Towards automatic EEG cyclic alternating pattern analysis: a systematic review.

This study conducted a systematic review to determine the feasibility of automatic Cyclic Alternating Pattern (CAP) analysis. Specifically, this review followed the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to address the formulated research question: is automatic CAP analysis viable for clinical application? From the identified 1,280 articles, the review included 35 studies that proposed various methods for examining CAP, including the classification of A phase, their subtypes, or the CAP cycles. Three main trends were observed over time regarding A phase classification, starting with mathematical models or features classified with a tuned threshold, followed by using conventional machine learning models and, recently, deep learning models. Regarding the CAP cycle detection, it was observed that most studies employed a finite state machine to implement the CAP scoring rules, which depended on an initial A phase classifier, stressing the importance of developing suitable A phase detection models. The assessment of A-phase subtypes has proven challenging due to various approaches used in the state-of-the-art for their detection, ranging from multiclass models to creating a model for each subtype. The review provided a positive answer to the main research question, concluding that automatic CAP analysis can be reliably performed. The main recommended research agenda involves validating the proposed methodologies on larger datasets, including more subjects with sleep-related disorders, and providing the source code for independent confirmation.

Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review.

The ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters. These studies suggest the dominance of banana fingers as input data, a sensor camera as the preferred capturing device, and appropriate features, such as color, that can provide better detection. Among six stages of ripeness, the studies employing the four mentioned stages performed better in terms of accuracy and coefficient of determination value. Among all the works for detecting ripeness stages prediction, convolutional neural networks were found to perform sufficiently well with large datasets, whereas conventional artificial neural networks and support vector machines attained better performance for sensor-related data. However, insufficient information on the dataset and capturing device, limited data availability, and exploitation of data augmentation techniques are limitations in existing studies. Thus, effectively addressing these shortcomings and close collaboration with experts to predict the ripeness stages should be pursued.

Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images.

The number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on people's health and the economy worldwide. For COVID-19 detection, reverse transcription-polymerase chain reaction testing is the benchmark. However, this test takes a long time and necessitates a lot of laboratory resources. A new trend is emerging to address these limitations regarding the use of machine learning and deep learning techniques for automatic analysis, as these can attain high diagnosis results, especially by using medical imaging techniques. However, a key question arises whether a chest computed tomography scan or chest X-ray can be used for COVID-19 detection. A total of 17,599 images were examined in this work to develop the models used to classify the occurrence of COVID-19 infection, while four different classifiers were studied. These are the convolutional neural network (proposed architecture (named, SCovNet) and Resnet18), support vector machine, and logistic regression. Out of all four models, the proposed SCoVNet architecture reached the best performance with an accuracy of almost 99% and 98% on chest computed tomography scan images and chest X-ray images, respectively.

A-phase index: an alternative view for sleep stability analysis based on automatic detection of the A-phases from the cyclic alternating pattern.

STUDY OBJECTIVES: Sleep stability can be studied by evaluating the cyclic alternating pattern (CAP) in electroencephalogram (EEG) signals. The present study presents a novel approach for assessing sleep stability, developing an index based on the CAP A-phase characteristics to display a sleep stability profile for a whole night's sleep. METHODS: Two ensemble classifiers were developed to automatically score the signals, one for "A-phase" and the other for "non-rapid eye movement" estimation. Both were based on three one-dimension convolutional neural networks. Six different inputs were produced from the EEG signal to feed the ensembles' classifiers. A proposed heuristic-oriented search algorithm individually tuned the classifiers' structures. The outputs of the two ensembles were combined to estimate the A-phase index (API). The models can also assess the A-phase subtypes, their API, and the CAP cycles and rate. RESULTS: Four dataset variations were considered, examining healthy and sleep-disordered subjects. The A-phase average estimation's accuracy, sensitivity, and specificity range was 82%-87%, 72%-80%, and 82%-88%, respectively. A similar performance was attained for the A-phase subtype's assessments, with an accuracy range of 82%-88%. Furthermore, in the examined dataset's variations, the API metric's average error varied from 0.15 to 0.25 (with a median range of 0.11-0.24). These results were attained without manually removing wake or rapid eye movement periods, leading to a methodology suitable to produce a fully automatic CAP scoring algorithm. CONCLUSIONS: Metrics based on API can be understood as a new view for CAP analysis, where the goal is to produce and examine a sleep stability profile.

