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Application of new class of antisymmetric wavelet filter banks for obstructive sleep apnea diagnosis using ECG signals
Abstract and Figures
Obstructive sleep apnea (OSA) is a sleep disorder caused due to interruption
of breathing resulting in insufficient oxygen to the human body and brain. If
the OSA is detected and treated at an early stage the possibility of severe
health impairment can be mitigated. Therefore, an accurate automated OSA
detection system is indispensable. Generally, OSA based computer aided diag-
nosis (CAD) system employs multi-channel, multi-signal physiological signals.
However, there is a great need for single-channel bio-signal based low-power,
portable OSA-CAD system which can be used at home. In this study, we pro-
pose single-channel electrocardiogram (ECG) based OSA-CAD system using a
new class of optimal biorthogonal antisymmetric wavelet filter bank (BAWFB).
In this class of filter bank, all filters are of even length. The filter bank design
problem is transformed into a constrained optimization problem wherein the
objective is to minimize either frequency-spread for the given time-spread or
time-spread for the given frequency-spread. The optimization problem is for-
mulated as a semi-definite programming (SDP) problem. In the SDP problem,
the objective function (time-spread or frequency-spread), constraints of perfect
reconstruction (PR) and zero moment (ZM) are incorporated in their time do-
main matrix formulations. The global solution for SDP is obtained using interior
point algorithm. The newly designed BAWFB is used for the classification of
OSA using ECG signals taken from the physionet’s Apnea-ECG database. The
ECG segments of 1 minute duration are decomposed into six wavelet subbands
(WSBs) by employing the proposed BAWFB. Then, the fuzzy entropy (FE) and log-energy (LE) features are computed from all six WSBs. The FE and LE
features are classified into normal and OSA groups using least squares support
vector machine (LS-SVM) with 35-fold cross-validation strategy. The proposed
OSA detection model achieved the average classification accuracy, sensitivity,
specificity and F-score of 90.11%, 90.87% 88.88% and 0.92, respectively. The
performance of model is found to be better than the existing works in detecting
OSA using the same database. Thus, the proposed automated OSA detection
system is accurate, cost-effective and ready to be tested with huge database.
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... These rules are based on a deep understanding of the underlying principles and physics of the problem, and are often tailored to specific applications. Fourier transforms and wavelet transforms have commonly been employed for signal preprocessing [13][14][15][16][17]. Some statistical methods, such as mean absolute deviation and entropy, are subsequently applied to prepare the features for computer classification. ...
Objective
Our objective was to create a machine learning architecture capable of identifying obstructive sleep apnea (OSA) patterns in single-lead electrocardiography (ECG) signals, exhibiting exceptional performance when utilized in clinical data sets.
Methods
We conducted our research using a data set consisting of 1656 patients, representing a diverse demographic, from the sleep center of China Medical University Hospital. To detect apnea ECG segments and extract apnea features, we utilized the EfficientNet and some of its layers, respectively. Furthermore, we compared various training and data preprocessing techniques to enhance the model’s prediction, such as setting class and sample weights or employing overlapping and regular slicing. Finally, we tested our approach against other literature on the Apnea-ECG database.
Results
Our research found that the EfficientNet model achieved the best apnea segment detection using overlapping slicing and sample-weight settings, with an AUC of 0.917 and an accuracy of 0.855. For patient screening with AHI > 30, we combined the trained model with XGBoost, leading to an AUC of 0.975 and an accuracy of 0.928. Additional tests using PhysioNet data showed that our model is comparable in performance to existing models regarding its ability to screen OSA levels.
Conclusions
Our suggested architecture, coupled with training and preprocessing techniques, showed admirable performance with a diverse demographic dataset, bringing us closer to practical implementation in OSA diagnosis.
Trial registration The data for this study were collected retrospectively from the China Medical University Hospital in Taiwan with approval from the institutional review board CMUH109-REC3-018.
