3^rd World Congress on Sleep Disorders and Therapeutics November 14-15, 2022 Amsterdam, Netherlands

Biography:

Ankit A. Bhurane is currently working with the Department of Electronics and Communication, Visvesvaraya National Institute of Technology Nagpur. He received his B.E. in Electronics and Communication, M.Tech. in Electronics, and Ph.D. in 2008, 2011, and 2016 respectively from SGBAU University, SGGS Nanded, and IIT Bombay. He has published papers in various international conferences and journals. He is also a reviewer for SCI journals indexed by Springer and Elsevier. He also has published four patent applications filed in the Indian Patent Office. His research interests include biomedical signal processing, scalable video coding, and green communication.

Abstract:

Healthy sleep is an essential criterion for a healthy life. However, people nowadays face many sleeping disorders, making their lives miserable. Hence, proper sleep monitoring is essential for detecting and diagnosing such sleep disorders. To address this issue, we can use the cyclic alternating patterns (CAP) to monitor the CAP phases, which tells us about the sleep quality and different disorders like insomnia, PLM, RBD, etc. This process of detecting CAP is time-consuming, hectic, and prone to errors. Therefore, there is a need to create an automatic detection of CAP phases for swift monitoring. A hierarchical approach to identify sleep disorders and classifying CAP sleep phases can be suitable in this regard. Single-channel EEG CAP sequence can be utilized to classify the data into healthy or unhealthy. Further, the sleep disorder of unhealthy sequence can be classified among rapid eye movement behavior disorder (RBD), narcolepsy (NARCO), periodic leg movement disorder (PLM), nocturnal frontal lobe epilepsy (NFLE), and insomnia (INS). Even further, the CAP phase of given sequence can be identified using our previous work. The best model for healthy, unhealthy, and disease classification can be obtained by long short-term memory (LSTM) and convolutional neural network (CNN). The same models, when evaluated using a dataset of only phase can give better accuracy for healthy-unhealthy classification and disease classification, respectively, indicating the significance of phase B for sleep disorder identification.

Biography:

Ankit A. Bhurane is currently working with the Department of Electronics and Communication, Visvesvaraya National Institute of Technology Nagpur. He received his B.E. in Electronics and Communication, M.Tech. in Electronics, and Ph.D. in 2008, 2011, and 2016 respectively from SGBAU University, SGGS Nanded, and IIT Bombay. He has published papers in various international conferences and journals. He is also a reviewer for SCI journals indexed by Springer and Elsevier. He also has published four patent applications filed in the Indian Patent Office. His research interests include biomedical signal processing, scalable video coding, and green communication

Abstract:

Healthy sleep is an essential criterion for a healthy life. However, people nowadays face many sleeping disorders, making their lives miserable. Hence, proper sleep monitoring is essential for detecting and diagnosing such sleep disorders. To address this issue, we can use the cyclic alternating patterns (CAP) to monitor the CAP phases, which tells us about the sleep quality and different disorders like insomnia, PLM, RBD, etc. This process of detecting CAP is time-consuming, hectic, and prone to errors. Therefore, there is a need to create an automatic detection of CAP phases for swift monitoring. A hierarchical approach to identify sleep disorders and classifying CAP sleep phases can be suitable in this regard. Single-channel EEG CAP sequence can be utilized to classify the data into healthy or unhealthy. Further, the sleep disorder of unhealthy sequence can be classified among rapid eye movement behavior disorder (RBD), narcolepsy (NARCO), periodic leg movement disorder (PLM), nocturnal frontal lobe epilepsy (NFLE), and insomnia (INS). Even further, the CAP phase of given sequence can be identified using our previous work. The best model for healthy, unhealthy, and disease classification can be obtained by long short-term memory (LSTM) and convolutional neural network (CNN). The same models, when evaluated using a dataset of only phase can give better accuracy for healthy-unhealthy classification and disease classification, respectively, indicating the significance of phase B for sleep disorder identification.

