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Journal of Medical Engineering &... Jul 2023Phonocardiogram signal (PCG) has been the subject of several signal processing studies, where researchers applied various analysis techniques and extracted numerous...
Phonocardiogram signal (PCG) has been the subject of several signal processing studies, where researchers applied various analysis techniques and extracted numerous features for different purposes, like cardiac pathologies identification, healthy/pathologic case discrimination, and severity assessment. When talking about cardiac severity, many think directly about the intensity or energy of the signal as the most reliable parameter. However, cardiac severity is not always reflected by the intensity or energy of the signal but includes other variables as well. In this paper, we will discuss the probability of having a Discrete Wavelet Transform (DWT) parameter that discriminates, identifies, and assesses the pathological cardiac severity levels, a parameter that takes into consideration other variables and elements for the severity study. For this purpose, we studied six PCGs signals that contain reduced murmurs (clicks) and eight murmur signals with four different cardiac severity levels. We extracted the Entropy of Approximation Coefficients (EAC) from the Discrete Wavelet Transform (DWT) sub-bands as the feature to study in this novel approach. The Energetic Ratio (ER) served as a reference parameter to evaluate the EAC evolution, due to its proven efficiency in cardiac severity tracking. While the DWT-EAC algorithm results revealed that the EAC provides better results for the paper purposes, the One versus All Support Vector Machine (OVA-SVM) classifier affirmed the efficiency of the Entropy of Approximation Coefficients (EAC) for cardiac severity assessment and proved the accuracy of this novel approach.
Topics: Humans; Signal Processing, Computer-Assisted; Wavelet Analysis; Heart Murmurs; Algorithms; Probability
PubMed: 38393735
DOI: 10.1080/03091902.2024.2310157 -
Tidsskrift For Den Norske Laegeforening... Feb 2024Ventricular septal rupture (VSR) following acute myocardial infarction is rare in the modern revascularisation era. Nevertheless, clinical awareness is paramount, as...
BACKGROUND
Ventricular septal rupture (VSR) following acute myocardial infarction is rare in the modern revascularisation era. Nevertheless, clinical awareness is paramount, as presentation may vary.
CASE PRESENTATION
A middle-aged male with no history of cardiovascular disease developed progressive heart failure symptoms while travelling abroad. Initial workup revealed a prominent systolic murmur, but findings were inconsistent with acute coronary syndrome. Transthoracic echocardiogram showed a small hypokinetic area in the basal septum, preserved left ventricular function and no significant valvulopathy. Despite the absence of chest pain, an invasive angiography revealed occlusion of a septal branch emerging from the left anterior descending artery, otherwise patent coronary arteries. Despite administration of diuretics, the patient remained symptomatic and presented two months later to his primary care provider with a persisting systolic murmur. He was subsequently referred to the outpatient cardiology clinic where echocardiography revealed a large VSR involving the basal anteroseptum of the left ventricle with a significant left-to-right shunt. After accurate radiological and haemodynamic assessment of the defect, he successfully underwent elective surgical repair.
INTERPRETATION
Although traditionally associated with large transmural myocardial infarctions, VSR may arise also from minor, subclinical events. A new-onset murmur is a valuable hint for the alert clinician.
Topics: Humans; Male; Middle Aged; Systolic Murmurs; Myocardial Infarction; Ventricular Septal Rupture; Echocardiography; Dyspnea
PubMed: 38349103
DOI: 10.4045/tidsskr.23.0373 -
Frontiers in Cardiovascular Medicine 2023This study aims to assess the ability of state-of-the-art machine learning algorithms to detect valvular heart disease (VHD) from digital heart sound recordings in a...
OBJECTIVE
This study aims to assess the ability of state-of-the-art machine learning algorithms to detect valvular heart disease (VHD) from digital heart sound recordings in a general population that includes asymptomatic cases and intermediate stages of disease progression.
METHODS
We trained a recurrent neural network to predict murmurs from heart sound audio using annotated recordings collected with digital stethoscopes from four auscultation positions in 2,124 participants from the Tromsø7 study. The predicted murmurs were used to predict VHD as determined by echocardiography.
