting the HS into four fundamental components each with distinct second-order structure. The identified boundaries are then utilized for automated classification of pathological HS using the continuous density hidden Markov model (CD-HMM). The MSAR formulated in a state-space form is able to capture simultaneously both the continuous hidden dynamics in HS, and the regime switching in the dynamics using a discrete Markov chain. This overcomes the limitation of HMM which uses a single-layer of discrete states. We introduce three schemes for model estimation: (1.) switching Kalman filter (SKF); (2.) refined SKF; (3.) fusion of SKF and the duration-dependent Viterbi algorithm (SKF-Viterbi). Results: The proposed methods are evaluated on Physionet/CinC Challenge 2016 database. The SKF-Viterbi significantly outperforms SKF by improvement of segmentation accuracy from 71% to 84.2%. The use of CD-HMM as a classifier and Mel-frequency cepstral coefficients (MFCCs) as features can characterize not only the normal and abnormal morphologies of HS signals but also morphologies considered as unclassifiable (denoted as X-Factor). It gives classification rates with best gross F1 score of 90.19 (without X-Factor) and 82.7 (with X-Factor) for abnormal beats. Conclusion: The proposed MSAR approach for automatic localization and detection of pathological HS shows a noticeable performance on large HS dataset. Significance: It has potential applications in heart monitoring systems to assist cardiologists for pre-screening of heart pathologies.
Iran University of Science and Technology（伊朗科技大学）；Sharif University of Technology（谢里夫理工大学）
Cardiovascular disease is one of the leading causes of death according to WHO. Phonocardiography (PCG) is a costeffective, non-invasive method suitable for heart monitoring. The main aim of this work is to classify heart sounds into normal/abnormal categories. Heart sounds are recorded using different stethoscopes, thus varying in the domain. Based on recent studies, this variability can affect heart sound classification. This work presents a Siamese network architecture for learning the similarity between normal vs. normal or abnormal vs. abnormal signals and the difference between normal vs.…
University of Augsburg（奥格斯堡大学）；The University of Tokyo（东京大学）；Shenzhen University General Hospital（深圳大学总医院）；Wenzhou Medical University First Affiliated Hospital（温州医科大学附属第一医院）；Imperial College London（帝国理工学院）
Cardiovascular diseases are the leading cause of deaths and severely threaten human health in daily life. On the one hand, there have been dramatically increasing demands from both the clinical practice and the smart home application for monitoring the heart status of subjects suffering from chronic cardiovascular diseases. On the other hand, experienced physicians who can perform an efficient auscultation are still lacking in terms of number. Automatic heart sound classification leveraging the power of advanced signal processing and machine learning technologies has shown encouraging results.…
Scaling to Multimodal and Multichannel Heart Sound Classification with Synthetic and Augmented Biosignals
School of Electrical Engineering, Computing, and Mathematical Sciences (EECMS), Faculty of Science and Engineering, Curtin University, Bentley, WA 6102, Australia（电气工程、计算与数学科学学院（EECMS），科学与工程学院， Curtin 大学，Bentley，WA 6102，澳大利亚）
H-Infinity Filter Enhanced CNN-LSTM for Arrhythmia Detection from Heart Sound Recordings
Department of Computer Science and Engineering（计算机科学与工程系）；National Institute of Technology Karnataka, Surathkal, Karnataka, India 575025（卡纳塔克邦苏拉塔卡尔国家理工学院）
本文提出CNN-H-Infinity-LSTM架构，通过引入受控制理论H-Infinity滤波器启发的可训练参数，增强模型鲁棒性与泛化能力，在PhysioNet CinC Challenge 2016数据集上实现99.42%准确率，有效解决心音记录心律失常检测问题。
University of Guilan（吉兰大学）；Alzahra University（阿扎赫拉大学）；Michigan State University（密歇根州立大学）
Phonocardiogram (PCG) signal analysis is a critical, widely-studied technology to noninvasively analyze the heart's mechanical activity. Through evaluating heart sounds, this technology has been chiefly leveraged as a preliminary solution to automatically diagnose Cardiovascular diseases among adults; however, prenatal tasks such as fetal gender identification have been relatively less studied using fetal Phonocardiography (FPCG). In this work, we apply common PCG signal processing techniques on the gender-tagged Shiraz University Fetal Heart Sounds Database and study the applicability of prev…