Multiple Time Series Fusion Based on LSTM: An Application to CAP A Phase Classification Using EEG.

The Cyclic Alternating Pattern (CAP) is a periodic activity detected in the electroencephalogram (EEG) signals. This pattern was identified as a marker of unstable sleep with several possible clinical applications; however, there is a need to develop automatic methodologies to facilitate real-world applications based on CAP assessment. Therefore, a deep learning-based EEG channels' feature level fusion was proposed in this work and employed for the CAP A phase classification. Two optimization algorithms optimized the channel selection, fusion, and classification procedures. The developed methodologies were evaluated by fusing the information from multiple EEG channels for patients with nocturnal frontal lobe epilepsy and patients without neurological disorders. Results showed that both optimization algorithms selected a comparable structure with similar feature level fusion, consisting of three electroencephalogram channels (Fp2-F4, C4-A1, F4-C4), which is in line with the CAP protocol to ensure multiple channels' arousals for CAP detection. Moreover, the two optimized models reached an area under the receiver operating characteristic curve of 0.82, with average accuracy ranging from 77% to 79%, a result in the upper range of the specialist agreement and best state-of-the-art works, despite a challenging dataset. The proposed methodology also has the advantage of providing a fully automatic analysis without requiring any manual procedure. Ultimately, the models were revealed to be noise-resistant and resilient to multiple channel loss, being thus suitable for real-world application.

Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection.

Methodologies for automatic non-rapid eye movement and cyclic alternating pattern analysis were proposed to examine the signal from one electroencephalogram monopolar derivation for the A phase, cyclic alternating pattern cycles, and cyclic alternating pattern rate assessments. A population composed of subjects free of neurological disorders and subjects diagnosed with sleep-disordered breathing was studied. Parallel classifications were performed for non-rapid eye movement and A phase estimations, examining a one-dimension convolutional neural network (fed with the electroencephalogram signal), a long short-term memory (fed with the electroencephalogram signal or with proposed features), and a feed-forward neural network (fed with proposed features), along with a finite state machine for the cyclic alternating pattern cycle scoring. Two hyper-parameter tuning algorithms were developed to optimize the classifiers. The model with long short-term memory fed with proposed features was found to be the best, with accuracy and area under the receiver operating characteristic curve of 83% and 0.88, respectively, for the A phase classification, while for the non-rapid eye movement estimation, the results were 88% and 0.95, respectively. The cyclic alternating pattern cycle classification accuracy was 79% for the same model, while the cyclic alternating pattern rate percentage error was 22%.

A method based on cardiopulmonary coupling analysis for sleep quality assessment with FPGA implementation.

The relevance of sleep quality examination for clinical diagnosis is increasing with the discovery of new relationships with several diseases and the overall wellness. This assessment is commonly performed by conducting interviews with the subjects, evaluating the self-report and psychological variables. However, this approach has a major constraint since the subject is a poor self-observer of sleep behaviors. To address this issue, a method based on the examination of a physiological signal was developed. Specifically, the single-lead electrocardiogram signal was examined to estimate the cardiopulmonary coupling between the electrocardiogram derived respiration signal and the normal-to-normal sinus interbeat interval series. A one dimensional array was created from the coupling signal and was fed to a convolutional neural network to estimate the sleep quality. The age-related cyclic alternating pattern rate percentages in healthy subjects was considered as the classification reference. An accuracy of 91 % was attained by the developed model, with an area under the receiver operating characteristic curve of 97 %. The performance is in the upper range of the reported performance by the works presented in the state of the art, advocating the relevance of the proposed method. The model was implemented in a small field programmable gate array board. Hence, a home monitoring device was created, composed of a processing unit, a sensing module and a display unit. The device is resilient, easy to self-assemble and operate, and can conceivably be employed for clinical analysis.

