Trends in voice characteristics in patients with heart failure (VENTURE) in Switzerland: Protocol for a longitudinal observational pilot study

INTRODUCTION: Heart Failure (HF) is a major health and economic issue worldwide. HF-related expenses are largely driven by hospital admissions and re-admissions, many of which are potentially preventable. Current self-management programs, however, have failed to reduce hospital admissions. This may be explained by their low predictive power for decompensation and high adherence requirements. Slight alterations in the voice profile may allow to detect decompensation in HF patients at an earlier stage and reduce hospitalizations. This pilot study investigates the potential of voice as a digital biomarker to predict health status deterioration in HF patients. METHODS AND ANALYSIS: In a two-month longitudinal observational study, we collect voice samples and HF-related quality-of-life questionnaires from 35 stable HF patients. Patients use our developed study application installed on a tablet at home during the study period. From the collected data, we use signal processing to extract voice characteristics from the audio samples and associate them with the answers to the questionnaire data. The primary outcome will be the correlation between voice characteristics and HF-related quality-of-life health status. ETHICS AND DISSEMINATION: The study was reviewed and approved by the Cantonal Ethics Committee Zurich (BASEC ID:2022-00912). Results will be published in medical and technical peer-reviewed journals

Smartphone-basierte Hustenerkennung – Auf dem Weg zu einem digitalen Biomarker für chronische Atemwegserkrankungen

Thema: Husten ist das häufigste Symptom, bei dem Personen ärztlichen Rat suchen. Die gewöhnliche Erkältung stellt die bekannteste Ursache dar. Darüber hinaus sind steigende Hustenraten mit einer Verschlechterung des Gesundheitszustands bei Krankheiten wie Asthma und COPD assoziiert. Infolgedessen wurden viele Anstrengungen unternommen, um ein objektives Mass für Husten zu schaffen. Bis heute gibt es jedoch keine standardisierte Methode, und es gibt keinen ausreichend validierten generischen Hustenmonitor, der im Handel erhältlich und klinisch akzeptabel ist. Zielsetzung: Ziel dieses Projekts ist es, die Smartphone-basierte Hustenerkennung über 24 Stunden bei 22 COPD-Patienten zu validieren und die erkannten Hustenzahlen mit menschlichen Annotatoren zu vergleichen. Methode: Für das Android-Betriebssystem wurde eine App zur Hustenerkennung entwickelt. Die Detektion des Hustens basiert auf einen Ensemble-Klassifikator von fünf Convolutional Neural Networks. Die App fungiert auch als Audiorecorder, sodass die Erkennung anschliessend verifiziert werden kann. Zusätzlich werden Hustendetektionen an einen Studienserver gesendet und können in Echtzeit über einen Web-Client verfolgt werden. Ergebnisse: Für das Trainieren von Hustenklassifikationsmodellen wurden Audiodaten von 94 Erwachsenen mit Asthma (57% Frauen, Durchschnittsalter 43 Jahre) verwendet, die über 29 Nächte aufgezeichnet wurden. Insgesamt wurden 704.697 Geräusche benutzt, von denen 30.304 als Husten identifiziert wurden. Der Ensemble-Klassifikator wurde vor der Studie auf dem PC evaluiert und schnitt mit einem Matthews-Korrelationskoeffizienten von 94.4% ab. Ob diese Ergebnisse auf dem Smartphone mit COPD Patienten im Krankenhauszimmer oder zu Hause reproduzierbar sind, wird in diesem Projekt erforscht. Fazit: Smartphone-basierte Hustenerkennung kann einen skalierbaren, kostengünstigen Marker für chronische Atemwegserkrankungen liefern

Lena: a Voice-Based Conversational Agent for Remote Patient Monitoring in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. To manage the increasing number of COPD patients and reduce the social and economic burden of treatment, healthcare providers have sought to implement remote patient monitoring (RPM). Screen-based RPM applications, such as filling self-reports on the smartphone or computer, have been shown to increase the quality of life, reduce the frequency and severity of exacerbations, and increase physical activity in patients with COPD. These applications, however, are not without challenges for the elderly target population. They are often used on devices designed by and for a different age group, which makes filling out self-reports prone to error and induces fears of technology malfunctions. Voice-based conversational agents (VCAs) are available on more than 2.5 billion devices and are increasingly present in homes worldwide. Aside from their commercial success, VCAs are also credited with several functionalities, such as hands-free use, that make their adoption in healthcare attractive, especially for the elderly. In this work, we investigate the potential of VCAs for RPM of COPD. Specifically, we designed and evaluated Lena, a single-board computer-based VCA framed as a digital member of the medical team. Lena acts as RPM for the early prediction of COPD exacerbations by asking ten symptom-related questions to determine the patient’s daily health status. This paper presents the patients’ feedback after their interaction with Lena. Patients evaluated the acceptability of the system. Notably, all patients could imagine using the system once a day in the context of a larger study and wished to integrate Lena into their daily routine.ISSN:1613-007

