Vocal Biomarker Is Associated With Hospitalization and Mortality Among Heart Failure Patients

Clinical Utility of Machine Learning-Derived Vocal Biomarkers in the Management of Heart Failure

Background

This study aimed to systematically evaluate voice symptoms during heart failure (HF) treatments and to exploratorily extract HF-related vocal biomarkers.

Methods and Results

This single-center, prospective study longitudinally acquired 839 audio files from 59 patients with acute decompensated HF. Patients' voices were analyzed along with conventional HF indicators (New York Heart Association [NYHA] class, presence of pulmonary congestion and pleural effusion on chest X-ray, and B-type natriuretic peptide [BNP]) and GOKAN scores based on the assessment of a cardiologist. Machine-learning (ML) models to estimate HF conditions were created using a Light Gradient Boosting Machine. Voice analysis identified 27 acoustic features that correlated with conventional HF indicators and GOKAN scores. When creating ML models based on the acoustic features, there was a significant correlation between actual and ML-derived BNP levels (r=0.49; P<0.001). ML models also identified good diagnostic accuracies in determining HF conditions characterized by NYHA class ≥2, BNP ≥300 pg/mL, presence of pulmonary congestion or pleural effusion on chest X-ray, and decompensated HF (defined as NYHA class ≥2 and BNP levels ≥300 pg/mL; accuracy: 75.1%, 69.1%, 68.7%, 66.4%, and 80.4%, respectively).

Conclusions

The present study successfully extracted HF-related acoustic features that correlated with conventional HF indicators. Although the data are preliminary, ML models based on acoustic features (vocal biomarkers) have the potential to infer various HF conditions, which warrant future studies.

Artificial intelligence-enhanced patient evaluation: bridging art and science

The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.

Landmark-based analysis of speech differentiates conversational from clear speech in speakers with muscle tension dysphonia

This study evaluated the feasibility of differentiating conversational and clear speech produced by individuals with muscle tension dysphonia (MTD) using landmark-based analysis of speech (LMBAS). Thirty-four adult speakers with MTD recorded conversational and clear speech, with 27 of them able to produce clear speech. The recordings of these individuals were analyzed with the open-source LMBAS program, SpeechMark®, matlab Toolbox version 1.1.2. The results indicated that glottal landmarks, burst onset landmarks, and the duration between glottal landmarks differentiated conversational speech from clear speech. LMBAS shows potential as an approach for detecting the difference between conversational and clear speech in dysphonic individuals.

Acoustic Voice and Speech Biomarkers of Treatment Status during Hospitalization for Acute Decompensated Heart Failure

This study investigates acoustic voice and speech features as biomarkers for acute decompensated heart failure (ADHF), a serious escalation of heart failure symptoms including breathlessness and fatigue. ADHF-related systemic fluid accumulation in the lungs and laryngeal tissues is hypothesized to affect phonation and respiration for speech. A set of daily spoken recordings from 52 patients undergoing inpatient ADHF treatment was analyzed to identify voice and speech biomarkers for ADHF and to examine the trajectory of biomarkers during treatment. Results indicated that speakers produce more stable phonation, a more creaky voice, faster speech rates, and longer phrases after ADHF treatment compared to their pre-treatment voices. This project builds on work to develop a method of monitoring ADHF using speech biomarkers and presents a more detailed understanding of relevant voice and speech features.

Speech biomarkers of risk factors for vascular dementia in people with mild cognitive impairment

Introduction

In this study we intend to use speech analysis to analyze the cognitive impairments caused by pathologies of vascular origin such as diabetes, hypertension, hypercholesterolemia and heart disease, predictors of the development of vascular dementia.

Methods

In this study, 40 participants with mild cognitive impairment were asked to read while being recorded and they were inquired about their history of the aforementioned conditions. Their speech was then analyzed.

Results

We found that some speech parameters of frequencies and syllabic rhythm vary due to these pathologies. In addition, we conducted a discriminant analysis in which we found that diabetes and hypertension can be predicted with an accuracy over 95% with few speech parameters, and hypercholesterolemia and heart disease with an accuracy over 80%.

Discussion

The predictor parameters found are heterogeneous, including voice quality, amplitude, frequency, and rhythm parameters. This result may lead to investigate why such important qualitative changes occur in the voice of older adults with these pathologies. Rather than trying to find a diagnostic procedure already existing in classical medicine, we expect this finding to contribute to explore the causes and concomitant pathologies of these diseases. We discuss the implications of behavioral traits, such as speech, as digital biomarkers.