... Typically, traditional OSA screening and diagnosis require the use of polysomnography (PSG) [6]. This method requires patients to undergo overnight observation in a sleep center, where their sleep information is recorded, including electroencephalography (EEG), electromyography (EMG), electrocardiography (ECG), respiratory flow, and blood oxygen saturation (SpO 2 ), among others [7]. This process is not only inconvenient for the patient, but also requires manual annotation of the PSG records by physicians, which is timeconsuming and requires full-time monitoring. ...
Objective:Growing attention has been paid recently to electrocardiogram (ECG) based obstructive sleep apnea (OSA) detection, with some progresses been made on this topic. However, the lack of data, low data quality, and incomplete data labeling hinder the application of deep learning to OSA detection, which in turn affects the overall generalization capacity of the network. Methods: To address these issues, we propose the ResT-ECGAN framework. It uses a one-dimensional generative adversarial network (ECGAN) for sample generation, and integrates it into ResTNet for OSA detection. ECGAN filters the generated ECG signals by incorporating the concept of fuzziness, effectively increasing the amount of high-quality data. ResT-Net not only alleviates the problems caused by deepening the network but also utilizes multihead attention mechanisms to parallelize sequence processing and extract more valuable OSA detection features by leveraging contextual information. Results: Through extensive experiments, we verify that ECGAN can effectively improve the OSA detection performance of ResT-Net. Using only ResT-Net for detection, the accuracy on the Apnea-ECG and private databases is 0.885 and 0.837, respectively. By adding ECGAN-generated data augmentation, the accuracy is increased to 0.893 and 0.848, respectively. Conclusion and significance: Comparing with the state-of-the-art deep learning methods, our method outperforms them in terms of accuracy. This study provides a new approach and solution to improve OSA detection in situations with limited labeled samples
... More than one-third of people are expected to have temporary insomnia at some point during their lives. According to the studies [12][13][14], nearly 40% of such people are at risk of developing chronic and long-term insomnia. Other medical issues that can be increased by a lack of sleep include anxiety, depression, respiratory difficulties, seizures, a weaker immune system, obesity, diabetes, high blood pressure, cardiac illness, and even heart disease [15][16][17][18][19]. ...
Polysomnograms (PSGs), commonly conducted in sleep laboratories, serve as the gold standard for sleep analysis. Among the vital PSG components, the electroencephalogram (EEG) stands out, yet its recording and analysis pose technical challenges, particularly within home settings. PSG procedures involve intricate sleep labs and the attachment of multiple electrodes to subjects’ bodies, making them less patient-friendly. The discomfort of wearing electrodes on the skull cap in an altered sleep environment can adversely impact sleep quality and data accuracy. In contrast, electrocardiogram (ECG) signals present a more accessible option for home-based sleep monitoring due to their simpler recording and analysis. Leveraging ECG signals for automated insomnia detection holds promise in enhancing practicality. Consequently, this study aims to develop an automated approach solely utilizing ECG signals, conveniently captured through wearable devices, for precise insomnia identification. For the automated identification of insomniac subjects, the proposed study uses the Deep Wavelet Scattering Network (DWSN) network. The extracted DWSN-based features of the ECG signals have been applied to different machine-learning algorithms to identify insomnia. The proposed method was validated on three different datasets, namely the Wisconsin Sleep Cohort (WSC) dataset (n = 308; where n = number of subjects), the Sleep Disorder Research Centre (SDRC) dataset (n = 22), and the Cyclic Alternating Pattern (CAP) dataset (n = 25). Our proposed method obtained the highest classification accuracy of 99.9% using the Weighted K-Nearest Neighbour (WKNN) classifier, and a Kappa value of 0.993 with the WSC dataset. Similarly, the highest classification accuracy of 99.60% for the SDRC dataset was obtained using the Trilayered Neural Network (TNN) classifier with a Kappa value of 0.991. The highest classification accuracy of 99% was obtained for the CAP dataset using the Ensemble of Bagged Tree (EBT) classifier with a 0.979 Kappa value. The proposed study suggests an automated, computerized method for creating a machine learning model with explainable artificial intelligence (XAI) capabilities, employing DWSN-based characteristics to distinguish healthy subjects and insomnia subjects. To gain an understanding of the model, the study uses feature ranking based on SHAP (Shapley Additive exPlanations). The proposed study is also the first of its kind to provide the highest accuracy for the classification of insomnia using a huge database. Hence, our model is more generalized as it used diverse and large-scale databases. The suggested study outperformed all previous methods in terms of efficiency, dependability, and accuracy. Thus, the proposed method can potentially aid in the clinical identification of insomnia.