On the use of patterns obtained from LSTM and feature-based methods for time series analysis: application in automatic classification of the CAP A phase subtypes.

Objective. The cyclic alternating pattern is a marker of sleep instability identified in the electroencephalogram signals whose sequence of transient variations compose the A phases. These phases are divided into three subtypes (A1, A2, and A3) according to the presented patterns. The traditional approach of manually scoring the cyclic alternating pattern events for the full night is unpractical, with a high probability of miss classification, due to the large quantity of information that is produced during a full night recording. To address this concern, automatic methodologies were proposed using a long short-term memory to perform the classification of one electroencephalogram monopolar derivation signal.Approach. The proposed model is composed of three classifiers, one for each subtype, performing binary classification in a one versus all procedure. Two methodologies were tested: feed the pre-processed electroencephalogram signal to the classifiers; create features from the pre-processed electroencephalogram signal which were fed to the classifiers (feature-based methods).Main results. It was verified that the A1 subtype classification performance was similar for both methods and the A2 subtype classification was higher for the feature-based methods. However, the A3 subtype classification was found to be the most challenging to be performed, and for this classification, the feature-based methods were superior. A characterization analysis was also performed using a recurrence quantification analysis to further examine the subtypes characteristics.Significance. The average accuracy and area under the receiver operating characteristic curve for the A1, A2, and A3 subtypes of the feature-based methods were respectively: 82% and 0.92; 80% and 0.88; 85% and 0.86.

Particle Swarm Optimisation: A Historical Review Up to the Current Developments.

The Particle Swarm Optimisation (PSO) algorithm was inspired by the social and biological behaviour of bird flocks searching for food sources. In this nature-based algorithm, individuals are referred to as particles and fly through the search space seeking for the global best position that minimises (or maximises) a given problem. Today, PSO is one of the most well-known and widely used swarm intelligence algorithms and metaheuristic techniques, because of its simplicity and ability to be used in a wide range of applications. However, in-depth studies of the algorithm have led to the detection and identification of a number of problems with it, especially convergence problems and performance issues. Consequently, a myriad of variants, enhancements and extensions to the original version of the algorithm, developed and introduced in the mid-1990s, have been proposed, especially in the last two decades. In this article, a systematic literature review about those variants and improvements is made, which also covers the hybridisation and parallelisation of the algorithm and its extensions to other classes of optimisation problems, taking into consideration the most important ones. These approaches and improvements are appropriately summarised, organised and presented, in order to allow and facilitate the identification of the most appropriate PSO variant for a particular application.

Greedy based convolutional neural network optimization for detecting apnea.

BACKGROUND AND OBJECTIVE: Sleep apnea is a common sleep disorder, usually diagnosed using an expensive, highly specialized, and inconvenient test called polysomnography. A single SpO2 sensor based on an automated classification system can be developed to simplify the apnea detection. The main objective of this work is to develop a classifier based on a convolution neural network with the capability of detecting apnea events from one dimensional SpO2 signal. However, to find an optimum convolution neural network structure is a daunting task is usually done by a trial-and-error method. To solve this problem, a method is proposed to save time and simplify the process of searching for an optimum convolution neural network structure. METHODS: Greedy based optimization is proposed to search for an optimized convolution neural network structure. Three different variants of greedy based optimization are proposed: the topology transfer, the weighted-topology transfer with rough estimation, and the weighted-topology transfer with fine tuning. The subject independent and the cross-database test are performed for the analysis. RESULTS: Considering the balance between the execution time and the performance, the weighted-topology transfer with rough estimation is the best. An accuracy of 88.49% for the HuGCDN2008 database and 95.14% for the Apnea-ECG database are obtained for apnea events detection per minute. Regarding the apnea patient detection, also referred to as global classification, an accuracy of 95.71% is achieved for the HuGCDN2008 database, and 100% is achieved for the AED database without removing any subjects from both databases. CONCLUSIONS: The proposed one-dimensional convolution neural network performs better in a similar situation than those presented in the literature. The greedy based methods, mainly the weighted-topology transfer with rough estimation, is an alternative method to extensive trial and error method.

