Obstructive Sleep Apnea Detection based on ECG Signal using Statistical Features of Wavelet Subband

One of the respiratory disorders is obstructive sleep apnea (OSA). OSA occurs when a person sleeps. OSA causes breathing to stop momentarily due to obstruction in the airways. In this condition, people with OSA will be deprived of oxygen, sleep awake and short of breath. Diagnosis of OSA by a doctor can be done by confirming the patient\u27s complaints during sleep, sleep patterns, and other symptoms that point to OSA. Another way of diagnosing OSA is a polysomnography (PSG) examination in the laboratory to analyze apnea and hypopnea. However, this examination tends to be high cost and time consuming. An alternative diagnostic tool is an electrocardiogram (ECG) examination referring to changes in the mechanism of ECG-derived respiration (EDR). So digital ECG signal analysis is a potential tool for OSA detection. Therefore, in this study, it is proposed to classify OSA based on ECG signals using wavelets and statistical parameters. Statistical parameters include mean, variance, skewness kurtosis entropy calculated on the signal decomposition results. The validation performance of the proposed method is carried out using a support vector machine, k-nearest neighbor (k-NN), and ensemble classifier. The proposed method produces the highest accuracy of 89.2% using a bagged tree where all features are used as predictors. From this study, it is hoped that ECG signal analysis can be used to complete clinical diagnosis in detecting OSA

Respiratory Anomaly Detection using Reflected Infrared Light-wave Signals

In this study, we present a non-contact respiratory anomaly detection method using incoherent light-wave signals reflected from the chest of a mechanical robot that can breathe like human beings. In comparison to existing radar and camera-based sensing systems for vitals monitoring, this technology uses only a low-cost ubiquitous light source (e.g., infrared light emitting diode) and sensor (e.g., photodetector). This light-wave sensing (LWS) system recognizes different breathing anomalies from the variations of light intensity reflected from the chest of the robot within a 0.5m-1.5m range. The anomaly detection model demonstrates up to 96.6% average accuracy in classifying 7 different types of breathing data using machine learning. The model can also detect faulty data collected by the system that does not contain breathing information. The developed system can be utilized at home or healthcare facilities as a smart, non-contact and discreet respiration monitoring method.Comment: 5 pages, 4 figures, submitted to IEEE conferenc

Characterization and processing of novel neck photoplethysmography signals for cardiorespiratory monitoring

Epilepsy is a neurological disorder causing serious brain seizures that severely affect the patients' quality of life. Sudden unexpected death in epilepsy (SUDEP), for which no evident decease reason is found after post-mortem examination, is a common cause of mortality. The mechanisms leading to SUDEP are uncertain, but, centrally mediated apneic respiratory dysfunction, inducing dangerous hypoxemia, plays a key role. Continuous physiological monitoring appears as the only reliable solution for SUDEP prevention. However, current seizure-detection systems do not show enough sensitivity and present a high number of intolerable false alarms. A wearable system capable of measuring several physiological signals from the same body location, could efficiently overcome these limitations. In this framework, a neck wearable apnea detection device (WADD), sensing airflow through tracheal sounds, was designed. Despite the promising performance, it is still necessary to integrate an oximeter sensor into the system, to measure oxygen saturation in blood (SpO2) from neck photoplethysmography (PPG) signals, and hence, support the apnea detection decision. The neck is a novel PPG measurement site that has not yet been thoroughly explored, due to numerous challenges. This research work aims to characterize neck PPG signals, in order to fully exploit this alternative pulse oximetry location, for precise cardiorespiratory biomarkers monitoring. In this thesis, neck PPG signals were recorded, for the first time in literature, in a series of experiments under different artifacts and respiratory conditions. Morphological and spectral characteristics were analyzed in order to identify potential singularities of the signals. The most common neck PPG artifacts critically corrupting the signal quality, and other breathing states of interest, were thoroughly characterized in terms of the most discriminative features. An algorithm was further developed to differentiate artifacts from clean PPG signals. Both, the proposed characterization and classification model can be useful tools for researchers to denoise neck PPG signals and exploit them in a variety of clinical contexts. In addition to that, it was demonstrated that the neck also offered the possibility, unlike other body parts, to extract the Jugular Venous Pulse (JVP) non-invasively. Overall, the thesis showed how the neck could be an optimum location for multi-modal monitoring in the context of diseases affecting respiration, since it not only allows the sensing of airflow related signals, but also, the breathing frequency component of the PPG appeared more prominent than in the standard finger location. In this context, this property enabled the extraction of relevant features to develop a promising algorithm for apnea detection in near-real time. These findings could be of great importance for SUDEP prevention, facilitating the investigation of the mechanisms and risk factors associated to it, and ultimately reduce epilepsy mortality.Open Acces

