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1.
Physiol Meas ; 2021 Apr 01.
Artigo em Inglês | MEDLINE | ID: mdl-33794516

RESUMO

In this perspective paper, we aim to highlight the potential of sleep as an auspicious time for diagnosis, management and therapy of non-sleep-specific pathologies. Sleep has a profound influence on the physiology of body systems and biological processes. Molecular studies have shown circadian-regulated shifts in protein expression patterns across human tissues, further emphasizing the unique functional, behavioral and pharmacokinetic landscape of sleep. Thus, many pathological processes are also expected to exhibit sleep-specific manifestations. Modern advances in biosensor technologies have enabled remote, non-invasive recording of a growing number of physiologic parameters and biomarkers. Sleep is an ideal time frame for the collection of long and clean physiological time series data which can then be analyzed using data-driven algorithms such as deep learning. Here, we introduce key clinical studies in selected medical fields, which leveraged novel technologies and the advantageous period of sleep to diagnose, monitor and treat pathologies. We then discuss possible opportunities to further harness this new paradigm and modern technologies to explore human health and disease during sleep and to advance the development of novel clinical applications - from sleep medicine to medicine during sleep.

3.
Physiol Meas ; 41(10): 104001, 2020 Nov 06.
Artigo em Inglês | MEDLINE | ID: mdl-32932240

RESUMO

OBJECTIVE: In this research, we introduce a new methodology for atrial fibrillation (AF) diagnosis during sleep in a large population sample at risk of sleep-disordered breathing. APPROACH: The approach leverages digital biomarkers and recent advances in machine learning (ML) for mass AF diagnosis from overnight-hours of single-channel electrocardiogram (ECG) recording. Four databases, totaling n = 3088 patients and p = 26 913 h of continuous single-channel electrocardiogram raw data were used. Three of the databases (n = 125, p = 2513) were used for training a ML model in recognizing AF events from beat-to-beat time series. Visit 1 of the sleep heart health study database (SHHS1, n = 2963, p = 24 400) was used as the test set to evaluate the feasibility of identifying prominent AF from polysomnographic recordings. By combining AF diagnosis history and a cardiologist's visual inspection of individuals suspected of having AF (n = 118), a total of 70 patients were diagnosed with prominent AF in SHHS1. MAIN RESULTS: Model prediction on SHHS1 showed an overall [Formula: see text]and [Formula: see text] in classifying individuals with or without prominent AF. [Formula: see text] was non-inferior (p = 0.03) for individuals with an apnea-hypopnea index (AHI) ≥15 versus AHI < [Formula: see text]. Over 22% of correctly identified prominent AF rhythm cases were not previously documented as AF in SHHS1. SIGNIFICANCE: Individuals with prominent AF can be automatically diagnosed from an overnight single-channel ECG recording, with an accuracy unaffected by the presence of moderate-to-severe obstructive sleep apnea. This approach enables identifying a large proportion of AF individuals that were otherwise missed by regular care.

4.
Physiol Meas ; 41(10): 10TR01, 2020 11 10.
Artigo em Inglês | MEDLINE | ID: mdl-32947271

RESUMO

Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper, we present a review of remote health monitoring initiatives taken in 20 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy.


Assuntos
Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/fisiopatologia , Monitorização Fisiológica/métodos , Pneumonia Viral/diagnóstico , Pneumonia Viral/fisiopatologia , Telemedicina/métodos , Infecções por Coronavirus/epidemiologia , Humanos , Pandemias , Pneumonia Viral/epidemiologia
5.
J Mol Cell Cardiol ; 143: 85-95, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-32339564