Biography:

I've got my doctoral degree in 2020 and master degree in 2009 from Zhejiang Chinese Medical University, Hangzhou and bachelor’s degree from Hebei Medical University, Shijiazhuang. I have published 15 papers in English or in Chinese in reputed journals. Now I am an clinician working in The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China and held various social positions related to acupuncture and moxibustion.

Abstract:

Acupuncture can effectively improve the sleep state , and most PD patients have sleep disorders. In thise used acupuncture to intervene in the sleep state of PDSD, so as to observe the changes and dose effect of Acutreatment on PDSD. 57 patients with PDSD, during medical treatment, aged 40–70 years were recruited to enroll in this trial. Each participant completed one condition, namely, Acutreatment (n = 30) and sham Acutreatment (placebo, stick flat needle on skin, n = 27). The Acutreatment was applied for 30 min once a day for a 30-day observation. UPDRSIII scores for motor symptom assessment and sleeping quality were assessed by PDSS-2, ESS as well as ActiGraph. Scale evaluation was made on the first day of admission and the thirtieth day. There were significant differences on all outcome indicators, except UPDRSIII, on day 30 compared with day 1 (P < 0:01). Compared with sham Acutreatment therapy, Acutreatment therapy has better performance in sleep latency, total sleep time, and sleep efficiency (P < 0:01). ActiGraph indicated that sleep efficiency of sham or Acutreatment in day 6 was significantly lower than that in day 5 (P < 0:05 and P < 0:01) and Acutreatment in day 7 was significantly lower than that in day 6 (P < 0:01). The sleep efficiency of Acutreatment in days 5, 6, and 7 was significantly higher than that in sham Acutreatment (P < 0:01). Moreover, Acutreatment in days 26, 27, and 28 was significantly higher than that in sham Acutreatment (P < 0:01). There was a close correlation between the difference of UPDRSIII and PDSS-2 (r = 0:5090, P < 0:05), sleep latency (r = 0:7201, P < 0:01), TST (r = −0:6136, P < 0:01), and sleep efficiency (r = −0:6707, P < 0:01). The sleep condition of PDSD patients can be improved by acupuncture, which can effectively relieve sleep quality, can also be shown by ActiGraph, and shows a dose-response relationship. Future research should explore Acutreatment with a larger sample size and compare the Acutreatment protocol goal formation of the system scheme. cu

Biography:

Prof (Dr) Mayank Shukla, has a PhD in Sports Medicine and Physiotherapy, and MPT in Cardiopulmonary PT, he has been trained at the best hospitals including the AIIMS, New Delhi. He has18 years+ of teaching and clinical experience, along with international teaching experience at LTU-Sweden. He has 18 publications, &  3 published patents. One of his papers is part of WHO Covid-19 database.

Abstract:

Covid-19 induced lockdown had restricted outdoor workouts. Indoor workouts were largely unsupervised. Video assisted supervision of exercise and yoga sessions was also used in various reported studies. Whether video supervision is any better than without video supervision for Suryanamaskar, (SN) practice was the research question explored by us in this study. N=60 individuals (30 in each group), were analysed using insomnia severity index (ISI), for the effect of SN with or without video-based supervision. SN was taught to both the groups by a physiotherapist trained in yoga using video-based call, however video-based supervision was the independent variable. Practice of SN was recommended in a natural atmosphere with direct exposure to sun light and fresh air. The improvement is seen for ISI in both phases with or without the video calling in pre and post analysis (p<0.05), but no additional effect of video-based supervision on the ISI (p>0.05) improvement is seen.

Biography:

Individual with over twenty six (26) years’ experience with a diverse background in Healthcare, and Pharmaceutical industries. Possesses a high degree of technical and business acumen, exceptional analytical, organizational and communication skills. Held many academic, senior and management positions; worked in and directed all aspects research and regulatory activities, throughout process and methods development, validation, technology transfer, and registration. Conducted researches, trainings and other activities throughout the Canada and abroad, including UAE, Jordan, UK, India, and Kingdom of Saudi Arabia.