Evaluation of Speech and Pause Alterations in Patients With Acute and Chronic Heart Failure

Background

Acute heart failure is the most frequent cause of unplanned hospital admission in elderly patients. Various biomarkers have been evaluated to better assess the status of these patients and prevent decompensation. Recently, voice has been suggested as a cost‐effective and noninvasive way to monitor disease progression. This study evaluates speech and pause alterations in patients with acute decompensated and stable heart failure. Specifically, we aim to identify a vocal biomarker that could be used to monitor patients with heart failure and to prevent decompensation.

Methods and Results

Speech and pause patterns were evaluated in 68 patients with acute and 36 patients with stable heart failure. Voice recordings were performed using a web‐browser based application that consisted of 5 tasks. Speech and pause patterns were automatically extracted and compared between acute and stable patients and with clinical markers. Compared with stable patients, pause ratio was up to 14.9% increased in patients with acute heart failure. This increase was largely independent of sex, age, and ejection fraction and persisted in patients with lower degrees of edema or dyspnea. Furthermore, pause ratio was positively correlated with NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide) after controlling for acute versus stable heart failure. Collectively, our findings indicate that the pause ratio could be useful in identifying acute heart failure, particularly in patients who do not display traditional indicators of decompensation.

Conclusions

Speech and pause patterns are altered in patients with acute heart failure. Particularly, we identified pause ratio as an easily interpretable vocal biomarker to support the monitoring of heart failure decompensation.

Computerized analysis of speech and voice for Parkinson's disease: A systematic review

BACKGROUND AND OBJECTIVE

Speech impairment is an early symptom of Parkinson's disease (PD). This study has summarized the literature related to speech and voice in detecting PD and assessing its severity.

METHODS

A systematic review of the literature from 2010 to 2021 to investigate analysis methods and signal features. The keywords "Automatic analysis" in conjunction with "PD speech" or "PD voice" were used, and the PubMed and ScienceDirect databases were searched. A total of 838 papers were found on the first run, of which 189 were selected. One hundred and forty-seven were found to be suitable for the review. The different datasets, recording protocols, signal analysis methods and features that were reported are listed. Values of the features that separate PD patients from healthy controls were tabulated. Finally, the barriers that limit the wide use of computerized speech analysis are discussed.

RESULTS

Speech and voice may be valuable markers for PD. However, large differences between the datasets make it difficult to compare different studies. In addition, speech analytic methods that are not informed by physiological understanding may alienate clinicians.

CONCLUSIONS

The potential usefulness of speech and voice for the detection and assessment of PD is confirmed by evidence from the classification and correlation results.

Predicting frailty in older adults using vocal biomarkers: a cross-sectional study

BACKGROUND

Frailty is a common issue in the aging population. Given that frailty syndrome is little discussed in the literature on the aging voice, the current study aims to examine the relationship between frailty and vocal biomarkers in older people.

METHODS

Participants aged ≥ 60 years visiting geriatric outpatient clinics were recruited. They underwent frailty assessment (Cardiovascular Health Study [CHS] index; Study of Osteoporotic Fractures [SOF] index; and Fatigue, Resistance, Ambulation, Illness, and Loss of weight [FRAIL] index) and were asked to pronounce a sustained vowel /a/ for approximately 1 s. Four voice parameters were assessed: average number of zero crossings (A1), variations in local peaks and valleys (A2), variations in first and second formant frequencies (A3), and spectral energy ratio (A4).

RESULTS

Among 277 older adults, increased A1 was associated with a lower likelihood of frailty as defined by SOF (odds ratio [OR] 0.84, 95% confidence interval [CI] 0.74-0.96). Participants with larger A2 values were more likely to be frail, as defined by FRAIL and CHS (FRAIL: OR 1.41, 95% CI 1.12-1.79; CHS: OR 1.38, 95% CI 1.10-1.75). Sex differences were observed across the three frailty indices. In male participants, an increase in A3 by 10 points increased the odds of frailty by almost 7% (SOF: OR 1.07, 95% CI 1.02-1.12), 6% (FRAIL: OR 1.06, 95% CI 1.02-1.11), or 6% (CHS: OR 1.06, 95% CI 1.01-1.11). In female participants, an increase in A4 by 0.1 conferred a significant 2.8-fold (SOF: OR 2.81, 95% CI 1.71-4.62), 2.3-fold (FRAIL: OR 2.31, 95% CI 1.45-3.68), or 2.8-fold (CHS: OR 2.82, 95% CI 1.76-4.51, CHS) increased odds of frailty.

CONCLUSIONS

Vocal biomarkers, especially spectral-domain voice parameters, might have potential for estimating frailty, as a non-invasive, instantaneous, objective, and cost-effective estimation tool, and demonstrating sex differences for individualised treatment of frailty.

