Sai Sri Sathya

Bio: Sai Sri Sathya is an academic researcher from Prin. L. N. Welingkar Institute of Management Development and Research. The author has contributed to research in topics: Obstructive sleep apnea & Sleep apnea. The author has an hindex of 1, co-authored 1 publications receiving 7 citations.

Design and preliminary evaluation of a wearable device for mass-screening of sleep apnea

Abstract: Between 7–18 million Americans suffer from sleep disordered breathing (SDB), including those who suffer from obstructive sleep apnea (OSA). Despite this high prevalence and burden of OSA, existing diagnostic techniques remain impractical for widespread screening. In this study, we introduce a new model for OSA screening and describe an at-home wearable sleep mask (named ARAM) that can robustly track the wearers' sleep patterns. This monitoring is achieved using select sensors that enable screening and monitoring in a form-factor that can be easily self-instrumented. Based on feedback from sleep doctors and technicians, we incorporate the most valuable sensors for OSA diagnosis, while maintaining ease-of-use and comfort for the patient. We discuss the results of preliminary field trials, where both our sleep mask and a commercially available device were worn simultaneously to evaluate our device's robustness. Based on these results, we discuss next steps for the design of the screening system, including analyses techniques that would provide more efficient screening than existing systems.

Portable Detection of Apnea and Hypopnea Events Using Bio-Impedance of the Chest and Deep Learning

Abstract: Sleep apnea is one of the most common sleep-related breathing disorders. It is diagnosed through an overnight sleep study in a specialized sleep clinic. This setup is expensive and the number of beds and staff are limited, leading to a long waiting time. To enable more patients to be tested, and repeated monitoring for diagnosed patients, portable sleep monitoring devices are being developed. These devices automatically detect sleep apnea events in one or more respiration-related signals. There are multiple methods to measure respiration, with varying levels of signal quality and comfort for the patient. In this study, the potential of using the bio-impedance (bioZ) of the chest as a respiratory surrogate is analyzed. A novel portable device is presented, combined with a two-phase Long Short-Term Memory (LSTM) deep learning algorithm for automated event detection. The setup is benchmarked using simultaneous recordings of the device and the traditional polysomnography in 25 patients. The results demonstrate that using only the bioZ, an area under the precision-recall curve of 46.9% can be achieved, which is on par with automatic scoring using a polysomnography respiration channel. The sensitivity, specificity and accuracy are 58.4%, 76.2% and 72.8% respectively. This confirms the potential of using the bioZ device and deep learning algorithm for automatically detecting sleep respiration events during the night, in a portable and comfortable setup.

A New Wearable System for Home Sleep Apnea Testing, Screening, and Classification.

Abstract: We propose an unobtrusive, wearable, and wireless system for the pre-screening and follow-up in the domestic environment of specific sleep-related breathing disorders. This group of diseases manifests with episodes of apnea and hypopnea of central or obstructive origin, and it can be disabling, with several drawbacks that interfere in the daily patient life. The gold standard for their diagnosis and grading is polysomnography, which is a time-consuming, scarcely available test with many wired electrodes disseminated on the body, requiring hospitalization and long waiting times. It is limited by the night-by-night variability of sleep disorders, while inevitably causing sleep alteration and fragmentation itself. For these reasons, only a small percentage of patients achieve a definitive diagnosis and are followed-up. Our device integrates photoplethysmography, an accelerometer, a microcontroller, and a bluetooth transmission unit. It acquires data during the whole night and transmits to a PC for off-line processing. It is positioned on the nasal septum and detects apnea episodes using the modulation of the photoplethysmography signal during the breath. In those time intervals where the photoplethysmography is detecting an apnea, the accelerometer discriminates obstructive from central type thanks to its excellent sensitivity to thoraco-abdominal movements. Tests were performed on a hospitalized patient wearing our integrated system and the type III home sleep apnea testing recommended by The American Academy of Sleep Medicine. Results are encouraging: sensitivity and precision around 90% were achieved in detecting more than 500 apnea episodes. Least thoraco-abdominal movements and body position were successfully classified in lying down control subjects, paving the way toward apnea type classification.

Speech as a Biomarker for Obstructive Sleep Apnea Detection

Abstract: Obstructive sleep apnea (OSA) is a prevalent sleep disorder, responsible for a decrease of people’s quality of life, and significant morbidity and mortality associated with hypertension and cardiovascular diseases. OSA is caused by anatomical and functional alterations in the upper airways, thus we hypothesize that the speech properties of OSA patients are altered, making it possible to detect OSA through voice analysis. To address this hypothesis, we collected speech recordings from 25 OSA subjects and 20 controls, designed a feature set, and compared different machine learning algorithms for binary classification. We achieved a True-Positive-Rate of 88% and a True-Negative-Rate of 80% with a majority vote ensemble of SVM, LDA and kNN classifiers. These results were validated with in-the-wild data acquired from Youtube. Moreover, the negative impact of sleep disorders on working memory was also shown by the results obtained in one of the recorded verbal tasks.

Technology-enabled examinations of cardiac rhythm, optic nerve, oral health, tympanic membrane, gait and coordination evaluated jointly with routine health screenings: an observational study at the 2015 Kumbh Mela in India

Abstract: Objectives Technology-enabled non-invasive diagnostic screening (TES) using smartphones and other point-of-care medical devices was evaluated in conjunction with conventional routine health screenings for the primary care screening of patients. Design Dental conditions, cardiac ECG arrhythmias, tympanic membrane disorders, blood oxygenation levels, optic nerve disorders and neurological fitness were evaluated using FDA-approved advanced smartphone powered technologies. Routine health screenings were also conducted. A novel remote web platform was developed to allow expert physicians to examine TES data and compare efficacy with routine health screenings. Setting The study was conducted at a primary care centre during the 2015 Kumbh Mela in Maharashtra, India. Participants 494 consenting 18–90 years old adults attending the 2015 Kumbh Mela were tested. Results TES and routine health screenings identified unique clinical conditions in distinct patients. Intraoral fluorescent imaging classified 63.3% of the population with dental caries and periodontal diseases. An association between poor oral health and cardiovascular illnesses was also identified. Tympanic membrane imaging detected eardrum abnormalities in 13.0% of the population, several with a medical history of hearing difficulties. Gait and coordination issues were discovered in eight subjects and one subject had arrhythmia. Cross-correlations were observed between low oxygen saturation and low body mass index (BMI) with smokers (p=0.0087 and p=0.0122, respectively), and high BMI was associated with elevated blood pressure in middle-aged subjects. Conclusions TES synergistically identified clinically significant abnormalities in several subjects who otherwise presented as normal in routine health screenings. Physicians validated TES findings and used routine health screening data and medical history responses for comprehensive diagnoses for at-risk patients. TES identified high prevalence of oral diseases, hypertension, obesity and ophthalmic conditions among the middle-aged and elderly Indian population, calling for public health interventions.