Assessing aortic stenosis using sample entropy of the phonocardiographic signal in dogs

Use of proposed systolic and myocardial performance indices derived from simultaneous ECG and PCG recordings to assess cardiac function in healthy Beagles

Background and Aim:

Cardiac time intervals (CTIs) can provide important information on the electrical and mechanical properties of the heart. We hypothesized that cardiac function can be described using the combined power of electrocardiography (ECG) and phonocardiography (PCG) signals. This study aimed to (1) validate a novel custom device in measuring CTI parameters; (2) compare CTI parameters with a commercially available device and standard transthoracic echocardiography (STE); and (3) compare calculated systolic performance index (SPI) and myocardial performance index (MPI) with Tei index from the STE.

Materials and Methods:

This study determined CTIs based on simultaneous ECG and PCG recordings in 14 healthy Beagle dogs using the custom-built device. These CTI parameters were compared with a commercially available device (Eko DUO ECG + Digital Stethoscope; Eko DUO) and the STE. Agreement of CTI parameters between the custom device and the commercially available device or STE was evaluated. Calculated SPI and MPI based on Wigger’s diagram were proposed, compared with SPI and Tei index, and correlated with STE parameters.

Results:

We found that the ECG and PCG parameters measured from the custom-built device did not differ from the commercially available device and the STE. By combining ECG and PCG signals, we established CTI parameters in healthy dogs including indices for systolic function (SPI: QS1/S1S2) and global cardiac function {F1 ([QS1+S2]/S1S2), F2 ([RS1+S2]/S1S2), and F3 (RS1 + [QS2-QT]/S1S2)}. The SPI, F2, and F3 were comparable with echocardiographic parameters describing systolic (Pre-ejection period/left ventricular ejection time [LVET]) and Tei index ([MCOdur-LVET]/LVET), respectively. Only SPI and F3 were correlated significantly with MCOdur and heart rate, respectively.

Conclusion:

We have validated the use of the custom-built device to describe CTIs that are comparable to the commercially available device and STE in healthy Beagles. The proposed SPI and MPI derived from CTI parameters can be useful in clinical practice to describe the cardiac function, especially in areas where access to STE is constrained.

Models and Techniques to Study Aortic Valve Calcification in Vitro, ex Vivo and in Vivo. An Overview

Aortic valve stenosis secondary to aortic valve calcification is the most common valve disease in the Western world. Calcification is a result of pathological proliferation and osteogenic differentiation of resident valve interstitial cells. To develop non-surgical treatments, the molecular and cellular mechanisms of pathological calcification must be revealed. In the current overview, we present methods for evaluation of calcification in different ex vivo, in vitro and in vivo situations including imaging in patients. The latter include echocardiography, scanning with computed tomography and magnetic resonance imaging. Particular emphasis is on translational studies of calcific aortic valve stenosis with a special focus on cell culture using human primary cell cultures. Such models are widely used and suitable for screening of drugs against calcification. Animal models are presented, but there is no animal model that faithfully mimics human calcific aortic valve disease. A model of experimentally induced calcification in whole porcine aortic valve leaflets ex vivo is also included. Finally, miscellaneous methods and aspects of aortic valve calcification, such as, for instance, biomarkers are presented.

Deep Layer Kernel Sparse Representation Network for the Detection of Heart Valve Ailments from the Time-Frequency Representation of PCG Recordings

The heart valve ailments (HVAs) are due to the defects in the valves of the heart and if untreated may cause heart failure, clots, and even sudden cardiac death. Automated early detection of HVAs is necessary in the hospitals for proper diagnosis of pathological cases, to provide timely treatment, and to reduce the mortality rate. The heart valve abnormalities will alter the heart sound and murmurs which can be faithfully captured by phonocardiogram (PCG) recordings. In this paper, a time-frequency based deep layer kernel sparse representation network (DLKSRN) is proposed for the detection of various HVAs using PCG signals. Spline kernel-based Chirplet transform (SCT) is used to evaluate the time-frequency representation of PCG recording, and the features like L1-norm (LN), sample entropy (SEN), and permutation entropy (PEN) are extracted from the different frequency components of the time-frequency representation of PCG recording. The DLKSRN formulated using the hidden layers of extreme learning machine- (ELM-) autoencoders and kernel sparse representation (KSR) is used for the classification of PCG recordings as normal, and pathology cases such as mitral valve prolapse (MVP), mitral regurgitation (MR), aortic stenosis (AS), and mitral stenosis (MS). The proposed approach has been evaluated using PCG recordings from both public and private databases, and the results demonstrated that an average sensitivity of 100%, 97.51%, 99.00%, 98.72%, and 99.13% are obtained for normal, MVP, MR, AS, and MS cases using the hold-out cross-validation (CV) method. The proposed approach is applicable for the Internet of Things- (IoT-) driven smart healthcare system for the accurate detection of HVAs.