Left ventricular assist devices (LVADs) are surgically implanted mechanical pumps that improve survival rates for individuals with advanced heart failure. While life-saving, LVAD therapy is also associated with high morbidity, which can be partially attributed to the difficulties in identifying an LVAD complication before an adverse event occurs. Methods that are currently used to monitor for complications in LVAD-supported individuals require frequent clinical assessments at specialized LVAD centers. Remote analysis of digitally recorded precordial sounds has the potential to provide an inexp…
Harokopio University of Athens（哈罗科皮奥雅典大学）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
Heart murmurs are abnormal sounds present in heartbeats, caused by turbulent blood flow through the heart. The PhysioNet 2022 challenge targets automatic detection of murmur from audio recordings of the heart and automatic detection of normal vs. abnormal clinical outcome. The recordings are captured from multiple locations around the heart. Our participation investigates the effectiveness of selfsupervised learning for murmur detection. We train the layers of a backbone CNN in a self-supervised way with data from both this year's and the 2016 challenge. We use two different augmentations on e…
Pulse wave measurements provide vital information in medical diagnosis. For this reason, a measurement system is developed for determining the transient time of the pulse wave between the heart and the ear. To detect pressure variations in the sealed ear canal, caused by the arriving pulse wave, an in-ear sensor is developed which uses heart sounds as time reference. Furthermore, for extracting the heart sounds from the pressure measurements and calculating the pulse wave transient time, a MATLAB-based algorithm is described. An embedded microcontroller based measurement board is presented, wh…
Monash University（莫纳什大学）；Shiraz University（设拉子大学）；Emory University（埃默里大学）；Monash Children’s Hospital（莫纳什儿童医院）；University British Columbia（不列颠哥伦比亚大学）；BC Children’s Hospital Research Institute（不列颠哥伦比亚儿童医院研究所）
Stethoscope-recorded chest sounds provide the opportunity for remote cardio-respiratory health monitoring of neonates. However, reliable monitoring requires high-quality heart and lung sounds. This paper presents novel Non-negative Matrix Factorisation (NMF) and Non-negative Matrix Co-Factorisation (NMCF) methods for neonatal chest sound separation. To assess these methods and compare with existing single-source separation methods, an artificial mixture dataset was generated comprising of heart, lung and noise sounds. Signal-to-noise ratios were then calculated for these artificial mixtures. T…
Obtaining high-quality heart and lung sounds enables clinicians to accurately assess a newborn's cardio-respiratory health and provide timely care. However, noisy chest sound recordings are common, hindering timely and accurate assessment. A new Non-negative Matrix Co-Factorisation-based approach is proposed to separate noisy chest sound recordings into heart, lung, and noise components to address this problem. This method is achieved through training with 20 high-quality heart and lung sounds, in parallel with separating the sounds of the noisy recording. The method was tested on 68 10-second…
University of Moratuwa（莫拉图瓦大学）；Lady Ridgeway Hospital for Children（里奇韦夫人儿童医院）
Diagnosing pre-existing heart diseases early in life is important as it helps prevent complications such as pulmonary hypertension, heart rhythm problems, blood clots, heart failure and sudden cardiac arrest. To identify such diseases, phonocardiogram (PCG) and electrocardiogram (ECG) waveforms convey important information. Therefore, effectively using these two modalities of data has the potential to improve the disease screening process. We evaluate this hypothesis on a subset of the PhysioNet Challenge 2016 Dataset which contains simultaneously acquired PCG and ECG recordings. Our novel Dua…
Dalian University of Technology（大连理工大学）；Dalian Minzu University（大连民族大学）