Abstract:

Objective

The study aimed to investigate the association between sleeping behavior (specifically sleep duration), body mass index (BMI), eating habits, and psychological mood depression among adolescents in the Emirate of Abu Dhabi- UAE.

Methods and materials

A subsample of three hundred and ninety-five participants (209 females and 186 males) from middle and high schools (aged 12–18 years) in the emirate of Abu Dhabi completed the surveys in the presence of their parents and two research assistants. Measures of daytime sleepiness and other sleep parameters (sleep duration on weekdays and weekends), eating habits, and mood depression questionnaires were reported.

Results

Differences in BMI between males and females were statistically significant (26.12 ± 4.5 vs. 24.4 ± 4.3; p< 0.01). There was a negative linear association (p < 0.01) between the students’ BMI and the weekday/ weekend sleep duration. The average weekday and weekend sleep duration ranged from 5.7 hours (weekdays) to 9.3 hours(weekend). The study showed that an increase in BMI was correlated to mood depression (r = 0.396, p<0.01). In terms of eating habits, there was a significant association between eating unhealthy food and sleep duration; 72.6% of students who slept less than 6 hours reported unhealthy eating habits (p <0.05).

Conclusion

The study showed a clear association between short sleep duration and obesity among adolescents in the UAE. This relationship between sleep duration and obesity is less studied and less understandable. Future research about exploring how sleeping behaviors can affect obesity during adolescence can support understanding this association and create an effective intervention.

Biography:

William D. Moscoso-Barrera is an electronic engineer with a doctorate in Applied Medicine and Biomedicine, who works at the Faculty of Engineering of the University of La Sabana and the Faculty of Engineering and Basic Sciences of the Central University in Colombia. He has articles in reputable journals and 4 patents for medical devices. He has a particular interest in the development of biomedical technologies focused on sleep medicine, respiration, and disability.

Abstract:

The present work focuses on the development of two biomedical devices for patients with Obstructive Sleep Apnea Syndrome (OSAS). The first device is a minimalist polysomnograph that allows the detection of apneas and hypopneas, and the second is a rehabilitation device through electrostimulation (ES) in the submental and intraoral areas.This research arises from the need for more affordable technologies for the measurement of sleep disorders and for the alternative treatment of OSAS, such as EE, which has shown efficacy in recent studies by reducing the Apnea Hypopnea Index (AHI) in patients. The development of the first device, a minimalist polysomnograph based on the measurement and analysis of electroencephalography, electromyography, respiratory effort, oxygen saturation and nasal temperature signals, consisted of four stages: (i) analysis of sleep tests with which two algorithms: detection of awake/asleep sleep states and detection of apneas and hypopneas, which were implemented in Matlab software, (ii) analysis of requirements and sensors necessary for the implementation of the biomedical device (iii) design and implementation of the polysomnograph divided into hardware design with all the electronic instrumentation and software design with the processing system implemented in an electronic card and the programming of a touch screen; and (iv) some preliminary technical tests of the biomedical device. The development of the second device is an electrostimulator with four channels associated with four stimulation points (one point in the submental region and 3 intraoral). The design process has four stages: (i) an analysis of the literature establishing the variables and thresholds necessary for the design, (ii) tests with two commercial electrotherapy equipment, reviewing thresholds and sensations in volunteers, (iii) the development of the biomedical device than with a hardware and software design, the latter with programming of a microcontroller and design of a mobile application and (iv) the technical validations carried out on the equipment, reviewing repeatability and stability of the variables: current intensity, frequency and variable load.