RESULTS
The presence of aortic stenosis (AS) was detected with a sensitivity of 90.9%, a specificity of 94.5%, and an area under the curve (AUC) of 0.979 (CI: 0.963-0.995). At least moderate AS was detected with an AUC of 0.993 (CI: 0.989-0.997). Moderate or greater aortic and mitral regurgitation (AR and MR) were predicted with AUC values of 0.634 (CI: 0.565-703) and 0.549 (CI: 0.506-0.593), respectively, which increased to 0.766 and 0.677 when clinical variables were added as predictors. The AUC for predicting symptomatic cases was higher for AR and MR, 0.756 and 0.711, respectively. Screening jointly for symptomatic regurgitation or presence of stenosis resulted in an AUC of 0.86, with 97.7% of AS cases ( = 44) and all 12 MS cases detected.
CONCLUSIONS
The algorithm demonstrated excellent performance in detecting AS in a general cohort, surpassing observations from similar studies on selected cohorts. The detection of AR and MR based on HS audio was poor, but accuracy was considerably higher for symptomatic cases, and the inclusion of clinical variables improved the performance of the model significantly.
PubMed: 38328674
DOI: 10.3389/fcvm.2023.1170804 -
Frontiers in Pediatrics 2023To create a brief, acceptable, innovative method for self-paced learning to enhance recognition of pediatric heart murmurs by medical students, and to demonstrate this...
OBJECTIVE
To create a brief, acceptable, innovative method for self-paced learning to enhance recognition of pediatric heart murmurs by medical students, and to demonstrate this method's effectiveness in a randomized, controlled trial.
MATERIALS AND METHODS
A curriculum of six 10-min online learning modules was designed to enable deliberate practice of pediatric cardiac auscultation, using recordings of patients' heart murmurs. Principles of andragogy and multimedia learning were applied to optimize acquisition of this skill. A pretest and posttest, given 4 weeks apart, were created using additional recordings and administered to 87 3rd-year medical students during their pediatric clerkship. They were randomized to have access to the modules after the pretest or after the posttest, and asked to use at least the first 2 of the modules.
RESULTS
47 subjects comprised the Intervention group, and 40 subjects the Control group. On our primary outcome, distinguishing innocent from pathological with at least moderate confidence, the posttest scores were significantly higher for the Intervention group (60.5%) than for the Control group (20.0%). For our secondary outcomes, the 2 groups also differed significantly in the ability to distinguish innocent from pathological murmurs, and in identifying the actual diagnosis. On all 3 outcomes, those Intervention group subjects who accessed 4-6 modules scored higher than those who accessed 0-3 modules, who in turn scored higher than the Control group.
SUMMARY
Applying current principles of adult learning, we have created a teaching program for medical students to learn to recognize common pediatric murmurs. Its effectiveness was demonstrated in a randomized, controlled trial. The program results in a meaningful gain in this skill from 1 h of self-paced training with high acceptance to learners.
PubMed: 38293663
DOI: 10.3389/fped.2023.1283306 -
Cardiology in the Young Apr 2024Left ventricular tumour is a rare condition in children. The causes include vegetations, thrombus, and fibroma. 2-year-old asymptomatic female presented with an innocent...
Left ventricular tumour is a rare condition in children. The causes include vegetations, thrombus, and fibroma. 2-year-old asymptomatic female presented with an innocent heart murmur at 6 months of age. Subsequent follow-ups at 18 months of age showed left ventricular mass. Surgical pathology revealed "nodular fasciitis." This type of tumour has never been described in the heart before.
Topics: Child; Humans; Female; Child, Preschool; Fasciitis; Heart Neoplasms; Fibroma; Heart Ventricles; Heart Murmurs
PubMed: 38282536
DOI: 10.1017/S1047951124000052 -
Animals : An Open Access Journal From... Jan 2024Mitral and aortic valve insufficiencies have been commonly reported in horses. The objective of this study was to establish the use of acoustic cardiography (Audicor) in...
