Investigation of automated sleep staging from cardiorespiratory signals regarding clinical applicability and robustness

2022 ◽  
Vol 71 ◽  
pp. 103047
Author(s):  
Miriam Goldammer ◽  
Sebastian Zaunseder ◽  
Moritz D. Brandt ◽  
Hagen Malberg ◽  
Felix Gräßer
Keyword(s):  
Sleep Staging ◽  
Clinical Applicability

Aplicabilidade clínica das técnicas de liberação manual diafragmática: uma revisão sistemática

2020 ◽  
Vol 18 (3) ◽  
pp. 111-116
Author(s):  
Dulce Marieli Danieli ◽  
Fabíola De Almeida Gomes ◽  
Bruna Eibel ◽  
William Dhein
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Gastroesophageal Reflux ◽  
Randomized Clinical Trials ◽  
Experimental Studies ◽  
Scar Tissue ◽  
Low Back ◽  
Musculoskeletal Manipulations ◽  
Clinical Applicability ◽  
Quasi Experimental

INTRODUÇÃO: O diafragma é o principal músculo respiratório e desempenha um papel importante na respiração e na regulação fisiológica. Uma terapia que visa melhorar essas condições referentes ao diafragma, é a técnica de liberação manual diafragmática. OBJETIVO: O objetivo deste estudo foi verificar a aplicabilidade clínica das técnicas manuais de liberação diafragmática e identificar as principais técnicas, populações investigadas, variáveis avaliadas e seus desfechos. MÉTODOS: Foram pesquisadas as seguintes bases de dados: PubMed, Scielo e Science Direct, com os descritores “Diaphragm [Mesh]” e “Musculoskeletal Manipulations [Mesh]” com seus correspondentes no mesmo idioma. Foram incluídos ensaios clínicos randomizados, não randomizados, estudos semi, quase-experimentais e estudos pilotos ou de caso, que abordaram técnicas de liberação manuais diafragmáticas.RESULTADOS: Há variadas técnicas de liberação diafragmática, sendo as mais mencionadas: normalização dos pilares do diafragma, alongamento e estiramento do diafragma, relaxamento dos pilares do diafragma. Além disso, as técnicas de liberação diafragmática vêm sendo associadas a protocolos de terapia manipulativa osteopática (TMO). As principais populações estudadas foram de pacientes saudáveis, com lombalgia, cervicalgia, osteoartrite, asmáticos, doença pulmonar obstrutiva crônica, constipados, cardiopatas e com refluxo gastroesofágico. Os principais desfechos avaliados são variáveis musculoesqueléticas (dor, flexibilidade, amplitude, espessura diafragmática), variáveis cardiorrespiratórias (pressão inspiratória/expiratória máxima (PImax e Pemax), mobilidade torácica, frequência cardíaca e respiratória), qualidade de vida e disfunções gastrointestinais/gastroesofágicas. CONCLUSÃO: A aplicabilidade clínica das técnicas de liberação diagramática está sendo investigada associado com outras técnicas osteopáticas, em protocolos de TMO em pacientes saudáveis, pneumopatas, cardiopatas, gestantes, em cicatriz pós-cirúrgica, constipados, com refluxo gastroesofágico, osteoartrite, cervicalgia e com lombalgia. Evidencia-se: diminuição ou eliminação das dores musculoesqueléticas, aumento da flexibilidade, ADM, Pimáx e Pemáx, aumento da mobilidade torácica, aumento da qualidade de vida, diminuição do inchaço e dor abdominal e sem efeito em cardiopatas.ABSTRACT. Clinical applicability of manual diaphragmatic release techniques: a systematic review.BACKGROUND: The diaphragm is the main respiratory muscle and plays an important role in breathing and physiological regulation. A therapy that aims to improve these conditions regarding the diaphragm, is the manual diaphragmatic release technique.OBJECTIVE: The aim of this study was to verify the clinical applicability of manual diaphragmatic release techniques and searching the main techniques, population, evaluated variables, and outcomes. METHODS: The following electronic databases were searched: PubMed, Scielo, and Science Direct, with the descriptors “Diaphragm [Mesh]” and “Musculoskeletal Manipulations [Mesh]” with their correspondents in the same language. There were included randomized clinical trial, non-randomized clinical trials, semi, and quasi-experimental studies, and pilot or case studies, which addressed manual diaphragmatic release techniques.RESULTS: There are various diaphragmatic release techniques, the most mentioned are: normalization of the diaphragm pillars, stretching of the diaphragm, relaxation of the diaphragm pillars, and protocols for osteopathic manipulative therapy (OMT) for the diaphragm. The main populations studied were healthy patients, with low back pain, asthmatics, chronic pulmonary obstructive disease, constipated, cardiac patients, and gastroesophageal reflux. The main outcomes assessed are musculoskeletal variables (pain, flexibility, range of motion, diaphragmatic thickness), cardiorespiratory variables (maximal inspiratory/expiratory pressure (MIP and MEP), chest mobility, heart, and respiratory rate), quality of life, and gastrointestinal/ gastroesophageal disorders.CONCLUSION: The clinical applicability of diagrammatic release techniques is being investigated in association with other osteopathic techniques, in protocols of OMT in healthy subjects, patients with lung diseases, heart disease, pregnant women, scar tissue, constipated, with gastroesophageal reflux, osteoarthritis, cervicalgia and with low back pain. There is evidence of reduction and elimination of musculoskeletal pain, increased MIP, increased chest mobility, an increase in health quality, a decrease of bloating and abdominal pain related to constipation, and a decrease of reflux symptoms.

