Cardiac Sounds Research Articles

Do basic auscultation skills need to be resuscitated? A new strategy for improving competency among nursing students

BackgroundAuscultation of heart and lung sounds is a foundational competency for Registered Nurses (RNs). Precise and timely assessments are important for the early detection and recognition of the deteriorating patient. Studies have shown that improved teaching methods that incorporate emerging technologies and address different learning styles are needed to improve competency in auscultation. MethodUndergraduate nursing students (n = 127) were randomized into treatment and control groups. The control group received the usual preparation in auscultation learning strategies. The treatment group received the usual training plus three auscultation learning sessions that were each 2 h in length (cardiac, pulmonary and mixed sounds). ResultsThe virtual auscultation teaching strategy had a significant impact on undergraduate nursing student's competency in recognizing heart murmurs. The treatment group also had increased scores compared to the control group increased scores in distinguishing normal versus abnormal heart and lung sounds, identification of crackles and diminished breath sounds. ConclusionVirtual auscultation as a teaching strategy was shown to have a positive impact on undergraduate student nurse competence in accurately identifying heart and lung sounds.

Abstract 17500: A Crisis of the Heart

A 59 year old female with past medical history of metastatic neuroendocrine tumor without cardiac involvement was admitted for transarterial catheter embolization (TACE) procedure of liver lesions. During the TACE procedure, she developed hypertensive urgency treated with intravenous beta blocker therapy and continuous octreotide for carcinoid reaction. The following day she developed acute pulmonary edema and labile blood pressures requiring low dose norepinephrine. Octreotide infusion was up-titrated to 500 mcg/hour with concern for carcinoid crisis. On physical exam, she was tachycardic and regular without notable murmurs or extra cardiac sounds, no elevated jugular venous distension, and no lower extremity edema. She had bilateral crackles on pulmonary exam and was diaphoretic and delirious. There were sub-millimeter lateral ST- segment elevations on her electrocardiogram but her troponin levels were elevated to 14 ng/dL. Point of care cardiac ultrasound demonstrated severely impaired left ventricular systolic function without regional wall motion abnormalities. Coronary angiogram revealed diffuse coronary vasospasm and elevated left ventricular end-diastolic pressure. On right heart catheterization, she had elevated filling pressures, pulmonary arterial oxygen saturation of 25%, and cardiac index of 1.42 L/min/m2. Our patient was diagnosed with cardiogenic shock secondary to octreotide mediated coronary vasospasm causing diffuse myocardial injury. Continued multidisciplinary discussions were needed to determine appropriate vasopressor and inotropic agent use to support cardiac function without triggering further hormonal dysregulation during carcinoid crisis. Her cardiac function eventually improved with left ventricular Impella support and gradual down-titration of octreotide dose. Norepinephrine, epinephrine, and phenylephrine were avoided in favor of vasopressin, and dobutamine was switched to milrinone to prevent further sympathetic activation. She was able to wean off Impella and pressor support and had improvement of her left ventricular function on repeat echocardiography one month later. She has not had recurrence of heart failure symptoms and continues to follow up with oncology via tele-visits.