An Oximetry Based Wireless Device for Sleep Apnea Detection.

Sleep related disorders can severely disturb the quality of sleep. Among these disorders, obstructive sleep apnea (OSA) is highly prevalent and commonly undiagnosed. Polysomnography is considered to be the gold standard exam for OSA diagnosis. Even though this multi-parametric test provides highly accurate results, it is time consuming, labor-intensive, and expensive. A non-invasive and easy to self-assemble home monitoring device was developed to address these issues. The device can perform the OSA diagnosis at the patient's home and a specialized technician is not required to supervise the process. An automatic scoring algorithm was developed to examine the blood oxygen saturation signal for a minute-by-minute OSA assessment. It was performed by analyzing statistical and frequency-based features that were fed to a classifier. Afterward, the ratio of the number of minutes classified as OSA to the time in bed in minutes was compared with a threshold for the global (subject-based) OSA diagnosis. The average accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve for the minute-by-minute assessment were, respectively, 88%, 80%, 91%, and 0.86. The subject-based accuracy was 95%. The performance is in the same range as the best state of the art methods for the models based only on the blood oxygen saturation analysis. Therefore, the developed model has the potential to be employed in clinical analysis.

Matrix of Lags: A tool for analysis of multiple dependent time series applied for CAP scoring.

BACKGROUND: Multiple methods have been developed to assess what happens between and within time series. In a particular type of these series, the previous values of the currently observed series are contingent on the lagged values of another series. These cases can commonly be addressed by regression. However, a model selection criteria should be employed to evaluate the compromise between the amount of information provided and the model complexity. This is the basis for the development of the Matrix of Lags (MoL), a tool to study dependent time series. METHODS: For each input, multiple regressions were applied to produce a model for each lag and a model selection criterion identifies the lags that will populate an auxiliary matrix. Afterwards, the energy of the lags (that are in the auxiliary matrix) was used to define a row of the MoL. Therefore, each input corresponds to a row of the MoL. To test the proposed tool, the heart rate variability and the electrocardiogram derived respiration were employed to perform the indirect estimation of the electroencephalography cyclic alternating pattern (CAP) cycles. Therefore, a support vector machine was fed with the MoL to perform the CAP cycle classification for each input signal. Multiple tests were carried out to further examine the proposed tool, including the effect of balancing the datasets, application of other regression methods and employment of two feature section models. The first was based on sequential backward selection while the second examined characteristics of a return map. RESULTS: The best performance of the subject independent model was attained by feeding the lags, selected by sequential backward selection, to a support vector machine, achieving an average accuracy, sensitivity, specificity and area under the receiver operating characteristic curve of, respectively, 77%, 71%, 82% and 0.77. CONCLUSIONS: The developed model allows to perform a measurement of a characteristic marker of sleep instability (the CAP cycle) and the results are in the upper bound of the specialist agreement range with visual analysis. Thus, the developed method could possibly be used for clinical diagnosis.

A Systematic Review of Detecting Sleep Apnea Using Deep Learning.

Sleep apnea is a sleep related disorder that significantly affects the population. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score. Numerous researchers have proposed and implemented automatic scoring processes to address these issues, based on fewer sensors and automatic classification algorithms. Deep learning is gaining higher interest due to database availability, newly developed techniques, the possibility of producing machine created features and higher computing power that allows the algorithms to achieve better performance than the shallow classifiers. Therefore, the sleep apnea research has currently gained significant interest in deep learning. The goal of this work is to analyze the published research in the last decade, providing an answer to the research questions such as how to implement the different deep networks, what kind of pre-processing or feature extraction is needed, and the advantages and disadvantages of different kinds of networks. The employed signals, sensors, databases and implementation challenges were also considered. A systematic search was conducted on five indexing services from 2008-2018. A total of 255 papers were found and 21 were selected by considering the inclusion and exclusion criteria, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach.

Sleep quality of subjects with and without sleep-disordered breathing based on the cyclic alternating pattern rate estimation from single-lead ECG.

