Atrial arrhythmias in adults with congenital heart disease. Listening to your heart sound can save your life

Wearable cardioverter-defibrillator (life-vest): A feasible bridging treatment in adult congenital heart disease

BACKGROUND

Wearable cardioverter-defibrillators (WCDs) are currently used in patients at temporarily heightened risk for sudden cardiac death (SCD) who are temporarily unable to receive an implantable cardioverter-defibrillator (ICD). WCD can safely record and terminate life-threatening arrhythmias through a non-invasive electrode-based system. The current clinical indications for WCD use are varied and keep evolving as experience with this technology increases.

METHODS

We reviewed and explored the data behind indications for WCD use and discuss its usefulness in congenital heart disease (CHD) patients.

RESULTS

We considered 8 consecutive patients (mean age 35.25 years, range 18-51 years, average duration of WCD use 4 months, range 3-6 months) with complex CHD, in which a WCD was used between June 2018 and January 2022. No sustained ventricular arrhythmias requiring shocks were recorded in the observation period. No inappropriate shocks were recorded. All the patients showed a good compliance and a very high mean wear time per day (21.2 ± 1 h a day). Four patients implanted a permanent device (3 CRT-D, 1 ICD), three underwent cardiac surgery at the end of the WCD period and one is still on the waiting list for the operation.

CONCLUSIONS

Larger trial could confirm the possible conceivable benefit from an extended use of the WCD in certain populations with complex CHD as in our case series, especially in patients with life-treating ventricular arrhythmias waiting for surgery for residual cardiac defects or in the early phases following the surgical/hemodynamic interventions, patients with tachycardiomyopathy expected to improve after the arrhythmias are removed and patients awaiting implantation of an ICD at high risk due to active infection.

Blind Monaural Source Separation on Heart and Lung Sounds Based on Periodic-Coded Deep Autoencoder

Auscultation is the most efficient way to diagnose cardiovascular and respiratory diseases. To reach accurate diagnoses, a device must be able to recognize heart and lung sounds from various clinical situations. However, the recorded chest sounds are mixed by heart and lung sounds. Thus, effectively separating these two sounds is critical in the pre-processing stage. Recent advances in machine learning have progressed on monaural source separations, but most of the well-known techniques require paired mixed sounds and individual pure sounds for model training. As the preparation of pure heart and lung sounds is difficult, special designs must be considered to derive effective heart and lung sound separation techniques. In this study, we proposed a novel periodicity-coded deep auto-encoder (PC-DAE) approach to separate mixed heart-lung sounds in an unsupervised manner via the assumption of different periodicities between heart rate and respiration rate. The PC-DAE benefits from deep-learning-based models by extracting representative features and considers the periodicity of heart and lung sounds to carry out the separation. We evaluated PC-DAE on two datasets. The first one includes sounds from the Student Auscultation Manikin (SAM), and the second is prepared by recording chest sounds in real-world conditions. Experimental results indicate that PC-DAE outperforms several well-known separation works in terms of standardized evaluation metrics. Moreover, waveforms and spectrograms demonstrate the effectiveness of PC-DAE compared to existing approaches. It is also confirmed that by using the proposed PC-DAE as a pre-processing stage, the heart sound recognition accuracies can be notably boosted. The experimental results confirmed the effectiveness of PC-DAE and its potential to be used in clinical applications.

Force Trends and Pulsatility for Catheter Contact Identification in Intracardiac Electrograms during Arrhythmia Ablation

The intracardiac electrical activation maps are commonly used as a guide in the ablation of cardiac arrhythmias. The use of catheters with force sensors has been proposed in order to know if the electrode is in contact with the tissue during the registration of intracardiac electrograms (EGM). Although threshold criteria on force signals are often used to determine the catheter contact, this may be a limited criterion due to the complexity of the heart dynamics and cardiac vorticity. The present paper is devoted to determining the criteria and force signal profiles that guarantee the contact of the electrode with the tissue. In this study, we analyzed 1391 force signals and their associated EGM recorded during 2 and 8 s, respectively, in 17 patients (82 ± 60 points per patient). We aimed to establish a contact pattern by first visually examining and classifying the signals, according to their likely-contact joint profile and following the suggestions from experts in the doubtful cases. First, we used Principal Component Analysis to scrutinize the force signal dynamics by analyzing the main eigen-directions, first globally and then grouped according to the certainty of their tissue-catheter contact. Second, we used two different linear classifiers (Fisher discriminant and support vector machines) to identify the most relevant components of the previous signal models. We obtained three main types of eigenvectors, namely, pulsatile relevant, non-pulsatile relevant, and irrelevant components. The classifiers reached a moderate to sufficient discrimination capacity (areas under the curve between 0.84 and 0.95 depending on the contact certainty and on the classifier), which allowed us to analyze the relevant properties in the force signals. We conclude that the catheter-tissue contact profiles in force recordings are complex and do not depend only on the signal intensity being above a threshold at a single time instant, but also on time pulsatility and trends. These findings pave the way towards a subsystem which can be included in current intracardiac navigation systems assisted by force contact sensors, and it can provide the clinician with an estimate of the reliability on the tissue-catheter contact in the point-by-point EGM acquisition procedure.