... More than one-third of people are expected to have temporary insomnia at some point during their lives. According to the studies [12][13][14], nearly 40 % of such people are at risk of developing chronic and long-term insomnia. Other medical issues that can be increased by a lack of sleep include anxiety, depression, respiratory difficulties, seizures, a weaker immune system, obesity, diabetes, high blood pressure, cardiac illness, and even heart disease [15][16][17][18]. ...
Polysomnograms (PSGs), commonly conducted in sleep laboratories, serve as the gold standard for sleep analysis. Among the vital PSG components, the electroencephalogram (EEG) stands out, yet its recording and analysis pose technical challenges, particularly within home settings. PSG procedures involve intricate sleep labs and the attachment of multiple electrodes to subjects' bodies, making them less patient-friendly. The discomfort of wearing electrodes on the skull cap in an altered sleep environment can adversely impact sleep quality and data accuracy. In contrast, electrocardiogram (ECG) signals present a more accessible option for home-based sleep monitoring due to their simpler recording and analysis. Leveraging ECG signals for automated insomnia detection holds promise in enhancing practicality. Consequently, this study aims to develop an automated approach solely utilizing ECG signals, conveniently captured through wearable devices, for precise insomnia identification. For the automated identification of insomniac subjects, the proposed study uses the Deep Wavelet Scattering Network (DWSN) network. The extracted DWSN-based features of the ECG signals have been applied to different machine-learning algorithms to identify insomnia. 1 The proposed method was validated on three different datasets, namely the Wis-consin Sleep Cohort (WSC) dataset (n = 308; where n= number of subjects), the Sleep Disorder Research Centre (SDRC) dataset (n = 22), and the Cyclic Alternating Pattern (CAP) dataset (n = 25). Our proposed method obtained the highest classification accuracy of 99.9 % using the Weighted K-Nearest Neighbour (WKNN) classifier, and a Kappa value of 0.993 with the WSC dataset. Similarly, the highest classification accuracy of 99.60 % for the SDRC dataset was obtained using the Trilayered Neural Network (TNN) classifier with a Kappa value of 0.991. The highest classification accuracy of 99 % was obtained for the CAP dataset using the Ensemble of Bagged Tree (EBT) classifier with a 0.979 Kappa value. The proposed study suggests an automated, computerized method for creating a machine learning model with explainable artificial intelligence (XAI) capabilities, employing DWSN-based characteristics to distinguish healthy subjects and insomnia subjects. To gain an understanding of the model, the study uses feature ranking based on SHAP (Shapley Additive exPlanations). The proposed study is also the first of its kind to provide the highest accuracy for the classification of insomnia using a huge database. Hence, our model is more generalized as it used diverse and large-scale databases. The suggested study outperformed all previous methods in terms of efficiency, dependability, and accuracy. Thus, the proposed method can potentially aid in the clinical identification of insomnia.
... Dyssomnias such as insomnia, hypersomnia, narcolepsy, and sleep apnea; parasomnias such as sleepwalking and rapid eye movement (REM) sleep behavior disorder (SBD); bruxism; and circadian rhythm sleep disorders are examples of sleep disorders. [4][5][6] Sleep deprivation and excessive sleep are connected to a variety of chronic health issues, including heart disease and diabetes. 7,8 Sleep difficulties can also be a symptom of medical and neurological issues such as congestive heart failure, osteoarthritis, and Parkinson's disease. ...