CARDIAC TAMPONADE FROM RAPID UREMIC PERICARDITIS IN PATIENT WITH WARFARIN OVER-ANTICOAGULATION

SESSION TITLE: Medical Student/Resident Cardiovascular Disease Posters SESSION TYPE: Med Student/Res Case Rep Postr PRESENTED ON: October 18-21, 2020 INTRODUCTION: Cardiac tamponade constitutes a medical emergency, which requires prompt recognition, diagnosis and treatment that is geared towards preventing cardiovascular collapse and cardiac arrest. CASE PRESENTATION: 81-year-old male patient with end stage renal disease on hemodialysis, paroxysmal atrial fibrillation on warfarin anticoagulation, diabetes mellitus type II, and morbid obesity, who was evaluated for history of 24-hour progression of general malaise, orthopnea, dyspnea on exertion, and dizziness after missing two sessions of hemodialysis. Physical exam was remarkable for borderline low blood pressure, +3 leg edema, distended jugular veins, bibasilar crackles, distant heart sounds, absence of cardiac murmurs or heart sounds. ECG showed pulsus alternans and low voltage criteria. Limited bedside echocardiogram was remarkable for a moderate pericardial effusion with minimal ventricular and atrial compression abnormalities. Patient was provided hemodialysis for volume overload without hemodynamic decompensation and was monitored with telemetry and bedside hemodynamics. Shortly after hemodialysis, patient developed worsening shortness of breath and became hypotensive. Patient was started on norepinephrine vasopressor therapy. A bedside limited echocardiogram showed diastolic right ventricular and right atrial collapse, non-collapsibility of the inferior vena cava and severe pericardial effusion that duplicated in size. Bedside emergent subxiphoid echocardiography-guided pericardiocentesis with instillation of agitated saline was performed with removal of about 400cc of serosanguineous fluid with resolution of hypotension and electric alternans. Labs reflected marked uremia, elevated ESR and C-reactive protein and warfarin overanticoagulation with INR of 4.5. Pericardial fluid resulted with a culture-negative exudative effusion composed of bloody material and inflammatory cells. Chest non-contrast computed tomography showed nonspecific mild mediastinal adenopathy. Positron emission tomography–computed tomography showed no significant F-18 Fluorodeoxyglucose avid lesions that were identified. Patient was provided ultrafiltration for 5 days, and started on colchicine and indomethacin with weekly follow-up of inflammatory markers and echocardiography, with marked improvement of pericardial effusion size and return of normal hemodynamics. DISCUSSION: Uremic pericarditis was identified in setting of acute pericarditis in a patient with over-anticoagulation. The hemorrhagic component resulted in rapid fluid accumulation and tamponade. The instillation of agitated saline can help assess for inadvertent chamber puncture during procedure. CONCLUSIONS: Uremic pericarditis is now an uncommon cause of pericarditis, but it can be exacerbated in the setting of inflammatory pericarditis and over-anticoagulation, leading to rapid accumulation of bloody pericardial fluid and cardiac tamponade. Reference #1: Maggiolini S, Gentile G, Farina A, et al. Safety, efficacy, and complications of pericardiocentesis by real-time echo-monitored procedure. Am J Cardiol 2016;117:1369-74. Reference #2: Sadjadi SA, Mashahdian A. Uremic pericarditis: a report of 30 cases and review of the literature. Am J Case Rep. 2015;16:169-173. Reference #3: Osuch JR, Khandekar JD, Fry WA. Emergency subxiphoid pericardial decompression for malignant pericardial effusion. Am Surg. 1985;51(6):298-300. DISCLOSURES: No relevant relationships by VICTOR MOLINA-LOPEZ, source=Web Response No relevant relationships by Hector Santiago, source=Web Response No relevant relationships by Francisco Tirado-Polo, source=Web Response

Carcinoma arising from an oesophageal duplication cyst in an adult cat

A 17‐year‐old female Siamese cat was referred for a history of progressive dyspnoea, depression and anorexia. The cat was tachypnoeic, dyspnoeic and the thoracic auscultation revealed muffled cardiac and respiratory sounds. Radiographs of the thorax demonstrated pleural effusion and a well‐circumscribed soft tissue mass in the caudodorsal aspect of the mediastinum, most likely consistent with an oesophageal mass. CT findings demonstrated a cystic‐like mass in the caudal mediastinum, which was located in the right wall of the oesophagus, pleural effusion and subpleural nodules. Microscopic examination revealed that the wall of the cystic‐like mass had an inner mucosal layer lined by squamous epithelium and contained two muscle layers, consistent with an oesophageal duplication cyst (ODC). A carcinoma was found in the ODC as well as pleural carcinomatosis. To the authors’ knowledge, this is the first case of a neoplastic ODC.

Robust heart sound segmentation based on spectral change detection and genetic algorithms

Listening to cardiac sounds can quickly provide information about the functioning of the heart. The heart sound signal, also known as the phonocardiogram (PCG), plays an essential role in automatic auscultation. Segmentation of the PCG signal into its fundamental parts can significantly facilitate any further analysis. In this work, we propose a new method that segments the PCG into fundamental heart sounds and silences. This method can be divided into two stages: Detection and Selection. In the first stage, a function whose maxima indicate the presence of sound events is generated based on the calculation of the spectral flux, a measure of how quickly the spectrum of the PCG signal is changing with respect to time. In the second stage, the position of the beginning and termination of the fundamental heart sounds is detected by analyzing and selectively choosing the time positions of the maxima in the detection function. This selection is solved as an optimization problem through the estimation of an ideal detection function, whose solution is found using two genetic algorithms: a simple genetic algorithm (SGA) and differential evolution (DE). The proposed method was evaluated using the PhysioNet/CinC Challenge dataset, comprising more than 3,000 PCGs. Our results exhibit a mean F1 score of 87.5% and 93.6% for the SGA and DE variants, respectively. The proposed system is robust and highly modular, which simplifies the reuse of specific parts to evaluate algorithm variants. The implementation of the proposed method is available as open-source software.

