Heart Sound Segmentation-An Event Detection Approach Using Deep Recurrent Neural Networks.

Phonocardiography (PCG) is a noninvasive method for obtaining recordings of cardiovascular sound, that is, acoustic phenomena that are perceivable by auscultation at the cardiac region on the chest wall. Essentially, vibrations of the chest surface are observed that are generated in cardiac structures and transmitted to the chest wall. In physiological terms, heart sounds and murmurs are mentioned: Heart sounds have a transient, musical character; murmurs, except for the musical types, have a random, noisy character. Normal heart sounds are initiated by valve closing (the first and the second sound) or by filling phenomena (the third and the fourth sound); besides the normal ones pathological sounds exist, for example, generated at opening of degenerated valves. Heart murmurs of the random type generally result from turbulence in blood flow; the latter can be caused by flow through a narrowed valve opening or a leaking valve. Besides a small number of normal types, most heart murmurs are pathological and many of these can be related to malfunctioning of cardiac valves. Phonocardiography and auscultation thus aim at the recognition of pathological sounds and murmurs and their relation to malfunctioning heart structures. Recording of heart sounds at the chest surface, as for any vibration recording, requires a vibration transducer (also called a heart vibration pickup, a heart sound microphone), a suitable amplifier with specific frequency characteristics and a storage, visualization, and processing device. Contact transducers (accelerometers) and air‐coupled transducers are used. The first type can be indicated as absolute, the second as relative as it measures a weighted difference in vibration between the air chamber surface and the surface at the edge of the rigid housing of the transducer. The sensing chest piece of the (electronic) stethoscope accords with the second type. Generally, quantitative measurement requires a transducer that does not affect the vibration to be measured. This is hard to achieve in vibration measurement on soft tissue as is the case for the chest wall. Affecting the vibrating surface is, for example, a technique applied in heart auscultation: with the use of a stiff membrane and by applying firm pressure on the stethoscope chest piece higher frequencies are better observed. In order to obtain equivalent results as from auscultation (due to the qualities of the human hearing sense) high pass frequency filter sets have to be applied on recorded vibrations in phonocardiography. A single signal representation does not provide the same information as heard with the stethoscope. On the other hand, heart sound recording can exceed the hearing abilities and inaudible sounds can be recorded. In conventional phonocardiography, signals are stored on paper with a suitable graphic recorder; Evidently, modern systems make use of information technology for storage, processing, and visualization. As a result of different transducers, an important interference with the chest wall vibration and different signal conditioning, heart sound recording is not standardized, as compared with electrocardiography, which has a clear millivolt scale on its ordinate axis: The method can be indicated semiquantitative as results depend on the measuring apparatus used. Nevertheless, knowledge of heart sounds and murmurs has been greatly increased with this technique. Signal analysis, more specifically time–frequency analysis, has proven to be very useful in the identification and classification of heart sound components and murmurs, and their relation to cardiac structures and hemodynamic variables. Whereas auscultation still holds its position as a diagnostic tool for general physicians and cardiologists, phonocardiography in its classical form has lost interest mostly as a result of the availability of techniques as Doppler echocardiography and cardiac imaging methods, which give more direct information on cardiac structures and hemodynamic variables. The historical value of conventional phonocardiography has to be stressed. On the other hand, recording and processing of heart sounds continue to be subjects of scientific research and remain beneficial for training and for supporting of diagnosis of physicians. Electronic stethoscopes coupled to a laptop and connected to the internet for automated or for remote diagnosis by a specialist may grow in importance in the next few years.

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.

