- Email: [email protected]
Introduction to the special issue on Advances in Biomedical Signal and Image Processing, and Biometrics
Computers and Electrical Engineering 40 (2014) 1714–1716
Contents lists available at ScienceDirect
Computers and Electrical Engineering journal homepage: www.elsevier.com/locate/compeleceng
Editorial
Introduction to the special issue on Advances in Biomedical Signal and Image Processing, and Biometrics The world population s increasing by almost three humans per second. Each one of these people lives, thinks, enjoys, worries, mourns, and daydreams with, and within, a very complex but marvelously organized body. Such a system is influenced by its environment, triggered by a large number of stimuli, and is able to adapt itself to constructive and destructive waves of changes in a continuously varying earth. To understand the structure and functions of human body and to understand various biological and biochemical processes involved in human physical and mental development, not only the physiology of the organs but also their functions and perhaps their metabolic activities have to be reviewed. Most of these studies are with the help of physiological and biological signs and symptoms measured from the human body invasively or noninvasively through a variety of sensors and instruments developed through decades. These measurements can be in the form of time series of electric potentials from brain and muscles, images of different modalities, image sequences, heart and lung sounds, blood pressure, eye pressure, body temperature, skin impedance, enzyme, statistics, and many other bio-information and metrics. The systems for measuring such information have been improved in recent years in the light of advances in science and technology. In this direction, in parallel with development of new measurement tools and systems, many mathematical and signal processing algorithms have also been introduced and implemented by thousands of researchers. With the help of such new achievements, diagnoses, treatment, and monitoring of diseases have become much easier, more reliable, faster, and therefore, more lives are saved. Analysis, understanding, detection, estimation, and modeling of the signal events and trends, the way they evolve, and the abnormalities and anomalies affecting them have attracted many researchers around the globe. Biological effects and signals are probably the only type of phenomena which constantly evolve in various aspects (domains). As an example, concentration of bacteria in one point spreads in different speeds to the nearby cells and tissues depending on many other biochemical and biological factors. This represents evolutions in both time and space. As another example, the electroencephalography (EEG) signals from the medial temporal lobes of the epileptic patients can go from chaotic to rhythmic before the seizure onset and during the ictal period the frequency of the signals decreases slowly. The evolution in such signals is therefore in both time and frequency. In another example, consider the changes in sleep EEG signals. They evolve in their patterns during the four stages of sleep. Understanding and analysis of such patterns reveal much information about human body activities and abnormalities. More importantly, connectivity of the brain lobes varies due to development of cognitive processes such as mental fatigue, and brain disorders such as dementia or Alzheimer’s. Processing of the signals from the brain can significantly improve clinical diagnosis and monitoring of the diseases and abnormalities. Mutation in genome sequences is a key to understanding of some life-affecting/threatening physiological changes within a live organism. Anomalies in heart beat sequence can be a sign of stroke. Generation of medial temporal spikes can be a sign of seizure development in animals and human. Many of these signals can be fleeting sources which are transient with no regularities in their occurrence. Consequently, the normal body activity changes due to such abnormalities. Coherency, synchronization, interaction, and stimulation of human organism involve transmission and processing of multiple sources by a complicated sensory network within the body. Isolation, discrimination, separation, clustering, and classification of these activities are the initial steps toward recognition, modelling, and monitoring of such functionalities. In addition, the activities of overall human body changes and the symptoms evolve due to various cyclic or chaotic natural states of the body as for seizure, stages of sleep, breathing, heart rate, blood circulation, attention, etc. Characterization, monitoring, quantification, and tracking of such evolution provide an in depth understanding of human organism. Introducing new data collection modalities and the techniques to process those data before their automatic or non-automatic analysis for health screening have attracted much attention by both engineers and clinicians within the past three decades. Opening new laboratories for development of rehabilitative equipment, new biomaterials, and automation of diag-
http://dx.doi.org/10.1016/j.compeleceng.2014.05.011 0045-7906/Ó 2014 Published by Elsevier Ltd.