Automatic Segmentation and Classification of Heart Sounds Using Modified Empirical Wavelet Transform and Power Features

A system for the automatic classification of cardiac sounds can be of great help for doctors in the diagnosis of cardiac diseases. Generally speaking, the main stages of such systems are (i) the pre-processing of the heart sound signal, (ii) the segmentation of the cardiac cycles, (iii) feature extraction and (iv) classification. In this paper, we propose methods for each of these stages. The modified empirical wavelet transform (EWT) and the normalized Shannon average energy are used in pre-processing and automatic segmentation to identify the systolic and diastolic intervals in a heart sound recording; then, six power characteristics are extracted (three for the systole and three for the diastole)—the motivation behind using power features is to achieve a low computational cost to facilitate eventual real-time implementations. Finally, different models of machine learning (support vector machine (SVM), k-nearest neighbor (KNN), random forest and multilayer perceptron) are used to determine the classifier with the best performance. The automatic segmentation method was tested with the heart sounds from the Pascal Challenge database. The results indicated an error (computed as the sum of the differences between manual segmentation labels from the database and the segmentation labels obtained by the proposed algorithm) of 843,440.8 for dataset A and 17,074.1 for dataset B, which are better values than those reported with the state-of-the-art methods. For automatic classification, 805 sample recordings from different databases were used. The best accuracy result was 99.26% using the KNN classifier, with a specificity of 100% and a sensitivity of 98.57%. These results compare favorably with similar works using the state-of-the-art methods.

Emergency typhlectomy for caecal strangulation caused by chronic diaphragmatic hernia in a dog

A two‐year‐old female entire Catahoula leopard dog was referred for investigation and management of suspected chronic diaphragmatic hernia, 11 months post road traffic accident. The dog had a two‐month history of progressive lethargy and one‐week history of vomiting and anorexia. Physical examination revealed a slight increase in respiratory effort with normal respiratory rate. Cardiac and pulmonary sounds were muffled on thoracic auscultation and intestinal loops were not immediately palpable in the abdomen. Thoracic radiographs acquired before referral were suggestive of a diaphragmatic hernia, which was later confirmed on abdominal and thoracic ultrasound. Cardiorespiratory deterioration was evident eight hours post presentation and an emergency exploratory laparotomy was performed. The small intestines, caecum, and the ascending and transverse colon were found to have herniated into the thorax. Marked caecal necrosis was found intraoperatively. A typhlectomy and repair of the diaphragmatic defect were performed. The patient recovered uneventfully and was in good health nine months postoperatively.

Desarrollo de una aplicación para configurar el teléfono inteligente como fonendoscopio para profesionales sanitarios con deficiencias auditivas