Trends in voice characteristics in patients with heart failure (VENTURE) in Switzerland: Protocol for a longitudinal observational pilot study.

IntroductionHeart Failure (HF) is a major health and economic issue worldwide. HF-related expenses are largely driven by hospital admissions and re-admissions, many of which are potentially preventable. Current self-management programs, however, have failed to reduce hospital admissions. This may be explained by their low predictive power for decompensation and high adherence requirements. Slight alterations in the voice profile may allow to detect decompensation in HF patients at an earlier stage and reduce hospitalizations. This pilot study investigates the potential of voice as a digital biomarker to predict health status deterioration in HF patients.Methods and analysisIn a two-month longitudinal observational study, we collect voice samples and HF-related quality-of-life questionnaires from 35 stable HF patients. Patients use our developed study application installed on a tablet at home during the study period. From the collected data, we use signal processing to extract voice characteristics from the audio samples and associate them with the answers to the questionnaire data. The primary outcome will be the correlation between voice characteristics and HF-related quality-of-life health status.Ethics and disseminationThe study was reviewed and approved by the Cantonal Ethics Committee Zurich (BASEC ID:2022-00912). Results will be published in medical and technical peer-reviewed journals

Nighttime Continuous Contactless Smartphone-Based Cough Monitoring for the Ward: Validation Study

Background:Clinical deterioration can go unnoticed in hospital wards for hours. Mobile technologies such as wearables and smartphones enable automated, continuous, noninvasive ward monitoring and allow the detection of subtle changes in vital signs. Cough can be effectively monitored through mobile technologies in the ward, as it is not only a symptom of prevalent respiratory diseases such as asthma, lung cancer, and COVID-19 but also a predictor of acute health deterioration. In past decades, many efforts have been made to develop an automatic cough counting tool. To date, however, there is neither a standardized, sufficiently validated method nor a scalable cough monitor that can be deployed on a consumer-centric device that reports cough counts continuously. These shortcomings limit the tracking of coughing and, consequently, hinder the monitoring of disease progression in prevalent respiratory diseases such as asthma, chronic obstructive pulmonary disease, and COVID-19 in the ward. Objective:This exploratory study involved the validation of an automated smartphone-based monitoring system for continuous cough counting in 2 different modes in the ward. Unlike previous studies that focused on evaluating cough detection models on unseen data, the focus of this work is to validate a holistic smartphone-based cough detection system operating in near real time. Methods:Automated cough counts were measured consistently on devices and on computers and compared with cough and noncough sounds counted manually over 8-hour long nocturnal recordings in 9 patients with pneumonia in the ward. The proposed cough detection system consists primarily of an Android app running on a smartphone that detects coughs and records sounds and secondarily of a backend that continuously receives the cough detection information and displays the hourly cough counts. Cough detection is based on an ensemble convolutional neural network developed and trained on asthmatic cough data. Results:In this validation study, a total of 72 hours of recordings from 9 participants with pneumonia, 4 of whom were infected with SARS-CoV-2, were analyzed. All the recordings were subjected to manual analysis by 2 blinded raters. The proposed system yielded a sensitivity and specificity of 72% and 99% on the device and 82% and 99% on the computer, respectively, for detecting coughs. The mean differences between the automated and human rater cough counts were −1.0 (95% CI −12.3 to 10.2) and −0.9 (95% CI −6.5 to 4.8) coughs per hour within subject for the on-device and on-computer modes, respectively. Conclusions:The proposed system thus represents a smartphone cough counter that can be used for continuous hourly assessment of cough frequency in the ward.ISSN:2561-326

TripletCough: Cougher Identification and Verification from Contact-Free Smartphone-Based Audio Recordings Using Metric Learning

