Improved cardiac auscultation competency interweaving visual, auditory, and tactile stimuli: a preliminary study.

Methods:   A pilot randomized controlled trial was conducted at our institution's simulation center with 32 first year medical students from a single medical institution. Participants were randomly divided into two equal groups and completed an educational module the identification and pathophysiology of five common cardiac sounds. The control group utilized traditional education methods, while the interventional group incorporated multisensory stimuli. Afterwards, participants listened to randomly selected cardiac sounds and competency data was collected through a multiple-choice post-assessment in both groups. Mann-Whitney U test was used to analyze the data. Results: Data were analyzed using the Mann-Whitney U test. Diagnostic accuracy was significantly higher in the multisensory group (Mdn=100%) compared to the control group (Mdn=60%) on the post-assessment (U=73.5, p<0.042). Likewise, knowledge acquisition was substantially better in the multisensory group (Mdn=80%) than in the control group (Mdn=50%) (U= 49, p<0.031). Conclusions: These findings suggest the incorporation of multisensory stimuli significantly improves cardiac auscultation competency. Given its cost-effectiveness and simplicity, this approach offers a viable alternative to more expensive simulation technologies like the Harvey simulator, particularly in settings with limited resources. Consequently, this teaching modality holds promise for global applicability, addressing the worldwide deterioration in cardiac auscultation skills and potentially leading to better patient outcomes. Future studies should broaden the sample size, span multiple institutions, and investigate long-term retention rates.

Accurate Localization of First and Second Heart Sounds via Template Matching in Forcecardiography Signals.

Cardiac auscultation is an essential part of physical examination and plays a key role in the early diagnosis of many cardiovascular diseases. The analysis of phonocardiography (PCG) recordings is generally based on the recognition of the main heart sounds, i.e., S1 and S2, which is not a trivial task. This study proposes a method for an accurate recognition and localization of heart sounds in Forcecardiography (FCG) recordings. FCG is a novel technique able to measure subsonic vibrations and sounds via small force sensors placed onto a subject's thorax, allowing continuous cardio-respiratory monitoring. In this study, a template-matching technique based on normalized cross-correlation was used to automatically recognize heart sounds in FCG signals recorded from six healthy subjects at rest. Distinct templates were manually selected from each FCG recording and used to separately localize S1 and S2 sounds, as well as S1-S2 pairs. A simultaneously recorded electrocardiography (ECG) trace was used for performance evaluation. The results show that the template matching approach proved capable of separately classifying S1 and S2 sounds in more than 96% of all heartbeats. Linear regression, correlation, and Bland-Altman analyses showed that inter-beat intervals were estimated with high accuracy. Indeed, the estimation error was confined within 10 ms, with negligible impact on heart rate estimation. Heart rate variability (HRV) indices were also computed and turned out to be almost comparable with those obtained from ECG. The preliminary yet encouraging results of this study suggest that the template matching approach based on normalized cross-correlation allows very accurate heart sounds localization and inter-beat intervals estimation.

HBNET: A blended ensemble model for the detection of cardiovascular anomalies using phonocardiogram.

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.

AI diagnosis of heart sounds differentiated with super StethoScope.

In the aging global society, heart failure and valvular heart diseases, including aortic stenosis, are affecting millions of people and healthcare systems worldwide. Although the number of effective treatment options has increased in recent years, the lack of effective screening methods is provoking continued high mortality and rehospitalization rates. Appropriately, auscultation has been the primary option for screening such patients, however, challenges arise due to the variability in auscultation skills, the objectivity of the clinical method, and the presence of sounds inaudible to the human ear. To address challenges associated with the current approach towards auscultation, the hardware of Super StethoScope was developed. This paper is composed of (1) a background literature review of bioacoustic research regarding heart disease detection, (2) an introduction of our approach to heart sound research and development of Super StethoScope, (3) a discussion of the application of remote auscultation to telemedicine, and (4) results of a market needs survey on traditional and remote auscultation. Heart sounds and murmurs, if collected properly, have been shown to closely represent heart disease characteristics. Correspondingly, the main characteristics of Super StethoScope include: (1) simultaneous collection of electrocardiographic and heart sound for the detection of heart rate variability, (2) optimized signal-to-noise ratio in the audible frequency bands, and (3) acquisition of heart sounds including the inaudible frequency ranges. Due to the ability to visualize the data, the device is able to provide quantitative results without disturbance by sound quality alterations during remote auscultations. An online survey of 3648 doctors confirmed that auscultation is the common examination method used in today's clinical practice and revealed that artificial intelligence-based heart sound analysis systems are expected to be integrated into clinicians' practices. Super StethoScope would open new horizons for heart sound research and telemedicine.

