Acoustic-based rule out of stable coronary artery disease: the FILTER-SCAD trial.

BACKGROUND AND AIMS: Overtesting of low-risk patients with suspect chronic coronary syndrome (CCS) is widespread. The acoustic-based coronary artery disease (CAD) score has superior rule-out capabilities when added to pre-test probability (PTP). FILTER-SCAD tested whether providing a CAD score and PTP to cardiologists was superior to PTP alone in limiting testing. METHODS: At six Danish and Swedish outpatient clinics, patients with suspected new-onset CCS were randomised to either standard diagnostic examination (SDE) with PTP, or SDE plus CAD score, and cardiologists provided with corresponding recommended diagnostic flowcharts. The primary endpoint was cumulative number of diagnostic tests at one year and key safety endpoint major adverse cardiac events (MACE). RESULTS: In total 2008 patients (46% male, median age 63 years) were randomised from October 2019 to September 2022. When randomised to CAD score (n=1002), it was successfully measured in 94.5%. Overall, 13.5% had PTP ≤5%, and 39.5% had CAD score ≤20. Testing was deferred in 22% with no differences in diagnostic tests between groups (p for superiority =0.56). In the PTP ≤5% subgroup, the proportion with deferred testing increased from 28% to 52% (p<0.001). Overall MACE was 2.4 per 100 person-years. Non-inferiority regarding safety was established, absolute risk difference 0.4% (95% CI -1.85 to 1.06) (p for non-inferiority = 0.005). No differences were seen in angina-related health status or quality of life. CONCLUSIONS: The implementation strategy of providing cardiologists with a CAD score alongside SDE did not reduce testing overall but indicated a possible role in patients with low CCS likelihood. Further strategies are warranted to address resistance to modifying diagnostic pathways in this patient population.

Deep learning fusion framework for automated coronary artery disease detection using raw heart sound signals.

One of the most common cardiovascular diseases is coronary artery disease (CAD). Thus, it is crucial for early CAD diagnosis to control disease progression. Computer-aided CAD detection often converts heart sounds into graphics for analysis. However, this method relies heavily on the subjective experience of experts. Therefore, in this study, we proposed a method for CAD detection using raw heart sound signals by constructing a fusion framework with two CAD detection models: a multidomain feature model and a medical multidomain feature fusion model. We collected heart sound signal datasets from 400 participants, extracting 206 multidomain features and 126 medical multidomain features. The designed framework fused the same one-dimensional deep learning features with different multidomain features for CAD detection. The experimental results showed that the multidomain feature model and the medical multidomain feature fusion model achieved areas under the curve (AUC) of 94.7 % and 92.7 %, respectively, demonstrating the effectiveness of the fusion framework in integrating one-dimensional and cross-domain heart sound features through deep learning algorithms, providing an effective solution for noninvasive CAD detection.

The role of cardiac acoustic biomarkers in monitoring patients with heart failure: A systematic literature review.

Heart failure (HF) creates a considerable clinical, humanistic and economic burden on patients and caregivers as well as on healthcare systems. To attenuate the significant burden of HF, there is a need for enhanced management of patients with HF. The use of digital tools for remote non-invasive monitoring of heart parameters is gaining traction, and cardiac acoustic biomarkers (CABs) have been proposed as a complementary set of measures to assess heart function alongside traditional methods such as electrocardiogram and echocardiography. We conducted a systematic literature review to evaluate associations between CABs and HF outcomes. Embase and MEDLINE databases were searched for recent studies published between 2013 and 2023 that evaluated CABs in patients with HF. Additional grey literature (i.e., conference, congress and pre-print publications from January 2021 to May 2023) searches were included. Two reviewers independently examined all articles; a third resolved conflicts. Data were extracted from articles meeting inclusion criteria. Extracted studies underwent quality and bias assessments using the Joanna Briggs Institute (JBI) critical appraisal tools. In total, 3074 records were screened, 73 full-text articles were assessed for eligibility and 27 publications were included. Third heart sound (S3) and electromechanical activation time (EMAT) were the CABs most often reported in the literature for monitoring HF. Fifteen publications discussed changes in S3 characteristics and its role in HF detection or outcomes: six studies highlighted S3 assessment among various groups of patients with HF; four studies evaluated the strength or amplitude of S3 with clinical outcomes; five studies assessed the relationship between S3 presence and clinical outcomes; and one study assessed both S3 presence and amplitude in relation to HF clinical outcomes. Eleven publications reported on EMAT and its derivatives: five studies on the relationship between EMAT and HF and six studies on the association of EMAT and HF clinical outcomes. Studies reporting the first and fourth heart sound, left ventricular ejection time and systolic dysfunction index were limited. Published literature supported S3 and EMAT as robust CAB measures in HF that may have value in remote clinical monitoring and management of patients with HF. Additional studies designed to test the predictive power of these CABs, and others less well-characterized, are needed. This work was funded by Astellas Pharma Inc.