Sleep affects the functioning of all biological processes in the human body. Sleep disorders disrupt the well‐being of an individual and can potentially lead to other health complications. It affects a large part of the population, and their timely and efficient detection is crucial for improving the quality of health. Sleep disorders are generally analyzed using polysomnogram (PSG) signals, which must be captured in sophisticated laboratory settings. However, electrocardiogram (ECG) signals provide information about the electrical activity of the heart, which is strongly linked with disturbances in sleep and can be used as an indicator of disordered sleep. Along with that, the recording of ECG signals is much easier than other signals and can be done remotely without causing any significant disturbance to the subject. This brings practicality to the systems that are already being employed for sleep disorder detection. In this paper, we present a method for identifying types of sleep disorders using ECG signals. The objective of this paper is to investigate the use of ECG signals for the automated identification of insomnia, narcolepsy, periodic leg movement (PLM), rapid eye movement (REM) behavior disorder (RBD), and nocturnal frontal lobe epilepsy (NFLE) against healthy subjects. We aim to develop a machine learning‐based algorithm that automatically classifies ECG signals into these sleep disorder categories. The cyclic alternating pattern (CAP) sleep database was used in this study, which contains PSG recordings from individuals with and without sleep disorders, including insomnia, narcolepsy, PLM, RBD, and NFLE. A wavelet scattering network has been used to extract features from the ECG signals. Various classifiers were tested, and the ensemble bag of trees classifier provided the optimum performance. An overall accuracy of 98% was obtained for the identification of sleep disorders, along with a classification accuracy of 99.37%, 99.45%, 99.23%, 99.4%, and 99.65% for insomnia, narcolepsy, NFLE, PLM, and RBD, respectively, for binary classification against healthy subject data. Our results show that wavelet scattering network and ensemble of bagged tree (EbagT) classification can accurately identify various sleep disorders. They provided evidence that ECG signals can be used to identify and diagnose sleep disorders, which could lead to the development of accurate detection of these sleep disorders. Also, our study demonstrated the effectiveness of the proposed algorithm in identifying multiple sleep disorders, which could lead to more efficient and cost‐effective diagnosis and treatment of sleep disorders.
Sleep apnea (SA) is considered one of the most dangerous sleep disorders. That happens when a person is sleeping, his or her breathing repeatedly stops and starts. In order to develop therapies and management strategies that will be effective in treating SA, it is critical to precisely diagnose sleep apnea episodes. In this study, the single-lead electrocardiogram (ECG), one of the most physiologically pertinent markers for SA, is analyzed to identify the SA issue. In this paper, a novel signal processing method is proposed, in which noise filtering is added and the detection of R peaks is utilized. Particularly, the Teager Energy Operator (TEO) algorithm is applied to detect R peaks and then obtain the RR intervals and amplitudes. Afterward, the SE-ResNeXt 50 deep learning model, which has never been used in SA detection before, is used as a classifier to perform the objective. The proposed model, which is a variation of ResNet 50, has the ability to use global information to highlight helpful information while allowing for feature recalibration. In order to confirm the proposed method, the benchmark dataset PhysioNet ECG Sleep Apnea v1.0.0 is used. Results are better than current research, with 89.21% accuracy, 90.29% sensitivity, and 87.36% specificity. This is also clear evidence that the ECG signals can be taken advantage of to efficiently detect SA.
Obstructive sleep apnea (OSAS), which is one of the leading sleep disorders and can result in death if not diagnosed and treated early, is most often confused with snoring. OSAS disease, the prevalence of which varies between 0.9% and 1.9% in Turkey, is a serious health problem that occurs as a result of complete or partial obstruction of the respiratory tract during sleep, resulting in sleep disruption, poor quality sleep, paralysis and even death in sleep. Polysomnography signal recordings (PSG) obtained from sleep laboratories are used for the diagnosis of OSAS, which is related to factors such as the individual's age, gender, neck diameter, smoking-alcohol consumption, and the occurrence of other sleep disorders. Polysomnography is used in the diagnosis and treatment of sleep disorders such as snoring, sleep apnea, parasomnia (abnormal behaviors during sleep), narcolepsy (sleep attacks that develop during the day) and restless legs syndrome. It allows recording various parameters such as brain waves, eye movements, heart and chest activity measurement, respiratory activities, and the amount of oxygen in the blood with the help of electrodes placed in different parts of the patient's body during night sleep. In this article, the performance of PSG signal data for the diagnosis of sleep apnea was examined on the basis of both signal parameters and the method used. First, feature extraction was made from PSG signals, then the feature vector was classified with artificial neural networks, Support Vector Machine (SVM), K-Nearest Neighbors (k-NN) and Logistic Regression (LR).