RESUMO

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a stress-induced ventricular arrhythmia associated with rhythm disturbance and impaired sinoatrial node cell (SANC) automaticity (pauses). Mutations associated with dysfunction of Ca2+-related mechanisms have been shown to be present in CPVT. These dysfunctions include impaired Ca2+ release from the ryanodine receptor (i.e., RyR2R4496C mutation) or binding to calsequestrin 2 (CASQ2). In SANC, Ca2+ signaling directly and indirectly mediates pacemaker function. We address here the following research questions: (i) what coupled-clock mechanisms and pathways mediate pacemaker mutations associated with CPVT in basal and in response to ß-adrenergic stimulation? (ii) Can different mechanisms lead to the same CPVT-related pacemaker pauses? (iii) Can the mutation-induced deteriorations in SANC function be reversed by drug intervention or gene manipulation? We used a numerical model of mice SANC that includes membrane and intracellular mechanisms and their interconnected signaling pathways. In the basal state of RyR2R4496C SANC, the model predicted that the Na+-Ca2+ exchanger current (INCX) and T-type Ca2+ current (ICaT) mediate between changes in Ca2+ signaling and SANC dysfunction. Under ß-adrenergic stimulation, changes in cAMP-PKA signaling and the sodium currents (INa), in addition to INCX and ICaT, mediate between changes in Ca2+ signaling and SANC automaticity pauses. Under basal conditions in Casq2-/-, the same mechanisms drove changes in Ca2+ signaling and subsequent pacemaker dysfunction. However, SANC automaticity pauses in response to ß-AR stimulation were mediated by ICaT and INa. Taken together, distinct mechanisms can lead to CPVT-associated SANC automaticity pauses. In addition, we predict that specifically increasing SANC cAMP-PKA activity by either a pharmacological agent (IBMX, a phosphodiesterase (PDE) inhibitor), gene manipulation (overexpression of adenylyl cyclase 1/8) or direct manipulation of the SERCA phosphorylation target through changes in gene expression, compensate for the impairment in SANC automaticity. These findings suggest new insights for understanding CPVT and its therapeutic approach.

6.
Physiol Meas ; 41(4): 044007, 2020 05 12.
Artigo em Inglês | MEDLINE | ID: mdl-32272456

RESUMO

OBJECTIVE: Portable oximetry has been shown to be a promising candidate for large-scale obstructive sleep apnea screening. In polysomnography (PSG), the gold standard OSA diagnosis test, the oxygen desaturation index (ODI) is usually computed from desaturation events occurring during sleep periods only, i.e. overnight desaturations occurring during or overlapping with a wake state are excluded. However, for unattended home oximetry, all desaturations are taken into account since no reference electroencephalogram is available for sleep staging. We aim to evaluate the hypothesis that the predictive power of oximetry for OSA screening is not impaired when reference sleep stages are not available. APPROACH: We used a PSG clinical database of 887 individuals from a representative São Paulo (Brazil) population sample. Using features derived from the oxygen saturation time series and demographic information, OxyDOSA, a published machine learning model, was trained to distinguish between non-OSA and OSA individuals using the ODI computed while including versus excluding overnight desaturations overlapping with a wake period, thus mimicking portable and PSG oximetry analyses, respectively. MAIN RESULTS: When excluding wake desaturations, the OxyDOSA model had an AUROC = 94.9 ± 1.6, Se = 85.9 ± 2.8, Sp = 90.1 ± 2.6 and F1 = 86.4 ± 2.7. When considering wake desaturations, the OxyDOSA model had an AUROC = 94.4 ± 1.6, Se = 88.0 ± 2.0, Sp = 87.7 ± 2.9 and F1 = 86.2 ± 2.4. Non-inferiority was demonstrated (p = 0.049) at a tolerance level of 3%. In addition, analysis of the desaturations excluded by PSG oximetry analysis suggests that up to 21% of the total number of desaturations might actually be related to apneas or hypopneas. SIGNIFICANCE: This analysis of a large representative population sample provided strong evidence that the predictive power of oximetry for OSA screening using the OxyDOSA model is not impaired when reference sleep stages are not available. This finding motivates the usage of portable oximetry for OSA screening.

7.
Sleep ; 43(1)2020 01 13.
Artigo em Inglês | MEDLINE | ID: mdl-31930346
8.
IEEE Rev Biomed Eng ; 13: 51-73, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-31478873

RESUMO

Fetal electrocardiography (fECG) is a promising alternative to cardiotocography continuous fetal monitoring. Robust extraction of the fetal signal from the abdominal mixture of maternal and fetal electrocardiograms presents the greatest challenge to effective fECG monitoring. This is mainly due to the low amplitude of the fetal versus maternal electrocardiogram and to the non-stationarity of the recorded signals. In this review, we highlight key developments in advanced signal processing algorithms for non-invasive fECG extraction and the available open access resources (databases and source code). In particular, we highlight the advantages and limitations of these algorithms as well as key parameters that must be set to ensure their optimal performance. Improving or combining the current or developing new advanced signal processing methods may enable morphological analysis of the fetal electrocardiogram, which today is only possible using the invasive scalp electrocardiography method.