Biography:

Abstract:

The prenatal stress (PNS) in rodents appears to impair the circadian rhythm of sleep/wake cycle. The novel compound Piromelatine (Pir), a mixed melatonin type 1 and 2 and serotonin (5-HT) 1A/1D receptor agonist and 5-HT2B receptor antagonist, is designed for the treatment of insomnia. With this study we explored the effects of Pir on the altered motor activity, sleep architecture and circadian rhythmicity of sleep/wake cycle in prenatally stressed male and female offspring rats. Adult female Sprague–Dawley rats were exposed to different types of stressor from day 7 until birth and adult male and female offspring were explored. Piromelatine was administered at a dose of 20 mg/kg/day for the period of 7 days on 60-day old male and female offspring. Electroencephalographic (EEG), electromyographic (EMG) and home cage motor activity recordings were done for up to 24 h and were analyzed under basal conditions (12:12 LD cycle+vehicle), groups: C-veh, C-Pir, C-Pir- Luzinodol (Luz), PNS+vehicle, PNS+Pir and PNS+Pir+Luz (10 mg/kg) conditions. The PNS exposure impaired the home cage locomotion and sleep/wake cycle indicated by increased motor activity, decreased NREM sleep, elevated REM sleep and wake pattern in both male and female offspring in a sex-dependent manner. Chronic Pir treatment improved the PNS-impaired locomotion, sleep architecture and sleep-wake cycle, while this effect was suppressed by the melatonin receptor antagonist Luz. Current findings suggest that that the novel melatoninergic drug Pir is able to exert sex-dependent beneficial effects on PNS-induced alteration of home cage motor activity, rhythmicity of sleep/wake cycle and sleep architecture. 

Biography:

International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Japan.

Abstract:

The sleep cycle alternates between REM (rapid eye movement) and NREM (non-rapid movement) sleep, which is a highly characteristic feature of sleep. However, the mechanisms by which this cycle is generated are totally unknown. We found that a periodic transient increase of dopamine (DA) level in the basolateral amygdala (BLA) during non-rapid eye movement (NREM) sleep terminates NREM sleep and initiates REM sleep. DA acts on dopamine receptor D2 (Drd2)-expressing neurons in the BLA to induce a transition from NREM to REM sleep. This mechanism also plays a role in cataplectic attack, which is a pathological intrusion of REM sleep into wakefulness in narcoleptics. These results show a critical role of DA signaling in the amygdala in REM sleep regulation and provide a neuronal basis of sleep cycle generation.

Biography:

Health Care executive with career track record of achievement in health insurance service industry, DME healthcare consulting and entrepreneurship .For 10 years was professor at undergraduate and graduate levels at USH and UPR. The last 15 years we have been developing our Sleep Lab and converted into an AASM triple certification for PSG, HST and DME Sleep Center. Grown from 2 beds in one location to 15 beds in 2 locations. Next Tampa Flacross selling strategies, DME, and reciprocity with Puerto Rico Center especiallyformedicaltourism.Also will start a Interamerican University of PR. 

Abstract:

Healthcare services in the EU accounts for an average of 10% of GDP among the members' states. There are different methods of payments to providers of services including government and different private payers. All payments are based on the country or market that the Sleep Center serves. Across the EU and the majority of the World, payers require different types of evidence for reimbursement. Not only the payer, but patients also expect to receive a service based on these expectations. As a Sleep Center, we should perform a Gap Analysis which measures the difference between patient and payer’s expectations for the amount of money paid. Expectation will be divided in two categories: Clinical & Technical Services and Patient’s Experience. During this presentation we will focus on the patient as center of the process complemented by the clinical and technical portion of services at the Sleep Center. During the conference the audience will understand the differences between the services and products companies. They will also understand patient’s behavior, journey, expectations, points of differentiation, positioning, 7 P’s of services, social networking strategies, B2B-B2C strategies, tangible/intangible actions, account based targeting, the strategy of being more human and converting patients to sleep ambassadors. As a case study, we will also present the evolution of our Sleep Center in Puerto Rico and the way we integrated the most important part (Clinical) to the patient services using the above tools for business development in and out the island.