Editorial / Computers and Electrical Engineering 40 (2014) 1714–1716
1715
nostic tools have become widely popular. In addition to publication of enormous number of articles, the research in biomedical engineering has been further empowered by publishing a number of very useful books from researches in medicine, dentistry, physics, math, and engineering. Although major research has been dedicated to analysis of signals such as electrocardiogram (ECG), electroencephalography (EEG), and electromyogram (EMG), there have been tremendous activities in some new data acquisition modalities such as functional magnetic resonance imaging (fMRI) sequences, and near infrared spectroscopy (NIRS) imaging. magnetoencephalography (MEG), as another brain scanning tool, avoids the problem of non-homogeneity of the head, as a transferring medium, in significantly better localization of synaptic sources within the brain. Joint EEG-fMRI or EEG-NIRS allows exploitation of both high time resolution of EEGs and good spatial resolution of fMRI or NIRS in better detecting and locating event and movement related brain sources. Other areas of interest by recent workers have included developing tools and algorithms for processing of heart and lung sounds, cardiovascular MRI, human joint sounds, impedance imaging, and microwave imaging. In addition, segmentation of eye retina images, vascular systems, and the assessment of a series of physiological, pathological, or biological measurements have been widely researched. In another domain, advancing in biometrics, not only for security and surveillance, but also for diagnosis of diseases, disorders, and physical abnormalities in human, have brought eye pattern recognition, cochlear technology, and micro body sensors into the research front. These new modalities complement the traditional finger print, face, and gait recognition systems. In this special issue, there is no room to cover all the above aspects. Instead, only a few topics are explored and examined by some prominent researchers. These topics are summarized and their importance acknowledged here. In the first paper, entitled An Efficient New Method for the Detection of QRS in Electrocardiogram’’ the authors have revived the traditional research in QRS detection by introducing a simple and efficient method for QRS detection from ECG. In this research the QRS peaks are enhanced considerably and therefore, the detection rate and accuracy are improved. The second paper, A Coherent Direction of Arrival Estimation Method using a Single Pulse, has a mathematical and signal processing view point in addressing the problem of estimation of direction or angle of arrival, as a useful technique for localization of signal sources. A single-pulse approach has been developed. Vijean et al. in their paper Classification of Mental Tasks using Stockwell Transform, put one step forward in developing BCI systems by transforming the EEG data using Stockwell transform to better capture the dynamics of the signals. It is shown that the signatures of movement related brain activities are better classified. In an interesting FPGA implementation, the authors of the 4th paper, FPGA Implementation of Image Processing Technique for Blood Samples Characterization, demonstrate a parallel structure of their FPGA-based image processing technique for handling a large number of blood samples. Their algorithm shows improvement in terms of speed, accuracy, and the processing power. Some recent research has been focused on automatic diagnosis and assessment of diabetes level for Indian community. The 5th paper of this special issue addresses this problem using machine learning techniques. It however, doesn’t provide a good description of the data and its significance. Janjarasjitt and Loparo in the 6th paper, Examination of Scale-Invariant Characteristics of Epileptic EEGs Using WaveletBased Analysis, recall the problem of seizure detection and prediction by including a wavelet-based analysis of intracranial EEG dynamics. From their achieved results they claim that the self-similar or scale-invariant characteristics of the intracranial EEG data obtained during ictal period are significantly different from the normal or interictal periods. In the last paper, Electrocardiogram beat classification has been considered using a new approach. The empirical mode decomposition (EMD) and singular value decomposition (SVD) approaches have been used for feature extraction. For the classification, the directed acyclic graph (DAG) support vector machine (SVM) has been proposed. The parameters of SVM have been optimized using particle swarm optimization approach. The results indicate a step forward toward automatic ECG beat classification which can be very useful in many clinical applications. It is hoped that the above manuscripts generate new seeds for future thoughts. Guest Editors Mohammad Reza Daliri Faculty of Electrical Engineering, Iran University of Science and Technology (IUST), Iran and the School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Iran E-mail address: [email protected] Saeid Sanei Department of Computing, University of Surrey, UK E-mail address: [email protected]
1716
Editorial / Computers and Electrical Engineering 40 (2014) 1714–1716
Mohammad Reza Daliri is an Associate Professor in the Department of Biomedical Engineering, Faculty of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran. His main research interests are computational and cognitive neuroscience, brain signal processing, pattern recognition, and computer vision (mainly for biomedical applications).
Saeid Sanei received his PhD from Imperial College London in 1991. Since then he has been a member of academic staff in Iran, Singapore, and the United Kingdom. Currently, he is Deputy Head of Computing Department, Faculty of Engineering and Physical Sciences, University of Surrey, UK. He is the author of three monograms in biomedical signal processing and approximately 300 peer reviewed papers. His research interests include adaptive and nonlinear signal processing and machine learning.