Assuntos
Eletrocardiografia , Coração Fetal/diagnóstico por imagem , Monitorização Fetal , Processamento de Sinais Assistido por Computador , Algoritmos , Feminino , Frequência Cardíaca Fetal/fisiologia , Humanos , Gravidez
9.
EClinicalMedicine ; 11: 81-88, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31317133

RESUMO

Background: The growing awareness for the high prevalence of obstructive sleep apnea (OSA) coupled with the dramatic proportion of undiagnosed individuals motivates the elaboration of a simple but accurate screening test. This study assesses, for the first time, the performance of oximetry combined with demographic information as a screening tool for identifying OSA in a representative (i.e. non-referred) population sample. Methods: A polysomnography (PSG) clinical database of 887 individuals from a representative population sample of São Paulo's city (Brazil) was used. Using features derived from the oxygen saturation signal during sleep periods and demographic information, a logistic regression model (termed OxyDOSA) was trained to distinguish between non-OSA and OSA individuals (mild, moderate, and severe). The OxyDOSA model performance was assessed against the PSG-based diagnosis of OSA (AASM 2017) and compared to the NoSAS and STOP-BANG questionnaires. Findings: The OxyDOSA model had mean AUROC = 0.94 ±â€¯0.02, Se = 0.87 ±â€¯0.04 and Sp = 0.85 ±â€¯0.03. In particular, it did not miss any of the 75 severe OSA individuals. In comparison, the NoSAS questionnaire had AUROC = 0.83 ±â€¯0.03, and missed 23/75 severe OSA individuals. The STOP-BANG had AUROC = 0.77 ±â€¯0.04 and missed 14/75 severe OSA individuals. Interpretation: We provide strong evidence on a representative population sample that oximetry biomarkers combined with few demographic information, the OxyDOSA model, is an effective screening tool for OSA. Our results suggest that sleep questionnaires should be used with caution for OSA screening as they fail to identify many moderate and even some severe cases. The OxyDOSA model will need to be further validated on data recorded using overnight portable oximetry.

11.
Prenat Diagn ; 39(3): 178-187, 2019 02.
Artigo em Inglês | MEDLINE | ID: mdl-30602066

RESUMO

OBJECTIVE: To assess whether noninvasive fetal electrocardiography (NI-FECG) enables the diagnosis of fetal arrhythmias. METHODS: A total of 500 echocardiography and NI-FECG recordings were collected from pregnant women during a routine medical visit in this multicenter study. All the cases with fetal arrhythmias (n = 12) and a matching number of control (n = 14) were used. Two perinatal cardiologists analyzed the extracted NI-FECG while blinded to the echocardiography. The NI-FECG-based diagnosis was compared with the reference fetal echocardiography diagnosis. RESULTS: NI-FECG and fetal echocardiography agreed on all cases (Ac = 100%) on the presence of an arrhythmia or not. However, in one case, the type of arrhythmia identified by the NI-FECG was incorrect because of the low resolution of the extracted fetal P-wave, which prevented resolving the mechanism (2:1 atrioventricular conduction) of the atrial tachycardia. CONCLUSION: It is possible to diagnose fetal arrhythmias using the NI-FECG technique. However, this study identifies that improvement in algorithms for reconstructing the P-wave is critical to systematically resolve the mechanisms underlying the arrhythmias. The elaboration of a NI-FECG Holter device will offer new opportunities for fetal diagnosis and remote monitoring of problematic pregnancies because of its low-cost, noninvasiveness, portability, and minimal setup requirements.


Assuntos
Arritmias Cardíacas/diagnóstico , Eletrocardiografia , Doenças Fetais/diagnóstico , Coração Fetal , Feminino , Humanos , Gravidez
12.
Front Physiol ; 9: 1390, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30337883

