Mohammadreza Balouchestani

Low Power Wireless Body Area Networks with Compressed sensing theory

Wireless Body Area Networks (WBANs) consist of small intelligent wireless sensors attached on or implanted in the body. These wireless sensors are responsible for collecting, processing, and transmitting vital information such as: blood pressure, heart rate, respiration rate, electrocardiographic (ECG), electroencephalography (EEG) and oxygenation signals to provide continuous health monitoring with real-time feedback to the users and medical centers. In order to fully exploit the benefits of WBANs for important applications such as Electronic Health (EH), Mobile Health (MH), and Ambulatory Health Monitoring (AHM), the power consumption must be minimized. Since Wireless Nodes (WNs) in WBANs are usually driven by battery power consumption is the most important factor to determine the life of WBANs. This paper presents the applications of Compressed Sensing (CS) theory in WBANs. We have achieved networks with low-sampling rate and low-power consumption on a number of applications. A combination of CS theory to WBANs is the optimal solution for achieving the networks with low-sampling rate and low-power consumption. Our simulation results in ECG signals show that sampling rate can be reduced t0 25% and power consumption to 35% without sacrificing performances by employing the CS theory to WBANs.

Increasing the reliability of wireless sensor network with a new testing approach based on compressed sensing theory

Wireless Sensor Networks (WSNs) consist of a large number of wireless nodes and are responsible for sensing, processing and monitoring environmental data. WSNs suffer of some problems such as limited processing capability, low storage capacity, limited time of testing and limited reliability. The Compressed sensing theory holds promising improvements to these parameters. Compressed Sensing shows that spars signals such as signals of WSNs can be exactly reconstructed from a small number of random linear measurements. With this in mind, we introduce a new mechanism of testing in wireless sensor network with compressed sensing theory in order to design a robust WSN with high reliability factor. This paper gives a background of compressed sensing theory, and then describes important concepts in wireless sensor networks, and finally our research combines the compressed sensing theory with wireless sensor network to introduce a new method for testing of wireless sensor networks with compressed sensing theory.

Wireless Body Area Networks with compressed sensing theory

With the rapid advancements of Wireless Sensor Networks (WSNs), wireless communication, and electronic technologies the area of wireless networks has grown significantly supporting a range of applications of Wireless Body Area Networks (WBANs) including Electronic Health (EH) and Mobile health (MH). Wireless Body Area Networks (WBANs) consist of small intelligent wireless sensors attached on or implanted in the body. These wireless sensors are responsible for collecting, processing, and transmitting vital information such as: blood pressure, heart rate, respiration rate, electrocardiographic (ECG), electroencephalography (EEG), oxygenation signals, respiratory rate, and temperature to provide continuous health monitoring with real-time feedback to the users and medical centers. In order to fully exploit the benefits of WBANs for important applications such as EH, MH, and Ambulatory Health Monitoring (AHM), the power consumption must be minimized. Each Wireless Node (WN) in WBANs must be designed to manage its local power supply in order to maximize total network lifetime. With this in mind, we want to employ Compressed Sensing (CS) to WBANs theory as a new sampling procedure to reduce load of sampling rate and minimize power consumption. Our simulation results show that sampling rate can be reduced to 30% of Nyquist Rate (NR) and power consumption to 40% in ECG signals without sacrificing reliability and availability by employing the CS theory to WBANs. This paper presents a novel sampling approach to WBANs using compressive sensing methods to WBANs.

Robust Wireless Sensor Networks with Compressed Sensing Theory

Wireless Sensor Networks (WSNs) consist of a large number of Wireless Nodes (WNs) each with sensing, processing, communication and power supply units to monitor the real-world environment information. The WSNs are responsible to sense, collect, process and transmit information such as pressure, temperature, position, flow, vibration, force, humidity, pollutants and biomedical signals like heart-rate and blood pressure. The ideal WSNs are networked to consume very limited power and are capable of fast data acquisition. The problems associated with WSNs are limited processing capability, low storage capacity, limited energy and global traffic. Also, WSNs have a finite life dependent upon initial power supply capacity and duty cycle. The WSNs are usually driven by a battery. Therefore, the primary limiting factor for the lifetime of a WN is the power supply. That is why; each WN must be designed to manage its local power supply of energy in order to maximize total network lifetime [5]. The life expectancy of a WSN for a given battery capacity can be enhanced by minimizing power consumption during the operation of the network. The CS theory solves the aforementioned problem by reducing the sampling rate throughout the network. A combination of CS theory to WSNs is the optimal solution for achieving the networks with low-sampling rate and low-power consumption. Our simulation results show that sampling rate can reduce to 30% and power consumption to 40% without sacrificing performances by employing the CS theory to WSNs. This paper presents a novel sampling approach using compressive sensing methods to WSNs. First, an overview of compressed sensing is presented. Second, CS in WSNs is investigated. Third, the simulation results on the sampling rate in WSNs are shown.

Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing

Surface Electromyography (sEMG) is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1) they are not able to provide real-time monitoring; (2) they suffer from long processing time and low speed; (3) they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS) architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC). At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL) framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR). In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD) to 24%, Root Mean Squared Error (RMSE) to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR) for the reconstruction process.