This paper aims to classify a single PCG recording as normal or abnormal for computer-aided diagnosis. The proposed framework for this challenge has four steps: preprocessing, feature extraction, training and validation. In the preprocessing step, a recording is segmented into four states, i.e., the first heart sound, systolic interval, the second heart sound, and diastolic interval by the Springer Segmentation algorithm. In the feature extraction step, the authors extract 324 features from multi-domains to perform classification. A back propagation neural network is used as predication model.…
Universiti Teknologi Malaysia（马来西亚工艺大学）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats. We design a 1D-CNN that directly learns features from raw heart-sound signals, and a 2D-CNN that takes inputs of two- dimensional time-frequency feature maps based on Mel-frequency cepstral coefficients (MFCC). We further develop a time-frequency CNN ensemble (TF-ECNN) combining the 1D-CNN and 2D-CNN based on score-level fusion of the class probabilities. On the large PhysioNet CinC challenge 2016 database, the proposed CNN …
A New Non-Negative Matrix Factorization Approach for Blind Source Separation of Cardiovascular and Respiratory Sound Based on the Periodicity of Heart and Lung Function
Facultad de Ingeniería, Universidad de Buenos Aires（布宜诺斯艾利斯大学工程学院）；Instituto Argentino de Matemática ”Alberto P. Calderón”, CONICET（国家科学技术研究委员会“阿尔贝托·P·卡尔德隆”阿根廷数学研究所）
In this work, a novel stack of well-known technologies is presented to determine an automatic method to segment the heart sounds in a phonocardiogram (PCG). We will show a deep recurrent neural network (DRNN) capable of segmenting a PCG into its main components and a very specific way of extracting instantaneous frequency that will play an important role in the training and testing of the proposed model. More specifically, it involves a Long Short-Term Memory (LSTM) neural network accompanied by the Fourier Synchrosqueezed Transform (FSST) used to extract instantaneous time-frequency features …
Monash University（莫纳什大学）；Emory University（埃默里大学）；University of British Columbia（不列颠哥伦比亚大学）
Digital stethoscopes in combination with telehealth allow chest sounds to be easily collected and transmitted for remote monitoring and diagnosis. Chest sounds contain important information about a newborn's cardio-respiratory health. However, low-quality recordings complicate the remote monitoring and diagnosis. In this study, a new method is proposed to objectively and automatically assess heart and lung signal quality on a 5-level scale in real-time and to assess the effect of signal quality on vital sign estimation. For the evaluation, a total of 207 10s long chest sounds were taken from 1…
Body sounds provide rich information about the state of the human body and can be useful in many medical applications. Auscultation, the practice of listening to body sounds, has been used for centuries in respiratory and cardiac medicine to diagnose or track disease progression. To date, however, its use has been confined to clinical and highly controlled settings. Our work addresses this limitation: we devise a chest-mounted wearable for continuous monitoring of body sounds, that leverages data processing algorithms that run on-device. We concentrate on the detection of heart sounds to perfo…
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Robert Bosch Research and Technology Center（罗伯特·博世研究与技术中心）
Automatic heart sound abnormality detection can play a vital role in the early diagnosis of heart diseases, particularly in low-resource settings. The state-of-the-art algorithms for this task utilize a set of Finite Impulse Response (FIR) band-pass filters as a front-end followed by a Convolutional Neural Network (CNN) model. In this work, we propound a novel CNN architecture that integrates the front-end bandpass filters within the network using time-convolution (tConv) layers, which enables the FIR filter-bank parameters to become learnable. Different initialization strategies for the learn…
King’s College London（伦敦国王学院）；Kuwait College of Science and Technology（科威特科技学院）；Université du Luxembourg（卢森堡大学）；Carthage University（迦太基大学）；Université de Kindu（金杜大学）
Time-Frequency Distributions (TFDs) support the heart sound characterisation and classification in early cardiac screening. However, despite the frequent use of TFDs in signal analysis, no study comprehensively compared their performances on deep learning for automatic diagnosis. Furthermore, the combination of signal processing methods as inputs for Convolutional Neural Networks (CNNs) has been proved as a practical approach to increasing signal classification performance. Therefore, this study aimed to investigate the optimal use of TFD/ combined TFDs as input for CNNs. The presented results…