Obstructive sleep apnea (OSA) is a condition that influences many people and is determined by events of reduced respiratory airflow during sleep. However, electrocardiogram (ECG)-based detection of OSA is more suitable for noninvasive requirements and instrument limitations of wearable portable devices. As compared to earlier electrocardiogram (ECG) based OSA detection systems, deep learning approaches demonstrate substantial promises and advantages. This research presents a model for the detection of OSA from a single-lead ECG using a 1D convolutional neural network (1D-CNN). The performance of the proposed model is estimated on the well-established PhysioNet Apnea-ECG dataset. This dataset includes seventy ECG recordings, but only thirty-five released datasets are used in this research. The accuracy, precision, sensitivity, specificity, and F1 scores of the proposed model were evaluated as 94.77±1.35%, 93.80±2.253%, 92.55±4.57%, 96.14±1.66%, and 93.07±2.03%, respectively. The accuracy of the proposed model can be further improved for large datasets. Moreover, the proposed method can be implemented in wearable devices, which could monitor/detect OSA in the home setting and assist the medical expert.
Sleep apnea is a common sleep disorder. Traditional testing and diagnosis heavily rely on the expertise of physicians, as well as analysis and statistical interpretation of extensive sleep testing data, resulting in time-consuming and labor-intensive processes. To address the problems of complex feature extraction, data imbalance, and low model capacity, we proposed an automatic sleep apnea classification model (CA-EfficientNet) based on the wavelet transform, a lightweight neural network, and a coordinated attention mechanism. The signal is converted into a time–frequency image by wavelet transform and put into the proposed model for classification. The effects of input time window, wavelet transform type and data balancing on the classification performance are considered, and a cost-sensitive algorithm is introduced to more accurately distinguish between normal and abnormal breathing events. PhysioNet apnea ECG database was used for training and evaluation. The 3-min 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%, sensitivity was 88.9%, specificity was 96.2% and most indexes were better than other related work.
The sleep apnea is a disease in which there is the absence of airflow
during respiration for at least 10 seconds. It may occur several times during the
night sleep. This disease can lead to many types of cardiovascular diseases. To
detect this disease, signals obtained from many channels of polysomnography
(PSG) are to be observed visually by physicians for the long duration. This
procedure is expensive, time-consuming, and subjective. Hence, it is required
to build an automated system to detect the sleep apnea with few channels. This
paper uses single-lead electrocardiogram (ECG) signal to detect apneic and
non-apneic events. The proposed method uses tunable-Q wavelet transform
(TQWT) based filter-bank instead of TQWT to decompose the segment of
ECG signal into several constant bandwidth sub-band signals. Then centered
correntropys (CCEs) are computed from the various sub-band signals. The
obtained features are then fed to the various classifiers to select the optimum
performing classifier. In this work, we have obtained the highest classification
accuracy (ACC), specificity (SPE), and sensitivity (SEN) of 92.78%, 93.91%,
and 90.95% respectively using random forest (RF) classifier. Hence, our developed prototype is ready for validation with the huge database and clinical
usage.