Introducción y objetivos. La hipoacusia supone un severo hándicap para cualquier profesional cuya actividad se base en el reconocimiento de sonidos. En el caso de profesionales sanitarios, la auscultación constituye una actividad rutinaria y el padecimiento de hipoacusia la limita en grado variable en función de la severidad de la misma. Aquellos profesionales sanitarios que por la severidad de su hipoacusia necesitan del uso de audífonos ven dificultadas las rutinas basadas en el uso del fonendoscopio. El objetivo del presente trabajo es describir el proceso llevado a cabo para desarrollar una aplicación para smartphones, que permita la reproducción en tiempo real, el registro y el análisis de sonidos para facilitar la labor de profesionales sanitarios con hipoacusia. Métodos. Hemos recogido somatosonidos cardiacos, pulmonares y abdominales de sujetos sanos y patológicos a fin de caracterizarles frecuencialmente. Posteriormente, la aplicación ha sido diseñada con el objetivo de facilitar la labor diagnóstica del profesional sanitario hipoacúsico, teniendo en cuenta la caracterización anterior para optimizar la escucha y el análisis de sonidos cardiacos, pulmonares y abdominales. Además, con el objetivo de maximizar el número de dispositivos compatibles, ha sido desarrollada para el sistema operativo Android, el más extendido del mercado. Resultados. Hemos desarrollado una App. para smartphones (a la que hemos llamado STETHOSCOPE) basados en Android que configura el teléfono como un fonendoscopio recogiendo el somatosonido a través de su micrófono (siendo posible utilizar exclusivamente el micrófono interno del smartphone o bien micrófonos externos de alta calidad a través de su conector JACK), procesando la señal hasta enviarla finalmente por Bluetooth a los audífonos del profesional hipoacúsico. Esta aplicación permite grabar y representar gráficamente sonidos cardiacos, pulmonares y abdominales en dispositivos Android y almacenarlos en formato WAV, según las recomendaciones del Instituto de Ingeniería Eléctrica y Electrónica (Institute of Electrical and Electronics Engineers, IEEE), utilizando una codificación FLOAT de 32 bits sin compresión posibilitando su archivo, comparación o compartición con otros profesionales. Conclusiones. En este estudio presentamos una aplicación destinada a utilizar el smartphone como fonendoscopio, haciendo llegar el sonido captado a la ayuda auditiva (por vía inalámbrica) del profesional sanitario hipoacúsico que lo precise.

A proposed algorithm for analysing heart sounds and calculating their time intervals

Abstract Heart sounds play a crucial role in the clinical assessment of patients. Stethoscopes are used for detecting heart sounds and diagnosing potential abnormal conditions. However, several parameters of the cardiac sounds cannot be extracted by traditional stethoscopes. This paper presents a proposed algorithm based on peaks detection. Besides its ability of filtering the heart sounds signals, the time intervals of these sounds in addition to the heart rate were calculated by the proposed algorithm in an efficient way. Signals of the heart sounds from two sources were used to evaluate the efficiency of the algorithm. The first source was the data recorded from 14 participants, whereas the second source was the free data set sponsored by PASCAL. The algorithm showed different performance accuracy for detecting the main heart sounds based on the source of the data used in the study. The accuracy was 93.6% when using the data recorded from the first source, whereas it was 76.194% for the data of the second source.

Detection of cardiac sounds components: a pilot study

<span>This paper presents a preliminary study related to the detection and identification of cardiac sounds components including first sound (S1), second sound (S2) and murmurs. Detection and identification of cardiac sounds are an important process in automated cardiac sound analysis system in order to automatically diagnose people who are having cardiovascular disorder and determine the existence of murmurs. Sixteen of recorded cardiac sounds (eight normal cardiac sounds, four abnormal cardiac sounds with systole murmur, and four abnormal cardiac sounds with diastole murmur) from PASCAL Classifying Heart Sounds Challenge database were examined for analysis. This work is significant in studying the time and time-frequency based detection of cardiac sounds components characteristics. In time-based analysis, envelope of signal energy was used to do the peak detection of S1, S2 and murmur and also analysis of cardiac cycle, systole and diastole duration. While time-frequency based analysis was used to determine the S1, S2 and murmur frequency range. The findings yield the overall accuracy of envelope-based detection for normal cardiac sound signal at 60.85% while for abnormal cardiac sound signal at 57.24%.</span>

PHONOCARDIOGRAPHY IMPROVES IDENTIFICATION OF MITRAL STENOSIS (MS) BY MEDICAL STUDENTS DURING STANDARDIZED AUSCULTATION ASSESSMENT

Phonocardiography (PCG), the graphic representation of cardiac vibration and sounds, previously limited to research, is now available for point-of-care (POC) assessment. Despite improved technology, there are few studies assessing whether PCG in addition to auscultation improves identification (ID)

Deep learning for heart disease detection through cardiac sounds

Abstract Most of death causes are related to cardiovascular disease. In fact, there are several anomalies afflicting the heart beat, for instance heart murmur or artefact. We propose a method for heart disease detection. By gathering a set of feature obtainable directly from cardiac sounds, we consider this feature vector as input for a deep neural network to discriminate whether a cardiac sound is belonging to an healthy or to a patient with a cardiac disease. The experiment we performed demonstrated the effectiveness of the proposed approach in real-world environment.

