The unique heart sound signature of children with pulmonary artery hypertension

Simulation of Acute Pulmonary Hypertension in Beagle Dogs

Acoustic cardiography (AC) combined with heart sound (HS) recording and electrocardiogram (ECG) provides a noninvasive and inexpensive way to understand the electrical mechanical activity of the heart. Pulmonary artery stenosis can cause hemodynamic abnormalities that might lead to pulmonary hypertension (PH). In this paper, we examined the relationships between the acoustic characteristics of the AC and hemodynamic changes in a beagle dog model of PH.Four healthy beagle dogs were injected with the prostaglandin endoperoxide receptor agonist U-44069 to induce acute PH states. AC was employed to analyze the process of pre-PH, intra-PH, and post-PH. Right ventricular blood pressure (RVBP) was measured via right cardiac catheterization, an invasive method performed in parallel for comparative hemodynamic evaluation. As RVBP increased or decreased, the HS features changed accordingly during acute PH occurrence and development. Right ventricular systolic blood pressure (RVSBP) significantly correlated with the minimum of the first HS (S1) amplitude (correlation coefficient (CC) = -0.82), energy of the S1 (CC = 0.86), energy of the second HS (S2) (CC = 0.67), entropy of the S1 (CC = -0.94), and ratio of electromechanical systolic time (EMST) to the cardiac cycle time (CC = 0.81). The two techniques (AC [HSs and ECG] versus right cardiac catheterization [RVBP]) were significantly correlated. Especially, the diastolic filling time (DFT) had a significant relationship with the right ventricular diastolic time (RVDT) (CC = 0.97), perfusion time (PT) (CC = 0.96), and cardiac cycle time (RR) (CC = 0.96). The CCs between the RVDT and the max dp/dt to min dp/dt, the EMST and the Q to min dp/dt, and the electromechanical activation time and the Q to max dp/dt were 0.95, 0.99, and 0.86, respectively. Furthermore, the logistic regression model with different combinations was used to identify the effective features for monitoring hemodynamic and pathophysiologic conditions.AC provided significant insight into mechanical dysfunction in a rapid and noninvasive way that could be used for early screening of PH.

Continuous and Noninvasive Estimation of Right Ventricle Systolic Blood Pressure Using Heart Sound Signal by Deep Bidirectional LSTM Network

Objective: Timely monitoring right ventricular systolic blood pressure (RVSBP) is helpful in the early detection of pulmonary hypertension (PH). However, it is not easy to monitor RVSBP directly. The objective of this paper is to develop a deep learning technique for RVSBP noninvasive estimation using heart sound (HS) signals supported by (electrocardiography) ECG signals without complex features extraction. Methods: Five beagle dog subjects were used. The medicine U-44069 was injected into the subjects to induce a wide range of RVSBP variation. The blood pressure in right ventricle, ECG of lead I and HS signals were recorded simultaneously. Thirty-two records were collected. The relations between RVSBP and cyclic HS signals were modeled by the Bidirectional Long Short-Term Memory (Bi-LSTM) network. Results: The mean absolute error (MAE) ± standard deviation (SD) inside record was 1.85 ± 1.82 mmHg. It was 4.37 ± 2.49 mmHg across record but within subject. The corrective factors were added after training the Bi-LSTM network across subjects. Finally, the MAE ± SD from 12.46 ± 6.56 mmHg dropped to 6.37 ± 4.90 mmHg across subjects. Significance: Our work was the first to apply the Bi-LSTM network to build relations between the HS signal and RVSBP. This work suggested a noninvasive and continuous RVSBP estimation using the HS signal supported by the ECG signal by deep learning architecture without the need of healthcare professionals.

Digital stethoscopes in paediatric medicine

AIM

To explore, synthesise and discuss currently available digital stethoscopes (DS) and the evidence for their use in paediatric medicine.

METHODS

Systematic review and narrative synthesis of digital stethoscope use in paediatrics following searches of OVID Medline, Embase, Scopus, PubMed and Google Scholar databases.

RESULTS

Six digital stethoscope makes were identified to have been used in paediatric focused studies so far. A total of 25 studies of DS use in paediatrics were included. We discuss the use of digital stethoscope technology in current paediatric medicine, comment on the technical properties of the available devices, the effectiveness and limitations of this technology, and potential uses in the fields of paediatrics and neonatology, from telemedicine to computer-aided diagnostics.