Novel Design of a Multimodal Technology-Based Smart Stethoscope for Personal Cardiovascular Health Monitoring.

Heart sounds and heart rate (pulse) are the most common physiological signals used in the diagnosis of cardiovascular diseases. Measuring these signals using a device and analyzing their interrelationships simultaneously can improve the accuracy of existing methods and propose new approaches for the diagnosis of cardiovascular diseases. In this study, we have presented a novel smart stethoscope based on multimodal physiological signal measurement technology for personal cardiovascular health monitoring. The proposed device is designed in the shape of a compact personal computer mouse for easy grasping and attachment to the surface of the chest using only one hand. A digital microphone and photoplehysmogram sensor are installed on the bottom and top surfaces of the device, respectively, to measure heart sound and pulse from the user's chest and finger simultaneously. In addition, a high-performance Bluetooth Low Energy System-on-Chip ARM microprocessor is used for pre-processing of measured data and communication with the smartphone. The prototype is assembled on a manufactured printed circuit board and 3D-printed shell to conduct an in vivo experiment to test the performance of physiological signal measurement and usability by observing users' muscle fatigue variation.

Abdominal cardiovascular sound recording and analysis using cardio-microphones.

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.

Automated Quality Assessment for Accelerometer-Based Heart Sounds Recorded with a Novel Subcutaneous Medical Implant.

In the context of monitoring patients with heart failure conditions, the automated assessment of heart sound quality is of major importance to insure the relevance of the medical analysis of the heart sound data. We propose in this study a technique of quality classification based on the selection of a small set of representative features. The first features are chosen to characterize whether the periodicity, complexity or statistical nature of the heart sound recordings. After segmentation process, the latter features are probing the detectability of the heart sounds in cardiac cycles. Our method is applied on a novel subcutaneous medical implant that combines ECG and accelerometric-based heart sound measurements. The actual prototype is in pre-clinical phase and has been implanted on 4 pigs, which anatomy and activity constitute a challenging environment for obtaining clean heart sounds. As reference quality labeling, we have performed a three-class manual annotation of each recording, qualified as "good", "unsure" and "bad". Our method allows to retrieve good quality heart sounds with a sensitivity and an accuracy of 82% ± 2% and 88% ± 6% respectively. Clinical Relevance- By accurately recovering high quality heart sound sequences, our method will enable to monitor reliable physiological indicators of heart failure complications such as decompensation.

The robustness of Random Forest and Support Vector Machine Algorithms to a Faulty Heart Sound Segmentation.

Cardiac auscultation is the key exam to screen cardiac diseases both in developed and developing countries. A heart sound auscultation procedure can detect the presence of murmurs and point to a diagnosis, thus it is an important first-line assessment and also cost-effective tool. The design automatic recommendation systems based on heart sound auscultation can play an important role in boosting the accuracy and the pervasiveness of screening tools. One such as step, consists in detecting the fundamental heart sound states, a process known as segmentation. A faulty segmentation or a wrong estimation of the heart rate might result in an incapability of heart sound classifiers to detect abnormal waves, such as murmurs. In the process of understanding the impact of a faulty segmentation, several common heart sound segmentation errors are studied in detail, namely those where the heart rate is badly estimated and those where S1/S2 and Systolic/Diastolic states are swapped in comparison with the ground truth state sequence. From the tested algorithms, support vector machine (SVMs) and random forest (RFs) shown to be more sensitive to a wrong estimation of the heart rate (an expected drop of 6% and 8% on the overall performance, respectively) than to a swap in the state sequence of events (an expected drop of 1.9% and 4.6%, respectively).

Simple Phonocardiography with the Ankle-Brachial Index Measurement System as a Tool for Detecting Fourth Heart Sounds of Hypertrophic Cardiomyopathy.

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.

Hemodynamics-driven mathematical model of first and second heart sound generation.

