Multimodal Remote Home Monitoring of Lung Transplant Recipients during COVID-19 Vaccinations: Usability Pilot Study of the COVIDA Desk Incorporating Wearable Devices.

Background and Objectives: Remote patient monitoring (RPM) of vital signs and symptoms for lung transplant recipients (LTRs) has become increasingly relevant in many situations. Nevertheless, RPM research integrating multisensory home monitoring in LTRs is scarce. We developed a novel multisensory home monitoring device and tested it in the context of COVID-19 vaccinations. We hypothesize that multisensory RPM and smartphone-based questionnaire feedback on signs and symptoms will be well accepted among LTRs. To assess the usability and acceptability of a remote monitoring system consisting of wearable devices, including home spirometry and a smartphone-based questionnaire application for symptom and vital sign monitoring using wearable devices, during the first and second SARS-CoV-2 vaccination. Materials and Methods: Observational usability pilot study for six weeks of home monitoring with the COVIDA Desk for LTRs. During the first week after the vaccination, intensive monitoring was performed by recording data on physical activity, spirometry, temperature, pulse oximetry and self-reported symptoms, signs and additional measurements. During the subsequent days, the number of monitoring assessments was reduced. LTRs reported on their perceptions of the usability of the monitoring device through a purpose-designed questionnaire. Results: Ten LTRs planning to receive the first COVID-19 vaccinations were recruited. For the intensive monitoring study phase, LTRs recorded symptoms, signs and additional measurements. The most frequent adverse events reported were local pain, fatigue, sleep disturbance and headache. The duration of these symptoms was 5-8 days post-vaccination. Adherence to the main monitoring devices was high. LTRs rated usability as high. The majority were willing to continue monitoring. Conclusions: The COVIDA Desk showed favorable technical performance and was well accepted by the LTRs during the vaccination phase of the pandemic. The feasibility of the RPM system deployment was proven by the rapid recruitment uptake, technical performance (i.e., low number of errors), favorable user experience questionnaires and detailed individual user feedback.

Privacy preserving synthetic respiratory sounds for class incremental learning

Digital health applications enable continuous and objective symptom reporting by remote monitoring at patients’ homes. These applications employ digital biomarker models that must be trained on high-quality data. Such datasets are classified as personal sensitive and, due to privacy and consent regulations, might only be available for a restricted duration. Accordingly, the extension and thus the life-cycle of digital biomarker classifiers is compromised. To address this problem, we present a Privacy Preserving Synthetic Data Generation pipeline. It follows the Generative Replay approach proposed in the Continual Learning literature: a Generative Adversarial Network (GAN) is trained to generate synthetic medical samples. To ensure that the identity of subjects from the GAN training set is not compromised, a privacy evaluation is performed on the synthetic samples. We test whether a sample can be related to a subject by training a Siamese Neural Network (SNN) with triplet-loss. A sample gets classified as anonymized when it is sufficiently distant to any of the subject clusters, or if k-anonymity with k=3 is given. Finally, only the anonymized synthetic data, which preserves the main characteristics of the original data, is stored for later model training. The applicability of the proposed method is demonstrated on the example of a Respiratory Sound Classifier, which enables the reporting of respiratory symptoms in the form of a diary. Such a diary is relevant for chronic or infectious pulmonary conditions such as Asthma, COPD, or COVID-19. The training of the WaveGAN with varying the number of iterations based on our dataset of respiratory sounds resulted in synthetic audios with changing psycho-acoustic appearance. From low to high iterations, the appearance changed from primitive robotic to a normal human style. Further, the identification of subjects based on respiratory sounds has proven to be possible by a threshold distance of the sound embeddings computed from the SNN. Accordingly, the method is applicable to evaluate the anonymity of the synthetic sounds. Finally, the original and anonymized synthetic audios were used to train two different Respiratory Sound Classifiers, with a resulting accuracy of 90% compared to 85%, respectively. We conclude: the proposed method enables class-incremental learning without the issue of catastrophic forgetting, at the cost of slight performance degradation.