We hypothesized that an automated speech- recognition-inspired classification algorithm could differentiate between the heart sounds in subjects with and without pulmonary hypertension (PH) and outperform physicians. Heart sounds, electrocardiograms, and mean pulmonary artery pressures (mPAp) were recorded simultaneously. Heart sound recordings were digitized to train and test speech-recognition-inspired classification algorithms. We used mel-frequency cepstral coefficients to extract features from the heart sounds. Gaussian-mixture models classified the features as PH (mPAp ≥ 25 mmHg) or normal (mPAp < 25 mmHg). Physicians blinded to patient data listened to the same heart sound recordings and attempted a diagnosis. We studied 164 subjects: 86 with mPAp ≥ 25 mmHg (mPAp 41 ± 12 mmHg) and 78 with mPAp < 25 mmHg (mPAp 17 ± 5 mmHg) (p < 0.005). The correct diagnostic rate of the automated speech-recognition-inspired algorithm was 74% compared to 56% by physicians (p = 0.005). The false positive rate for the algorithm was 34% versus 50% (p = 0.04) for clinicians. The false negative rate for the algorithm was 23% and 68% (p = 0.0002) for physicians. We developed an automated speech-recognition-inspired classification algorithm for the acoustic diagnosis of PH that outperforms physicians that could be used to screen for PH and encourage earlier specialist referral.

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

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.

New device for objective auscultation of the heart: Permanent audible and visual record of the heart acoustics

Cardiovascular diseases (CVDs) is considered a serious public health problem due to the uncertainty of its onset. Consuming wearable devices have increasing popularities for healthcare monitoring, and many of them are capable of continuous monitoring and early detection of CVDs. This paper proposes a framework for heart sound detection that can be considered for deployment on smart wearable devices to screen CVDs conveniently. A dual-stream convolutional neural network (DS-CNN) is developed to detect abnormal ones from short-term heart sound recordings. Preprocessing module is first employed for noise filtering and amplitude normalization. Then short-time Fourier transform and higher-order spectral are introduced for feature extraction, whose products are subsequently fed into the DS-CNN for screening abnormal heart sound signals. Two open accessible datasets are employed for performance evaluation. The results well demonstrate the classification accuracy of the proposed DS-CNN, and also indicate its advantages for adapting to heart sound recordings collected by different equipments.

Although auscultation of heart sounds is a commonly used diagnostic tool in preliminary investigation of heart function, no description of normal amplitudes of the two major heart sounds, the first (S1) and second (S2) heart sounds, and their relation to age, sex, and body mass index (BMI), exist. Heart sound recordings from 739 healthy subjects, from the Dan-NICAD study, recorded with Acarix CADScor System at the left fourth inter-costal space (IC4), were investigated. The S1 and S2 sounds were isolated using an automated segmentation algorithm and their absolute amplitudes, |S1| and |S2|, were tested for correlation with age, sex, and BMI. Mean S11 was 95.4 (±6.2) dB for men and 93.7 (±6.8) dB for women, which showed a significant difference (p<0.05). Mean |S2| was 91.9 (±5.1) dB for men and 91.8 (±5.6) dB for women, showing no significant difference (p=0.82). |S1| and |S2| were only weakly correlated with age (r=−0.084 and r=−0.069 respectively). Both |S1| and |S2| were well correlated with BMI (r=−0.42 and r=−0.50 respectively). The correlation between BMI and the amplitude of heart sounds is an important baseline knowledge when developing algorithms for automated analysis of heart sounds.

740-5 A New Intracavitary Cathether for Monitoring Contractility Through Endocardially Recorded First Heart Sound: Initial Clinical Validation

