Obstructive sleep apnea screening from unprocessed ECG signals using statistical modelling

Faal, Maryam; Almasganj, Farshad

doi:10.1016/j.bspc.2021.102685

Cited by 14 publications

(7 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the absence of heart diseases, ECG signals are highly structured, and individual signal components can be identified through visual inspection. The ECG trace is made up of several waves that are labelled P, QRS, and T. Each wave corresponds to a different physiological event during the cardiac cycle [7]. The breathing rate is linked to the heart rhythm via the autonomous nervous system [40].…”

Section: Electrocardiogram (Ecg)mentioning

confidence: 99%

“…Many patients have trouble sleeping in such an environment. Due to the presence of numerous leads and monitors, some patients report feeling constrained during in-laboratory PSGs, resulting in them spending more time in the supine position than they would during a typical night at home [7,57,58]. A PSG requires gathering 12 separate signals with a minimum of 22 lead wires linked to the patient's body, making a signal analysis difficult and causing discomfort to the patient [38].…”

Section: Polysomnography (Psg)mentioning

confidence: 99%

“…Sleep apnoea (SA) is one of the most common sleep disorders that deteriorates mental and cardiovascular health. It affects nearly 1 billion people around the world [6], and it is difficult to be diagnosed [7,8]. At least 20% of all adults in developed countries suffer in the home environment.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Environmental Benefits of Sleep Apnoea Detection in the Home Environment

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: Electrocardiogram (Ecg)mentioning

confidence: 99%

Section: Polysomnography (Psg)mentioning

confidence: 99%

See 1 more Smart Citation

Environmental Benefits of Sleep Apnoea Detection in the Home Environment

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Fatimah et al [12] used Fourier decomposition to transform ECG signals into frequency bands and calculated their features, including mean absolute deviation and entropy, to classify ECG segments using an SVM classifier and a Gaussian kernel. Faal et al [13] extracted ARIMA-EGARCH coefficients and used them as a feature vector to classify apneic and normal ECG segments. Using only eight features, the new ARIMA-EGARCH parameter-based method attained performance comparable to other methods.…”

Section: Introductionmentioning

confidence: 99%

Wavelet transform and deep learning-based obstructive sleep apnea detection from single-lead ECG signals

Lin

Zhang

Wu³

et al. 2023

Preprint

View full text Add to dashboard Cite

Sleep apnea is a common sleep disorder. To address the characteristics of ECG signals, we introduce a coordinate attention mechanism and propose an automatic sleep apnea classification model (CA-EfficientNet) based on wavelet transform and lightweight neural network. One-dimensional signals were converted into two-dimensional images by wavelet transform and in put into the proposed model for classification. The effects of input time window, wavelet transform type and data balance on classification performance were considered. PhysioNet apnea ECG database was used for training and evaluation. The 3-minute Frequency B-Spline wavelets transform of ECG signal was carried out, and Dice Loss was used to train the classification model of sleep breathing. The classification accuracy was 93.44%, precision was 93.2%, sensitivity was 88.9%, specificity was 96.2%, F1 score was 91%, and most indexes were better than other related work. Wavelet transform and CA-EfficientNet model provide a feasible diagnostic method for automatic classification of sleep apnea.

show abstract

“…These waves correspond to specific physiological events during the cardiac cycle (Almazaydeh et al, 2012;Faust, Kareem, et al, 2021). Figure 2.11 provides a schematic representation of a normal ECG, illustrating the different waveforms and their significance (Faal & Almasganj, 2021).…”

Section: Electrocardiogrammentioning

confidence: 99%

Sleep Apnoea Detection with Smart Internet of Things Technology

Barika

View full text Add to dashboard Cite

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.

show abstract

Obstructive sleep apnea screening from unprocessed ECG signals using statistical modelling

Cited by 14 publications

References 44 publications

Environmental Benefits of Sleep Apnoea Detection in the Home Environment

Environmental Benefits of Sleep Apnoea Detection in the Home Environment

Wavelet transform and deep learning-based obstructive sleep apnea detection from single-lead ECG signals

Sleep Apnoea Detection with Smart Internet of Things Technology

Contact Info

Product

Resources

About