Photoplethysmography and its application in clinical physiological measurement

Introduction. Fundamentals of Fuzzy Set Theory. Fuzzy Linear Programming. Fuzzy Nonlinear Programming. Interactive Multiobjective Linear Programming with Fuzzy Parameters. Interactive Multiobjective Nonlinear Programming with Fuzzy Parameters. Interactive Computer Programs. Some Applications. Further Research Directions. Index.

Fuzzy sets and interactive multiobjective optimization

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.

A Review of Obstructive Sleep Apnea Detection Approaches

During the last two decades, a substantial amount of research efforts has been intended for support vector machine at the application of various data mining tasks. Data Mining is a pioneering and attractive research area due to its huge application areas and task primitives. Support Vector Machine (SVM) is playing a decisive role as it provides techniques those are especially well suited to obtain results in an efficient way and with a good level of quality. In this paper, we survey the role of SVM in various data mining tasks like classification, clustering, prediction, forecasting and others applications. In broader point of view, we have reviewed the number of research publications that have been contributed in various internationally reputed journals for the data mining applications and also suggested a possible no. of issues of SVM. The main aim of this paper is to extrapolate the various areas of SVM with a basis of understanding the technique and a comprehensive survey, while offering researchers a modernized picture of the depth and breadth in both the theory and applications.

/pdf/a-comprehensive-survey-on-support-vector-machine-in-data-3dwml8it6v.pdf

A Comprehensive Survey on Support Vector Machine in Data Mining Tasks: Applications & Challenges

Readmission prediction using deep learning on electronic health records.

The identification of patients at high risk of unplanned readmission is an important component of discharge planning strategies aimed at preventing unwanted returns to hospital. The aim of this study was to investigate the factors associated with unplanned readmission in a Sydney hospital. We developed and compared validated readmission risk scores using routinely collected hospital data to predict 7-day, 30-day and 60-day all-cause unplanned readmission. A combination of gradient boosted tree algorithms for variable selection and logistic regression models was used to build and validate readmission risk scores using medical records from 62,235 live discharges from a metropolitan hospital in Sydney, Australia. The scores had good calibration and fair discriminative performance with c-statistic of 0.71 for 7-day and for 30-day readmission, and 0.74 for 60-day. Previous history of healthcare utilization, urgency of the index admission, old age, comorbidities related to cancer, psychosis, and drug-abuse, abnormal pathology results at discharge, and being unmarried and a public patient were found to be important predictors in all models. Unplanned readmissions beyond 7 days were more strongly associated with longer hospital stays and older patients with higher number of comorbidities and higher use of acute care in the past year. This study demonstrates similar predictors and performance to previous risk scores of 30-day unplanned readmission. Shorter-term readmissions may have different causal pathways than 30-day readmission, and may, therefore, require different screening tools and interventions. This study also re-iterates the need to include more informative data elements to ensure the appropriateness of these risk scores in clinical practice.

/pdf/predicting-7-day-30-day-and-60-day-all-cause-unplanned-16hflxcn9b.pdf

Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital

The Support Vector Machine (SVM) is one of the most popular machine learning algorithms for classification and regression. SVM displays outstanding performance when utilized in many applications. However, different approaches have been proposed in order to improve its performance in general and in special cases. This paper proposes a new method of improving SVM performance in general. This method can be applied to all the types of SVMs that have differing kernel types. The key aim of the proposed approach is to transfer more information from the training phase to the testing phase. This information is obtained from the misclassified data of the training phase, which is ignored in the classic SVM. Experimental results from eleven datasets from the real online resources show that this approach can improve the classification performances of C-SVM and @n-SVM without adding any parameters to the learner algorithm. Statistical tests show significance in this improvement. The proposed method has been found to improve accuracies of C-SVM and @n-SVM in more than 67% of the experiments in which 11% of these improvements are more than 5%. It must be noted that the highest improvement found with this method was 25%.

Self-advising support vector machine

A multiobjective approach for solving cooperative n-person games

Sleep disorders are common in a general population. It effect one in 5 adults and has several short term and long term bad side effects on health. Sleep apnea (SA) is the most important and common component of sleep disorders. This paper presents an automatic approach for detecting apnea events by using few bio-singles that are related to breathe defect. This work uses only air flow, thoracic and abdominal respiratory movement as input. The proposed algorithm consists of three main parts which are signal segmentation, feature generation and classification. A new proposed segmentation method intelligently segments the input signals for further classification, then features are generated for each segment by wavelet packet coefficients and also original signals. In classification phase a unique parallel PSO-SVM algorithm is investigated. PSO used to tune SVM parameters, and also data reduction. Proposed parallel structure used to help PSO to search space more efficiently, also avoiding fast convergence and local optimal results that are common problem in similar parallel algorithms. Obtained results demonstrate that the proposed method is effective and robust in sleep apnea detection and statistical tests on the results shown superiority of it versus previous methods even with more input signals, and also versus single PSO-SVM. Using fewer signals means more comfortable to subject and also, reduction of cost during recording the data.

A novel partially connected cooperative parallel PSO-SVM algorithm: Study based on sleep apnea detection

We present a new model and a new approach for solving fuzzy linear programming (FLP) problems with various utilities for the satisfaction of the fuzzy constraints. The model, constructed as a multi-objective linear programming problem, provides flexibility for the decision maker (DM), and allows for the assignment of distinct weights to the constraints and the ob- jective function. The desired solution is obtained by solving a crisp problem controlled by a parameter. We establish the validity of the proposed model and study the eect of the control parameter on the solution. We also illus- trate the eciency of the model and present three algorithms for solving the FLP problem, the first of which obtains a desired solution by solving a single crisp problem. The other two algorithms, interact with the decision maker, and compute a solution which achieves a given satisfaction level. Finally, we present an illustrative example showing that the solutions obtained are often even more satisfactory than asked for.

https://ijfs.usb.ac.ir/article_198_7226b83fe87443caa21599dac85680c6.pdf

Yashar Maali

Papers

Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital

Self-advising support vector machine

A multiobjective approach for solving cooperative n-person games

A novel partially connected cooperative parallel PSO-SVM algorithm: Study based on sleep apnea detection

Fuzzy linear programming with grades of satisfaction in constraints