Dataset Information

Unsupervised Clustering of Heartbeat Dynamics Allows for Real Time and Personalized Improvement in Cardiovascular Fitness.

ABSTRACT: VO₂max index has a significant impact on overall health. Its estimation through wearables notifies the user of his level of fitness but cannot provide a detailed analysis of the time intervals in which heartbeat dynamics are changed and/or fatigue is emerging. Here, we developed a multiple modality biosignal processing method to investigate running sessions to characterize in real time heartbeat dynamics in response to external energy demand. We isolated dynamic regimes whose fraction increases with the VO₂max and with the emergence of neuromuscular fatigue. This analysis can be extremely valuable by providing personalized feedback about the user's fitness level improvement that can be realized by developing personalized exercise plans aimed to target a contextual increase in the dynamic regime fraction related to VO₂max increase, at the expense of the dynamic regime fraction related to the emergence of fatigue. These strategies can ultimately result in the reduction in cardiovascular risk.

SUBMITTER: Serantoni C

PROVIDER: S-EPMC9182749 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Unsupervised Clustering of Heartbeat Dynamics Allows for Real Time and Personalized Improvement in Cardiovascular Fitness.

Serantoni Cassandra C Zimatore Giovanna G Bianchetti Giada G Abeltino Alessio A De Spirito Marco M Maulucci Giuseppe G

Sensors (Basel, Switzerland) 20220524 11

VO2max index has a significant impact on overall health. Its estimation through wearables notifies the user of his level of fitness but cannot provide a detailed analysis of the time intervals in which heartbeat dynamics are changed and/or fatigue is emerging. Here, we developed a multiple modality biosignal processing method to investigate running sessions to characterize in real time heartbeat dynamics in response to external energy demand. We isolated dynamic regimes ...[more]

PMID: 35684596

Similar Datasets

Project description:IntroductionSignificant heterogeneity in cardiovascular disease (CVD) risk and healthcare resource allocation has been demonstrated in the United States, but optimal methods to capture heterogeneity in county-level characteristics that contribute to CVD mortality differences are unclear. We evaluated the feasibility of unsupervised machine learning (ML)-based phenomapping in identifying subgroups of county-level social and demographic risk factors with differential CVD outcomes.MethodsWe performed a cross-sectional study using county-level data from 2008 to 2018 from the Centers for Disease Control (CDC) WONDER platform and the 2020 Robert Wood Johnson County Health Rankings program. Unsupervised clustering was performed on 46 facets of population characteristics spanning the demographic, health behaviors, socioeconomic, and healthcare access domains. Spatial autocorrelation was assessed using the Moran's I test, and temporal trends in age-adjusted CVD outcomes were evaluated using linear mixed effect models and least square means.ResultsAmong 2676 counties, 4 county-level phenogroups were identified (Moran's I p-value <0.001). Phenogroup 1 (N = 924; 24.5%) counties were largely white, suburban households with high income and access to healthcare. Phenogroup 2 counties (N = 451; 16.9%) included predominantly Hispanic residents and below-average prevalence of CVD risk factors. Phenogroup 3 (N = 951; 35.5%) counties included rural, white residents with the lowest levels of access to healthcare. Phenogroup 4 (350; 13.1%) comprised counties with predominantly Black residents, substantial cardiovascular comorbidities, and physical and socioeconomic burdens. Least square means in age-adjusted cardiovascular mortality over time increased in a stepwise fashion from 223 in phenogroup 1 to 317 per 100,000 residents in phenogroup 4.ConclusionsUnsupervised ML-based clustering on county-level population characteristics can identify unique phenogroups with differential risk of CVD mortality. Phenogroup identification may aid in developing a uniform set of preventive initiatives for clustered counties to address regional differences in CVD mortality.

Project description:BackgroundThere is currently no staging system for cutaneous squamous cell carcinoma (cSCC) that is adapted to decision-making and universally used. Experts have unconscious ability to simplify the heterogeneity of clinical situations into a few relevant groups to drive their therapeutic decisions. Therefore, we have used unsupervised clustering of real cases by experts to generate an operational classification of cSCCs, an approach that was successful for basal cell carcinomas.ObjectivesTo generate a consensual and operational classification of cSCCs.MethodsUnsupervised independent clustering of 248 cases of cSCCs considered difficult-to-treat. Eighteen international experts from different specialties classified these cases into what they considered homogeneous clusters useful for management, each with freedom regarding clustering criteria. Convergences and divergences between clustering were analysed using a similarity matrix, the K-mean approach and the average silhouette method. Mathematical modelling was used to look for the best consensual clustering. The operability of the derived classification was validated on 23 new practitioners.ResultsDespite the high heterogeneity of the clinical cases, a mathematical consensus was observed. It was best represented by a partition into five clusters, which appeared a posteriori to describe different clinical scenarios. Applicability of this classification was shown by a good concordance (94%) in the allocation of cases between the new practitioners and the 18 experts. An additional group of easy-to-treat cSCC was included, resulting in a six-group final classification: easy-to-treat/complex to treat due to tumour and/or patient characteristics/multiple/locally advanced/regional disease/visceral metastases.ConclusionsGiven the methodology based on the convergence of unguided intuitive clustering of cases by experts, this new classification is relevant for clinical practice. It does not compete with staging systems, but they may complement each other, whether the objective is to select the best therapeutic approach in tumour boards or to design homogeneous groups for trials.

Dataset Information

Unsupervised Clustering of Heartbeat Dynamics Allows for Real Time and Personalized Improvement in Cardiovascular Fitness.

Publications

Unsupervised Clustering of Heartbeat Dynamics Allows for Real Time and Personalized Improvement in Cardiovascular Fitness.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets