scholarly journals Variability of sleep stage scoring in late midlife and early old age

2021 ◽  
Author(s):  
Daphne Chylinski ◽  
Christian Berthomier ◽  
Eric Lambot ◽  
Sonia Frenette ◽  
Marie Brandewinder ◽  
...  
Keyword(s):  
Old Age ◽  
Sleep Stage ◽  
Late Midlife ◽  
Sleep Stage Scoring ◽  
Early Old Age

scholarly journals Self-compassion as predictor of daily physical symptoms and chronic illness across older adulthood

2021 ◽  
pp. 135910532110023
Author(s):  
Heather Herriot ◽  
Carsten Wrosch
Keyword(s):  
Older Adults ◽  
Longitudinal Study ◽  
Chronic Illness ◽  
End Of Life ◽  
Old Age ◽  
Physical Symptoms ◽  
Older Adulthood ◽  
Self Compassion ◽  
Early Old Age

This study examined whether self-compassion could benefit daily physical symptoms and chronic illness in early and advanced old age. The hypotheses were evaluated in a 4-year longitudinal study of 264 older adults. Results showed that self-compassion predicted lower levels of daily physical symptoms across the study period in advanced, but not early, old age ( T-ratio = −1.93, p = 0.05). In addition, self-compassion was associated with fewer increases in chronic illness in advanced, but not early, old age ( T-ratio = − 2.45, p < 0.02). The results of this study suggest that self-compassion may be particularly adaptive towards the end of life.

Combining decision trees classifiers: a case study of automatic sleep stage scoring

2012 ◽  
Vol 2 (1) ◽  
pp. 60 ◽  
Author(s):  
Natheer Khasawneh ◽  
Stefan Conrad ◽  
Luay Fraiwan ◽  
Eyad Taqieddin ◽  
Basheer Khasawneh
Keyword(s):  
Decision Trees ◽  
Sleep Stage ◽  
Sleep Stage Scoring

Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA

2010 ◽  
Vol 49 (03) ◽  
pp. 230-237 ◽  
Author(s):  
K. Lweesy ◽  
N. Khasawneh ◽  
M. Fraiwan ◽  
H. Wenz ◽  
H. Dickhaus ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Discriminant Analysis ◽  
Sleep Stage ◽  
Continuous Wavelet ◽  
Eeg Signal ◽  
Linear Discriminant ◽  
Time Frequency ◽  
Mother Wavelets ◽  
Sleep Stage Scoring

Summary Background: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomno-graphic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Objectives: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. Methods: The use of different mother wave-lets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. Results: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. Conclusions: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

scholarly journals Benefits of Sustained Physical Activity From Middle-Age To Early Old Age On Quality Of Life In Early Old Age

2022 ◽  
Author(s):  
Mikyung Lee ◽  
Hyeonkyeong Lee ◽  
Ki Jun Song ◽  
Young-Me Lee
Keyword(s):  
Quality Of Life ◽  
Old Age ◽  
Secondary Data ◽  
Activity Levels ◽  
Related Quality ◽  
Health Related ◽  
Using Data ◽  
Middle Aged Adults ◽  
Early Old Age

Abstract This secondary data analysis study aimed to examine the changes in physical activities (PAs) over time (2009-2017) in the same participants and to determine an association between changes in PA and health-related quality of life (HRQoL) in early older adults (n=994) using data from the Korea Health Panel Survey. The HRQoL was measured using the EuroQol quality-of-life system and the amount of PA were grouped to 4 activity levels (remained inactive, became inactive, became active, and remained active). The association of changes in PA over 8 years with HRQoL was examined using logistic regression analysis while controlling for socioeconomic and behavioral factors. The total PA decreased from 1,859.72±1,760.01 MET-minutes in 2009 to 1,264.80 ±1,251.14 MET-minutes in 2017 (P < 0.001). In 2017, 142 (14.3%) remained inactive, whereas 419 (42.2%) remained active. The participants who remained inactive at early old age were more likely to be at the lowest 10% HRQoL of the sample (odds ratio = 1.95, 95% confidence interval = 1.09–3.48). This indicates that educating middle-aged adults who are relatively inactive must be a priority in order to maintain and improve PA, enhance HRQoL, and maximize the benefits of PA in old age.

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