RESUMO

Background: The time variation between consecutive heartbeats is commonly referred to as heart rate variability (HRV). Loss of complexity in HRV has been documented in several cardiovascular diseases and has been associated with an increase in morbidity and mortality. However, the mechanisms that control HRV are not well understood. Animal experiments are the key to investigating this question. However, to date, there are no standard open source tools for HRV analysis of mammalian electrocardiogram (ECG) data and no centralized public databases for researchers to access. Methods: We created an open source software solution specifically designed for HRV analysis from ECG data of multiple mammals, including humans. We also created a set of public databases of mammalian ECG signals (dog, rabbit and mouse) with manually corrected R-peaks (>170,000 annotations) and signal quality annotations. The platform (software and databases) is called PhysioZoo. Results: PhysioZoo makes it possible to load ECG data and perform very accurate R-peak detection (F 1 > 98%). It also allows the user to manually correct the R-peak locations and annotate low signal quality of the underlying ECG. PhysioZoo implements state of the art HRV measures adapted for different mammals (dogs, rabbits, and mice) and allows easy export of all computed measures together with standard data representation figures. PhysioZoo provides databases and standard ranges for all HRV measures computed on healthy, conscious humans, dogs, rabbits, and mice at rest. Study of these measures across different mammals can provide new insights into the complexity of heart rate dynamics across species. Conclusion: PhysioZoo enables the standardization and reproducibility of HRV analysis in mammalian models through its open source code, freely available software, and open access databases. PhysioZoo will support and enable new investigations in mammalian HRV research. The source code and software are available on www.physiozoo.com.

13.
Front Physiol ; 9: 1001, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30116198

RESUMO

Background: Power spectral density (PSD) analysis of the heartbeat intervals in the three main frequency bands [very low frequency (VLF), low frequency (LF), and high frequency (HF)] provides a quantitative non-invasive tool for assessing the function of the cardiovascular control system. In humans, these frequency bands were standardized following years of empirical evidence. However, no quantitative approach has justified the frequency cutoffs of these bands and how they might be adapted to other mammals. Defining mammal-specific frequency bands is necessary if the PSD analysis of the HR is to be used as a proxy for measuring the autonomic nervous system activity in animal models. Methods: We first describe the distribution of prominent frequency peaks found in the normalized PSD of mammalian data using a Gaussian mixture model while assuming three components corresponding to the traditional VLF, LF and HF bands. We trained the algorithm on a database of human electrocardiogram recordings (n = 18) and validated it on databases of dogs (n = 17) and mice (n = 8). Finally, we tested it to predict the bands for rabbits (n = 4) for the first time. Results: Double-logarithmic analysis demonstrates a scaling law between the GMM-identified cutoff frequencies and the typical heart rate (HRm): fVLF-LF = 0.0037⋅ HRm0.58 , fLF-HF = 0.0017⋅ HRm1.01 and fHFup = 0.0128⋅ HRm0.86 . We found that the band cutoff frequencies and Gaussian mean scale with a power law of 1/4 or 1/8 of the typical body mass (BMm), thus revealing allometric power laws. Conclusion: Our automated data-driven approach allowed us to define the frequency bands in PSD analysis of beat-to-beat time series from different mammals. The scaling law between the band frequency cutoffs and the HRm can be used to approximate the PSD bands in other mammals.

14.
Artigo em Inglês | MEDLINE | ID: mdl-28794892

RESUMO

BACKGROUND: Complete atrioventricular block in fetuses is known to be mostly associated with autoimmune disease and can be irreversible if no steroids treatment is provided. Conventional methods used in clinical practice for diagnosing fetal arrhythmia are limited since they do not reflect the primary electrophysiological conduction processes that take place in the myocardium. The non-invasive fetal electrocardiogram has the potential to better support fetal arrhythmias diagnosis through the continuous analysis of the beat to beat variation of the fetal heart rate and morphological analysis of the PQRST complex. CASE PRESENTATION: We present two retrospective case reports on which atrioventricular block diagnosis could have been supported by the non-invasive fetal electrocardiogram. The two cases comprised a 22-year-old pregnant woman with the gestational age of 31 weeks and a 25-year-old pregnant woman with the gestational age of 41 weeks. Both women were admitted to the Department of Maternal and Fetal Medicine at the Kyiv and Kharkiv municipal perinatal clinics. Patients were observed using standard fetal monitoring methods as well as the non-invasive fetal electrocardiogram. The non-invasive fetal electrocardiographic recordings were analyzed retrospectively, where it is possible to identify the presence of the atrioventricular block. CONCLUSIONS: This study demonstrates, for the first time, the feasibility of the non-invasive fetal electrocardiogram as a supplementary method to diagnose of the fetal atrioventricular block. Combined with current fetal monitoring techniques, non-invasive fetal electrocardiography could support clinical decisions.

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