Cough, a symptom associated with many prevalent respiratory diseases, can serve as a potential biomarker for diagnosis and disease progression. Consequently, the development of cough monitoring systems and, in particular, automatic cough detection algorithms have been studied since the early 2000s. Recently, there has been an increased focus on the efficiency of such algorithms, as implementation on consumer-centric devices such as smartphones would provide a scalable and affordable solution for monitoring cough with contact-free sensors. Current algorithms, however, are incapable of discerning between coughs of different individuals and, thus, cannot function reliably in situations where potentially multiple individuals have to be monitored in shared environments. Therefore, we propose a weakly supervised metric learning approach for cougher recognition based on smartphone audio recordings of coughs. Our approach involves a triplet network architecture, which employs convolutional neural networks (CNNs). The CNNs of the triplet network learn an embedding function, which maps Mel spectrograms of cough recordings to an embedding space where they are more easily distinguishable. Using audio recordings of nocturnal coughs from asthmatic patients captured with a smartphone, our approach achieved a mean accuracy of 88% (10% SD) on two-way identification tests with 12 enrollment samples and accuracy of 80% and an equal error rate (EER) of 20% on verification tests. Furthermore, our approach outperformed human raters with regard to verification tests on average by 8% in accuracy, 4% in false acceptance rate (FAR), and 12% in false rejection rate (FRR). Our code and models are publicly available.ISSN:2168-2194ISSN:2168-220

Smartphone-based cough monitoring as a near real-time digital pneumonia biomarker

Background Cough represents a cardinal symptom of acute respiratory tract infections. Generally associated with disease activity, cough holds biomarker potential and might be harnessed for prognosis and personalised treatment decisions. Here, we tested the suitability of cough as a digital biomarker for disease activity in coronavirus disease 2019 (COVID-19) and other lower respiratory tract infections. Methods We conducted a single-centre, exploratory, observational cohort study on automated cough detection in patients hospitalised for COVID-19 (n=32) and non-COVID-19 pneumonia (n=14) between April and November 2020 at the Cantonal Hospital St Gallen, Switzerland. Cough detection was achieved using smartphone-based audio recordings coupled to an ensemble of convolutional neural networks. Cough levels were correlated to established markers of inflammation and oxygenation. Measurements and main results Cough frequency was highest upon hospital admission and declined steadily with recovery. There was a characteristic pattern of daily cough fluctuations, with little activity during the night and two coughing peaks during the day. Hourly cough counts were strongly correlated with clinical markers of disease activity and laboratory markers of inflammation, suggesting cough as a surrogate of disease in acute respiratory tract infections. No apparent differences in cough evolution were observed between COVID-19 and non-COVID-19 pneumonia. Conclusions Automated, quantitative, smartphone-based detection of cough is feasible in hospitalised patients and correlates with disease activity in lower respiratory tract infections. Our approach allows for near real-time telemonitoring of individuals in aerosol isolation. Larger trials are warranted to decipher the use of cough as a digital biomarker for prognosis and tailored treatment in lower respiratory tract infections

Metabolically driven maturation of human-induced-pluripotent-stem-cell-derived cardiac microtissues on microfluidic chips

The immature physiology of cardiomyocytes derived from human induced pluripotent stem cells (hiPSCs) limits their utility for drug screening and disease modelling. Here we show that suitable combinations of mechanical stimuli and metabolic cues can enhance the maturation of hiPSC-derived cardiomyocytes, and that the maturation-inducing cues have phenotype-dependent effects on the cells' action-potential morphology and calcium handling. By using microfluidic chips that enhanced the alignment and extracellular-matrix production of cardiac microtissues derived from genetically distinct sources of hiPSC-derived cardiomyocytes, we identified fatty-acid-enriched maturation media that improved the cells' mitochondrial structure and calcium handling, and observed divergent cell-source-dependent effects on action-potential duration (APD). Specifically, in the presence of maturation media, tissues with abnormally prolonged APDs exhibited shorter APDs, and tissues with aberrantly short APDs displayed prolonged APDs. Regardless of cell source, tissue maturation reduced variabilities in spontaneous beat rate and in APD, and led to converging cell phenotypes (with APDs within the 300-450 ms range characteristic of human left ventricular cardiomyocytes) that improved the modelling of the effects of pro-arrhythmic drugs on cardiac tissue