Segmentation of Heart Sound Signal Based on Multi-Scale Feature Fusion and Multi-Classification of Congenital Heart Disease.

Analyzing heart sound signals presents a novel approach for early diagnosis of pediatric congenital heart disease. The existing segmentation algorithms have limitations in accurately distinguishing the first (S1) and second (S2) heart sounds, limiting the diagnostic utility of cardiac cycle data for pediatric pathology assessment. This study proposes a time bidirectional long short-term memory network (TBLSTM) based on multi-scale analysis to segment pediatric heart sound signals according to different cardiac cycles. Mel frequency cepstral coefficients and dynamic characteristics of the heart sound fragments were extracted and input into random forest for multi-classification of congenital heart disease. The segmentation model achieved an overall F1 score of 94.15% on the verification set, with specific F1 scores of 90.25% for S1 and 86.04% for S2. In a situation where the number of cardiac cycles in the heart sound fragments was set to six, the results for multi-classification achieved stabilization. The performance metrics for this configuration were as follows: accuracy of 94.43%, sensitivity of 95.58%, and an F1 score of 94.51%. Furthermore, the segmentation model demonstrates robustness in accurately segmenting pediatric heart sound signals across different heart rates and in the presence of noise. Notably, the number of cardiac cycles in heart sound fragments directly impacts the multi-classification of these heart sound signals.

The heart sound classification of congenital heart disease by using median EEMD-Hurst and threshold denoising method.

Heart sound signals are vital for the machine-assisted detection of congenital heart disease. However, the performance of diagnostic results is limited by noise during heart sound acquisition. A limitation of existing noise reduction schemes is that the pathological components of the signal are weak, which have the potential to be filtered out with the noise. In this research, a novel approach for classifying heart sounds based on median ensemble empirical mode decomposition (MEEMD), Hurst analysis, improved threshold denoising, and neural networks are presented. In decomposing the heart sound signal into several intrinsic mode functions (IMFs), mode mixing and mode splitting can be effectively suppressed by MEEMD. Hurst analysis is adopted for identifying the noisy content of IMFs. Then, the noise-dominated IMFs are denoised by an improved threshold function. Finally, the noise reduction signal is generated by reconstructing the processed components and the other components. A database of 5000 heart sounds from congenital heart disease and normal volunteers was constructed. The Mel spectral coefficients of the denoised signals were used as input vectors to the convolutional neural network for classification to verify the effectiveness of the preprocessing algorithm. An accuracy of 93.8%, a specificity of 93.1%, and a sensitivity of 94.6% were achieved for classifying the normal cases from abnormal one.

Importance of fourth heart sound and preserved left atrial function in wild-type transthyretin amyloidosis.