Recent Nano-based Therapeutic Intervention of Bioactive Sesquiterpenes: Prospects in Cancer Therapeutics

2020 ◽  
Vol 26 (11) ◽  
pp. 1138-1144 ◽  
Author(s):  
Mohammad A. Ansari ◽  
Khan F. Badrealam ◽  
Asrar Alam ◽  
Saba Tufail ◽  
Gulshan Khalique ◽  
...  
Keyword(s):  
Physicochemical Properties ◽  
Essential Oils ◽  
Plant Species ◽  
Bioactive Compounds ◽  
Therapeutic Intervention ◽  
Cancer Therapeutics ◽  
Therapeutic Interventions ◽  
Pharmacological Intervention ◽  
Clinical Applicability ◽  
Near Future

: In the recent scenario, nanotechnology-based therapeutics intervention has gained tremendous impetus all across the globe. Nano-based pharmacological intervention of various bioactive compounds has been explored on an increasing scale. Sesquiterpenes are major constituents of essential oils (EOs) present in various plant species which possess intriguing therapeutic potentials. However, owing to their poor physicochemical properties; they have pharmacological limitations. Recent advances in nano-based therapeutic interventions offer various avenues to improve their therapeutic applicability. Reckoning with these, the present review collates various nano-based therapeutic intervention of sesquiterpenes with prospective potential against various debilitating diseases especially cancer. In our viewpoint, considering the burgeoning advancement in the field of nanomedicine; in the near future, the clinical applicability of these nano-formulated sesquiterpenes can be foreseen with great enthusiasm.

scholarly journals Accurate Deep Learning-Based Sleep Staging in a Clinical Population with Suspected Obstructive Sleep Apnea

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Henri Korkalainen ◽  
Timo Leppanen ◽  
Juhani Aakko ◽  
Sami Nikkonen ◽  
Samu Kainulainen ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Deep Learning ◽  
Sleep Apnea ◽  
Clinical Population ◽  
Sleep Staging ◽  
Obstructive Sleep

scholarly journals THE CLINICAL APPLICABILITY OF CURRENT PROGNOSTIC MODELS IN FOLLICULAR LYMPHOMA: A SYSTEMATIC REVIEW

2021 ◽  
pp. 103418
Author(s):  
Jelena Jelicic ◽  
Thomas Stauffer Larsen ◽  
Zoran Bukumiric ◽  
Bosko Andjelic
Keyword(s):  
Systematic Review ◽  
Follicular Lymphoma ◽  
Prognostic Models ◽  
Clinical Applicability

scholarly journals A Multi-Class Automatic Sleep Staging Method Based on Photoplethysmography Signals

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 116
Author(s):  
Xiangfa Zhao ◽  
Guobing Sun
Keyword(s):  
Time Domain ◽  
Single Channel ◽  
Kappa Statistic ◽  
Gradient Boosting ◽  
Sleep Staging ◽  
Challenging Problem ◽  
Sleep State ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
The Time Domain

Automatic sleep staging with only one channel is a challenging problem in sleep-related research. In this paper, a simple and efficient method named PPG-based multi-class automatic sleep staging (PMSS) is proposed using only a photoplethysmography (PPG) signal. Single-channel PPG data were obtained from four categories of subjects in the CAP sleep database. After the preprocessing of PPG data, feature extraction was performed from the time domain, frequency domain, and nonlinear domain, and a total of 21 features were extracted. Finally, the Light Gradient Boosting Machine (LightGBM) classifier was used for multi-class sleep staging. The accuracy of the multi-class automatic sleep staging was over 70%, and the Cohen’s kappa statistic k was over 0.6. This also showed that the PMSS method can also be applied to stage the sleep state for patients with sleep disorders.

scholarly journals A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1562
Author(s):  
Syed Anas Imtiaz
Keyword(s):  
Systematic Review ◽  
Classification Accuracy ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Regulatory Requirements ◽  
Wearable System ◽  
Wearable Systems

Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages.

