Auscultation in noise: A program to develop a stethoscope capable of functioning during aeromedical transport

To develop a digital algorithm that detects first and second heart sounds, defines the systole and diastole, and characterises the systolic murmur. Heart sounds were recorded in 300 children with a cardiac murmur, using an electronic stethoscope. A Digital algorithm was developed for detection of first and second heart sounds. R-waves and T-waves in the electrocardiography were used as references for detection. The sound signal analysis was carried out using the short-time Fourier transform. The first heart sound detection rate, with reference to the R-wave, was 100% within 0.05-0.2R-R interval. The second heart sound detection rate between the end of the T-wave and the 0.6R-R interval was 97%. The systolic and diastolic phases of the cardiac cycle could be identified. Because of the overlap between heart sounds and murmur a systolic segment between the first and second heart sounds (20-70%) was selected for murmur analysis. The maximum intensity of the systolic murmur, its average frequency, and the mean spectral power were quantified. The frequency at the point with the highest sound intensity in the spectrum and its time from the first heart sound, the highest frequency, and frequency range were also determined. This method will serve as the foundation for computer-based detection of heart sounds and the characterisation of cardiac murmurs.

Cardiovascular diseases (CVD) are often identified by the audio characteristics of persistent heart and lung sounds of healthy and abnormal subjects. Quite often, these sounds recorded from various subjects are found mixed with each other which in turn leads to ambiguity in the diagnosis. Hence, accurate and precise separation of heart and lung sounds is the essential and foremost task in heart-lung sound analysis. Various signal processing techniques and algorithms have attempted to decompose the heart sound and lung sounds from mixed signals. However, many of them were implemented on bulky and standalone computerized systems and have limited exposure of low-power embedded implementation. In this work, we attempted to implement a novel framework for heart-lung sound separation on a low-power and resource-constrained embedded platform. As a part of the work, we have explored variational mode decomposition (VMD) algorithm to separate the heart and lung sounds from the mixed signals. The proposed framework is implemented on standalone Windows operating system and Raspberry PI-based embedded platform to emulate the offline and real-time scenarios, respectively. Further, the proposed framework is tested with most popular and publicly available heart and lung sound databases obtained from Michigan heart sound library and Littmann lung sound library. The obtained results on both stand alone and embedded platforms are compared with the state-of-the-art techniques. The obtained results on embedded platform are found promising as compared to that of standalone PC-based implementation. As the targeted embedded platform is a low-power, portable, and low-cost computational device, the precise implementation of this work can find fascinating applications in IoT-based heart sound analysis and handheld heart sound assessment devices.

A novel heart sound activity detection framework for automated heart sound analysis

The extraction of heart sound component from a composite signal of heart and lung is a quite challenging task in phonocardiogram signal analysis the first heart sound (S1) and the second heart sound (S2), produced by the closing of the atrioventricular valves and the closing of the semilunar valves, respectively, are the fundamental sounds of the heart. To accomplish this task a novel framework with intrinsic time scale decomposition (ITD) is designed. The capture of the PCG signal frequently hides the detection of the third heart sound (S3), which is necessary to identify cardiac failures. To separate S3, ITD method is deployed to enable signal decomposition into certain levels. Next, by applying smoothed pseudo-Wigner Ville distribution (SWVD) with reassignment, the location of S3 is detected. The proposed method is performed on 36 combinations consists of 144 cardiac cycles containing S3 obtained from different online databases. In comparison to existing approaches, the proposed work separates the S3 from other heart and lung sounds and the proposed method obtained the detection accuracy of S3 as 95.4%, which proves the superiority with other methods.

Heart sounds are a fundamental physiological variable that provide a unique insight into cardiac semiotics. However a deterministic and unambiguous association between noises in cardiac dynamics is far from being accomplished yet due to many and different overlapping events which contribute to the acoustic emission. The current computer-based capacities in terms of signal detection and processing allow one to move from the standard cardiac auscultation, even in its improved forms like electronic stethoscopes or hi-tech phonocardiography, to the extraction of information on the cardiac activity previously unexplored. In this report, we present a new equipment for the detection of heart sounds, based on a set of accelerometric sensors placed in contact with the chest skin on the precordial area, and are able to measure simultaneously the vibration induced on the chest surface by the heart's mechanical activity. By utilizing advanced algorithms for the data treatment, such as wavelet decomposition and principal component analysis, we are able to condense the spatially extended acoustic information and to provide a synthetical representation of the heart activity. We applied our approach to 30 adults, mixed per gender, age and healthiness, and correlated our results with standard echocardiographic examinations. We obtained a 93% concordance rate with echocardiography between healthy and unhealthy hearts, including minor abnormalities such as mitral valve prolapse.