AIMS: A fourth heart sound (S4) was reported to be almost never present in patients with amyloid light-chain cardiomyopathy. There have been no reports on S4 in patients with wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM). This study aimed to clarify the clinical implications of S4 in patients with ATTRwt-CM. METHODS AND RESULTS: Seventy-six patients with ATTRwt-CM (mean age: 80.4 ± 5.4 years, 68 males) who had undergone phonocardiography (PCG) were retrospectively assessed. We measured S4 amplitude on digitally recorded PCG. S4 was considered to be present when its amplitude was 1.0 mm or greater on the PCG. Distinct S4 was defined as S4 with an amplitude of 2.0 mm or greater, which is usually recognizable by auscultation. According to the rhythm and presence or absence of S4, the patients were divided into three groups, namely, sinus rhythm (SR) with S4, SR without S4, and non-SR. Non-SR consisted of atrial fibrillation, atrial flutter, and atrial tachycardia. Thirty-six patients were in SR and the remaining 40 patients were in non-SR. In the 36 patients in SR, S4 was shown by PCG to be present in 17 patients (47%), and distinct S4 was recognized in 7 patients (19%) by auscultation. In patients who were in SR, those with S4 had higher systolic blood pressure (124 ± 15 vs. 99 ± 8 mmHg, P < 0.001), lower level of plasma B-type natriuretic peptide (308 [interquartile range (IQR): 165, 354] vs. 508 [389, 765] pg/mL, P = 0.034) and lower level of high-sensitivity cardiac troponin T (0.068 [0.046, 0.089] vs. 0.109 [0.063, 0.148] ng/mL, P = 0.042) than those without S4. There was no significant difference in left atrium (LA) volume index or LA reservoir strain between patients with S4 and without S4. Patients with S4 had more preserved LA systolic function than those without S4 (peak atrial filling velocity: 53 ± 25 vs. 34 ± 9 cm/s, P = 0.033; LA contractile strain: 4.1 ± 2.1 vs. 1.6 ± 2.0%, P = 0.012). Patients in SR without S4 had worse short-term prognosis compared with the other two groups (generalized Wilcoxon test, P = 0.033). CONCLUSIONS: S4 was present in 47% of the patients in SR with ATTRwt-CM. Patients in SR without S4 had more impaired LA systolic function than those in SR with S4. The absence of S4 portends a poor short-term prognosis in patients with ATTRwt-CM.

NeoSSNet: Real-Time Neonatal Chest Sound Separation Using Deep Learning.

Goal: Auscultation for neonates is a simple and non-invasive method of diagnosing cardiovascular and respiratory disease. However, obtaining high-quality chest sounds containing only heart or lung sounds is non-trivial. Hence, this study introduces a new deep-learning model named NeoSSNet and evaluates its performance in neonatal chest sound separation with previous methods. Methods: We propose a masked-based architecture similar to Conv-TasNet. The encoder and decoder consist of 1D convolution and 1D transposed convolution, while the mask generator consists of a convolution and transformer architecture. The input chest sounds were first encoded as a sequence of tokens using 1D convolution. The tokens were then passed to the mask generator to generate two masks, one for heart sounds and one for lung sounds. Each mask is then applied to the input token sequence. Lastly, the tokens are converted back to waveforms using 1D transposed convolution. Results: Our proposed model showed superior results compared to the previous methods based on objective distortion measures, ranging from a 2.01 dB improvement to a 5.06 dB improvement. The proposed model is also significantly faster than the previous methods, with at least a 17-time improvement. Conclusions: The proposed model could be a suitable preprocessing step for any health monitoring system where only the heart sound or lung sound is desired.

Feasibility of self-measurement telemonitoring using a handheld heart sound recorder in patients with heart failure - SELPH multicenter pilot study.

BACKGROUND: Multi-parametric assessment, including heart sounds in addition to conventional parameters, may enhance the efficacy of noninvasive telemonitoring for heart failure (HF). We sought to assess the feasibility of self-telemonitoring with multiple devices including a handheld heart sound recorder and its association with clinical events in patients with HF. METHODS: Ambulatory HF patients recorded their own heart sounds, mono­lead electrocardiograms, oxygen saturation, body weight, and vital signs using multiple devices every morning for six months. RESULTS: In the 77 patients enrolled (63 ±â€¯13 years old, 84 % male), daily measurements were feasible with a self-measurement rate of >70 % of days in 75 % of patients. Younger age and higher Minnesota Living with Heart Failure Questionnaire scores were independently associated with lower adherence (p = 0.002 and 0.027, respectively). A usability questionnaire showed that 87 % of patients felt self-telemonitoring was helpful, and 96 % could use the devices without routine cohabitant support. Six patients experienced ten HF events of re-hospitalization and/or unplanned hospital visits due to HF. In patients who experienced HF events, a significant increase in heart rate and diastolic blood pressure and a decrease in the time interval from Q wave onset to the second heart sound were observed 7 days before the events compared with those without HF events. CONCLUSIONS: Self-telemonitoring with multiple devices including a handheld heart sound recorder was feasible even in elderly patients with HF. This intervention may confer a sense of relief to patients and enable monitoring of physiological parameters that could be valuable in detecting the deterioration of HF.