OBJECTIVE: The term sleep quality is widely used by researchers and clinicians despite the lack of a definitional consensus, due to different assumptions on quality quantification. It is usually assessed using subject self-reporting, a method that has a major limitation since the subject is a poor self-observer of their sleep behaviors. A more precise method requires the estimation of physiological signals through polysomnography, a procedure that has high costs, is uncomfortable for the subjects and it is unavailable to a large group of the world population. To address these issues, a sleep quality prediction method was developed based on the analysis of the cyclic alternating pattern rate estimated using a single-lead electrocardiogram. APPROACH: The algorithm analyzes the causality, entropy of the variability and connection of respiratory volume and the N-N interbeat intervals as features for a classifier to assess the cyclic alternating pattern and non-rapid eye movement periods. This information was then combined to estimate the cyclic alternating pattern rate and define the quality of sleep by considering the age-related cyclic alternating pattern rate percentages as a reference threshold. MAIN RESULTS: The best results were achieved using a deep stacked autoencoder as a classifier and employing the minimal-redundancy-maximal-relevance as feature selection algorithm. Data collected from three databases and one hospital were used for training and testing the algorithms, achieving an average accuracy of, respectively, 76% and 77% for the cyclic alternating pattern and non-rapid eye movement sleep classification. The predicted sleep quality achieved a high agreement when considering either the cyclic alternating pattern rate, the arousal index, apnea-hypopnea index or the sleep efficiency as quantification for sleep quality. A moderate correlation was achieved with the Epworth sleepiness score and Pittsburgh sleep quality index. Total sleep time presented a higher variation on the correlation analysis. SIGNIFICANCE: The developed method is capable of estimating the sleep quality and is characterized by a low intra-individual variability. It only requires a small number of sensors that can easily be self-assembled, and could possibly lead to new developments in sleep quality estimation by home monitoring devices.

A Review of Obstructive Sleep Apnea Detection Approaches.

Sleep disorders are a common health condition that can affect numerous aspects of life. Obstructive sleep apnea is one of the most common disorders and is characterized by a reduction or cessation of airflow during sleep. In many countries, this disorder is usually diagnosed in sleep laboratories, by polysomnography, which is an expensive procedure involving much effort for the patient. Multiple systems have been proposed to address this situation, including performing the examination and analysis in the patient's home, using sensors to detect physiological signals that are automatically analyzed by algorithms. However, the precision of these devices is usually not enough to provide clinical diagnosis. Therefore, the objective of this review is to analyze already existing algorithms that have not been implemented on hardware but have had their performance verified by at least one experiment that aims to detect obstructive sleep apnea to predict trends. The performance of different algorithms and methods for apnea detection through the use of different sensors (pulse oximetry, electrocardiogram, respiration, sound, and combined approaches) has been evaluated. 84 original research articles published from 2003 to 2017 with the potential to be promising diagnostic tools have been selected to cover multiple solutions. This paper could provide valuable information for those researchers who want to carry out a hardware implementation of potential signal processing algorithms.

Sleep Quality Estimation by Cardiopulmonary Coupling Analysis.

The gold standard for assessment of sleep quality is the polysomnography, where physiological signals are used to generate both quantitative and qualitative measurements. Despite the production of highly accurate results, polysomnography is a complex, uncomfortable, and expensive process, inaccessible to a large group of the population. Home monitoring devices were developed to address these issues, fitting the growing perspective of health care and focusing on prevention and wellness. The objective of this paper was to develop an algorithm capable of estimating the quality of sleep, by analyzing the cyclic alternating pattern rate. The algorithm uses a single-lead electrocardiogram to produce a spectrographic measure of the cardiopulmonary coupling that in turn was fed to a classifier to estimate the non-rapid eye movement sleep and the presence of the cyclic alternating pattern. Two classifiers were tested, a feedforward neural network and a deeply stacked autoencoder, with the second achieving better results, correctly classifying 77% of the subjects sleep quality (either good or bad). The developed method can be implemented in a home monitoring device to estimate the sleep quality in a non-invasive way and improve the detection of pathologies.

Devices for home detection of obstructive sleep apnea: A review.

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.