Contents lists available at ScienceDirect
Computers and Electrical Engineering journal homepage: www.elsevier.com/locate/compeleceng
Editorial
Introduction to the special issue on Advances in Biomedical Signal and Image Processing, and Biometrics The world population s increasing by almost three humans per second. Each one of these people lives, thinks, enjoys, worries, mourns, and daydreams with, and within, a very complex but marvelously organized body. Such a system is influenced by its environment, triggered by a large number of stimuli, and is able to adapt itself to constructive and destructive waves of changes in a continuously varying earth. To understand the structure and functions of human body and to understand various biological and biochemical processes involved in human physical and mental development, not only the physiology of the organs but also their functions and perhaps their metabolic activities have to be reviewed. Most of these studies are with the help of physiological and biological signs and symptoms measured from the human body invasively or noninvasively through a variety of sensors and instruments developed through decades. These measurements can be in the form of time series of electric potentials from brain and muscles, images of different modalities, image sequences, heart and lung sounds, blood pressure, eye pressure, body temperature, skin impedance, enzyme, statistics, and many other bio-information and metrics. The systems for measuring such information have been improved in recent years in the light of advances in science and technology. In this direction, in parallel with development of new measurement tools and systems, many mathematical and signal processing algorithms have also been introduced and implemented by thousands of researchers. With the help of such new achievements, diagnoses, treatment, and monitoring of diseases have become much easier, more reliable, faster, and therefore, more lives are saved. Analysis, understanding, detection, estimation, and modeling of the signal events and trends, the way they evolve, and the abnormalities and anomalies affecting them have attracted many researchers around the globe. Biological effects and signals are probably the only type of phenomena which constantly evolve in various aspects (domains). As an example, concentration of bacteria in one point spreads in different speeds to the nearby cells and tissues depending on many other biochemical and biological factors. This represents evolutions in both time and space. As another example, the electroencephalography (EEG) signals from the medial temporal lobes of the epileptic patients can go from chaotic to rhythmic before the seizure onset and during the ictal period the frequency of the signals decreases slowly. The evolution in such signals is therefore in both time and frequency. In another example, consider the changes in sleep EEG signals. They evolve in their patterns during the four stages of sleep. Understanding and analysis of such patterns reveal much information about human body activities and abnormalities. More importantly, connectivity of the brain lobes varies due to development of cognitive processes such as mental fatigue, and brain disorders such as dementia or Alzheimer’s. Processing of the signals from the brain can significantly improve clinical diagnosis and monitoring of the diseases and abnormalities. Mutation in genome sequences is a key to understanding of some life-affecting/threatening physiological changes within a live organism. Anomalies in heart beat sequence can be a sign of stroke. Generation of medial temporal spikes can be a sign of seizure development in animals and human. Many of these signals can be fleeting sources which are transient with no regularities in their occurrence. Consequently, the normal body activity changes due to such abnormalities. Coherency, synchronization, interaction, and stimulation of human organism involve transmission and processing of multiple sources by a complicated sensory network within the body. Isolation, discrimination, separation, clustering, and classification of these activities are the initial steps toward recognition, modelling, and monitoring of such functionalities. In addition, the activities of overall human body changes and the symptoms evolve due to various cyclic or chaotic natural states of the body as for seizure, stages of sleep, breathing, heart rate, blood circulation, attention, etc. Characterization, monitoring, quantification, and tracking of such evolution provide an in depth understanding of human organism. Introducing new data collection modalities and the techniques to process those data before their automatic or non-automatic analysis for health screening have attracted much attention by both engineers and clinicians within the past three decades. Opening new laboratories for development of rehabilitative equipment, new biomaterials, and automation of diag-
http://dx.doi.org/10.1016/j.compeleceng.2014.05.011 0045-7906/Ó 2014 Published by Elsevier Ltd.