Improved recovery of cardiac auscultation sounds using modified cosine transform and LSTM-based masking.

Obtaining accurate cardiac auscultation signals, including basic heart sounds (S1 and S2) and subtle signs of disease, is crucial for improving cardiac diagnoses and making the most of telehealth. This research paper introduces an innovative approach that utilizes a modified cosine transform (MCT) and a masking strategy based on long short-term memory (LSTM) to effectively distinguish heart sounds and murmurs from background noise and interfering sounds. The MCT is used to capture the repeated pattern of the heart sounds, while the LSTMs are trained to construct masking based on the repeated MCT spectrum. The proposed strategy's performance in maintaining the clinical relevance of heart sounds continues to demonstrate effectiveness, even in environments marked by increased noise and complex disruptions. The present work highlights the clinical significance and reliability of the suggested methodology through in-depth signal visualization and rigorous statistical performance evaluations. In comparative assessments, the proposed approach has demonstrated superior performance compared to recent algorithms, such as LU-Net and PC-DAE. Furthermore, the system's adaptability to various datasets enhances its reliability and practicality. The suggested method is a potential way to improve the accuracy of cardiovascular diagnostics in an era of rapid advancement in medical signal processing. The proposed approach showed an enhancement in the average signal-to-noise ratio (SNR) by 9.6 dB at an input SNR of - 6 dB and by 3.3 dB at an input SNR of 10 dB. The average signal distortion ratio (SDR) achieved across a variety of input SNR values was 8.56 dB.

Beat-to-beat alterations of acoustic intensity and frequency at the maximum power of heart sounds are associated with NT-proBNP levels.

Background: Auscultatory features of heart sounds (HS) in patients with heart failure (HF) have been studied intensively. Recent developments in digital and electrical devices for auscultation provided easy listening chances to recognize peculiar sounds related to diastolic HS such as S3 or S4. This study aimed to quantitatively assess HS by acoustic measures of intensity (dB) and audio frequency (Hz). Methods: Forty consecutive patients aged between 46 and 87 years (mean age, 74 years) with chronic cardiovascular disease (CVD) were enrolled in the present study after providing written informed consent during their visits to the Kitasato University Outpatient Clinic. HS were recorded at the fourth intercostal space along the left sternal border using a highly sensitive digital device. Two consecutive heartbeats were quantified on sound intensity (dB) and audio frequency (Hz) at the peak power of each spectrogram of S1-S4 using audio editing and recording application software. The participants were classified into three groups, namely, the absence of HF (n = 27), HF (n = 8), and high-risk HF (n = 5), based on the levels of NT-proBNP < 300, ≥300, and ≥900 pg/ml, respectively, and also the levels of ejection fraction (EF), such as preserved EF (n = 22), mildly reduced EF (n = 12), and reduced EF (n = 6). Results: The intensities of four components of HS (S1-S4) decreased linearly (p < 0.02-0.001) with levels of body mass index (BMI) (range, 16.2-33.0 kg/m2). Differences in S1 intensity (ΔS1) and its frequency (ΔfS1) between two consecutive beats were non-audible level and were larger in patients with HF than those in patients without HF (ΔS1, r = 0.356, p = 0.024; ΔfS1, r = 0.356, p = 0.024). The cutoff values of ΔS1 and ΔfS1 for discriminating the presence of high-risk HF were 4.0 dB and 5.0 Hz, respectively. Conclusions: Despite significant attenuations of all four components of HS by BMI, beat-to-beat alterations of both intensity and frequency of S1 were associated with the severity of HF. Acoustic quantification of HS enabled analyses of sounds below the audible level, suggesting that sound analysis might provide an early sign of HF.