Automatic Recognition of Fundamental Heart Sound Segments From PCG Corrupted With Lung Sounds and Speech

Automated recognition of fundamental heart sound segments (FHSS) from Phonocardiogram (PCG) is the preliminary step before clinical parameters extraction to detect the presence of abnormality if any. PCG acquisition systems are usually based on microphones. These microphones apart from cardiac sounds will also pick up non-cardiac sounds like lung sounds and speech. The recognition of FHSS is challenging in the presence of non-cardiac events. Deep learning techniques like convolutional neural network (CNN) and recurrent neural network (RNN) are suitable for automated FHSS. However, it will be shown that their performance is degraded in the presence of interference like lung sounds, and speech. Hence in this work, a combination of conventional signal processing technique with deep neural network (DNN) is proposed to enhance the accuracy of automated FHSS. The conventional signal processing technique is based on EWT which can adaptively design the filter banks based on the type of interference. For DNN, U-Net is considered. The method involves the segmentation of PCG using EWT and recognition of FHSS using U-Net based DNN. Envelope features are extracted from the EWT based reconstructed signal and used for training the U-Net based DNN to recognize FHSS. To further improve the recognition accuracy of FHSS, delineation parameters obtained from EWT are incorporated for temporal modeling with the outcomes of U-Net based DNN. The performance of the proposed method is analyzed using both real-time signals and signals taken from standard databases like the Physionet database, and Littmann’s lung sound library. Realtime PCG is acquired using an in-house developed PCG acquisition system. The proposed U-Net based DNN with the EWT method achieves FHSS recognition accuracy of 91.17% for PCG with lung sound interference and 90.78% for PCG with speech interference. The proposed method significantly improves the accuracy of FHSS recognition compared to long short term memory (LSTM), and gated recurrent unit (GRU).

P1259 When it rains, it pours: Late presentation STEMI in a patient with intricate symptomatology due to concomitant acute pancreatitis and pneumonia

Abstract Background ST segment elevation acute myocardial infarction and acute pancreatitis are diagnostic and therapeutic emergencies. The concomitance of these two pathologies produces intricate and confusing symptoms leading to late presentation. Clinical Case Description- We present the case of a 62-year-old male patient, non-smoker, admitted in the emergency department for intense, continuous epigastric pain with posterior radiation and dyspnea, started four days before. On admission we found hemodynamic stability, lung crackles in the ½ inferior of the right lung, SaO2= 94% under 3l/min O2, normal cardiac sounds, HR 102/min, mitral systolic murmur, BP 145/80mmHg, abdominal pain on palpation. Lab tests revealed elevated cardiac troponin values and detection of a fall of cTn values in dynamics (hs-cTnI 30372ng/l, TnI 73ng/ml), but also high serum transaminases, amylase and lipase (1278 U/l), with high inflammatory markers, leukocytosis and neutrophilia. ECG showed sinus rhythm, QRS axis 0 degrees, ST segment elevation in V1-V4, ST segment depression DI, aVL, negative and biphasic T waves in V5-V6, DI, DII, aVL, Q wave in DIII, aVF and poor R wave progression V1-V4. Admission echocardiography revealed dilated left cavities with severe left ventricular systolic dysfunction (EF 20%), akinesia of the anterior and inferolateral walls, moderate mitral and tricuspid regurgitation, grade III diastolic dysfunction and possible pulmonary arterial hypertension. Coronary angiography revealed distal stenosis of the left main, involving the bifurcation, 80% ostial LAD stenosis, proximal LAD occlusion, 30% ostial LCX. PCI with DES of the proximal LAD, and left main–LDA junction with overlapping was performed successfully. Abdominal echography showed increased volume of the pancreas and significant decrease in echogenicity. Abdominal CT scan assessed inflammatory changes in pancreas and peripancreatic fat (grade C Balthazar score), superior mesenteric artery partial thrombosis and alveolar consolidation in the right lung. Echocardiography reassessment four days later revealed a fresh apical thrombus of the left ventricle. He received treatment with low molecular weight heparin, dual antiplatelet therapy statin, loop diuretic, aldosterone antagonist, digoxin, antibiotics and food restriction with gradual oral realimentation. He needed positive inotropic support for the first 4 days from a total of 28 days of hospitalization. Conclusions ECG changes and prothrombotic status can occur in patients with acute pancreatitis. The diagnosis and management of STEMI concomitant with acute pancreatitis can be challenging. The multimodality imaging and interdisciplinary approach individualized to the patient’s clinical situation is important, especially when having safety concerns about the revascularization therapy, antiplatelet and anticoagulant therapy. Abstract P1259 Figure.