Obstrüktif uyku apne teşhisi için makine öğrenmesi tabanlı yeni bir yöntem geliştirilmesi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Obstrüktif Uyku Apne (OSA) uykuda solunumun durmasına bağlı olarak ortaya çıkan bir hastalıktır. Hastalığın teşhisi polisomnografi (PSG) cihazı kullanılarak uyku evreleme ve solunum skorlama adımları ile gerçekleştirilir. Sistem yapısı gereği teşhis sırasında hastaya birçok rahatsızlık vermektedir. Verilen rahatsızlıklara çözüm olabilecek, PSG cihazına alternatif sistemlere ihtiyaç duyulmaktadır. Bu tez çalışmasında, PSG cihazına alternatif yeni bir yaklaşım geliştirilmiştir. Bu yaklaşım ile PSG'ye alternatif, hastaya daha az rahatsızlık veren ve PSG kadar güvenilir bir cihazın oluşturulabileceği ispatlanmıştır. Çalışmada, 10 bireyden alınan Fotopletismografi (PPG) sinyali kullanılmıştır. Teşhis için PPG sinyali ve bu sinyalden türetilen Kalp Hızı Değişkeni (HRV) kullanılarak yapay zeka tabanlı teşhis algoritması tasarlanmıştır. Çalışma için PPG'den 46, HRV'den 40 adet olmak üzere toplam 86 özellik çıkarılmıştır. Çıkarılan özelliklerin, Mann-Whitney U Testi yöntemiyle, istatistiksel olarak, uyku uyanıklık ve anormal solunumsal olaylar (apne var - yok) için ayırt edici olup olmadığı tespit edilmeye çalışılmıştır. Ayrıca, özellikler, F-score özellik seçme yöntemleriyle 2 defa azaltılmış ve sınıflandırılmıştır. İstatistiksel sonuçlara göre, uyku evreleme işlemi için, 86 özellikten 75'inin uyku uyanıklık için anlamlı olduğu (p<0,05), solunum skorlamada ise 58 özelliğin anlamlı olduğu (p<0,05) tespit edilmiştir. Sınıflandırma sonuçlarına göre uyku evreleme 11 özellik ile, %84,93 duyarlılık, %97,40 özgüllük ve %91,09 sınıflandırma doğruluk oranı ile topluluk sınıflandırıcısı yardımıyla başarı ile sınıflandırılmıştır. Solunum skorlama işlemi, 86 özellik ile, %87,78 duyarlılık, %95,46 özgüllük ve %92,54 doğruluk oranı ile başarıyla gerçekleştirilmiştir. Bu çalışmada elde edilen sonuçlara göre, PPG sinyali ve bu sinyalden türetilen HRV özelliklerinin uyku evreleme ve solunum skorlama işleminde kullanılabileceği ve anlamlı sonuçlar vereceği kanısına varılmıştır. PPG sinyalinin kolay elde edilebilmesi ve HRV'nin PPG sinyalinden türetilmesi tek sinyal ile uyku evreleme ve solunum skorlama işleminin yapılabilmesinin önünü açmaktadır. Gerçek zamanlı çalışabilecek sistemlerde sinyalin kolay ölçülebilir ve kolay işlenebilir olması sistemlerin pratikliğini arttıracaktır.Obstructive Sleep Apnea (OSA) is a disease caused by breathlessness in sleep. Diagnosis of the disease is performed by polysomnography (PSG) device with sleep staging and respiratory scoring steps. The system structure causes many discomfort to the patient during diagnosis. Alternative systems are needed for the PSG device, which can be a solution to the inconveniences. In this thesis study, a new approach was developed to PSG device. This approach has been proven that an alternative to PSG is to create a device that is less disturbing to the patient and as reliable as PSG. In the study, a Photoplethysmography (PPG) signal from 10 individuals was used. For diagnosis, an artificial intelligence-based diagnostic algorithm is designed using PPG signal and Heart Rate Variable (HRV) derived from PPG. For the study, 86 features were extracted, 46 of PPG and 40 of HRV. Statistically, the Mann-Whitney U test was used to determine whether the extracted features were discriminatory for sleep – wakefulness and abnormal respiratory events (apnea present - absent). In addition, features are reduced by F-score property selection methods 2 times and classified. According to the statistical results, 75 of the 86 features were significant for sleep awake (p<0,05) and 58 for respiratory scoring (p<0,05). According to the classification results, the sleep classification was successfully classified with the help of ensemble classifier with 11 features, 84,93% sensitivity, 97,40% specificity and 91,09% classification accuracy. Respiratory scoring was successfully performed with 86 features with 87,78% sensitivity, 95.46% specificity and 92.54% classification accuracy. According to the results obtained in this study, it was concluded that features of the PPG signal and the HRV derived from PPG can be used in the sleep staging and respiratory scoring process and have meaningful results. The easy acquisition of the PPG signal and the derivation of the HRV from the PPG signal opens up the possibility of performing sleep staging and respiratory scoring with a single signal. In systems that can operate in real time, easy measurement and easy handling of the signal will increase the practicality of the systems