It's to introduce the structure and operating principle of two electronic stethoscopes,one with a simple noise-stop pickup,while the other one with an active inverting denoiser.By subjective auscultation comparison and biological sounds processing via spectral view software,it got visual effects and made objective comparison.The new active inverting denoised stethoscope,the circuit of which is stable and reliable,can get a better denoised effect than the simple noise-stop pickup stethoscope.The new one is easy to operate,and can display the heart sounds,breath sounds and bowel sounds.It can solve the problem of external noise interfering auscultation better than other means. Key words: Inverting denoised; Electronic stethoscope; Spectral view

Phonocardiogram (PCG), the digital recording of heart sounds is becoming increasingly popular as a primary detection system for diagnosing heart disorders and it is relatively inexpensive. Electrocardiogram (ECG) is used during the PCG in order to identify the systolic and diastolic parts manually. In this study a heart sound segmentation algorithm has been developed which separates the heart sound signal into these parts automatically. This study was carried out on 100 patients with normal and abnormal heart sounds. The algorithm uses discrete wavelet decomposition and reconstruction to produce PCG intensity envelopes and separates that into four parts: the first heart sound, the systolic period, the second heart sound and the diastolic period. The performance of the algorithm has been evaluated using 14,000 cardiac periods from 100 digital PCG recordings, including normal and abnormal heart sounds. In tests, the algorithm was over 93% correct in detecting the first and second heart sounds. The presented automatic segmentation algorithm using wavelet decomposition and reconstruction to select suitable frequency band for envelope calculations has been found to be effective to segment PCG signals into four parts without using an ECG.

In this paper, we accurately detect the state-sequence first heart sound(S1)-systole-second heart sound(S2)-diastole, i.e., the positions of S1 and S2, in heart sound recordings. We propose an event detection approach without explicitly incorporating a priori information of the state duration. This renders it also applicable to recordings with cardiac arrhythmia and extendable to the detection of extra heart sounds (third and fourth heart sound), heart murmurs, as well as other acoustic events. We use data from the 2016 PhysioNet/CinC Challenge, containing heart sound recordings and annotations of the heart sound states. From the recordings, we extract spectral and envelope features and investigate the performance of different deep recurrent neural network(DRNN) architectures to detect the state sequence. We use virtual adversarial training, dropout, and data augmentation for regularization. We compare our results with the state-of-the-art method and achieve an average score for the four events of the state sequence of ${\bf F}_{1}\approx 96$% on an independent test set. Our approach shows state-of-the-art performance carefully evaluated on the 2016 PhysioNet/CinC Challenge dataset. In this work, we introduce a new methodology for the segmentation of heart sounds, suggesting an event detection approach with DRNNs using spectral or envelope features.

An early detection of heart disease can save lives, caution individuals and also help to determine the type of treatment to be given to the patients. The first test of diagnosing a heart disease is through auscultation - listening to the heart sounds. The interpretation of heart sounds is subjective and requires a professional skill to identify the abnormalities in these sounds. A medical practitioner uses a stethoscope to perform an initial screening by listening for irregular sounds from the patient's chest. Later, echocardiography and electrocardiography tests are taken for further diagnosis. However, these tests are expensive and require specialized technicians to operate. A simple and economical way is vital for monitoring in homecare or rural hospitals and urban clinics. This dissertation is focused on developing a patient-centered device for initial screening of the heart sounds that is both low cost and can be used by the users on themselves, and later share the readings with the healthcare providers. An innovative mobile health service platform is created for analyzing and classifying heart sounds. Certain properties of heart sounds have to be evaluated to identify the irregularities such as the number of heart beats and gallops, intensity, frequency, and duration. Since heart sounds are generated in low frequencies, human ears tend to miss certain sounds as the high frequency sounds mask the lower ones. Therefore, this dissertation provides a solution to process the heart sounds using several signal processing techniques, identifies the features in the heart sounds and finally classifies them. This dissertation enables remote patient monitoring through the integration of advanced wireless communications and a customized low-cost stethoscope. It also permits remote management of patients' cardiac status while maximizing patient mobility. The smartphone application facilities recording, processing, visualizing, listening, and classifying heart sounds. The application also generates an electronic medical record, which is encrypted using the efficient elliptic curve cryptography and sent to the cloud, facilitating access to physicians for further analysis. Thus, this dissertation results in a patient-centered device that is essential for initial screening of the heart sounds, and could be shared for further diagnosis with the medical care practitioners.

Changes and murmurs of heart sounds often provide the earliest information of abnormal heart status, which makes heart sound monitor and diagnose an important mean for cardiovascular disease. Compared to other heart monitoring approaches, such as EGG, Color Doppler and Magnetic Resonance Imaging Diagnosis, heart sound detection is more portable, much safer and cheaper, and needs lower professional requirements, thus has a broader applicability. This paper aims to design a system for monitoring and analyzing heart sounds in noisy environments, which can achieve signal acquisition, automatic analysis and classification. For the signal acquisition part, a novel equipment prototype is designed by separately using the two channels of a digital sound, one to record mixed signals of heart sounds and some environmental noises, and the other to record only environmental noises as a reference, then the environmental noise can be filtered out based on Least Mean Square (LMS) adaptive noise cancellation to get purified heart sound. By disassembling and assembling a stethoscope and a recorder, this design is realized and verified. For the signal analysis and classification part, with the features extracted by wavelet decomposition and Normalized Average Shannon Energy, a novel fuzzy neural network method with structure learning is used for the heart sound classification. Experiments with real and simulated data demonstrated that our approach can correctly classify all the tested heart sounds with noises even for the ones with previous unseen heart diseases.