CONCLUSION

Further validation and testing of available DS devices is required. Comparison studies between different types of DS would be useful in identifying strengths and flaws of each DS as well as identifying clinical situations for which each may be most appropriately suited.

Identification of Pulmonary Hypertension Using Entropy Measure Analysis of Heart Sound Signal

This study introduced entropy measures to analyze the heart sound signals of people with and without pulmonary hypertension (PH). The lead II Electrocardiography (ECG) signal and heart sound signal were simultaneously collected from 104 subjects aged between 22 and 89. Fifty of them were PH patients and 54 were healthy. Eleven heart sound features were extracted and three entropy measures, namely sample entropy (SampEn), fuzzy entropy (FuzzyEn) and fuzzy measure entropy (FuzzyMEn) of the feature sequences were calculated. The Mann–Whitney U test was used to study the feature significance between the patient and health group. To reduce the age confounding factor, nine entropy measures were selected based on correlation analysis. Further, the probability density function (pdf) of a single selected entropy measure of both groups was constructed by kernel density estimation, as well as the joint pdf of any two and multiple selected entropy measures. Therefore, a patient or a healthy subject can be classified using his/her entropy measure probability based on Bayes’ decision rule. The results showed that the best identification performance by a single selected measure had sensitivity of 0.720 and specificity of 0.648. The identification performance was improved to 0.680, 0.796 by the joint pdf of two measures and 0.740, 0.870 by the joint pdf of multiple measures. This study showed that entropy measures could be a powerful tool for early screening of PH patients.

The Voice of the Heart: Vowel-Like Sound in Pulmonary Artery Hypertension

Increased blood pressure in the pulmonary artery is referred to as pulmonary hypertension and often is linked to loud pulmonic valve closures. For the purpose of this paper, it was hypothesized that pulmonary circulation vibrations will create sounds similar to sounds created by vocal cords during speech and that subjects with pulmonary artery hypertension (PAH) could have unique sound signatures across four auscultatory sites. Using a digital stethoscope, heart sounds were recorded at the cardiac apex, 2nd left intercostal space (2LICS), 2nd right intercostal space (2RICS), and 4th left intercostal space (4LICS) undergoing simultaneous cardiac catheterization. From the collected heart sounds, relative power of the frequency band, energy of the sinusoid formants, and entropy were extracted. PAH subjects were differentiated by applying the linear discriminant analysis with leave-one-out cross-validation. The entropy of the first sinusoid formant decreased significantly in subjects with a mean pulmonary artery pressure (mPAp) ≥ 25 mmHg versus subjects with a mPAp < 25 mmHg with a sensitivity of 84% and specificity of 88.57%, within a 10-s optimized window length for heart sounds recorded at the 2LICS. First sinusoid formant entropy reduction of heart sounds in PAH subjects suggests the existence of a vowel-like pattern. Pattern analysis revealed a unique sound signature, which could be used in non-invasive screening tools.

Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians

We hypothesized that an automated speech- recognition-inspired classification algorithm could differentiate between the heart sounds in subjects with and without pulmonary hypertension (PH) and outperform physicians. Heart sounds, electrocardiograms, and mean pulmonary artery pressures (mPAp) were recorded simultaneously. Heart sound recordings were digitized to train and test speech-recognition-inspired classification algorithms. We used mel-frequency cepstral coefficients to extract features from the heart sounds. Gaussian-mixture models classified the features as PH (mPAp ≥ 25 mmHg) or normal (mPAp < 25 mmHg). Physicians blinded to patient data listened to the same heart sound recordings and attempted a diagnosis. We studied 164 subjects: 86 with mPAp ≥ 25 mmHg (mPAp 41 ± 12 mmHg) and 78 with mPAp < 25 mmHg (mPAp 17 ± 5 mmHg) (p  < 0.005). The correct diagnostic rate of the automated speech-recognition-inspired algorithm was 74% compared to 56% by physicians (p = 0.005). The false positive rate for the algorithm was 34% versus 50% (p = 0.04) for clinicians. The false negative rate for the algorithm was 23% and 68% (p = 0.0002) for physicians. We developed an automated speech-recognition-inspired classification algorithm for the acoustic diagnosis of PH that outperforms physicians that could be used to screen for PH and encourage earlier specialist referral.