[Telemedicine in chronic heart failure-From clinical studies to standard care]

Telemedicine has the potential to solve many current and especially future challenges in medical care. Using the example of heart failure (HF), the transition of telemedicine from clinical studies to standard care is presented. In patients with chronic HF, randomized controlled trials have shown that telemedicine-based care leads to a reduction in mortality and cardiovascular morbidity. Based on these data, the Federal Joint Committee (G-BA) decided that for the first time a digital method should be introduced into standard care for high-risk patients with reduced left ventricular ejection fraction. In the future, this group of patients will be entitled to telemedical care using active rhythm devices or noninvasive measuring devices. The indications are assessed by the primary treating physician (PBA), who works together with a telemedicine center (TMZ) managed through cardiology that receives daily telemetric data and notifies the PBA of abnormal findings. Alternatively, a cardiologist PBA with an associated TMZ infrastructure can also provide telemedical care. In the future, advanced technologies such as artificial intelligence or mobile communication standard 5G will help to make telemedicine both widely available and usable for alternative sensor technology.

Remote Cardiac Monitoring in Patients With Heart Failure: A Review

Importance

Heart failure (HF) is often characterized by an insidious disease course leading to frequent rehospitalizations and a high use of ambulatory care. Remote cardiac monitoring is a promising approach to detect worsening HF early and intervene prior to an overt decompensation.

Observations

Recently, a multitude of novel technologies for remote cardiac monitoring (RCM) in patients with HF have been developed and are undergoing clinical trials. This development has been accelerated by the COVID-19 pandemic.

Conclusions and Relevance

This review summarizes the major clinical trials on RCM in patients with HF and present the most recent developments in noninvasive and invasive RCM technologies.

Identifying individuals with recent COVID-19 through voice classification using deep learning

Recently deep learning has attained a breakthrough in model accuracy for the classification of images due mainly to convolutional neural networks. In the present study, we attempted to investigate the presence of subclinical voice feature alteration in COVID-19 patients after the recent resolution of disease using deep learning. The study was a prospective study of 76 post COVID-19 patients and 40 healthy individuals. The diagnoses of post COVID-19 patients were based on more than the eighth week after onset of symptoms. Voice samples of an ‘ah’ sound, coughing sound and a polysyllabic sentence were collected and preprocessed to log-mel spectrogram. Transfer learning using the VGG19 pre-trained convolutional neural network was performed with all voice samples. The performance of the model using the polysyllabic sentence yielded the highest classification performance of all models. The coughing sound produced the lowest classification performance while the ability of the monosyllabic ‘ah’ sound to predict the recent COVID-19 fell between the other two vocalizations. The model using the polysyllabic sentence achieved 85% accuracy, 89% sensitivity, and 77% specificity. In conclusion, deep learning is able to detect the subtle change in voice features of COVID-19 patients after recent resolution of the disease.

Telemedizin bei Herzinsuffizienz – Translation von klinischen Studien in die Regelversorgung

Für telemedizinische Mitbetreuung von Patient*innen mit chronischer Herzinsuffizienz besteht Evidenz bezüglich der Senkung von Letalität und kardiovaskulärer Morbidität. Nach aktuellem Beschluss des Gemeinsamen Bundesausschusses (G-BA) wird erstmals eine digitale Methode in die Regelversorgung überführt. Hochrisikopatient*innen mit reduzierter linksventrikulärer Ejektionsfraktion haben künftig einen Leistungsanspruch, entweder mittels aktiver Implantate (ICD, CRT-P und CRT-D) oder mittels telemedizinischer Heimmessgeräte betreut zu werden. Die Indikation zum Telemonitoring stellt der/die primär behandelnde Arzt/Ärztin (PBA). Der tägliche Vitaldatentransfer erfolgt an ein kardiologisch geführtes Telemedizinzentrum (TMZ), welches den/die PBA bei auffälligen Befunden zeitnah benachrichtigt. Der/die PBA entscheidet im Einzelfall über den Bedarf einer telemedizinischen 24/7-Mitbetreuung. Ein/e kardiologische/r PBA mit einer TMZ-Infrastruktur kann die telemedizinische Mitbetreuung der eigenen Patient*innen übernehmen.

Artificial Intelligence Can Improve Patient Management at the Time of a Pandemic: The Role of Voice Technology

Artificial intelligence–driven voice technology deployed on mobile phones and smart speakers has the potential to improve patient management and organizational workflow. Voice chatbots have been already implemented in health care–leveraging innovative telehealth solutions during the COVID-19 pandemic. They allow for automatic acute care triaging and chronic disease management, including remote monitoring, preventive care, patient intake, and referral assistance. This paper focuses on the current clinical needs and applications of artificial intelligence–driven voice chatbots to drive operational effectiveness and improve patient experience and outcomes.