Pulse Rate Variability for the Assessment of Cardiovascular Changes

Pulse rate variability (PRV) describes the way pulse rate changes through time and is measured from pulsatile signals such as the photoplethysmogram (PPG). It has been proposed as a surrogate for heart rate variability (HRV). Nonetheless, the relationship between these variables is not entirely clear, probably due to both physiological and technical aspects involved in the extraction of PRV. Moreover, the effects of cardiovascular changes on PRV have not been elucidated. In this thesis, four studies were performed to (1) determine the best combination of some technical aspects for the extraction of PRV from PPG signals; (2) evaluate the relationship between PRV and HRV under different cardiovascular conditions; and (3) explore the effects of cardiovascular changes on PRV. First, PRV extraction gave lower errors when (1) signals were acquired for at least 120 s with a 256 Hz sampling rate and filtered with lower low cut-off frequencies and elliptic, equiripple or Parks-McClellan filter; (2) cardiac cycles were determined using the D2max algorithm and the a fiducial points; and (3) the Fast Fourier Transform was applied to obtain frequency spectra. Secondly, the relationship between HRV and PRV was found to be affected by cold exposure and changes in blood pressure, while PRV was found to be different at different body sites. Finally, PRV was affected by haemodynamic changes, such as target flow, stroke rate and blood pressure, both in an in-vitro model and in-vivo data. Additionally, PRV was found to be a potential tool for the estimation of blood pressure, with errors as low as 1:54 ± 0:17 mmHg, 1:07 ± 0:06 mmHg and 1:22 ± 0:09 mmHg for the estimation of systolic, diastolic and mean arterial pressure. Although more studies are needed to fully understand PRV and its clinical potential, PRV should not be regarded as the same as HRV, and it could be consider as a potential valuable biomarker for cardiovascular health

2021 ISHNE/ HRS/ EHRA/ APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society.

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/ Heart Rhythm Society/ European Heart Rhythm Association/ Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored

Papüloskuamöz Hastalıkların Belirlenmesi için Yapay Zeka Yöntemleriyle Kural Tabanlı Teşhis Algoritmalarının Geliştirilmesi