Fundamental heart sounds analysis using improved complete ensemble EMD with adaptive noise

Phonocardiogram (PCG) recordings contain valuable information about the functioning and state of the heart that is useful in the diagnosis of cardiovascular diseases. 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. The similarity in morphology and duration of these heart sounds and their superposition in the frequency domain makes it difficult to use them in computer systems to provide an automatic diagnosis. Therefore, in this paper, we analyzed these heart sounds in the intrinsic mode functions (IMF) domain, which were issued from two time-frequency decomposition techniques, the empirical mode decomposition (EMD) and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), with the aim of retrieving useful information on an expanded basis. The decomposition of PCG recordings into IMF allows representing the fundamental cardiac sounds in many oscillating components, increasing thus the observability of the system. Moreover, the time-frequency representation of PCG recordings could provide valuable information to automatically detect heart sounds and diagnose pathologies from characteristic patterns of these heart sounds in the IMF. The analysis was made through the variance and Shannon's entropy of the heart sounds, observed in time windows located among different IMF. In addition, we determined the frequencies ranges of the IMF from the decomposition of the PCG recordings using both techniques. Given that the frequency content of S1 and S2 is different but overlap each other, and the duration of these sounds are also different, these heart sounds were represented in different IMF with different variances and entropies, in both techniques, but the ICEEMDAN offers a more consistent decomposition of S1 and S2 (they were concentrated in IMF 4-6). The decomposition of PCG signals into IMF has allowed us to identify the frequency components of the IMF in which these sounds are found.

Evaluation of cardiac enzymes and acute phase response as biomarkers for rapid diagnosis of myocarditis in calves with FMD

Troponin-I, homocysteine, Creatine kinase-myocardial band, lactate dehydrogenase and acute phase response had been evaluated in calves with myocarditis due to FMD. The study was conducted on 52 local breed calves 1-6 months old and from both sexes, their dams have no history for vaccination against FMD and show classical foot and mouth disease signs. Ten clinically healthy calves of the same ages were considered as controls. Suspected calves neither show oral blisters, rope salivation, nor foot lesions. Diseased calves showed signs of dullness, in activity, panting with mouth breathing, unable to suck, recumbency, However, five of diseased calves were died within 24-72 hours and on macroscopic examinations of autopsied animals, necrotic myocarditis with pale foci with a zone of hyperemia which were present on the papillary and ventricular cardiac muscles, moreover, on histopathological examinations there were severe inflammatory cells infiltration in the interstitial of myocardial fibers with obvious area of coagulation of myocardial fibers and marked area of hyalinization, furthermore, severe mononuclear cells infiltration, mainly lymphocytes, with few neutrophils closed to necrotic myocardial fibers were also detected. Diagnosis of FMD virus was confirmed by using commercially NSP ELISA kits for foot and mouth. A significant increase (p>0.05)was encounteredin body temperature, respiratory and heart rates in diseased animals than in controls, Furthermore, abnormal cardiac sounds (organic murmurs) were indicated on auscultation of the heart. Results of hematological parameters shown a significant increase indicated in ESR values of diseased calves than in controls, moreover, total leukocyte count was increased significantly with significant lymphocytosis. Furthermore, the results were also showed significant increase in values of serum cardiac troponin, homocysteine, creatine kinase-myocardial band, lactate dehydrogenase, haptoglobin and fibrinogen in seropositive calves for FMD compared with controls. It can be concluded that determination of cardiac biomarkers and acute phase response concentration in calves with myocarditis can considered as a guide to quantify early heart damage.

