Atena Roshan Fekr

A Medical Cloud-Based Platform for Respiration Rate Measurement and Hierarchical Classification of Breath Disorders

The measurement of human respiratory signals is crucial in cyberbiological systems. A disordered breathing pattern can be the first symptom of different physiological, mechanical, or psychological dysfunctions. Therefore, a real-time monitoring of the respiration patterns, as well as respiration rate is a critical need in medical applications. There are several methods for respiration rate measurement. However, despite their accuracy, these methods are expensive and could not be integrated in a body sensor network. In this work, we present a real-time cloud-based platform for both monitoring the respiration rate and breath pattern classification, remotely. The proposed system is designed particularly for patients with breathing problems (e.g., respiratory complications after surgery) or sleep disorders. Our system includes calibrated accelerometer sensor, Bluetooth Low Energy (BLE) and cloud-computing model. We also suggest a procedure to improve the accuracy of respiration rate for patients at rest positions. The overall error in the respiration rate calculation is obtained 0.53% considering SPR-BTA spirometer as the reference. Five types of respiration disorders, Bradapnea, Tachypnea, Cheyn-stokes, Kaussmal, and Biots breathing are classified based on hierarchical Support Vector Machine (SVM) with seven different features. We have evaluated the performance of the proposed classification while it is individualized to every subject (case 1) as well as considering all subjects (case 2). Since the selection of kernel function is a key factor to decide SVMs performance, in this paper three different kernel functions are evaluated. The experiments are conducted with 11 subjects and the average accuracy of 94.52% for case 1 and the accuracy of 81.29% for case 2 are achieved based on Radial Basis Function (RBF). Finally, a performance evaluation has been done for normal and impaired subjects considering sensitivity, specificity and G-mean parameters of different kernel functions.

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results.

Design and Evaluation of an Intelligent Remote Tidal Volume Variability Monitoring System in E-Health Applications

A reliable long-term monitoring and diagnosis of breath disorders at an early stage provides an improvement of medical act, life expectancy, and quality of life while decreasing the costs of treatment and medical services. Therefore, a real-time unobtrusive monitoring of respiration patterns, as well as breath parameters, is a critical need in medical applications. In this paper, we propose an intelligent system for patient home care, capable of measuring respiration rate and tidal volume variability via a wearable sensing technology. The proposed system is designed particularly for the goal of diagnosis and treatment in patients with pathological breathing, e.g., respiratory complications after surgery or sleep disorders. The complete system was comprised of wearable calibrated accelerometer sensor, Bluetooth low energy, and cloud database. The experiments are conducted with eight subjects and the overall error in respiration rate calculation is obtained 0.29%±0.33% considering SPR-BTA spirometer as the reference. We also introduce a method for tidal volume variability estimation while validated using Pearson correlation. Furthermore, since it is essential to detect the critical events resulted from sudden rise or fall in per breath tidal volume of the patients, we provide a technique to automatically find the accurate threshold values based on each individual breath characteristics. Therefore, the system is able to detect the major changes, precisely by more than 98%, and provide immediate feedback such as sound alarm for round-the-clock respiration monitoring.

MSE minimization and fault-tolerant data fusion for multi-sensor systems

Multi-sensor data fusion is an efficient method to provide both accurate and fault-tolerant sensor readouts. Furthermore, detection of faults in a reasonably short amount of time is crucial for applications dealing with high risks. In order to deliver high accuracies for the sensor measurements, it is required to perform a calibration for each sensor. This paper focuses on designing a fault-tolerant calibrated multisensor system. First, the least squares method is applied to calibrate each sensor using a linear curve fitting function. Next, an analytical technique is proposed to carry out a fault-tolerant multi-sensor data fusion, while minimizing the Mean-Square-Error (MSE) for the final sensor readout. While our data fusion approach is applicable to different multi-sensor systems, the experimental results are shown for 16 temperature sensors, where an environmental thermal chamber was used as the reference model to calibrate the sensors and perform the measurements.

Respiration Disorders Classification With Informative Features for m-Health Applications

Respiratory disorder is a highly prevalent condition associated with many adverse health problems. As the current means of diagnosis are obtrusive and ill-suited for real-time m-health applications, we explore a convenient and low-cost automatic approach that uses wearable microelectromechanical system sensor technology. The proposed system introduces the use of motion sensors to detect the changes in the anterior-posterior diameter of the chest wall during breathing function as well as extracting the informative respiratory features to be used for breathing disorders classification. Extensive evaluations are provided on six well-known classifiers with novel feature extraction techniques to distinguish among eight different pathological breathing patterns. The effects of the number of sensors, sensor placement, as well as feature selection on the classification performance are discussed. The experimental results conducted with ten subjects show the best accuracy rates of 97.50% by support vector machine and 97.37% with decision tree bagging (DTB) with all features and after feature selection, correspondingly. Furthermore, a binary classification is proposed for distinguishing between healthy people and patients with breath problems. The different assessments of classification parameters are provided by measuring the accuracy, sensitivity, specificity, F1-score and Mathew correlation coefficient. The accuracy rates above 98% suggest superior performance of DTB in binary recognition supported by the suggested new features.