A heart sound segmentation algorithm has been developed which separates the heart sound signal into four parts: the first heart sound, the systolic period, the second heart sound, and the diastolic period. The algorithm uses discrete wavelet decomposition and reconstruction to produce phonocardiographic intensity envelopes. The performance of the algorithm has been evaluated using 14000 cardiac periods from 100 digital phonocardiographic recordings, including normal and abnormal heart sounds. In tests, the algorithm was over 93 percent correct in detecting the first and second heart sounds.Keywordsphonocardiographyauscultationfirst heart soundsecond heart soundsystolic perioddiastolic periodmurmurswavelet decompositionwavelet reconstructionsegmentationnormalized average Shannon energy

During lung sound recordings, heart sounds (HS) interfere with clinical interpretation of lung sounds over the low frequency components which is significant especially at low flow rates. Hence, it is desirable to cancel the effect of HS on lung sound records. In this paper, a novel HS cancellation method is presented. This method first localizes HS segments using multiresolution decomposition of the wavelet transform coefficients, then removes those segments from the original lung sound record and estimates the missing data via a 2D interpolation in the time-frequency (TF) domain. Finally, the signal is reconstructed into the time domain. To evaluate the efficiency of the TF filtering, the average power spectral density (PSD) of the original lung sound segments with and without HS over four frequency bands from 20 to 300 Hz were calculated and compared with the average PSD of the filtered signals. Statistical tests show that there is no significant difference between the average PSD of the HS-free original lung sounds and the TF-filtered signal for all frequency bands at both low and medium flow rates. It was found that the proposed method successfully removes HS from lung sound signals while preserving the original fundamental components of the lung sounds.

In this paper, we propose a robust classification method to distinguish between normal lung sounds from healthy subjects and abnormal lung sounds containing adventitious sounds from patients b y using lung sounds contaminated with heart sounds. Heart sounds make it difficult to perform the aforementioned classification with high accuracy. To address this problem, we propose the use of stochastic models to represent the acoustic feature of heart sounds in the classification method based on the maximum likelihood approach b y using hidden Markov models (HMMs) for the calculation of a more exact acoustic likelihood of lung sounds. Our method distinguishes between periods of adventitious sounds and heart sounds by yielding the most likely acoustic segment sequence for each respiration. For the test set of lung sounds contaminated with heart sounds, our classification method achieved a higher classification rate between normal and abnormal lung sounds compared to the conventional method without heart-sound models (88.6% vs. 87.1%, respectively). The classification of healthy and patient subjects b y using the proposed method also achieved a higher classification rate of 83.0% when two samples from different auscultation points were used.

Objective 2 independent studies were undertaken to assess the value of electronic stethoscopes in the training of pulmonary auscultation in the setting of classroom or bedside respectively.Methods 38 medical students without clinical experience were randomized into 2 groups in the classroom training section.17 students participated in a seminar on lung sounds during which electronic stethoscopes were used to play recorded lung sounds,and others received conventional formal teaching on chest auscultation as control group.Chest auscultation skills were evaluated before and after training,and questionnaires were done to evaluate the performance of electronic stethoscope compared with traditional stethoscope.In the bedside training session,30 patients participated in bedside training using electronic or traditional stethoscopes.Time of bedside training was recorded and questionnaires were done.Results Chest auscultation skills of students in both groups were increased after training.No differences in the examination score were observed between the two groups in the preliminary test(P =0.660).However,in the post-intervention assessment,the examination score of the students who had followed the electronic stethoscope training seminar was significantly greater than that of the controls (P =0.002).The answers to a feedback questionnaire indicated that electronic stethoscope was more useful for learning and examination compared with the traditional stethoscope.Time of bedside training was shortened and patients felt more comfortable with the use of electronic stethoscopes compared with the traditional stethoscopes.Conclusions Short time training of inexperienced medical students with electronic stethoscope significantly promoted their discrimination ability of lung sounds compared to students receiving only conventional teaching.The use of electronic stethoscope could also convenience bedside training by shortening training time. Key words: Electronic stethoscope ; Lung sounds; Auscultation; Bedside training

Developing explainable machine intelligence (XAI) models for heart sound abnormality detection is a crucial area of research aimed at improving the interpretability and transparency of machine learning algorithms in medical diagnostics. In this study, we propose a framework for building XAI models that can effectively detect abnormalities in heart sounds while providing interpretable explanations for their predictions. We leverage techniques such as SHapley additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) to generate explanations for model predictions, enabling clinicians to understand the rationale behind the algorithm’s decisions. Our approach involves preprocessing heart sound data, training machine learning models, and integrating XAI techniques to enhance the interpretability of the models. We evaluate the performance of our XAI models using standard metrics and demonstrate their effectiveness in accurately detecting heart sound abnormalities while providing insightful explanations for their predictions. This research contributes to the advancement of XAI in medical applications, particularly in the domain of cardiac diagnostics, where interpretability is crucial for clinical decision-making.