A Review of the Evidence for Use of the Home Sleep Apnea Test or Portable Monitoring in the Evaluation of Central Sleep Apnea

San, Kaung H.; Malhotra, Raman K.

doi:10.1007/s13665-021-00280-4

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…However, identifying the cause of health problems associated with SA can be challenging due to patients' unawareness of their awakenings (Kristiansen et al, 2021). Common symptoms of SA include daytime fatigue, loud snoring, breathing difficulties during sleep, trouble concentrating, restlessness, morning headaches, and dry mouth (Syeda Quratulain Ali et al, 2019;Gandhi et al, 2021;Kristiansen et al, 2018Kristiansen et al, , 2021Salari et al, 2022;San & Malhotra, 2021;Sweed et al, 2019) These symptoms,…”

Section: Sleep Apnoeamentioning

confidence: 99%

“…In developed countries, at least 20% of adults are estimated to suffer from SA (Banluesombatkul et al, 2019). SA is associated with various comorbidities, including high blood pressure (Kristiansen et al, 2021), CVD (Sweed et al, 2019), type 2 diabetes mellitus, and stroke (DM) (Gurrala et al, 2021;Kristiansen et al, 2021;San & Malhotra, 2021) This is primarily due to reduced travel for both patients and healthcare specialists, as well as the ability to share resources. The authors conducted a comprehensive review of 113 papers on SA detection systems and found that the key factor influencing the environmental impact of a system is whether the measurement evaluation is conducted online or offline.…”

Section: Introductionmentioning

confidence: 99%

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Sleep Apnoea Detection with Smart Internet of Things Technology

Barika

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Sleep apnoea (SA) is a hazardous condition characterized by interrupted breathing during sleep. This prevalent medical issue affects individuals of all ages, potentially leading to severe complications when untreated including, cardiovascular problems, diabetes, and daytime fatigue etc. Unfortunately, SA often remains undiagnosed due to the costly and inconvenient diagnostic procedures associated with it. It stands as a significant global health concern, impacting nearly one billion people worldwide, with a prevalence of 17 to 23% in women and 34 to 50% in men. SA is recognized as a risk factor for cardiovascular disorders (CVD) and carries substantial individual, societal, and economic burdens. The economic costs of SA diagnosis and treatment services run into billions of dollars annually. The reference standard for diagnosing SA is polysomnography (PSG), conducted in a laboratory setting by trained professionals. However, this process is time-consuming, susceptible to human error, and demands technical expertise for both execution and interpretation. The inconvenience of in-lab PSG has spurred the need for new, simplified methods. This thesis posits that Computer-Aided Diagnosis (CAD) systems can enhance diagnostic efficacy. To explore this hypothesis, the thesis introduces innovative real-time detection techniques for Obstructive Sleep Apnoea (OSA) and the development of a high-performance OSA detection system. This system, offering continuous OSA detection, addresses the practical challenges associated with traditional diagnostic approaches. The integration of Internet of Things (IoT) and advanced Artificial Intelligence (AI) technologies, with a focus on the Lifetouch sensor, represents a novel approach to improve the accuracy of OSA detection. This innovative strategy aims to overcome barriers to timely and reliable diagnosis and monitoring of sleep disorders. To thoroughly assess the algorithm, a clinical study enrolled 15 patients with a history of OSA. Simultaneously, standard PSG monitoring and diagnosis were conducted, serving as the benchmark for comparison. This dual approach ensured a robust evaluation of the DL algorithm's performance against established PSG methods, providing a comprehensive understanding of its capabilities in OSA detection. The trial results highlight the potential of the proposed technology model, showing a high level of patient acceptance and satisfaction with Lifetouch wearables. However, the identification of only two OSA cases among the 15 patients studied was lower than anticipated. These findings emphasize the need for improved detection methods, as addressed by the novel techniques introduced in this thesis. The results presented here also highlight the efficacy of the developed methods, showcasing their ability to deliver quick, reliable, and standardized analyses an essential step forward in overcoming the limitations of conventional diagnostic approaches.

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Section: Sleep Apnoeamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sleep Apnoea Detection with Smart Internet of Things Technology

Barika

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Environmental Benefits of Sleep Apnoea Detection in the Home Environment

et al. 2022

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Sleep Apnoea (SA) is a common chronic illness that affects nearly 1 billion people around the world, and the number of patients is rising. SA causes a wide range of psychological and physiological ailments that have detrimental effects on a patient’s wellbeing. The high prevalence and negative health effects make SA a public health problem. Whilst the current gold standard diagnostic procedure, polysomnography (PSG), is reliable, it is resource-expensive and can have a negative impact on sleep quality, as well as the environment. With this study, we focus on the environmental impact that arises from resource utilisation during SA detection, and we propose remote monitoring (RM) as a potential solution that can improve the resource efficiency and reduce travel. By reusing infrastructure technology, such as mobile communication, cloud computing, and artificial intelligence (AI), RM establishes SA detection and diagnosis support services in the home environment. However, there are considerable barriers to a widespread adoption of this technology. To gain a better understanding of the available technology and its associated strength, as well as weaknesses, we reviewed scientific papers that used various strategies for RM-based SA detection. Our review focused on 113 studies that were conducted between 2018 and 2022 and that were listed in Google Scholar. We found that just over 50% of the proposed RM systems incorporated real time signal processing and around 20% of the studies did not report on this important aspect. From an environmental perspective, this is a significant shortcoming, because 30% of the studies were based on measurement devices that must travel whenever the internal buffer is full. The environmental impact of that travel might constitute an additional need for changing from offline to online SA detection in the home environment.

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A Review of the Evidence for Use of the Home Sleep Apnea Test or Portable Monitoring in the Evaluation of Central Sleep Apnea

Cited by 2 publications

References 19 publications

Sleep Apnoea Detection with Smart Internet of Things Technology

Sleep Apnoea Detection with Smart Internet of Things Technology

Environmental Benefits of Sleep Apnoea Detection in the Home Environment

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