Intelligent methodologies for cardiac sound signals analysis and characterization in cepstrum and time‐scale domains

AbstractBiometric authentication is the process that allows an individual to be identified based on a set of unique biological features data. In this study, we present different experiments to use the cardiac sound signals (phonocardiogram “PCG”) as a biometric authentication trait. We have applied different features extraction approaches and different classification techniques to use the PCG as a biometric trait. Through all experiments, data acquisition is based on collecting the cardiac sounds from HSCT‐11 and PASCAL CHSC2011 datasets, while preprocessing is concerned with de‐noising of cardiac sounds using multiresolution‐decomposition and multiresolution‐reconstruction (MDR‐MRR). The de‐noised signal is then segmented based on frame‐windowing and Shanon energy (SE) methods. For feature extraction, Cepstral (Cp) domain (based on mel‐frequency) and time‐scale (T‐S) domain (based on Wavelet Transform) features are extracted from the de‐noised signal after segmentation. The features, extracted from the Cp‐domain and the T‐S domain, are fed to four different classifiers: Artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and K‐nearest neighbor (KNN). The performance of the classifications is assessed based on the k‐fold cross validation. The computation complexity of the feature extraction domains is expressed using the Big‐O measurements. The T‐S features are superior to PCG heart signals in terms of the classification accuracy. The experiments' results give the highest classification accuracy with lowest computation complexity for RF in the Cp domain and SVM and ANN in the T‐S domain.

Effect of Online Training Modules and Virtual Patients on First-Year Physician Assistant Students' Confidence Levels.

Pilot study assessing impact of virtual patients (VPs) and online modules on first-year physician assistant (PA) student confidence in documentation performance. Five VP cases (head, eyes, ears, nose, and throat; musculoskeletal; abdominal; neurological; and comprehensive history and physical examination) and one module (cardiac sounds) were implemented to complement the core PA curriculum. A VP-based protocol significantly improved student confidence for taking a history, performing a physical examination, and undertaking clinical reasoning as well as providing overall documentation confidence. These 3 subscales explain nearly half (49%) of overall pretraining confidence, increasing to nearly two-thirds (63%) of overall posttraining confidence. Stepwise regression finds confidence in clinical reasoning to be the only unique predictor of overall pretraining confidence. In contrast, confidence in history taking and physical exams uniquely predicts overall posttraining confidence. Virtual patient cases and online modules help improve overall and dimension-specific documentation confidence, with different dimensions driving overall confidence pre- and posttraining. Implications for job efficacy are discussed.

A hybrid algorithm for heart sounds segmentation based on phonocardiogram

For many years, heart function has been measured by the electrocardiogram (ECG) signal, while sounds produced in the heart can also contain information indicating normal or abnormal heart function. What has caused to restrict the use of the phonocardiography (PCG) signal was the lack of mastery of experts in the interpretation of these sounds, as well as its high potential for noise pollution. PCG is a non-invasive signal for monitoring physiological parameters of cardiac, which can make heart disease diagnostics more efficient. In recent years, attempts have been made to use PCG to detect heart disease independently without a need to match with the ECG. We propose a hybrid algorithm including empirical mode decomposition (EMD), Hilbert transform and Gaussian function for detecting heart sounds to distinguish first (S1) and second (S2) cardiac sounds by eliminating the effect of cardiac murmurs. In this article, 250 normal and 250 abnormal sound signals were examined. The overall positive predictivity of normal and abnormal S1 and S2 is 98.98%, 98.78, 98.78 and 98.37, respectively. Our results showed that the proposed method has a high potential for heart sounds determination, while maintains its simplicity and has a reasonable computational time.

Fetal heart rate estimation from a single phonocardiogram signal using non-negative matrix factorization.

Fetal heart rate (FHR) is an important characteristic in fetal well-being follow-up. It is classically estimated using the cardiotocogram (CTG) non-invasive reference technique. However, this latter presents some significant drawbacks. An alternative non-invasive solution based on the fetal phonocardiogram (fetal PCG) can be used. But most of proposed methods based on the PCG signal need to detect and to label the fetal cardiac S1 and S2 sounds, which may be a difficult task in certain conditions. Therefore, in this paper, we propose a new methodology for FHR estimation from fetal PCG with one single cardio-microphone and without the distinction constraint of heart sounds. The method is based on the non-negative matrix factorization (NMF) applied on the spectrogram of fetal PCG considered as a source-filter model. The proposed method provides satisfactory results on a preliminary dataset of abdominal PCG signals. When compared to the reference CTG, correlation on FHR estimations between PCG and CTG is around 90%.