The ability of clinicians, ie, 10 veterinary students, 10 general practitioners, and 10 board certified internists, to describe and interpret common normal and abnormal heart sounds was assessed. Recordings of heart sounds from 7 horses with a variety of normal and abnormal rhythms, heart sounds, and murmurs were analyzed by digital sonography. The perception of the presence or absence of the heart sounds S1, S2, and S4 was similar for clinicians irrespective of their level of training and was in agreement with the sonographic interpretation on 89, 82, and 78% of occasions, respectively. However, practitioners were less likely to correctly describe the presence of S3. The heart rhythm was correctly described as being regular or irregular on 89% of occasions, and this outcome was not affected by level of training. Differentiation of the type of irregularity was less reliable. The perception of the intensity of a heart murmur was accurate and correlated with the grade assigned in the living horses,R2= .68, and with sonographic measurements of the murmur's intensity,R2= .69. Clinicians overestimated the duration of cardiac murmurs, particularly that of the loud systolic murmur. Only diplomates could reliably differentiate systolic from diastolic murmurs. The ability to diagnose the underlying cardiac problem was significantly affected by training; diplomates, practitioners, and undergraduates made the correct diagnosis on 53, 33, and 29% of occasions, respectively. The poor diagnostic ability of practitioners and the lack of improvement in diagnostic skill after the 2nd year of veterinary school emphasizes the need for better teaching of these skills. Digital sonograms that combine sound files with synchronous visual interpretations may be useful in this regard.

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.

The computation of fast Fourier transform (FFT) plays a vital role in all fields of science and engineering, especially in the medical domain. Spectral results of the data convey more information to the clinicians to take handy decisions during the diagnosis. If a problem happens during the computation of FFT, the incorrect results misinterpret the information and hence the suggestions and decisions by practitioners would be the useless one moreover, it may yield harmful result to the patients. This paper deals with how to recover the spectrum from the faulty one with the heart sound case reports. The heart sound recording is the phonocardiogram (PCG) signal. Stethoscopes are commonly used to hear the heart sound for diagnosis. The two PCG signals of normal and abnormal subjects in each were analyzed in this paper. The normal subject produce clear lub and dub sounds, where the abnormal subject produce a kind of whistling or swishing sound in middle which may be due to the problem of septal defect in a heart. This paper analyzes four PCG signals spectrally with fault and the recovered ones along with the execution time results in each using Matlab R2016b tool.

The cardiac auscultation (CA) skills of paediatric residents and office-based paediatricians have recently been shown to be suboptimal. CA is known to have a high degree of specificity and sensitivity, and is inexpensive. New teaching aids and availability of surrogate patient heart sounds and murmurs now allow most physicians to acquire CA skills. These teaching aids should be available in all medical schools and in all postgraduate paediatric training programs. While the relationship between musicality and CA skill has not been proven, the author assumes this relationship to be valid. Specific learning objectives in CA should be established. Recognizing that the current trend is away from clinical examinations, students frequently are unaware that a CA learning deficit exists. Therefore, students' CA skills should be evaluated before medical school graduation and at the Royal College of Physicians and Surgeons specialty examination. Students with amusia (inability to distinguish pitch of sound) should be identified and consideration should be given to directing them away from a clinical specialty in which CA is important. Further study is required in the physiology of learning of CA. Appropriate action by medical school and paediatric postgraduate program directors and the Royal College will effect a higher standard of patient care by increasing CA skills, resulting in a more financially efficient system - something everyone is trying to achieve in these times of increasing medical care costs.

Only after a comprehensive examination of the precordium may one begin an auscultation of the heart. Inspection, palpation, percussion, and auscultation are all steps in the precordium examination procedure. Here, we only go into detail on cardiac auscultation. Heart sounds and murmurs are extremely hard to hear without the right equipment because their amplitude and frequency signals are so tiny. You may listen to heart sounds by placing your ear over your chest, using a stethoscope, or using a microphone. Graphics are also recorded for the sounds. The four auscultatory areas in our precordium region are the mitral area, tricuspid area, aortic area, and pulmonary area. Heart sounds has significant diagnostic significance in clinical practise because changes in heart sounds are indicative of heart valve dysfunction. With the use of a stethoscope or microphone, one may hear four different heart sounds: the first heart sound (LUB), also known as s1, the second heart sound (DUB), also known as s2, the third heart sound (s3), and the fourth heart sound (s4). Every normal person can hear their first and second heart sounds, but only those with abnormal cardiac issues can hear their third and fourth heart sounds. With the use of a stethoscope, murmurs may also be heard.

Prevalence and diagnostic value of precordial murmurs for valvular regurgitation in obese patients treated with dexfenfluramine