250 AI-Supported Sleep Staging from Activity and Heart Rate

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A101-A101
Author(s):  
Samadrita Chowdhury ◽  
TzuAn Song ◽  
Richa Saxena ◽  
Shaun Purcell ◽  
Joyita Dutta
Keyword(s):  
Heart Rate ◽  
Network Architecture ◽  
Class Imbalance ◽  
Nrem Sleep ◽  
Sleep Time ◽  
Deep Sleep ◽  
Sleep Staging ◽  
Temporal Correlations ◽  
Stage Classification ◽  
Small Form Factor

Abstract Introduction Polysomnography (PSG) is considered the gold standard for sleep staging but is labor-intensive and expensive. Wrist wearables are an alternative to PSG because of their small form factor and continuous monitoring capability. In this work, we present a scheme to perform such automated sleep staging via deep learning in the MESA cohort validated against PSG. This scheme makes use of actigraphic activity counts and two coarse heart rate measures (only mean and standard deviation for 30-s sleep epochs) to perform multi-class sleep staging. Our method outperforms existing techniques in three-stage classification (i.e., wake, NREM, and REM) and is feasible for four-stage classification (i.e., wake, light, deep, and REM). Methods Our technique uses a combined convolutional neural network coupled and sequence-to-sequence network architecture to appropriate the temporal correlations in sleep toward classification. Supervised training with PSG stage labels for each sleep epoch as the target was performed. We used data from MESA participants randomly assigned to non-overlapping training (N=608) and validation (N=200) cohorts. The under-representation of deep sleep in the data leads to class imbalance which diminishes deep sleep prediction accuracy. To specifically address the class imbalance, we use a novel loss function that is minimized in the network training phase. Results Our network leads to accuracies of 78.66% and 72.46% for three-class and four-class sleep staging respectively. Our three-stage classifier is especially accurate at measuring NREM sleep time (predicted: 4.98 ± 1.26 hrs. vs. actual: 5.08 ± 0.98 hrs. from PSG). Similarly, our four-stage classifier leads to highly accurate estimates of light sleep time (predicted: 4.33 ± 1.20 hrs. vs. actual: 4.46 ± 1.04 hrs. from PSG) and deep sleep time (predicted: 0.62 ± 0.65 hrs. vs. actual: 0.63 ± 0.59 hrs. from PSG). Lastly, we demonstrate the feasibility of our method for sleep staging from Apple Watch-derived measurements. Conclusion This work demonstrates the viability of high-accuracy, automated multi-class sleep staging from actigraphy and coarse heart rate measures that are device-agnostic and therefore well suited for extraction from smartwatches and other consumer wrist wearables. Support (if any) This work was supported in part by the NIH grant 1R21AG068890-01 and the American Association for University Women.

A VALIDATION, WITH NEW CLINICAL APPLICABILITY, OF A CLINICAL-GENETIC RISK MODEL THAT PREDICTS THROMBOSIS WITH HIGH SENSITIVITY IN PATIENTS WITH LYMPHOMA

2019 ◽  
Vol 37 ◽  
pp. 534-534
Author(s):  
M. Bastos-Oreiro ◽  
J. Ortiz ◽  
V. Pradillo ◽  
C. Martinez-Laperche ◽  
E. Salas ◽  
...  
Keyword(s):  
Genetic Risk ◽  
Risk Model ◽  
High Sensitivity ◽  
Clinical Genetic ◽  
Clinical Applicability

Assessment of the suitability of using a forehead EEG electrode set and chin EMG electrodes for sleep staging in polysomnography

2016 ◽  
Vol 25 (6) ◽  
pp. 636-645 ◽  
Author(s):  
Sami Myllymaa ◽  
Anu Muraja-Murro ◽  
Susanna Westeren-Punnonen ◽  
Taina Hukkanen ◽  
Reijo Lappalainen ◽  
...  
Keyword(s):  
Sleep Staging ◽  
Emg Electrodes
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