Papüloskuamöz deri hastalıkları halk arasında oldukça sık rastlanan ve kendine has morfolojik özellikleri olan deri hastalıkları grubudur. Papüloskuamöz deri hastalıklarının alt gruplarının belirtileri birbirine çok yakın olduğu için teşhis süreci bazı durumlarda zahmetlidir. Hastalığın teşhisi klinik muayenede konulabilir. Klinik muayenenin yetersiz olduğu durumlarda, tanı deri biyopsisi ile histopatolojik değerlendirme ile konulmaktadır. Bu süreçte dermatolog ve patoloğun uyumlu bir şekilde çalışması ve her iki hekimin de teşhis süreci ile ilgili bilgi birikiminin iyi olması gerekir. Bu yüzden Papüloskuamöz deri hastalıklarının tanısı deri biyopsisine ihtiyaç duyulmadan sadece klinik muayene ile dematolog tarafından konulabilmesi için daha basit, yüksek başarı oranına sahip ve klinikte kullanılabilir yöntemlere ihtiyaç duyulmaktadır. Bu çalışmanın amacı Papüloskuamöz deri hastalıklarının yüksek başarı oranı ile tespit edebilecek, klinikte dermatolog tarafından kullanılabilecek, yapay zeka yöntemleriyle geliştirilmiş kural tabanlı algoritma geliştirmektir. Çalışma kapsamında daha önce toplanmış veri seti kullanılmıştır. Veri setinde Papüloskuamöz deri hastalıklarının altı farklı alt grubu için klinik ve histopatolojik bulgular bulunmaktadır. Öncelikle veri seti ikişer sınıflı olacak şekilde gruplandırılmıştır. Daha sonra özellik seçme algoritmalarıyla klinik ve histopatolojik bulgular seçilmiştir. Daha sonra karar ağaçları yardımıyla kural tabanlı teşhis algoritmaları oluşturulmuştur. Çalışma sonucunda, sadece seçilmiş klinik bulgular kullanılarak ortalama %82.98 doğruluk oranı, 0.89 duyarlılık, 0.76 özgüllük oranıyla Papüloskuamöz deri hastalıkları kural tabanlı algoritmalar geliştirilmiştir. Sonuç olarak, bu çalışmada elde edilen sonuçlara göre, çalışma kapsamında geliştirilen algoritmalar, Papüloskuamöz deri hastalıklarının teşhisi için yapay zeka yöntemleriyle geliştirilen yüksek doğruluk oranına sahip kural tabanlı algoritmalar klinikte kullanılabilir

Development of Rule-Based Diagnostic Algorithms with Artificial Intelligence Methods to Identify Papulosquamous Diseases

Papüloskuamöz deri hastalıkları halk arasında oldukça sık rastlanan ve kendine has morfolojik özellikleri olan deri hastalıkları grubudur. Papüloskuamöz deri hastalıklarının alt gruplarının belirtileri birbirine çok yakın olduğu için teşhis süreci bazı durumlarda zahmetlidir. Hastalığın teşhisi klinik muayenede konulabilir. Klinik muayenenin yetersiz olduğu durumlarda, tanı deri biyopsisi ile histopatolojik değerlendirme ile konulmaktadır. Bu süreçte dermatolog ve patoloğun uyumlu bir şekilde çalışması ve her iki hekimin de teşhis süreci ile ilgili bilgi birikiminin iyi olması gerekir. Bu yüzden Papüloskuamöz deri hastalıklarının tanısı deri biyopsisine ihtiyaç duyulmadan sadece klinik muayene ile dematolog tarafından konulabilmesi için daha basit, yüksek başarı oranına sahip ve klinikte kullanılabilir yöntemlere ihtiyaç duyulmaktadır. Bu çalışmanın amacı Papüloskuamöz deri hastalıklarının yüksek başarı oranı ile tespit edebilecek, klinikte dermatolog tarafından kullanılabilecek, yapay zeka yöntemleriyle geliştirilmiş kural tabanlı algoritma geliştirmektir. Çalışma kapsamında daha önce toplanmış veri seti kullanılmıştır. Veri setinde Papüloskuamöz deri hastalıklarının altı farklı alt grubu için klinik ve histopatolojik bulgular bulunmaktadır. Öncelikle veri seti ikişer sınıflı olacak şekilde gruplandırılmıştır. Daha sonra özellik seçme algoritmalarıyla klinik ve histopatolojik bulgular seçilmiştir. Daha sonra karar ağaçları yardımıyla kural tabanlı teşhis algoritmaları oluşturulmuştur. Çalışma sonucunda, sadece seçilmiş klinik bulgular kullanılarak ortalama %82.98 doğruluk oranı, 0.89 duyarlılık, 0.76 özgüllük oranıyla Papüloskuamöz deri hastalıkları kural tabanlı algoritmalar geliştirilmiştir. Sonuç olarak, bu çalışmada elde edilen sonuçlara göre, çalışma kapsamında geliştirilen algoritmalar, Papüloskuamöz deri hastalıklarının teşhisi için yapay zeka yöntemleriyle geliştirilen yüksek doğruluk oranına sahip kural tabanlı algoritmalar klinikte kullanılabilir.Papulosquamous skin diseases are common skin diseases and have morphological features. The diagnosis process_x000D_ is sometimes troublesome, as the symptoms of the subgroups of papulosquamous skin diseases are very close to_x000D_ each other. The diagnosis of the disease can be made at the clinical examination. In cases where the clinical_x000D_ examination is insufficient, the diagnosis is made by histopathological evaluation by skin biopsy. In this process,_x000D_ dermatologists and pathologists should work in harmony, and both doctors should have a good knowledge of the_x000D_ diagnosis process. Therefore, more uncomplicated, higher success rate, and clinically practical methods are needed_x000D_ in order for Papulosquamous skin diseases to be established only by a clinical examination by a dermatologist_x000D_ without the need for a skin biopsy. This study aims to develop a rule-based algorithm that can detect_x000D_ Papulosquamous skin diseases with a high success rate, can be used by dermatologists in the clinic, developed_x000D_ with artificial intelligence methods. Within the scope of the study, the previously collected data set was used. The_x000D_ data set contains clinical and histopathological findings for six different subgroups of Papulosquamous skin_x000D_ diseases. Firstly, the data set is grouped into two classes. Then, clinical and histopathological findings were_x000D_ selected with feature selection algorithms. Then, rule-based diagnostic algorithms were created with the help of_x000D_ decision trees. As a result of the study, Papulosquamous skin diseases rule-based algorithms have been developed_x000D_ with an average of 82.98% accuracy rate, 0.89 sensitivity, and 0.76 specificity rate using only selected clinical_x000D_ findings. Consequently, according to the results obtained in this study, algorithms developed within the scope of_x000D_ the study, high-accuracy rule-based algorithms developed with artificial intelligence methods can be used in the_x000D_ clinic for the diagnosis of Papulosquamous skin diseases.In job-shop production systems, orders are assigned to work centers according to their routes, and their operations are performed in this order. Production is becoming more and more complex with the increasing number of product lines and work centers with different routes. Decisions to be made according to the realtime monitoring of a dynamic production environment have become important. With the Fourth Industrial Revolution, information technologies are widely used in industries. A large amount of data is obtained from production tools that are capable of communicating with each other by means of Industry 4.0 and the internet of things. In this study, a simulation model of a production system that can collect data in real-time via sensors in work centers has been created and operation conditions have been determined. Then, work center / machine loading strategies were compared according to the delay periods of the jobs. The simulation model with the best loading strategy was run according to three different demand rates. Then data related with the delay status of the orders and the status of the work centers was obtained. The data were evaluated with data mining classification algorithms and rules were determined for delayed jobs. These rules were added to the simulation model as a decision mechanism. When an order is received in this model, the expert system estimates whether or not there will be a delay, and makes a decision to outsource the order’s production if needed. This approach further reduces the number of delayed order

