Faisal Mushtaq

Pathophysiological responses to viral infections such as COVID-19 significantly affect sleep duration, sleep quality, and concomitant cardiorespiratory function. The widespread adoption of consumer smart bed technology presents a unique opportunity for unobtrusive, real-world, longitudinal monitoring of sleep and physiological signals, which may be valuable for infectious illness surveillance and early detection. During the COVID-19 pandemic, scalable and noninvasive methods for identifying… Show more

Pathophysiological responses to viral infections such as COVID-19 significantly affect sleep duration, sleep quality, and concomitant cardiorespiratory function. The widespread adoption of consumer smart bed technology presents a unique opportunity for unobtrusive, real-world, longitudinal monitoring of sleep and physiological signals, which may be valuable for infectious illness surveillance and early detection. During the COVID-19 pandemic, scalable and noninvasive methods for identifying subtle early symptoms in naturalistic settings became increasingly important. Existing digital health studies have largely relied on wearables or patient self-reports, with limited adherence and recall bias. In contrast, smart bed–derived signals enable high-frequency objective data capture with minimal user burden. Show less

From punch cards to prompt engineering, this book traces the rich evolution of software development—from the rigor of Waterfall and the rise of Agile to the fluidity of DevOps, CI/CD, and continuous experimentation. Along the way, it unpacks real-world case studies, modern playbooks, and the metrics that drive engineering performance. But the future is already knocking. In the age of AI-powered collaboration and vibe-centric development, a new paradigm is emerging—one that prioritizes flow… Show more

From punch cards to prompt engineering, this book traces the rich evolution of software development—from the rigor of Waterfall and the rise of Agile to the fluidity of DevOps, CI/CD, and continuous experimentation. Along the way, it unpacks real-world case studies, modern playbooks, and the metrics that drive engineering performance. But the future is already knocking. In the age of AI-powered collaboration and vibe-centric development, a new paradigm is emerging—one that prioritizes flow, creativity, real-time feedback, and human–machine synergy. Enter "Vibe Coding," where AI pair programmers, voice-first IDEs, and developer experience redefine the SDLC as we know it. Whether you're a seasoned engineer, CTO, product leader, or AI enthusiast, this book offers a comprehensive roadmap for navigating—and shaping—the next decade of software innovation. Show less

Artificial intelligence is emerging as a transformative force in healthcare. This book, 'Role and Impact of AI in Healthcare,' explores the evolution of AI from its early developments to its significant impact on patient care and medical advancements.
The narrative takes readers through the history of AI in healthcare, highlighting technological innovations that have shaped its progress. AI's influence spans various areas, including disease diagnosis, personalized treatment plans, remote… Show more

Artificial intelligence is emerging as a transformative force in healthcare. This book, 'Role and Impact of AI in Healthcare,' explores the evolution of AI from its early developments to its significant impact on patient care and medical advancements.
The narrative takes readers through the history of AI in healthcare, highlighting technological innovations that have shaped its progress. AI's influence spans various areas, including disease diagnosis, personalized treatment plans, remote monitoring, and telemedicine, revolutionizing healthcare delivery and improving patient outcomes.
The book also addresses ethical considerations and regulatory frameworks related to AI's integration into healthcare, emphasizing the balance between innovation and patient privacy. Through real-world examples and success stories, it illustrates the positive effects of AI on patients and healthcare professionals.
Looking ahead, 'Role and Impact of AI in Healthcare' envisions a future with AI-driven advancements, cost reductions, and enhanced patient-centric care. This book serves as an informative guide to understanding the potential of AI in creating a healthier, more efficient healthcare system. Show less

Inadequate information exists regarding physiological changes post-COVID-19 infection. We used smart beds to record biometric data following COVID-19 infection in nonhospitalized patients. Recordings of daily biometric signals over 14 weeks in 59 COVID-positive participants’ homes in 2020 were compared with the same participants’ data from 2019. Participants completed a survey of demographic information, health conditions, COVID exposure and testing, and symptom prevalence/subjective severity… Show more

Inadequate information exists regarding physiological changes post-COVID-19 infection. We used smart beds to record biometric data following COVID-19 infection in nonhospitalized patients. Recordings of daily biometric signals over 14 weeks in 59 COVID-positive participants’ homes in 2020 were compared with the same participants’ data from 2019. Participants completed a survey of demographic information, health conditions, COVID exposure and testing, and symptom prevalence/subjective severity. Mean age was 47.5 years (standard deviation [SD] 9.5), mean body mass index was 30.1 kg/m2 (SD 7.1), and 46% were men. During acute infection, 64% exhibited 5–6 h increased sleep duration, 51% had increased movement, and 64% had increased breathing rate (BR). Nearly 34% had paradoxical bradycardia (decreased heart rate by ~ 10 BPM concomitant with elevated BR and/or fever), with more-severe symptoms. Smart beds can detect physiological changes during COVID-19. A subtype of acute response (paradoxical bradycardia) may predict delay recovery from COVID-19. Show less

In today’s fast-changing tech world, efficient business operations are crucial as data and AI redefine strategies, models, and outlooks. This book offers essential insights to leverage XOps, an umbrella term for various operational techniques like DevOps, SecOps, DataOps, and AIOps. XOps integrates development, operations, data management, AI, and security, making traditional operations outdated. The book explores how xOps enhances productivity, flexibility, and creativity in our data-driven… Show more