Estimating pressure gradients by auscultation: How technology (echocardiography) can help improve clinical skills

AIM

To extend our previously-published experience in estimating pressure gradients (PG) via physical examination in a large patient cohort.

METHODS

From January 1, 1997 through December 31, 2009, an attending pediatric cardiologist compared clinical examination (EXAM) with Doppler-echo (ECHO), in 1193 patients with pulmonic stenosis (PS, including tetralogy of Fallot), aortic stenosis (AS), and ventricular septal defect (VSD). EXAM PG estimates were based primarily on a murmur’s pitch, grade, and length. ECHO peak instantaneous PG was derived from the modified Bernoulli equation. Patients were 0-38.4 years old (median 4.8).

RESULTS

For all patients, EXAM correlated highly with ECHO: ECHO = 0.99 (EXAM) + 3.2 mmHg; r = +0.89; P < 0.0001. Agreement was excellent (mean difference = -2.9 ± 16.1 mmHg). In 78% of all patients, agreement between EXAM and ECHO was within 15 mmHg and within 5 mmHg in 45%. Clinical estimates of PS PG were more accurate than of AS and VSD. A palpable precordial thrill and increasing loudness of the murmur predicted higher gradients (P < 0.0001). Weight did not influence accuracy. A learning curve was evident, such that the most recent quartile of patients showed ECHO = 1.01 (EXAM) + 1.9, r = +0.92, P < 0.0001; during this time, the attending pediatric cardiologist had been > 10 years in practice.

CONCLUSION

Clinical examination can accurately estimate PG in PS, AS, or VSD. Continual correlation of clinical findings with echocardiography can lead to highly accurate diagnostic skills.

An Intelligent Phonocardiography for Automated Screening of Pediatric Heart Diseases

This paper presents a robust device for automated screening of pediatric heart diseases based on our unique processing method in murmur characterization; the Arash-Band method. The present study modifies the Arash-Band method and employs output of the modified method in conjunction with the two other original techniques to extract indicative feature vectors for the screening. The extracted feature vectors are classified by using the support vector machine method. Results show that the proposed modifications significantly enhances performance of the Arash-Band in terms of the both accuracy and sensitivity as the corresponding effect sizes are sufficiently large. The proposed algorithm has been incorporated into an Android-based tablet to constitute an intelligent phonocardiogram with the automatic screening capability. In order to obtain confidence interval of the accuracy and sensitivity, an inferable statistical test is applied on our database containing the phonocardiogram signals recorded from 263 of the referrals to a hospital. The expected value of the accuracy/sensitivity is estimated to be 87.45 % / 87.29 % with a 95 % confidence interval of (80.19 % - 92.47 %) / (76.01 % - 95.78 %) exhibiting superior performance than a pediatric cardiologist who relies on conventional or even computer-assisted auscultation.

Complexity-based analysis for the detection of heart murmurs

While a healthy human heart produce a rhythmic pattern of sounds, some heart disorder induce deviations perceived as abnormal sounds called murmurs. Despite many murmurs can be considered harmless, other constitute the first basis of a heart disorder. In this sense, a correct diagnosis remains essential; however, due to the subjectivity on using human ear to make diagnosis, automatic detection systems appear as useful tools for helping medical specialists on improving diagnosis accuracy. Complexity analysis has become one important tool for the study of physiological signals, because tracking sudden alteration on the inherent complexity on biological processes might be useful for detecting pathologies. The present paper presents a complexity-based analysis methodology, which uses regularity features for the detection of heart murmurs, including Approximate Entropy, Sample Entropy, Gaussian Kernel Approximate Entropy, and Fuzzy Entropy. The results show the high discriminative power, up to 90%, of the Gaussian Kernel Approximate Entropy and Fuzzy Entropy for the proposed labour.

Animal models of calcific aortic valve disease

Calcific aortic valve disease (CAVD), once thought to be a degenerative disease, is now recognized to be an active pathobiological process, with chronic inflammation emerging as a predominant, and possibly driving, factor. However, many details of the pathobiological mechanisms of CAVD remain to be described, and new approaches to treat CAVD need to be identified. Animal models are emerging as vital tools to this end, facilitated by the advent of new models and improved understanding of the utility of existing models. In this paper, we summarize and critically appraise current small and large animal models of CAVD, discuss the utility of animal models for priority CAVD research areas, and provide recommendations for future animal model studies of CAVD.