Hybrid Artificial Intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists

Background and Objective: Tourism and travel sector continues to grow by gaining an important place in the world economy and many countries want to increase their share in this sector. At the same time, it is known that today's consumer tourism and travel purchase decisions are influenced by social media. By examining the data of consumers on social media, it is possible for businesses to reach the right person and get more efficiency from high-cost promotion activities. The study aims to analyze the historical data of users on TripAdvisor with artificial intelligence methods to reveal a profile of consumers who might prefer Turkey. Methods: In this context, TripAdvisor, which is one of the best-known websites in the tourism sector, is an important source of data for countries to increase their share in the tourism market. Inferences can be made by using artificial intelligence methods and the data in TripAdvisor together. In this study, as a case study, the potentials of Chinese tourists to prefer Turkey are dealt because Turkey has increased its tourism targets ten folds for China and the year 2018 was declared as "Turkey Tourism Year" in China. In this context, this study aims to determine the potentials of Chinese tourists to prefer Turkey, by processing travel data histories obtained from TripAdvisor with artificial intelligence methods. It is expected that the study will contribute to the tourism sector as well as the academic literature. The study used the travel data history of Chinese tourists taken from TripAdvisor. Significant travel histories were selected by the F-score method. Depending on the selected and all travel histories of users, their travel preferences (Turkey/France) were classified by artificial intelligence algorithms. The developed model was tested with performance criteria. Results: At the end of the study, it was ensured that the Chinese, who would prefer Turkey, were determined with an accuracy rate of 75.25% and sensitivity rate of 0.76. Conclusions: It was observed that it is possible to find the tourists who will prefer Turkey by using the developed system. In other words, the study revealed that the countries can reach the individual instead of masses in their promotional activities