Editorial / Computers and Electrical Engineering 40 (2014) 1714–1716
1715
nostic tools have become widely popular. In addition to publication of enormous number of articles, the research in biomedical engineering has been further empowered by publishing a number of very useful books from researches in medicine, dentistry, physics, math, and engineering. Although major research has been dedicated to analysis of signals such as electrocardiogram (ECG), electroencephalography (EEG), and electromyogram (EMG), there have been tremendous activities in some new data acquisition modalities such as functional magnetic resonance imaging (fMRI) sequences, and near infrared spectroscopy (NIRS) imaging. magnetoencephalography (MEG), as another brain scanning tool, avoids the problem of non-homogeneity of the head, as a transferring medium, in significantly better localization of synaptic sources within the brain. Joint EEG-fMRI or EEG-NIRS allows exploitation of both high time resolution of EEGs and good spatial resolution of fMRI or NIRS in better detecting and locating event and movement related brain sources. Other areas of interest by recent workers have included developing tools and algorithms for processing of heart and lung sounds, cardiovascular MRI, human joint sounds, impedance imaging, and microwave imaging. In addition, segmentation of eye retina images, vascular systems, and the assessment of a series of physiological, pathological, or biological measurements have been widely researched. In another domain, advancing in biometrics, not only for security and surveillance, but also for diagnosis of diseases, disorders, and physical abnormalities in human, have brought eye pattern recognition, cochlear technology, and micro body sensors into the research front. These new modalities complement the traditional finger print, face, and gait recognition systems. In this special issue, there is no room to cover all the above aspects. Instead, only a few topics are explored and examined by some prominent researchers. These topics are summarized and their importance acknowledged here. In the first paper, entitled An Efficient New Method for the Detection of QRS in Electrocardiogram’’ the authors have revived the traditional research in QRS detection by introducing a simple and efficient method for QRS detection from ECG. In this research the QRS peaks are enhanced considerably and therefore, the detection rate and accuracy are improved. The second paper, A Coherent Direction of Arrival Estimation Method using a Single Pulse, has a mathematical and signal processing view point in addressing the problem of estimation of direction or angle of arrival, as a useful technique for localization of signal sources. A single-pulse approach has been developed. Vijean et al. in their paper Classification of Mental Tasks using Stockwell Transform, put one step forward in developing BCI systems by transforming the EEG data using Stockwell transform to better capture the dynamics of the signals. It is shown that the signatures of movement related brain activities are better classified. In an interesting FPGA implementation, the authors of the 4th paper, FPGA Implementation of Image Processing Technique for Blood Samples Characterization, demonstrate a parallel structure of their FPGA-based image processing technique for handling a large number of blood samples. Their algorithm shows improvement in terms of speed, accuracy, and the processing power. Some recent research has been focused on automatic diagnosis and assessment of diabetes level for Indian community. The 5th paper of this special issue addresses this problem using machine learning techniques. It however, doesn’t provide a good description of the data and its significance. Janjarasjitt and Loparo in the 6th paper, Examination of Scale-Invariant Characteristics of Epileptic EEGs Using WaveletBased Analysis, recall the problem of seizure detection and prediction by including a wavelet-based analysis of intracranial EEG dynamics. From their achieved results they claim that the self-similar or scale-invariant characteristics of the intracranial EEG data obtained during ictal period are significantly different from the normal or interictal periods. In the last paper, Electrocardiogram beat classification has been considered using a new approach. The empirical mode decomposition (EMD) and singular value decomposition (SVD) approaches have been used for feature extraction. For the classification, the directed acyclic graph (DAG) support vector machine (SVM) has been proposed. The parameters of SVM have been optimized using particle swarm optimization approach. The results indicate a step forward toward automatic ECG beat classification which can be very useful in many clinical applications. It is hoped that the above manuscripts generate new seeds for future thoughts. Guest Editors Mohammad Reza Daliri Faculty of Electrical Engineering, Iran University of Science and Technology (IUST), Iran and the School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Iran E-mail address: [email protected] Saeid Sanei Department of Computing, University of Surrey, UK E-mail address: [email protected]
1716
Editorial / Computers and Electrical Engineering 40 (2014) 1714–1716
Mohammad Reza Daliri is an Associate Professor in the Department of Biomedical Engineering, Faculty of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran. His main research interests are computational and cognitive neuroscience, brain signal processing, pattern recognition, and computer vision (mainly for biomedical applications).
Saeid Sanei received his PhD from Imperial College London in 1991. Since then he has been a member of academic staff in Iran, Singapore, and the United Kingdom. Currently, he is Deputy Head of Computing Department, Faculty of Engineering and Physical Sciences, University of Surrey, UK. He is the author of three monograms in biomedical signal processing and approximately 300 peer reviewed papers. His research interests include adaptive and nonlinear signal processing and machine learning.