Mitral and aortic valve insufficiencies have been commonly reported in horses. The objective of this study was to establish the use of acoustic cardiography (Audicor) in horses with aortic (AI) or mitral valve insufficiency (MI). A total of 17 healthy horses, 18 horses with AI, and 28 horses with MI were prospectively included. None of the horses was in heart failure. Echocardiography and Audicor analyses were conducted. Electromechanical activating time (EMAT), rate-corrected EMATc, left ventricular systolic time (LVST), rate-corrected LVSTc, and intensity and persistence of the third and fourth heart sound (S3, S4) were reported by Audicor. Graphical analysis of the three-dimensional (3D) phonocardiogram served to visually detect murmurs. Audicor snapshot variables were compared between groups using one-way ANOVA followed by Tukey's multiple-comparisons test. The association between Audicor snapshot variables and the corresponding echocardiographic variables was investigated by linear regression and Bland-Altman analyses. Heart murmurs were not displayed on Audicor phonocardiograms. No significant differences were found between Audicor variables obtained in clinically healthy horses and horses with valvular insufficiency. The Audicor device is unable to detect heart murmurs in horses. Audicor variables representing cardiac function are not markedly altered, and their association with corresponding echocardiographic variables is poor in horses with valvular insufficiency that are not in heart failure.
PubMed: 38275790
DOI: 10.3390/ani14020331 -
Frontiers in Artificial Intelligence 2023Heart sound detection technology plays an important role in the prediction of cardiovascular disease, but the most significant heart sounds are fleeting and may be...
Heart sound detection technology plays an important role in the prediction of cardiovascular disease, but the most significant heart sounds are fleeting and may be imperceptible. Hence, obtaining heart sound information in an efficient and accurate manner will be helpful for the prediction and diagnosis of heart disease. To obtain heart sound information, we designed an audio data analysis tool to segment the heart sounds from single heart cycle, and validated the heart rate using a finger oxygen meter. The results from our validated technique could be used to realize heart sound segmentation. Our robust algorithmic platform was able to segment the heart sounds, which could then be compared in terms of their difference from the background. A combination of an electronic stethoscope and artificial intelligence technology was used for the digital collection of heart sounds and the intelligent identification of the first (S1) and second (S2) heart sounds. Our approach can provide an objective basis for the auscultation of heart sounds and visual display of heart sounds and murmurs.
PubMed: 38274051
DOI: 10.3389/frai.2023.1309750 -
IEEE Journal of Biomedical and Health... Apr 2024One in every four newborns suffers from congenital heart disease (CHD) that causes defects in the heart structure. The current gold-standard assessment technique,...
One in every four newborns suffers from congenital heart disease (CHD) that causes defects in the heart structure. The current gold-standard assessment technique, echocardiography, causes delays in the diagnosis owing to the need for experts who vary markedly in their ability to detect and interpret pathological patterns. Moreover, echo is still causing cost difficulties for low- and middle-income countries. Here, we developed a deep learning-based attention transformer model to automate the detection of heart murmurs caused by CHD at an early stage of life using cost-effective and widely available phonocardiography (PCG). PCG recordings were obtained from 942 young patients at four major auscultation locations, including the aortic valve (AV), mitral valve (MV), pulmonary valve (PV), and tricuspid valve (TV), and they were annotated by experts as absent, present, or unknown murmurs. A transformation to wavelet features was performed to reduce the dimensionality before the deep learning stage for inferring the medical condition. The performance was validated through 10-fold cross-validation and yielded an average accuracy and sensitivity of 90.23 % and 72.41 %, respectively. The accuracy of discriminating between murmurs' absence and presence reached 76.10 % when evaluated on unseen data. The model had accuracies of 70 %, 88 %, and 86 % in predicting murmur presence in infants, children, and adolescents, respectively. The interpretation of the model revealed proper discrimination between the learned attributes, and AV channel was found important (score 0.75) for the murmur absence predictions while MV and TV were more important for murmur presence predictions. The findings potentiate deep learning as a powerful front-line tool for inferring CHD status in PCG recordings leveraging early detection of heart anomalies in young people. It is suggested as a tool that can be used independently from high-cost machinery or expert assessment.
