Miniaturized wireless, skin-integrated sensor networks for quantifying full-body movement behaviors and vital signs in infants.

Early identification of atypical infant movement behaviors consistent with underlying neuromotor pathologies can expedite timely enrollment in therapeutic interventions that exploit inherent neuroplasticity to promote recovery. Traditional neuromotor assessments rely on qualitative evaluations performed by specially trained personnel, mostly available in tertiary medical centers or specialized facilities. Such approaches are high in cost, require geographic proximity to advanced healthcare resources, and yield mostly qualitative insight. This paper introduces a simple, low-cost alternative in the form of a technology customized for quantitatively capturing continuous, full-body kinematics of infants during free living conditions at home or in clinical settings while simultaneously recording essential vital signs data. The system consists of a wireless network of small, flexible inertial sensors placed at strategic locations across the body and operated in a wide-bandwidth and time-synchronized fashion. The data serve as the basis for reconstructing three-dimensional motions in avatar form without the need for video recordings and associated privacy concerns, for remote visual assessments by experts. These quantitative measurements can also be presented in graphical format and analyzed with machine-learning techniques, with potential to automate and systematize traditional motor assessments. Clinical implementations with infants at low and at elevated risks for atypical neuromotor development illustrates application of this system in quantitative and semiquantitative assessments of patterns of gross motor skills, along with body temperature, heart rate, and respiratory rate, from long-term and follow-up measurements over a 3-mo period following birth. The engineering aspects are compatible for scaled deployment, with the potential to improve health outcomes for children worldwide via early, pragmatic detection methods.

Development of a miniaturized mechanoacoustic sensor for continuous, objective cough detection, characterization and physiologic monitoring in children with cystic fibrosis.

Cough is an important symptom in children with acute and chronic respiratory disease. Daily cough is common in Cystic Fibrosis (CF) and increased cough is a symptom of pulmonary exacerbation. To date, cough assessment is primarily subjective in clinical practice and research. Attempts to develop objective, automatic cough counting tools have faced reliability issues in noisy environments and practical barriers limiting long-term use. This single-center pilot study evaluated usability, acceptability and performance of a mechanoacoustic sensor (MAS), previously used for cough classification in adults, in 36 children with CF over brief and multi-day periods in four cohorts. Children whose health was at baseline and who had symptoms of pulmonary exacerbation were included. We trained, validated, and deployed custom deep learning algorithms for accurate cough detection and classification from other vocalization or artifacts with an overall area under the receiver-operator characteristic curve (AUROC) of 0.96 and average precision (AP) of 0.93. Child and parent feedback led to a redesign of the MAS towards a smaller, more discreet device acceptable for daily use in children. Additional improvements optimized power efficiency and data management. The MAS's ability to objectively measure cough and other physiologic signals across clinic, hospital, and home settings is demonstrated, particularly aided by an AUROC of 0.97 and AP of 0.96 for motion artifact rejection. Examples of cough frequency and physiologic parameter correlations with participant-reported outcomes and clinical measurements for individual patients are presented. The MAS is a promising tool in objective longitudinal evaluation of cough in children with CF.

Synchronized wearables for the detection of haemodynamic states via electrocardiography and multispectral photoplethysmography.

Cardiovascular health is typically monitored by measuring blood pressure. Here we describe a wireless on-skin system consisting of synchronized sensors for chest electrocardiography and peripheral multispectral photoplethysmography for the continuous monitoring of metrics related to vascular resistance, cardiac output and blood-pressure regulation. We used data from the sensors to train a support-vector-machine model for the classification of haemodynamic states (resulting from exposure to heat or cold, physical exercise, breath holding, performing the Valsalva manoeuvre or from vasopressor administration during post-operative hypotension) that independently affect blood pressure, cardiac output and vascular resistance. The model classified the haemodynamic states on the basis of an unseen subset of sensor data for 10 healthy individuals, 20 patients with hypertension undergoing haemodynamic stimuli and 15 patients recovering from cardiac surgery, with an average precision of 0.878 and an overall area under the receiver operating characteristic curve of 0.958. The multinodal sensor system may provide clinically actionable insights into haemodynamic states for use in the management of cardiovascular disease.

Wireless broadband acousto-mechanical sensing system for continuous physiological monitoring.

The human body generates various forms of subtle, broadband acousto-mechanical signals that contain information on cardiorespiratory and gastrointestinal health with potential application for continuous physiological monitoring. Existing device options, ranging from digital stethoscopes to inertial measurement units, offer useful capabilities but have disadvantages such as restricted measurement locations that prevent continuous, longitudinal tracking and that constrain their use to controlled environments. Here we present a wireless, broadband acousto-mechanical sensing network that circumvents these limitations and provides information on processes including slow movements within the body, digestive activity, respiratory sounds and cardiac cycles, all with clinical grade accuracy and independent of artifacts from ambient sounds. This system can also perform spatiotemporal mapping of the dynamics of gastrointestinal processes and airflow into and out of the lungs. To demonstrate the capabilities of this system we used it to monitor constrained respiratory airflow and intestinal motility in neonates in the neonatal intensive care unit (n = 15), and to assess regional lung function in patients undergoing thoracic surgery (n = 55). This broadband acousto-mechanical sensing system holds the potential to help mitigate cardiorespiratory instability and manage disease progression in patients through continuous monitoring of physiological signals, in both the clinical and nonclinical setting.