New sampling approach for wireless ECG systems with compressed sensing theory

Wireless Body Area Networks (WBANs) consist of small intelligent biomedical wireless sensors attached on or implanted to the body to collect vital biomedical data such as electrocardiogram (ECG) signals to provide continuous health monitoring systems for diagnostic and therapeutic purposes. ECG signals are widely used in health care systems because they are noninvasive mechanisms to establish medical diagnosis of heart diseases. In order to fully exploit the benefits of WBANs to Electronic Health (EH), Mobile Health (MH), and Ambulatory Health Monitoring Systems (AHMS) the power consumption and sampling rate should be restricted to a minimum. With this in mind, Compressed Sensing (CS) procedure and the collaboration of Block Sparse Bayesian Learning (BSBL) framework is used to provide new sampling approach for wireless ECG systems with CS theory. Advanced wireless ECG systems based on our approach will be able to deliver healthcare not only to patients in hospital and medical centers; but also in their homes and workplaces thus offering cost saving, and improving the quality of life. Our simulation results illustrate 25% reduction of Percentage Root-mean-square Difference (PRD) and a good level of quality for Signal to Noise Ratio (SNR), sampling-rate, and power consumption.

Fast clustering algorithm for large ECG data sets based on CS theory in combination with PCA and K-NN methods.

Long-term recording of Electrocardiogram (ECG) signals plays an important role in health care systems for diagnostic and treatment purposes of heart diseases. Clustering and classification of collecting data are essential parts for detecting concealed information of P-QRS-T waves in the long-term ECG recording. Currently used algorithms do have their share of drawbacks: 1) clustering and classification cannot be done in real time; 2) they suffer from huge energy consumption and load of sampling. These drawbacks motivated us in developing novel optimized clustering algorithm which could easily scan large ECG datasets for establishing low power long-term ECG recording. In this paper, we present an advanced K-means clustering algorithm based on Compressed Sensing (CS) theory as a random sampling procedure. Then, two dimensionality reduction methods: Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) followed by sorting the data using the K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers are applied to the proposed algorithm. We show our algorithm based on PCA features in combination with K-NN classifier shows better performance than other methods. The proposed algorithm outperforms existing algorithms by increasing 11% classification accuracy. In addition, the proposed algorithm illustrates classification accuracy for K-NN and PNN classifiers, and a Receiver Operating Characteristics (ROC) area of 99.98%, 99.83%, and 99.75% respectively.

Increasing the reliability of wireless body area networks based on compressed sensing theory

A Wireless Body Area network (WBAN) is a special purpose of Wireless Sensor Networks (WSNs) to connect various Biomedical Wireless Sensors (BWSs) located inside and outside of the human body to collect and transmit vital signals. The collected biomedical data send out via Gate Way (GW) to external databases at the hospitals and medical centers for diagnostic and therapeutic purposes. To increase the reliability of WBANs the power consumption and sampling-rate should be minimized in the Multipath Fading Channels (MFCs) between BWSs and GW. That is why an improving of MFCs as well as a low sampling-rate channel model is inevitably required for WBANs to expand WBANs to important applications such as Electronic Health (EH) and Mobile Health (MH). With this in mind, Compressed Sensing (CS) theory, as a new sampling procedure, is employed to MFCs in order to minimize power consumption and sampling-rate. The MFCs and the collaboration from an important platform for CS theory in order to provide lowpower and low sampling-rate WBANs expected to increase a lot in the future. Advance WBANs with MFCs based on CS theory will be able to deliver healthcare not only to patients in hospital and medical centers; but also in their homes and workplaces thus offering cost saving, and improving the quality of life. The simulation results confirm that detection probability of biomedical signals at GW increases by 25%, which will result in an increment in the received signal amplitude at GW. Our simulation results also illustrate that satisfying quality for Bit Error Rate (BER) can be achieved with CS.

Advanced K-means clustering algorithm for large ECG data sets based on K-SVD approach

Wireless electrocardiography (ECG) systems are crucial in detecting and diagnosing heart disorders. Minimizing power consumption and sampling-rate should be the key aspects when designing wireless ECG systems. In order to achieve portability coupled with ultra-low power consumption and sampling-rate, clustering and classification algorithms play an important role in developing wireless ECG systems. Currently used algorithms do have their share of drawbacks: 1) clustering and classification cannot be done in real time; 2) implementing existing algorithms would lead to higher computational costs. These drawbacks motivated us in developing novel optimized clustering algorithm which could easily scan large ECG datasets for characteristic bio-markers. In this paper, we present an advanced K-means clustering algorithm based on K-Singular Value Decomposition (K-SVD) approach with a connection to Compressed Sensing (CS) theory, followed by sorting the data using a K-Nearest Neighbours (K-NN) classifier. The proposed algorithm outperforms existing algorithms by achieving a classification accuracy of 99.3%. This ability allows reducing 15% of Average Classification Error (ACE). The proposed algorithm also reduces the clustering energy consumption by increasing the classification performance.

High - Resolution QRS detection algorithm for wireless ECG systems based on compressed sensing theory

Wireless electrocardiogram (ECG) systems are responsible to collect and transmit the vital signals of cardiac patients wirelessly to medical centers for diagnostic and therapeutic purposes. ECG is a noninvasive technique widely used in health care systems for diagnosis of heart diseases. However, the use of conventional ECG system is restricted by patients mobility and the systems transmission capacity, and physical size. Aforementioned highlights the need and advantage of wireless ECG systems with low sampling-rate and low power consumption. With this in mind, Compressed Sensing (CS) procedure as a new sampling approach and the collaboration of wireless ECG framework is used to provide a robust high-resolution QRS detection algorithm in the hospitals and medical centers with high probability and enough accuracy. Advanced wireless ECG systems based on our approach will be able to deliver healthcare not only to patients in hospitals and medical centers; but also at their homes and workplaces thus offering cost saving, and improving the quality of life. Our simulation results for two records of ECG signals show an increment of 10% for sensitivity as well as 12% for the prediction level and good detection accuracy. The proposed algorithm also achieves significantly better detection rate in comparison with Empirical Mode Decomposition (EMD) method.