Sleep related disorder causes diminished quality of lives in human beings. Sleep scoring or sleep staging is the process of classifying various sleep stages which helps to detect the quality of sleep. The identification of sleep-stages using electroencephalogram (EEG) signals is an arduous task. Just by looking at an EEG signal, one cannot determine the sleep stages precisely. Sleep specialists may make errors in identifying sleep stages by visual inspection. To mitigate the erroneous identification and to reduce the burden on doctors, a computer-aided EEG based system can be deployed in the hospitals, which can help identify the sleep stages, correctly. Several automated systems based on the analysis of polysomnographic (PSG) signals have been proposed. A few sleep stage scoring systems using EEG signals have also been proposed. But, still there is a need for a robust and accurate portable system developed using huge dataset. In this study, we have developed a new single-channel EEG based sleep-stages identification system using a novel set of wavelet-based features extracted from a large EEG dataset. We employed a novel three-band time-frequency localized (TBTFL) wavelet filter bank (FB). The EEG signals are decomposed using three-level wavelet decomposition, yielding seven sub-bands (SBs). This is followed by the computation of discriminating features namely, log-energy (LE), signal-fractal-dimensions (SFD), and signal-sample-entropy (SSE) from all seven SBs. The extracted features are ranked and fed to the support vector machine (SVM) and other supervised learning classifiers. In this study, we have considered five different classification problems (CPs), (two-class (CP-1), three-class (CP-2), four-class (CP-3), five-class (CP-4) and six-class (CP-5)). The proposed system yielded accuracies of 98.3%, 93.9%, 92.1%, 91.7%, and 91.5% for CP-1 to CP-5, respectively, using 10-fold cross validation (CV) technique.
Obstructive Sleep Apnea (OSA) is one of the main under-diagnosed sleep disorder. It is an aggravating factor for several serious cardiovascular diseases, including stroke. There is, however, a lack of medical devices for long-term ambulatory monitoring of OSA since current systems are rather bulky, expensive, intrusive, and cannot be used for long-term monitoring in ambulatory settings. In this paper, we propose a wearable, accurate, and energy efficient system for monitoring obstructive sleep apnea on a long-term basis. As an embedded system for Internet of Things, it reduces the gap between home health-care and professional supervision. Our approach is based on monitoring the patient using a single-channel electrocardiogram signal. We develop an efficient time-domain analysis to meet the stringent resources constraints of embedded systems to compute the sleep apnea score. Our system, for a publicly available database (PhysioNet Apnea-ECG), has a classification accuracy of up to 88.2% for our new online and patient-specific analysis, which takes the distinct profile of each patient into account. While accurate, our approach is also energy efficient and can achieve a battery lifetime of 46 days for continuous screening of OSA.
Background and objective:
We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017.
Methods:
An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review.
Results:
During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input.
Conclusions:
This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis.
Sleep-related conditions require high-cost and low-comfort diagnosis at the hospital during one night or longer. To overcome this situation, this work aims to evaluate an unobtrusive monitoring technique for sleep apnea. This paper presents, for the first time, the evaluation of contactless capacitively-coupled electrocardiography (ccECG) signals for the extraction of sleep apnea features, together with a comparison of different signal quality indicators. A multichannel ccECG system is used to collect signals from 15 subjects in a sleep environment from different positions. Reference quality labels were assigned for every 30-s segment. Quality indicators were calculated, and their signal classification performance was evaluated. Features for the detection of sleep apnea were extracted from capacitive and reference signals. Sleep apnea features related to heart rate and heart rate variability achieved high similarity to the reference values, with p-values of 0.94 and 0.98, which is in line with the more than 95% beat-matching obtained. Features related to signal morphology presented lower similarity with the reference, although signal similarity metrics of correlation and coherence were relatively high. Quality-based automatic classification of the signals had a maximum accuracy of 91%. Best-performing quality indicators were based on template correlation and beat-detection. Results suggest that using unobtrusive cardiac signals for the automatic detection of sleep apnea can achieve similar performance as contact signals, and indicates clinical value of ccECG. Moreover, signal segments can automatically be classified by the proposed quality metrics as a pre-processing step. Including contactless respiration signals is likely to improve the performance and provide a complete unobtrusive cardiorespiratory monitoring solution; this is a promising alternative that will allow the screening of more patients with higher comfort, for a longer time, and at a reduced cost.