Comparison of Severity of Illness Scores and Artificial Intelligence Models That Are Predictive of Intensive Care Unit Mortality: Meta-analysis and Review of the Literature.

BACKGROUND: Severity of illness scores-Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score, and Sequential Organ Failure Assessment-are current risk stratification and mortality prediction tools used in intensive care units (ICUs) worldwide. Developers of artificial intelligence or machine learning (ML) models predictive of ICU mortality use the severity of illness scores as a reference point when reporting the performance of these computational constructs. OBJECTIVE: This study aimed to perform a literature review and meta-analysis of articles that compared binary classification ML models with the severity of illness scores that predict ICU mortality and determine which models have superior performance. This review intends to provide actionable guidance to clinicians on the performance and validity of ML models in supporting clinical decision-making compared with the severity of illness score models. METHODS: Between December 15 and 18, 2020, we conducted a systematic search of PubMed, Scopus, Embase, and IEEE databases and reviewed studies published between 2000 and 2020 that compared the performance of binary ML models predictive of ICU mortality with the performance of severity of illness score models on the same data sets. We assessed the studies' characteristics, synthesized the results, meta-analyzed the discriminative performance of the ML and severity of illness score models, and performed tests of heterogeneity within and among studies. RESULTS: We screened 461 abstracts, of which we assessed the full text of 66 (14.3%) articles. We included in the review 20 (4.3%) studies that developed 47 ML models based on 7 types of algorithms and compared them with 3 types of the severity of illness score models. Of the 20 studies, 4 (20%) were found to have a low risk of bias and applicability in model development, 7 (35%) performed external validation, 9 (45%) reported on calibration, 12 (60%) reported on classification measures, and 4 (20%) addressed explainability. The discriminative performance of the ML-based models, which was reported as AUROC, ranged between 0.728 and 0.99 and between 0.58 and 0.86 for the severity of illness score-based models. We noted substantial heterogeneity among the reported models and considerable variation among the AUROC estimates for both ML and severity of illness score model types. CONCLUSIONS: ML-based models can accurately predict ICU mortality as an alternative to traditional scoring models. Although the range of performance of the ML models is superior to that of the severity of illness score models, the results cannot be generalized due to the high degree of heterogeneity. When presented with the option of choosing between severity of illness score or ML models for decision support, clinicians should select models that have been externally validated, tested in the practice environment, and updated to the patient population and practice environment. TRIAL REGISTRATION: PROSPERO CRD42021203871; https://tinyurl.com/28v2nch8.

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.

BACKGROUND: Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient's health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. OBJECTIVE: This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. METHODS: The PubMed search engine was systematically searched by combining "deep learning" and keywords such as "ecg," "ekg," "electrocardiogram," "electrocardiography," and "electrocardiology." Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. RESULTS: We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. CONCLUSIONS: We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.

Soft skin-interfaced mechano-acoustic sensors for real-time monitoring and patient feedback on respiratory and swallowing biomechanics.

Swallowing is a complex neuromuscular activity regulated by the autonomic nervous system. Millions of adults suffer from dysphagia (impaired or difficulty swallowing), including patients with neurological disorders, head and neck cancer, gastrointestinal diseases, and respiratory disorders. Therapeutic treatments for dysphagia include interventions by speech-language pathologists designed to improve the physiology of the swallowing mechanism by training patients to initiate swallows with sufficient frequency and during the expiratory phase of the breathing cycle. These therapeutic treatments require bulky, expensive equipment to synchronously record swallows and respirations, confined to use in clinical settings. This paper introduces a wireless, wearable technology that enables continuous, mechanoacoustic tracking of respiratory activities and swallows through movements and vibratory processes monitored at the skin surface. Validation studies in healthy adults (n = 67) and patients with dysphagia (n = 4) establish measurement equivalency to existing clinical standard equipment. Additional studies using a differential mode of operation reveal similar performance even during routine daily activities and vigorous exercise. A graphical user interface with real-time data analytics and a separate, optional wireless module support both visual and haptic forms of feedback to facilitate the treatment of patients with dysphagia.

A transient, closed-loop network of wireless, body-integrated devices for autonomous electrotherapy.

Temporary postoperative cardiac pacing requires devices with percutaneous leads and external wired power and control systems. This hardware introduces risks for infection, limitations on patient mobility, and requirements for surgical extraction procedures. Bioresorbable pacemakers mitigate some of these disadvantages, but they demand pairing with external, wired systems and secondary mechanisms for control. We present a transient closed-loop system that combines a time-synchronized, wireless network of skin-integrated devices with an advanced bioresorbable pacemaker to control cardiac rhythms, track cardiopulmonary status, provide multihaptic feedback, and enable transient operation with minimal patient burden. The result provides a range of autonomous, rate-adaptive cardiac pacing capabilities, as demonstrated in rat, canine, and human heart studies. This work establishes an engineering framework for closed-loop temporary electrotherapy using wirelessly linked, body-integrated bioelectronic devices.