NEW & NOTEWORTHY: To the best of our knowledge, this is the first hemodynamic-based heart sound generation model embedded in a complete real-time computational model of the cardiovascular system. Simulated heart sounds are similar to experimental and clinical measurements, both quantitatively and qualitatively. Our model can be used to investigate the relationships between heart sound acoustic features and hemodynamic factors/anatomical parameters.

Contactless analysis of heart rate variability during cold pressor test using radar interferometry and bidirectional LSTM networks.

Contactless measurement of heart rate variability (HRV), which reflects changes of the autonomic nervous system (ANS) and provides crucial information on the health status of a person, would provide great benefits for both patients and doctors during prevention and aftercare. However, gold standard devices to record the HRV, such as the electrocardiograph, have the common disadvantage that they need permanent skin contact with the patient. Being connected to a monitoring device by cable reduces the mobility, comfort, and compliance by patients. Here, we present a contactless approach using a 24 GHz Six-Port-based radar system and an LSTM network for radar heart sound segmentation. The best scores are obtained using a two-layer bidirectional LSTM architecture. To verify the performance of the proposed system not only in a static measurement scenario but also during a dynamic change of HRV parameters, a stimulation of the ANS through a cold pressor test is integrated in the study design. A total of 638 minutes of data is gathered from 25 test subjects and is analysed extensively. High F-scores of over 95% are achieved for heartbeat detection. HRV indices such as HF norm are extracted with relative errors around 5%. Our proposed approach is capable to perform contactless and convenient HRV monitoring and is therefore suitable for long-term recordings in clinical environments and home-care scenarios.

Changes in acoustic cardiographic parameters before and after hemodialysis are associated with overall and cardiovascular mortality in hemodialysis patients.

Acoustic cardiography can provide simultaneous electrocardiography and acoustic cardiac data to assess the electronic and mechanical heart functions. The aim of this study was to assess whether changes in acoustic cardiographic parameters (ACPs) before and after hemodialysis (HD) are associated with overall and cardiovascular (CV) mortality in HD patients. A total of 162 HD patients was enrolled and ACPs were measured before and after HD, including left ventricular systolic time (LVST), systolic dysfunction index (SDI), third (S3) and fourth (S4) heart sounds, and electromechanical activation time (EMAT). During a follow-up of 2.9 years, 25 deaths occurred with 16 from CV causes. Multivariate analysis showed that high △SDI (per 1; hazard ratio [HR], 2.178; 95% confidence interval [CI], 1.189-3.990), high △EMAT (per 1%; HR, 2.218; 95% CI 1.382-3.559), and low △LVST (per 1 ms; HR, 0.947; 95% CI 0.912-0.984) were independently associated with increased overall mortality. In addition, high △EMAT (per 1%; HR, 2.141; 95% CI 1.117-4.102), and low △LVST (per 1 ms; HR, 0.777; 95% CI 0.637-0.949) were associated with increased CV mortality. In conclusion, the changes in ACPs before and after HD may be a useful clinical marker and stronger prognostic marker of overall and CV mortality than ACPs before HD.

Analysis of the four heart sounds statistical study and spectro-temporal characteristics.

Heart auscultation has been recognised for a long time as an important tool for the diagnosis of heart disease; it is the most common and widely recommended method to screen for structural abnormalities of the cardiovascular system. Detecting relevant characteristics and forming a diagnosis based on the sounds heard through a stethoscope, however, is a skill that can take years to be acquired and refine. The efficiency and accuracy of diagnosis based on heart sound auscultation can be improved considerably by using digital signal processing techniques to analyse phonocardiographic (PCG) signals. The study of the functioning of the heart is very important for the diagnosis of different cardiac pathologies. The phonocardiogram signal (PCG) is the signal generated after conversion of the sound noises coming from the heart into an electrical signal, it groups together a set of four cardiac noises (S1, S2, S3, S4) which are in direct correlation with cardiac activity. The short-term Fourier Transform (STFT) is an analytical technique that describes the evolution of the time and frequency behaviour of these four heart sounds. A statistical study has been carried out in this direction in order to better highlight the characteristics of the PCG signal. A fairly high number of cycles (twenty) was used to further refine the expected results. The objective of this paper is to use a statistical analysis based on the results obtained by the use of The STFT technic this in order to find statistical parameters (mean, standard deviation, etc.) which can give us a clear vision of the electrophysiological behaviour of the phonocardiogram signal. This aspect has not been done so far and which however can give appreciable practical results.

