Identification of sleep apnea events using discrete wavelet transform of respiration, ECG and accelerometer signals

Sleep apnea is a common sleep disorder in which patient sleep patterns are disrupted due to recurrent pauses in breathing or by instances of abnormally low breathing. Current gold standard tests for the detection of apnea events are costly and have the addition of long waiting times. This paper investigates the use of cheap and easy to use sensors for the identification of sleep apnea events. Combinations of respiration, electrocardiography (ECG) and acceleration signals were analysed. Results show that using features, formed using the discrete wavelet transform (DWT), from the ECG and acceleration signals provided the highest classification accuracy, with an F1 score of 0.914. However, the novel employment of just the accelerometer signal during classification provided a comparable F1 score of 0.879. By employing one or a combination of the analysed sensors a preliminary test for sleep apnea, prior to the requirement for gold standard testing, can be performed

Self-adjustable domain adaptation in personalized ECG monitoring integrated with IR-UWB radar

To enhance electrocardiogram (ECG) monitoring systems in personalized detections, deep neural networks (DNNs) are applied to overcome individual differences by periodical retraining. As introduced previously [4], DNNs relieve individual differences by fusing ECG with impulse radio ultra-wide band (IR-UWB) radar. However, such DNN-based ECG monitoring system tends to overfit into personal small datasets and is difficult to generalize to newly collected unlabeled data. This paper proposes a self-adjustable domain adaptation (SADA) strategy to prevent from overfitting and exploit unlabeled data. Firstly, this paper enlarges the database of ECG and radar data with actual records acquired from 28 testers and expanded by the data augmentation. Secondly, to utilize unlabeled data, SADA combines self organizing maps with the transfer learning in predicting labels. Thirdly, SADA integrates the one-class classification with domain adaptation algorithms to reduce overfitting. Based on our enlarged database and standard databases, a large dataset of 73200 records and a small one of 1849 records are built up to verify our proposal. Results show SADA\u27s effectiveness in predicting labels and increments in the sensitivity of DNNs by 14.4% compared with existing domain adaptation algorithms

A review of ECG-based diagnosis support systems for obstructive sleep apnea

Humans need sleep. It is important for physical and psychological recreation. During sleep our consciousness is suspended or least altered. Hence, our ability to avoid or react to disturbances is reduced. These disturbances can come from external sources or from disorders within the body. Obstructive Sleep Apnea (OSA) is such a disorder. It is caused by obstruction of the upper airways which causes periods where the breathing ceases. In many cases, periods of reduced breathing, known as hypopnea, precede OSA events. The medical background of OSA is well understood, but the traditional diagnosis is expensive, as it requires sophisticated measurements and human interpretation of potentially large amounts of physiological data. Electrocardiogram (ECG) measurements have the potential to reduce the cost of OSA diagnosis by simplifying the measurement process. On the down side, detecting OSA events based on ECG data is a complex task which requires highly skilled practitioners. Computer algorithms can help to detect the subtle signal changes which indicate the presence of a disorder. That approach has the following advantages: computers never tire, processing resources are economical and progress, in the form of better algorithms, can be easily disseminated as updates over the internet. Furthermore, Computer-Aided Diagnosis (CAD) reduces intra- and inter-observer variability. In this review, we adopt and support the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy

A Panoramic Study of Obstructive Sleep Apnea Detection Technologies

This study offers a literature research reference value for bioengineers and practitioner medical doctors. It could reduce research time and improve medical service efficiency regarding Obstructive Sleep Apnea (OSA) detection systems. Much of the past and the current apnea research, the vital signals features and parameters of the SA automatic detection are introduced.The applications for the earlier proposed systems and the related work on real-time and continuous monitoring of OSA and the analysis is given. The study concludes with an assessment of the current technologies highlighting their weaknesses and strengths which can set a roadmap for researchers and clinicians in this rapidly developing field of study

SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal

Sleep apnea (SA) is the most commonly known sleeping disorder characterized by pauses of airflow to the lungs and often results in day and night time symptoms such as impaired concentration, depression, memory loss, snoring, nocturnal arousals, sweating and restless sleep. Obstructive Sleep Apnea (OSA), the most common SA, is a result of a collapsed upper respiratory airway, which is majorly undiagnosed due to the inconvenient Polysomnography (PSG) testing procedure at sleep labs. This paper introduces an automated approach towards identifying sleep apnea. The idea is based on efficient feature extraction of the electrocardiogram (ECG) signal by employing a hybrid of signal processing techniques and classification using a linear-kernel Support Vector Machine (SVM). The optimum set of RR-interval features of the ECG signal yields a high classification accuracy of 97.1% when tested on the Physionet Apnea-ECG recordings. The results provide motivating insights towards future developments of convenient and effective OSA screening setups.http://dx.doi.org/10.18201/ijisae.7907

Detection of Obstructive Sleep Apnea from ECG Signal using SVM based Grid Search

Obstructive Sleep Apnea is one common form of sleep apnea and is now tested by means of a process called Polysomnography which is time-consuming, expensive and also requires a human observer throughout the study of the subject which makes it inconvenient and new detection techniques are now being developed to overcome these difficulties. Heart rate variability has proven to be related to sleep apnea episodes and thus the features from the ECG signal can be used in the detection of sleep apnea. The proposed detection technique uses Support Vector Machines using Grid search algorithm and the classifier is trained using features based on heart rate variability derived from the ECG signal. The developed system is tested using the dataset and the results show that this classification system can recognize the disorder with an accuracy rate of 89%. Further, the use of the grid search algorithm has made this system a reliable and an accurate means for the classification of sleep apnea and can serve as a basis for the future development of its screening