Tidal volume variability and respiration rate estimation using a wearable accelerometer sensor

The measurement of respiration rate and tidal volume variability are critical to the diagnosis and monitoring of a wide range of breath disorders as well as being useful broader parameters of a patients condition. This paper presents a portable real-time platform designed to support a computationally efficient human respiratory tracking system for medical applications. The proposed system is designed particularly for patients with breathing problems (e.g. respiratory complications after surgery) or sleep disorders. We introduce the use of accelerometer sensor to detect changes in the anterior-posterior diameter of the chest; whereas these changes provide an accurate measurement of respiration rate as well as tidal volume variability. The complete system was comprised of wearable calibrated accelerometer sensor, Bluetooth Low Energy (BLE) and cloud database. The experiments are conducted with 8 subjects and the overall error in respiration rate calculation is obtained 0.2% considering SPR-BTA spirometer as the reference. We also present a method for Tidal Volume variability (TVvar) estimation while validated using Pearson correlation. The mean value of the correlation coefficient between TVvar derived from the accelerometer and spirometer for all subjects and three breath patterns is 0.87 which shows a high correspondence of two signals. Furthermore, the results indicate that the accelerometer driven TVvar achieves the average MSE 1.6E-03±3.69E-03 compared to the reference.

Design of an e-Health Respiration and Body Posture Monitoring System and Its Application for Rib Cage and Abdomen Synchrony Analysis

Automated methods of real-time and un-obstructive patients respiration and position monitoring have been subjects of interests in e-health applications. The present study implements a low-cost and convenient monitoring system for patients with breathing problems or sleep disorders. We have also captured the Rib Cage (RC) and Abdomen (AB) movements using accelerometer sensors. In addition to measurement of phase shift between the chest wall compartments, the impacts of different body positions on AB and RC motions have been investigated. The performance of the presented system is evaluated and the average Mean Square Error (MSE) of 0.14 is achieved for three breath timing variables. Moreover, the overall errors of phase angles for paradoxical and synchronous breath patterns are 0.25°±0.06 and 0.26°±6E-03, respectively. The system properly indicates a significant increase in the degree of ribcage and abdomen asynchrony in the paradoxical breathing compared to normal pattern.

Development of a Remote Monitoring System for Respiratory Analysis

In order to prevent the lack of appropriate respiratory ventilation which causes brain damage and critical problems, it is required to continuously monitor the breathing signal of a patient. There are different conventional methods for capturing respiration signal, such as polysomnography and spirometer. In spite of their accuracy, these methods are expensive and could not be integrated in a body sensor network. In this work, we present a real-time cloud-based respiration monitoring platform which allows the patient to continue treatment and diagnosis from different places such as home. These remote services are designed for patients who suffer from breathing problems or sleep disorders. Our system includes calibrated accelerometer sensor, Bluetooth Low Energy (BLE) and cloud database. Based on the high correlation between spirometer and accelerometer signals, the Detrended Fluctuation Analysis (DFA) has been applied on respiration signals. The obtained results show that DFA can be used as an efficient feature while classifying the healthy people from patients suffering from breath abnormalities.

Multi-sensor blind recalibration in mHealth applications

This paper considers the problem of self-calibration of multi-sensor systems for health care cyber-biological systems, such as closed-loop glucose control. The recalibration method is performed periodically in the cloud resulted in significant advantages over traditional methods, including increased on-line accessibility and fast automated recovery from failures. Since the size of dataset has direct impact on the recalibration quality, we use cloud database which let us have a more complete recalibration dataset compared to limited on-board logging at different times and situations. Three methods are presented and evaluated in terms of accuracy and time. The proposed Minimum Mean Square Error (MMSE) recalibration method delivers the superior precision compared to other two techniques which are based on average and correlation. While all these approaches are generic and applicable to different medical multi-sensor systems, the experimental results are evaluated on temperature sensors due to their simple and reliable setup.

Affordable erehabilitation monitoring platform

People who have suffered a motor function disability need to practice appropriate rehabilitation treatments. Motion sensors such as accelerometer and gyroscope in fact are increasingly being embedded in wearable computing devices and can provide a quantitative measure of the human movement for assessment. In this paper, we present a low-cost eRehabilitation platform employing efficient algorithms to provide high accuracy feedback. The provided online rehabilitation service is removing the traditional face-to-face services by using cutting-edge mobile and sensors technologies. It allows doctors to give the patients qualitative feedback and track their progress over time. This system considers the variability in movement speed and accurate angle measurements. To this end, the golden standard pattern collected under physiotherapist supervision is compared with the patients exercises based on Dynamic Time Warping (DTW) algorithm. The experiments were conducted in a laboratory with different subjects, and results confirm that low-cost MEMS technology achieves an acceptable accuracy level in real-time rehabilitation monitoring. We also address different encountered issues and discuss how to efficiently tackle with them.