Queensland University of Technology（昆士兰科技大学）；M3DICINE Pty Ltd（M3DICINE私人有限公司）
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. 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. The proposed method attains state-of-the-art performance on multiple benchmarks including b…
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 paper 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 …
University of Illinois, Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Mitsubishi Electric Research Labs（三菱电机研究实验室）
There exists an unequivocal distinction between the sound produced by a static source and that produced by a moving one, especially when the source moves towards or away from the microphone. In this paper, we propose to use this connection between audio and visual dynamics for solving two challenging tasks simultaneously, namely: (i) separating audio sources from a mixture using visual cues, and (ii) predicting the 3D visual motion of a sounding source using its separated audio. Towards this end, we present Audio Separator and Motion Predictor (ASMP) -- a deep learning framework that leverages…
A novel approach for analysing cardiac rhythm data is presented in this paper. Heartbeats are decomposed into the five fundamental $P$, $Q$, $R$, $S$ and $T$ waves plus an error term to account for artefacts in the data which provides a meaningful, physical interpretation of the heart's electric system. The morphology of each wave is concisely described using four parameters that allow to all the different patterns in heartbeats be characterized and thus differentiated This multi-purpose approach solves such questions as the extraction of interpretable features, the detection of the fiducial m…
Southeast University（东南大学）；Emory University School of Medicine（埃默里大学医学院）；Georgia Institute of Technology（佐治亚理工学院）
Objective: This paper presents a novel heart sound segmentation algorithm based on Temporal-Framing Adaptive Network (TFAN), including state transition loss and dynamic inference for decoding the most likely state sequence. Methods: In contrast to previous state-of-the-art approaches, the TFAN-based method does not require any knowledge of the state duration of heart sounds and is therefore likely to generalize to non sinus rhythm. The TFAN-based method was trained on 50 recordings randomly chosen from Training set A of the 2016 PhysioNet/Computer in Cardiology Challenge and tested on the othe…
Diffusion-Based Heart Sound Generation: Evaluation with Physiological Signal Metrics, Classifiers, and Expert Listening
Department of Information Science & Engineering, KTH Royal Institute of Technology（信息科学与工程系，皇家理工学院）；Rutherford Appleton Laboratory（拉瑟福德·苹果顿实验室）；Peng Cheng Laboratory（鹏城实验室）；Department of Engineering, King’s College London（工程系，伦敦国王学院）
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；King Saud University（沙特国王大学）；Taiz University（塔伊兹大学）
The alarmingly high mortality rate and increasing global prevalence of cardiovascular diseases signify the crucial need for early detection schemes. Phonocardiogram (PCG) signals have been historically applied in this domain owing to its simplicity and cost-effectiveness. In this paper, we propose CardioXNet, a novel lightweight end-to-end CRNN architecture for automatic detection of five classes of cardiac auscultation namely normal, aortic stenosis, mitral stenosis, mitral regurgitation and mitral valve prolapse using raw PCG signal. The process has been automated by the involvement of two l…
The heart sound signals (Phonocardiogram - PCG) enable the earliest monitoring to detect a potential cardiovascular pathology and have recently become a crucial tool as a diagnostic test in outpatient monitoring to assess heart hemodynamic status. The need for an automated and accurate anomaly detection method for PCG has thus become imminent. To determine the state-of-the-art PCG classification algorithm, 48 international teams competed in the PhysioNet (CinC) Challenge at 2016 over the largest benchmark dataset with 3126 records with the classification outputs, normal (N), abnormal (A) and u…