In today’s fast-changing tech world, efficient business operations are crucial as data and AI redefine strategies, models, and outlooks. This book offers essential insights to leverage XOps, an umbrella term for various operational techniques like DevOps, SecOps, DataOps, and AIOps. XOps integrates development, operations, data management, AI, and security, making traditional operations outdated. The book explores how xOps enhances productivity, flexibility, and creativity in our data-driven, AI-powered era, transforming business operations. It details the evolution of operations, the importance of XOps, and its components, like SecOps, DevOps, and AIOps, with practical applications and case studies. Embracing xOps involves challenges, which are addressed with effective strategies and future trends. This book serves as a strategic guide for business leaders, IT professionals, and students to thrive in the age of data and AI, promoting an innovative mindset for success in today’s digital world. Show less

Snoring and obstructive sleep apnea (OSA) are bidirectionally associated. The majority of OSA patients snore and a substantial percentage of those who snore have OSA. The intensity and the acoustic properties of snoring in apnea patients have been shown to correlate with OSA presence. The goal of this research is to use objective sleep data collected by the Sleep Number platform and survey responses to examine the relationship between snoring, sleep metrics, demographics, and OSA.

Introduction: Insomnia causes serious adverse health effects and is estimated to affect 10–30% of the worldwide population. This study leverages personalized fine-tuned machine learning algorithms to detect insomnia risk based on questionnaire and longitudinal objective sleep data collected by a smart bed platform.

Methods: Users of the Sleep Number smart bed were invited to participate in an IRB approved study which required them to respond to four questionnaires (which included the… Show more

Introduction: Insomnia causes serious adverse health effects and is estimated to affect 10–30% of the worldwide population. This study leverages personalized fine-tuned machine learning algorithms to detect insomnia risk based on questionnaire and longitudinal objective sleep data collected by a smart bed platform.

Methods: Users of the Sleep Number smart bed were invited to participate in an IRB approved study which required them to respond to four questionnaires (which included the Insomnia Severity Index; ISI) administered 6 weeks apart from each other in the period from November 2021 to March 2022. For 1,489 participants who completed at least 3 questionnaires, objective data (which includes sleep/wake and cardio-respiratory metrics) collected by the platform were queried for analysis. An incremental, passive-aggressive machine learning model was used to detect insomnia risk which was defined by the ISI exceeding a given threshold. Three ISI thresholds (8, 10, and 15) were considered. The incremental model is advantageous because it allows personalized fine-tuning by adding individual training data to a generic model.

Results: The generic model, without personalizing, resulted in an area under the receiving-operating curve (AUC) of about 0.5 for each ISI threshold. The personalized fine-tuning with the data of just five sleep sessions from the individual for whom the model is being personalized resulted in AUCs exceeding 0.8 for all ISI thresholds. Interestingly, no further AUC enhancements resulted by adding personalized data exceeding ten sessions.

Discussion: These are encouraging results motivating further investigation into the application of personalized fine tuning machine learning to detect insomnia risk based on longitudinal sleep data and the extension of this paradigm to sleep medicine. Show less

The Sleep Number smart bed uses embedded ballistocardiography, together with network connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing rate (BR), and sleep vs. wake states. This study evaluated the performance of the smart bed relative to polysomnography (PSG) in estimating epoch-by-epoch HR, BR, sleep vs. wake, mean overnight HR and BR, and summary sleep variables. Forty-five participants (aged 22–64 years; 55% women) slept one night on the smart bed… Show more

The Sleep Number smart bed uses embedded ballistocardiography, together with network connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing rate (BR), and sleep vs. wake states. This study evaluated the performance of the smart bed relative to polysomnography (PSG) in estimating epoch-by-epoch HR, BR, sleep vs. wake, mean overnight HR and BR, and summary sleep variables. Forty-five participants (aged 22–64 years; 55% women) slept one night on the smart bed with standard PSG. Smart bed data were compared to PSG by Bland–Altman analysis and Pearson correlation for epoch-by-epoch HR and epoch-by-epoch BR. Agreement in sleep vs. wake classification was quantified using Cohen’s kappa, ROC analysis, sensitivity, specificity, accuracy, and precision. Epoch-by-epoch HR and BR were highly correlated with PSG (HR: r = 0.81, |bias| = 0.23 beats/min; BR: r = 0.71, |bias| = 0.08 breaths/min), as were estimations of mean overnight HR and BR (HR: r = 0.94, |bias| = 0.15 beats/min; BR: r = 0.96, |bias| = 0.09 breaths/min). Calculated agreement for sleep vs. wake detection included kappa (prevalence and bias-adjusted) = 0.74 ± 0.11, AUC = 0.86, sensitivity = 0.94 ± 0.05, specificity = 0.48 ± 0.18, accuracy = 0.86 ± 0.11, and precision = 0.90 ± 0.06. For all-night summary variables, agreement was moderate to strong. Overall, the findings suggest that the Sleep Number smart bed may provide reliable metrics to unobtrusively characterize human sleep under real life-conditions. Show less

Pathophysiologic responses to viral respiratory challenges such as SARS-CoV-2 may affect sleep duration, quality and concomitant cardiorespiratory function. Unobtrusive and ecologically valid methods to monitor longitudinal sleep metrics may therefore have practical value for surveillance and monitoring of infectious illnesses. We leveraged sleep metrics from Sleep Number 360 smart bed users to build a COVID-19 predictive model.