Noninvasive Vocal Biomarker is Associated With Severe Acute Respiratory Syndrome Coronavirus 2 Infection

Objective

To investigate the association of voice analysis with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

Patients and Methods

A vocal biomarker, a unitless scalar with a value between 0 and 1, was developed based on 434 voice samples. The biomarker training was followed by a prospective, multicenter, observational study. All subjects were tested for SARS-CoV-2, had their voice recorded to a smartphone application, and gave their informed consent to participate in the study. The association of SARS-CoV-2 infection with the vocal biomarker was evaluated.

Results

The final study population included 80 subjects with a median age of 29 [range, 23 to 36] years, of whom 68% were men. Forty patients were positive for SARS-CoV-2. Infected patients were 12 times more likely to report at least one symptom (odds ratio, 11.8; P<.001). The vocal biomarker was significantly higher among infected patients (OR, 0.11; 95% CI, 0.06 to 0.17 vs OR, 0.19; 95% CI, 0.12 to 0.3; P=.001). The area under the receiver operating characteristic curve evaluating the association of the vocal biomarker with SARS-CoV-2 status was 72%. With a biomarker threshold of 0.115, the results translated to a sensitivity and specificity of 85% (95% CI, 70% to 94%) and 53% (95% CI, 36% to 69%), respectively. When added to a self-reported symptom classifier, the area under the curve significantly improved from 0.775 to 0.85.

Conclusion

Voice analysis is associated with SARS-CoV-2 status and holds the potential to improve the accuracy of self-reported symptom-based screening tools. This pilot study suggests a possible role for vocal biomarkers in screening for SARS-CoV-2-infected subjects.

Smart Wearables for Cardiac Monitoring-Real-World Use beyond Atrial Fibrillation

The possibilities and implementation of wearable cardiac monitoring beyond atrial fibrillation are increasing continuously. This review focuses on the real-world use and evolution of these devices for other arrhythmias, cardiovascular diseases and some of their risk factors beyond atrial fibrillation. The management of nonatrial fibrillation arrhythmias represents a broad field of wearable technologies in cardiology using Holter, event recorder, electrocardiogram (ECG) patches, wristbands and textiles. Implementation in other patient cohorts, such as ST-elevation myocardial infarction (STEMI), heart failure or sleep apnea, is feasible and expanding. In addition to appropriate accuracy, clinical studies must address the validation of clinical pathways including the appropriate device and clinical decisions resulting from the surrogate assessed.

Digital Health Applications in Heart Failure: a Critical Appraisal of Literature

Purpose of the review

Despite advancements in the diagnostic and therapeutic armamentarium, heart failure (HF) remains a major public health concern in the USA and worldwide. Digital health applications hold promise to bridge this gap and improve HF care. This review will provide the reader with a concise overview of the current digital health applications in HF, the main challenges to its use, and discuss the future of digital health for promoting care for HF patients.

Recent findings

Emerging evidence continues to support the potential role of digital health across the continuum of HF disease process including primary prevention, early detection, disease management, and reducing associated morbidity. There is also increasing emphasis on the need to pursue rigorous investigations to validate these promising claims, with some successful stories that have changed clinical practices.

Summary

Digital health technologies have emerged as potentially useful tools to complement HF care in both research and clinical realms. As digital technologies continue to play an increasing role in transforming healthcare delivery, creating the framework for its effective use would be necessary to ensure that digital health applications consistently improve outcomes and enhance care for HF patients.

Feasibility of Incorporating Voice Technology and Virtual Assistants in Cardiovascular Care and Clinical Trials

PURPOSE OF REVIEW

With the rising cost of cardiovascular clinical trials, there is interest in determining whether new technologies can increase cost effectiveness. This review focuses on current and potential uses of voice-based technologies, including virtual assistants, in cardiovascular clinical trials.

RECENT FINDINGS

Numerous potential uses for voice-based technologies have begun to emerge within cardiovascular medicine. Voice biomarkers, subtle changes in speech parameters, have emerged as a potential tool to diagnose and monitor many cardiovascular conditions, including heart failure, coronary artery disease, and pulmonary hypertension. With the increasing use of virtual assistants, numerous pilot studies have examined whether these devices can supplement initiatives to promote transitional care, physical activity, smoking cessation, and medication adherence with promising initial results. Additionally, these devices have demonstrated the ability to streamline data collection by administering questionnaires accurately and reliably. With the use of these technologies, there are several challenges that must be addressed before wider implementation including respecting patient privacy, maintaining regulatory standards, acceptance by patients and healthcare providers, determining the validity of voice-based biomarkers and endpoints, and increased accessibility.

