scholarly journals A Case Report of Sleep-Related Painful Erection Successfully Treated with Paroxetine

2021 ◽  
Vol 6 (5) ◽  
Author(s):  
Lin S ◽  
Lu H ◽  
Wang D ◽  
Wang J ◽  
Dai B ◽  
...  
Keyword(s):  
Sleep Stage ◽  
Rare Condition ◽  
Therapeutic Strategies ◽  
Single Daily Dose ◽  
Sleep Monitoring ◽  
Chinese Herbal ◽  
Hyperthermia Therapy ◽  
Penile Erections ◽  
Daily Dose

Sleep-Related Painful Erection (SRPE) is a rare condition characterized by recurrent, painful penile erections occurring when awakening from the Rapid Eye Movement (REM) sleep stage. The cause of SRPE is still unknown, the therapeutic strategies still in an expert-based opinion phase and there is no consensus yet. We present a case of a 23-year-old patient suffering from SRPE for 1 year, the smart bracelet which has a sleep monitoring function showed his sleep was fragmented by awakenings at the end of all the REM period. Several treatments such as tamsulosin and highfrequency hyperthermia therapy and Chinese herbal medicine did not prompt any improvement of his condition, but after taking a single daily dose of paroxetine 20mg for twelves weeks, both the frequency and intensity of SRPE gradually decreased. Even though the antidepressants to which paroxetine belongs were included as one of the abandoned treatments in recent review, in our case, paroxetine showed a long-term and stable effect on patients with SRPE, it indicates that the therapeutic effect of paroxetine on SRPE deserves further study and observation.

Effects of Allopurinol (Zyloric®) in Patients with Long-Term Indwelling Urethral Catheters

1983 ◽  
Vol 11 (2) ◽  
pp. 116-119 ◽  
Author(s):  
Allan Eddeland ◽  
Hans Hedelin
Keyword(s):  
Single Daily Dose ◽  
Hospitalized Patients ◽  
Blind Study ◽  
Double Blind Study ◽  
Double Blind ◽  
Indwelling Catheters ◽  
Catheter Encrustation ◽  
Daily Dose

A randomized double-blind study of the effect of allopurinol on the need for catheter attention and the amount of catheter encrustation has been conducted in hospitalized patients with long-term indwelling catheters. Allopurinol 300 mg as a single daily dose significantly reduced the frequency of need for catheter attention including catheter change. There was no significant effect on the quantity of catheter encrustation.

scholarly journals Treatment of Hyperthyroidism with a Small Single Daily Dose of Methimazole: A Prospective Long-Term Follow-Up Study.

1997 ◽  
Vol 44 (4) ◽  
pp. 553-558 ◽  
Author(s):  
YASUO MASHIO ◽  
MUTSUO BENIKO ◽  
AKIRA MATSUDA ◽  
SHIGEKI KOIZUMI ◽  
KUMIKO MATSUYA ◽  
...  
Keyword(s):  
Single Daily Dose ◽  
Follow Up Study ◽  
Daily Dose ◽  
Long Term Follow Up

scholarly journals Ubi-SleepNet

2021 ◽  
Vol 5 (4) ◽  
pp. 1-33
Author(s):  
Bing Zhai ◽  
Yu Guan ◽  
Michael Catt ◽  
Thomas Plötz
Keyword(s):  
Rem Sleep ◽  
Large Scale ◽  
Sleep Stage ◽  
Sleep Monitoring ◽  
Ubiquitous Sensing ◽  
Fusion Methods ◽  
Practical Tool ◽  
Research Grade ◽  
Open Question

Sleep is a fundamental physiological process that is essential for sustaining a healthy body and mind. The gold standard for clinical sleep monitoring is polysomnography(PSG), based on which sleep can be categorized into five stages, including wake/rapid eye movement sleep (REM sleep)/Non-REM sleep 1 (N1)/Non-REM sleep 2 (N2)/Non-REM sleep 3 (N3). However, PSG is expensive, burdensome and not suitable for daily use. For long-term sleep monitoring, ubiquitous sensing may be a solution. Most recently, cardiac and movement sensing has become popular in classifying three-stage sleep, since both modalities can be easily acquired from research-grade or consumer-grade devices (e.g., Apple Watch). However, how best to fuse the data for greatest accuracy remains an open question. In this work, we comprehensively studied deep learning (DL)-based advanced fusion techniques consisting of three fusion strategies alongside three fusion methods for three-stage sleep classification based on two publicly available datasets. Experimental results demonstrate important evidences that three-stage sleep can be reliably classified by fusing cardiac/movement sensing modalities, which may potentially become a practical tool to conduct large-scale sleep stage assessment studies or long-term self-tracking on sleep. To accelerate the progression of sleep research in the ubiquitous/wearable computing community, we made this project open source, and the code can be found at: https://github.com/bzhai/Ubi-SleepNet.

scholarly journals Automating sleep stage classification using wireless, wearable sensors

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Alexander J. Boe ◽  
Lori L. McGee Koch ◽  
Megan K. O’Brien ◽  
Nicholas Shawen ◽  
John A. Rogers ◽  
...  
Keyword(s):  
High Resolution ◽  
Sleep Stage ◽  
Low Cost ◽  
Wearable Sensors ◽  
Sleep Stages ◽  
Sleep Monitoring ◽  
Promising Alternative ◽  
High Resolution Data ◽  
Sleep Stage Classification

AbstractPolysomnography (PSG) is the current gold standard in high-resolution sleep monitoring; however, this method is obtrusive, expensive, and time-consuming. Conversely, commercially available wrist monitors such as ActiWatch can monitor sleep for multiple days and at low cost, but often overestimate sleep and cannot differentiate between sleep stages, such as rapid eye movement (REM) and non-REM. Wireless wearable sensors are a promising alternative for their portability and access to high-resolution data for customizable analytics. We present a multimodal sensor system measuring hand acceleration, electrocardiography, and distal skin temperature that outperforms the ActiWatch, detecting wake and sleep with a recall of 74.4% and 90.0%, respectively, as well as wake, non-REM, and REM with recall of 73.3%, 59.0%, and 56.0%, respectively. This approach will enable clinicians and researchers to more easily, accurately, and inexpensively assess long-term sleep patterns, diagnose sleep disorders, and monitor risk factors for disease in both laboratory and home settings.

scholarly journals EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal

2021 ◽  
Vol 15 ◽  
Author(s):  
Jiahao Fan ◽  
Chenglu Sun ◽  
Meng Long ◽  
Chen Chen ◽  
Wei Chen
Keyword(s):  
Neural Network ◽  
Single Channel ◽  
Sleep Stage ◽  
Sleep Staging ◽  
Eeg Signals ◽  
Sleep Monitoring ◽  
Sleep Stage Classification ◽  
Sequential Information ◽  
Deep Learning Model

In recent years, automatic sleep staging methods have achieved competitive performance using electroencephalography (EEG) signals. However, the acquisition of EEG signals is cumbersome and inconvenient. Therefore, we propose a novel sleep staging approach using electrooculogram (EOG) signals, which are more convenient to acquire than the EEG. A two-scale convolutional neural network first extracts epoch-wise temporary-equivalent features from raw EOG signals. A recurrent neural network then captures the long-term sequential information. The proposed method was validated on 101 full-night sleep data from two open-access databases, the montreal archive of sleep studies and Sleep-EDF, achieving an overall accuracy of 81.2 and 76.3%, respectively. The results are comparable to those models trained with EEG signals. In addition, comparisons with six state-of-the-art methods further demonstrate the effectiveness of the proposed approach. Overall, this study provides a new avenue for sleep monitoring.

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