Classification of heart sounds based on the combination of the modified frequency wavelet transform and convolutional neural network.

We purpose a novel method that combines modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN) for classifying normal and abnormal heart sounds. A hidden Markov model is used to find the position of each cardiac cycle in the heart sound signal and determine the exact position of the four periods of S1, S2, systole, and diastole. Then the one-dimensional cardiac cycle signal was converted into a two-dimensional time-frequency picture using the MFSWT. Finally, two CNN models are trained using the aforementioned pictures. We combine two CNN models using sample entropy (SampEn) to determine which model is used to classify the heart sound signal. We evaluated our model on the heart sound public dataset provided by the PhysioNet Computing in Cardiology Challenge 2016. Experimental classification performance from a 10-fold cross-validation indicated that sensitivity (Se), specificity (Sp) and mean accuracy (MAcc) were 0.95, 0.93, and 0.94, respectively. The results showed the proposed method can classify normal and abnormal heart sounds with efficiency and high accuracy. Graphical abstract Block diagram of heart sound classification.

[Evaluation of the use of a simulation software in the learning of cardiopulmonary auscultation in undergraduate medical students]. / Evaluation de l'utilisation d'un logiciel de simulation dans l'apprentissage de l'auscultation cardio-pulmonaire chez les étudiants en premier cycle d'études médicales.

INTRODUCTION: Medsounds™ software allows to create an auscultation learning platform, by providing real pre-recorded cardiopulmonary sounds on virtual chests. The study aimed at comparing the skills in cardiopulmonary auscultation between students who benefited from this platform and students who did not have access to it. METHODS: A controlled trial was conducted with 2nd year medical students randomised into three groups. Groups A, B and C received 10 h of cardiopulmonary clinical training. In addition, group B benefited from an online access to the educative platform, and group C had a demonstration of the platform during their clinical training, then an online access. The main outcome was a 3-point multiple-choice questionnaire based on 2 original case vignettes about the description of cardiopulmonary sounds. The secondary outcome was the faculty exam on high-fidelity cardiopulmonary simulator. RESULTS: Groups A and B included 127 students, and group C 117. Students in group C had a significantly higher score than those in group A (1.72/3 versus 1.48/3; p = 0.02), without difference between the groups B and C. Students who actually had a demonstration of the platform and used it at home had a higher score than those who did not use it (1.87 versus 1.51; p = 0.01). Students who had a demonstration of the platform before using it performed a better pulmonary examination on high-fidelity simulators. CONCLUSION: The supervised use of an online auscultation simulation software in addition to the traditional clinical training seems to improve the auscultation performances of undergraduated medical students.

Automated heart sound classification system from unsegmented phonocardiogram (PCG) using deep neural network.

Given the patient to doctor ratio of 50,000:1 in low income and middle-income countries, there is a need for automated heart sound classification system that can screen the Phonocardiogram (PCG) records in real-time. This paper proposes deep neural network architectures such as a one-dimensional convolutional neural network (1D-CNN) and Feed-forward Neural Network (F-NN) for the classification of unsegmented phonocardiogram (PCG) signal. The research paper aims to automate the feature engineering and feature selection process used in the analysis of the PCG signal. The original PCG signal is down-sampled at 500 Hz. Then they are divided into smaller time segments of 6 s epochs. Savitzky-Golay filter is used to suppress the high-frequency noises in the signal by data point smoothening. The processed data was then provided as an input to the proposed deep neural network (DNN) architectures. 1081 PCG records were used for training and validating the proposed DNN models. The Feed-forward Neural Network model with five hidden layers provided a better overall accuracy of 0.8565 with a sensitivity of 0.8673, and specificity of 0.8475. The balanced accuracy of the model was found to be 0.8574. The performance of the model was also studied using the Receiver Operating Characteristic (ROC) plot, which produced an Area Under the Curve (AUC) value of 0.857. The classification accuracy of the proposed models was compared to the related works on PCG signal analysis for cardiovascular disease detection. The DNN models studied in this study provided comparable performance in heart sound classification without the requirement of feature engineering and segmentation of heart sound signals.