Topics: Adolescent; Child; Humans; Infant, Newborn; Heart Auscultation; Deep Learning; Heart Murmurs; Phonocardiography; Auscultation; Heart Defects, Congenital
PubMed: 38261492
DOI: 10.1109/JBHI.2024.3357506 -
Journal of Zoo and Wildlife Medicine :... Jan 2024The asymptomatic and slow progressive nature of cardiopathies represents a risk to the welfare of avian species in human care. Diagnosis and treatment of cardiac disease...
The asymptomatic and slow progressive nature of cardiopathies represents a risk to the welfare of avian species in human care. Diagnosis and treatment of cardiac disease in birds pose a challenge due to unique anatomic and physiologic characteristics. Comprehensive cardiac assessments with diagnostic tools such as echocardiography, color-Doppler, the biomarker cardiac troponin I (cTn1), and cholesterol serum concentrations have been utilized in different bird species with varying success. Saddle-billed storks () have been maintained in human care for over 80 yrs and several institutions have noted heart murmurs and cardiomegaly. Despite these findings, peer-reviewed literature describing cardiopathies is lacking for this species. This case series documents the identification of mitral valve regurgitation in saddle-billed storks in a breeding center. Transcoelomic echocardiography using a ventromedial approach with a two-chambered view and color Doppler was utilized. Echocardiographic measurements were taken and compared 1 yr later in most of the birds. There was left atrial enlargement and worsened mitral regurgitation in one geriatric patient, and no progression of the disease in two young birds. Serum samples showed that cTn1 had different concentrations depending on the severity of the disease, whereas cholesterol was within reference range for all birds. Treatment with digoxin and pimobendan was recommended in one bird, serum concentrations of digoxin were tested in a 6-mon span, results were within therapeutic range, and there were no overt adverse effects. There was a suspected genetic component in this population, as four of the five birds with confirmed mitral regurgitation were related.
Topics: Animals; Humans; Mitral Valve Insufficiency; Birds; Echocardiography; Digoxin; Cholesterol
PubMed: 38252011
DOI: 10.1638/2023-0039 -
The Canadian Veterinary Journal = La... Jan 2024Cardiovascular dysfunction associated with acute kidney injury has been recently described in veterinary medicine, but limited information is available for cats with...
BACKGROUND
Cardiovascular dysfunction associated with acute kidney injury has been recently described in veterinary medicine, but limited information is available for cats with urinary tract obstruction (UTO).
OBJECTIVE
This retrospective study aimed to describe the type, frequency, timeline, and risk factors for cardiovascular events (CVEs) in cats treated for acute UTO.
ANIMALS AND PROCEDURES
Medical records of cats admitted to the intensive care unit for either upper (ureteral: UUTO) or lower (urethral: LUTO) UTO from 2016 to 2021 were reviewed. Cardiovascular events were defined as development of arrhythmia, heart murmur or gallop sound, clinical signs consistent with fluid overload (CRFO), or decreased tissue perfusion (DTP).
RESULTS
One hundred and sixty-eight cats with UTO were recruited (56 with UUTO and 112 with LUTO). Cardiovascular events were reported in 61.9% of cases, including arrhythmia (33.6%), gallop rhythm (28.1%), heart murmur (15.3%), CRFO (14.4%), and DTP (8.6%). Potassium concentration, preexisting chronic kidney disease, and renal pelvic dilation at abdominal ultrasonography were associated with CVE occurrence in multivariate analysis.
CONCLUSIONS
This study highlighted frequent CVEs in cats treated for UTO, with a potential strong impact on outcome. Therefore, cardiovascular parameters of cats with preexisting chronic kidney disease or those admitted with hyperkalemia or renal pelvic dilation should be closely monitored.
Topics: Cats; Animals; Retrospective Studies; Urethral Diseases; Kidney; Renal Insufficiency, Chronic; Arrhythmias, Cardiac; Heart Murmurs; Cardiovascular Diseases; Cat Diseases; Urethral Obstruction; Ureteral Obstruction
PubMed: 38164379
DOI: No ID Found