SUMMARY

Voice technology represents a novel and promising tool for cardiovascular clinical trials; however, research is still required to understand how it can be best harnessed.

Digital Health in Cardiac Rehabilitation and Secondary Prevention: A Search for the Ideal Tool

Digital health is becoming more integrated in daily medical practice. In cardiology, patient care is already moving from the hospital to the patients' homes, with large trials showing positive results in the field of telemonitoring via cardiac implantable electronic devices (CIEDs), monitoring of pulmonary artery pressure via implantable devices, telemonitoring via home-based non-invasive sensors, and screening for atrial fibrillation via smartphone and smartwatch technology. Cardiac rehabilitation and secondary prevention are modalities that could greatly benefit from digital health integration, as current compliance and cardiac rehabilitation participation rates are low and optimisation is urgently required. This viewpoint offers a perspective on current use of digital health technologies in cardiac rehabilitation, heart failure and secondary prevention. Important barriers which need to be addressed for implementation in medical practice are discussed. To conclude, a future ideal digital tool and integrated healthcare system are envisioned. To overcome personal, technological, and legal barriers, technological development should happen in dialog with patients and caregivers. Aided by digital technology, a future could be realised in which we are able to offer high-quality, affordable, personalised healthcare in a patient-centred way.

[eHealth-smart devices revolutionizing cardiology]

Hintergrund

Der Nutzen von Smart Devices wie Handys und Smartwatches in der Kardiologie nimmt deutlich zu. Der gehäufte Einsatz wird vor allem auch von Patienten und der Industrie vorangetrieben.

Fragestellung

Welche Möglichkeiten bieten Smart Devices in der Kardiologie?

Material und Methode

Es wurde eine selektive Literaturrecherche durchgeführt. Naturwissenschaftliche und klinische Studien über die verschiedenen Einsatzmöglichkeiten der technischen Mittel wurden interpretiert.

Ergebnisse

Der Besitz und Gebrauch von Smartphones in Deutschland ist im weltweiten Vergleich sehr hoch. Dies ermöglicht einen sehr breiten Einsatz dieser Technologie auch im medizinischen Bereich. Die Anwendungsmöglichkeiten sind vielfältig und reichen von der Nutzung als Nachschlagewerk über einen „clinical decision support“ bis hin zur Erfassung von Biosignalen. Gerade die Kombination aus Biosignalmessung und Weiterverarbeitung der Information durch künstliche Intelligenz (KI) ermöglicht eine deutliche Verbesserung der bisherigen Diagnosemethoden und erlaubt extrem genaue Vorhersagen verschiedener kardiovaskulärer Krankheitsverläufe.

Schlussfolgerung

Smart Devices werden in der Kardiologie zunehmend im klinischen Alltag genutzt. Aufgrund der technischen Möglichkeiten wird der Einsatz sehr wahrscheinlich weiter steigen und einige Bereiche der Kardiologie deutlich verändern.

Vocal Biomarker Is Associated With Hospitalization and Mortality Among Heart Failure Patients

Background The purpose of this article is to evaluate the association of voice signal analysis with adverse outcome among patients with congestive heart failure (CHF). Methods and Results The study cohort included 10 583 patients who were registered to a call center of patients who had chronic conditions including CHF in Israel between 2013 and 2018. A total of 223 acoustic features were extracted from 20 s of speech for each patient. A biomarker was developed based on a training cohort of non-CHF patients (N=8316). The biomarker was tested on a mutually exclusive CHF study cohort (N=2267) and was evaluated as a continuous and ordinal (4 quartiles) variable. Median age of the CHF study population was 77 (interquartile range 68-83) and 63% were men. During a median follow-up of 20 months (interquartile range 9-34), 824 (36%) patients died. Kaplan-Meier survival analysis showed higher cumulative probability of death with increasing quartiles (23%, 29%, 38%, and 54%; P<0.001). Survival analysis with adjustment to known predictors of poor survival demonstrated that each SD increase in the biomarker was associated with a significant 32% increased risk of death during follow-up (95% CI, 1.24-1.41, P<0.001) and that compared with the lowest quartile, patients in the highest quartile were 96% more likely to die (95% CI, 1.59-2.42, P<0.001). The model consistently demonstrated an independent association of the biomarker with hospitalizations during follow-up (P<0.001). Conclusions Noninvasive vocal biomarker is associated with adverse outcome among CHF patients, suggesting a possible role for voice analysis in telemedicine and CHF patient care.