scholarly journals Dr. Ed. Bérillon. Hypnotism and mental orthopedics. — Paris, 1898

2020 ◽  
Vol VII (3) ◽  
pp. 156-157
Author(s):  
B. I. Vorotynskiy

In this brochure, the author continues to defend and develop further his view on the meaning of hypnotism in its application to pedagogy, a view expressed by him back in 1886 at the Nancy congress. Dr. Brillon is an advocate of the belief that hypnosis can be of great service to the interests of pedagogy. Numerous experiments carried out on two different classes of society convinced the author that children from 5 to 15 years old generally quite easily fall into hypnosis. It is difficult for hypnosis to be given to those who have severely expressed signs of severe neuropathic inheritance. Children-idiotes do not fall into hypnosis; Although feeble-minded children fall asleep, their sleep is usually not deep, it is impossible to induce automatism in them, and it is also impossible to achieve the fulfillment of suggestion after hypnosis. Children with the stigmata of hysteria succumb to hypnotic suggestion, but it is possible to evoke deep sleep in them only after a series of preparatory sessions.

2017 ◽  
Vol 19 (1) ◽  
pp. 7-54 ◽  
Author(s):  
Richard Skues

In 1892–3 Freud published his first substantial case history, which concerned a patient treated by means of hypnotic suggestion. For some years this has been one of the few remaining of Freud's dedicated cases histories where the patient has not been identified. More recently, however, two publications independently arrived at the conclusion that the patient was none other than Freud's wife, Martha. This paper sets out the reasons why this identification should always have been treated with suspicion, even if the real identity was not known. Nevertheless, the paper goes on to offer a more plausible identification from among Freud's known social circle. The second part of the paper questions the circumstances under which the original misidentification could plausibly have been sustained in the face of such glaring evidence to the contrary. It concludes that, among other reasons, recent tendencies in controversies about Freud's trustworthiness have the hazard of leading to unreliable assumptions about Freud's honesty being taken as a basis for sound historical investigation.


SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A78-A78
Author(s):  
Zahra Mousavi ◽  
Jocelyn Lai ◽  
Asal Yunusova ◽  
Alexander Rivera ◽  
Sirui Hu ◽  
...  

Abstract Introduction Sleep disturbance is a transdiagnostic risk factor that is so prevalent among emerging adults it is considered to be a public health epidemic. For emerging adults, who are already at greater risk for psychopathology, the COVID-19 pandemic has disrupted daily routines, potentially changing sleep patterns and heightening risk factors for the emergence of affective dysregulation, and consequently mood-related disturbances. This study aimed to determine whether variability in sleep patterns across a 3-month period was associated with next-day positive and negative affect, and affective dynamics, proximal affective predictors of depressive symptoms among young adults during the pandemic. Methods College student participants (N=20, 65% female, Mage=19.80, SDage=1.0) wore non-invasive wearable devices (the Oura ring https://ouraring.com/) continuously for a period of 3-months, measuring sleep onset latency, sleep efficiency, total sleep, and time spent in different stages of sleep (light, deep and rapid eye movement). Participants reported daily PA and NA using the Positive and Negative Affect Schedule on a 0-100 scale to report on their affective state. Results Multilevel models specifying a within-subject process of the relation between sleep and affect revealed that participants with higher sleep onset latency (b= -2.98, p<.01) and sleep duration on the prior day (b= -.35, p=.01) had lower PA the next day. Participants with longer light sleep duration had lower PA (b= -.28, p=.02), whereas participants with longer deep sleep duration had higher PA (b= .36, p=.02) the next day. On days with higher total sleep, participants experienced lower NA compared to their own average (b= -.01, p=.04). Follow-up exploratory bivariate correlations revealed significant associations between light sleep duration instability and higher instability in both PA and NA, whereas higher deep sleep duration was linked with lower instability in both PA and NA (all ps< .05). In the full-length paper these analyses will be probed using linear regressions controlling for relevant covariates (main effects of sleep, sex/age/ethnicity). Conclusion Sleep, an important transdiagnostic health outcome, may contribute to next-day PA and NA. Sleep patterns predict affect dynamics, which may be proximal predictors of mood disturbances. Affect dynamics may be one potential pathway through which sleep has implications for health disparities. Support (if any):


2021 ◽  
Vol 63 (4) ◽  
pp. 355-371
Author(s):  
David J. Acunzo ◽  
David A. Oakley ◽  
Devin B. Terhune
Keyword(s):  

2021 ◽  
Author(s):  
Masao Ishizawa ◽  
Takuya Uchiumi ◽  
Miki Takahata ◽  
Michiyasu Yamaki ◽  
Toshiaki Sato

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A101-A101
Author(s):  
Samadrita Chowdhury ◽  
TzuAn Song ◽  
Richa Saxena ◽  
Shaun Purcell ◽  
Joyita Dutta

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.


Author(s):  
Lahiru J. Ekanayake ◽  
Ruwan D. Nawarathna ◽  
Saluka R. Kodituwakku ◽  
Roshan D. Yapa ◽  
Amalka J. Pinidiyaarachchi

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