An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively.
Alcoholism is a critical disorder related to the central nervous
system, caused due to the repeated and excessive consumption of alcohol.
The electroencephalogram (EEG) signals are used to depict the brain activities. It can also be employed for the diagnosis of subjects consuming excessive alcohol. In this study, we have developed an automated system for the
classification of alcoholic and normal EEG signals using a recently designed
duration-bandwidth product (DBP) optimized three-band orthogonal wavelet
filter bank (TBOWFB) and log-energy (LE). First, we obtain sub-bands (SBs)
of EEG signals using the TBOWFB. Then, we use logarithms of the energies
of the SBs as the discriminating features which are fed to the least square
support vector machine (LS-SVM) for the discrimination of normal and al-
coholic EEG signals. We have achieved the classification accuracy (CA) of
97.08%, with ten-fold cross validation strategy. The proposed model presents
a promising performance, and therefore it can be used in a practical setup
to assist the medical professional in the diagnosis of alcoholism using EEG
signals automatically.
Obstructive sleep apnea (OSA) is the most common sleep-related breathing disorder that potentially threatened people's cardiovascular system. As an alternative to polysomnography for OSA detection, ECG-based methods have been developed for several years. However, previous work is focused on feature engineering, which is highly dependent on the prior knowledge of human experts and maybe subjective. Moreover, feature engineering also highlights the prominent shortcoming of current learning algorithms that the features are unable to extracted and organized from the data. In this study, we proposed a method to detect OSA based on deep neural network and Hidden Markov model (HMM) using single-lead ECG signal. The method utilized sparse auto-encoder to learn features, which belongs to unsupervised learning that only requires unlabeled ECG signals. Two types classifiers (SVM and ANN) are used to classify the features extracted from the sparse auto-encoder. Considering the temporal dependency, HMM was adopted to improve the classification accuracy. Finally, a decision fusion method is adopted to improve the classification performance. About 85% classification accuracy is achieved in the per-segment OSA detection, and the sensitivity is up to 88.9%. Based on the results of per-segment OSA detection, we perfectly separate the OSA recording from normal with accuracy of 100%. Experimental results demonstrated that our proposed method is reliable for OSA detection.
One of the most common sleep-related disorders is obstructive sleep apnea, characterized by a reduction of airflow while breathing during sleep and cause significant health problems. This disorder is mainly diagnosed in sleep labs with polysomnography, involving high costs and stress for the patient. To address this situation multiple systems have been proposed to conduct the examination and analysis in the patient’s home, using sensors to detect physiological signals that are examined by algorithms. The objective of this research is to review publications that show the performance of different devices for ambulatory diagnosis of sleep apnea. Commercial systems that were examined by an independent research group and validated research projects were selected. In total 117 articles were analysed, including a total of 50 commercial devices. Each article was evaluated according to diagnostic elements, level of automatisation implemented and the deducted level of evidence and quality rating. Each device was categorized using the SCOPER categorization system, including an additional proposed category, and a final comparison was performed to determine the sensors that provided the best results.
This letter presents an automated obstructive sleep apnoea (OSA) detection method with high accuracy, based on a deep learning framework employing convolutional neural network. The proposed work develops a system that takes single lead electrocardiography signals from patients for analysis and detects the OSA condition of the patient. The results show that the proposed method has some advantages in solving such problems and it outperforms the existing methods significantly. The present scheme eliminates the requirement of separate feature extraction and classification algorithms for the detection of OSA. The proposed network performs both feature learning and classifies the features in a supervised manner. The scheme is computation-intensive, but can achieve very high degree of accuracy—on an average a margin of more than 9% compared to other published literature till date. The method also has a good immunity to the contamination of the signals by noise. Even with pessimistic signal to noise ratio values considered here, the methods already reported are not able to outshine the present method. The software for the algorithm reported here can be a good contender to constitute a module that can be integrated with a portable medical diagnostic system.