A Review of Computer-Aided Heart Sound Detection Techniques.

Cardiovascular diseases have become one of the most prevalent threats to human health throughout the world. As a noninvasive assistant diagnostic tool, the heart sound detection techniques play an important role in the prediction of cardiovascular diseases. In this paper, the latest development of the computer-aided heart sound detection techniques over the last five years has been reviewed. There are mainly the following aspects: the theories of heart sounds and the relationship between heart sounds and cardiovascular diseases; the key technologies used in the processing and analysis of heart sound signals, including denoising, segmentation, feature extraction and classification; with emphasis, the applications of deep learning algorithm in heart sound processing. In the end, some areas for future research in computer-aided heart sound detection techniques are explored, hoping to provide reference to the prediction of cardiovascular diseases.

Cardiac auscultation skills among junior doctors: effects of sound simulation lesson.

OBJECTIVES: To evaluate the effect of a sound simulation lesson to improve cardiac auscultation skills among junior doctors. METHODS: This study is based on the design of test comparison before and after educational intervention using a convenient sample. For 50 junior doctors in Japan, diagnostic accuracy before and after a sound simulation lesson for cardiac auscultation skills was compared. There were 15 doctors who experienced cardiology rotation. The lesson used seven abnormal cardiac recordings (third heart sound, double gallop, aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, and pericardial friction rub). At tests before and after the lesson, the doctors listened to random sound outputs of the same seven recordings, chose diagnostic findings from multiple-choice items, and obtained individual diagnostic accuracy based on the total number of choosing correct findings. Top 10 doctors obtaining the greatest individual accuracy received a commendation. RESULTS: Pre-lesson diagnostic accuracy was not different between doctors with cardiology rotation training (total diagnostic accuracy of the group, 27/105 [26%]) and those without cardiology rotation (70/245 [29%]). Compared to pre-lesson, post-lesson total diagnostic accuracy significantly improved with about two-folds (97/350 [28%] vs 170/350 [61%]; McNemar Test, p<0.0001). The improvement was significant for double gallop (5/50 [10%] vs. 15/50 [30%]), mitral stenosis (0/50 [0%] vs. 6/50 [12%]), and pericardial friction rub (1/50 [2%] vs. 35/50 [70%]). CONCLUSIONS: The use of a simple sound simulation lesson may help junior doctors to learn cardiac auscultation skills. Clinician educators are encouraged to use this strategy in addition to cardiology rotation training.

Evaluation of left ventricular systolic function using synchronized analysis of heart sounds and the electrocardiogram.

BACKGROUND: Heart failure is a major health concern and often requires echocardiography to confirm the diagnosis. We introduce a new method that uses a wearable heart sound and electrocardiogram (ECG) device that can be used in the outpatient setting. OBJECTIVE: The purpose of this study was to determine the value of synchronized analysis of heart sounds and ECG in identifying patients with depressed left ventricular ejection fraction (dLVEF) <50%. METHODS: One hundred eighty-nine patients (76 with dLVEF; 113 with normal ejection fraction) were enrolled. All were admitted to the hospital because of dyspnea or chest discomfort. N-Terminal pro-B-type natriuretic peptide (NT-proBNP) was measured in all patients. LVEF was determined by echocardiography. Heart sound and ECG signals were simultaneously recorded using the wearable synchronized phonocardiogram and ECG device. Heart sound and ECG signals were automatically analyzed using wavelet analysis and utilized to determine electromechanical activation time (EMAT), EMAT/RR, S1-S2 time, and S1-S2/RR. RESULTS: EMAT in the dLVEF group was significantly higher than that in the control group (159.82 ± 83 ms vs 91.58 ± 28 ms). Pearson correlation test showed a negative correlation between EMAT and LVEF (r = -0.449; P <.001). Receiver operating characteristic curve analysis demonstrated that the sensitivity and specificity of EMAT ≥104 ms for the diagnosis of EF <50% were 92.1% and 92%, respectively. Patients with intermediate NT-proBNP values were identified as dLVEF by EMAT ≥104 ms, with sensitivity of 93.5% and specificity of 92.8%. CONCLUSION: The heart sound and ECG signal index EMAT contributes to the diagnosis of EF <50% and is especially helpful in patients with an inconclusive NT-proBNP value.