Bitter Flavored, Soft Composites for Wearables Designed to Reduce Risks of Choking in Infants.

Wireless, skin-integrated devices for continuous, clinical-quality monitoring of vital signs have the potential to greatly improve the care of patients in neonatal and pediatric intensive-care units. These same technologies can also be used in the home, across a broad spectrum of ages, from beginning to end of life. Although miniaturized forms of such devices minimize patient burden and improve compliance, they represent life-threatening choking hazards for infants. A materials strategy is presented here to address this concern. Specifically, composite materials are introduced as soft encapsulating layers and gentle adhesives that release chemical compounds designed to elicit an intense bitter taste when placed in the mouth. Reflexive reactions to this sensation strongly reduce the potential for ingestion, as a safety feature. The materials systems described involve a non-toxic bitterant (denatonium benzoate) as a dopant in an elastomeric (poly(dimethylsiloxane)) or hydrogel matrix. Experimental and computational studies of these composite materials and the kinetics of release of the bitterant define the key properties. Incorporation into various wireless skin-integrated sensors demonstrates their utility in functional systems. This simple strategy offers valuable protective capabilities, with broad practical relevance to the welfare of children monitored with wearable devices.

Differential cardiopulmonary monitoring system for artifact-canceled physiological tracking of athletes, workers, and COVID-19 patients.

Soft, skin-integrated electronic sensors can provide continuous measurements of diverse physiological parameters, with broad relevance to the future of human health care. Motion artifacts can, however, corrupt the recorded signals, particularly those associated with mechanical signatures of cardiopulmonary processes. Design strategies introduced here address this limitation through differential operation of a matched, time-synchronized pair of high-bandwidth accelerometers located on parts of the anatomy that exhibit strong spatial gradients in motion characteristics. When mounted at a location that spans the suprasternal notch and the sternal manubrium, these dual-sensing devices allow measurements of heart rate and sounds, respiratory activities, body temperature, body orientation, and activity level, along with swallowing, coughing, talking, and related processes, without sensitivity to ambient conditions during routine daily activities, vigorous exercises, intense manual labor, and even swimming. Deployments on patients with COVID-19 allow clinical-grade ambulatory monitoring of the key symptoms of the disease even during rehabilitation protocols.

Wireless, Skin-Interfaced Devices for Pediatric Critical Care: Application to Continuous, Noninvasive Blood Pressure Monitoring.

Indwelling arterial lines, the clinical gold standard for continuous blood pressure (BP) monitoring in the pediatric intensive care unit (PICU), have significant drawbacks due to their invasive nature, ischemic risk, and impediment to natural body movement. A noninvasive, wireless, and accurate alternative would greatly improve the quality of patient care. Recently introduced classes of wireless, skin-interfaced devices offer capabilities in continuous, precise monitoring of physiologic waveforms and vital signs in pediatric and neonatal patients, but have not yet been employed for continuous tracking of systolic and diastolic BP-critical for guiding clinical decision-making in the PICU. The results presented here focus on materials and mechanics that optimize the system-level properties of these devices to enhance their reliable use in this context, achieving full compatibility with the range of body sizes, skin types, and sterilization schemes typically encountered in the PICU. Systematic analysis of the data from these devices on 23 pediatric patients, yields derived, noninvasive BP values that can be quantitatively validated against direct recordings from arterial lines. The results from this diverse cohort, including those under pharmacological protocols, suggest that wireless, skin-interfaced devices can, in certain circumstances of practical utility, accurately and continuously monitor BP in the PICU patient population.

Mechano-acoustic sensing of physiological processes and body motions via a soft wireless device placed at the suprasternal notch.

Skin-mounted soft electronics that incorporate high-bandwidth triaxial accelerometers can capture broad classes of physiologically relevant information, including mechano-acoustic signatures of underlying body processes (such as those measured by a stethoscope) and precision kinematics of core-body motions. Here, we describe a wireless device designed to be conformally placed on the suprasternal notch for the continuous measurement of mechano-acoustic signals, from subtle vibrations of the skin at accelerations of around 10-3 m s-2 to large motions of the entire body at about 10 m s-2, and at frequencies up to around 800 Hz. Because the measurements are a complex superposition of signals that arise from locomotion, body orientation, swallowing, respiration, cardiac activity, vocal-fold vibrations and other sources, we exploited frequency-domain analysis and machine learning to obtain-from human subjects during natural daily activities and exercise-real-time recordings of heart rate, respiration rate, energy intensity and other essential vital signs, as well as talking time and cadence, swallow counts and patterns, and other unconventional biomarkers. We also used the device in sleep laboratories and validated the measurements using polysomnography.