Imperial College London

Dr Syed Anas Imtiaz

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 6297anas.imtiaz Website

 
 
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Location

 

907Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

32 results found

Imtiaz S, 2021, A systematic review of sensing technologies for wearable sleep staging, Sensors, Vol: 21, ISSN: 1424-8220

Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages.

Journal article

Liu D, Imtiaz S, 2020, Studying the effects of compression in EEG-based wearable sleep monitoring systems, IEEE Access, Vol: 8, Pages: 168486-168501, ISSN: 2169-3536

Long-term sleep monitoring through the use of wearable EEG-based systems generates large volumes of data that need to be either locally stored or wireless transmitted. Compression of data can play a vital role to reduce the power consumption of these already resource-constrained systems. While compression methods can result in significantly reduced data storage and transmission requirements, the loss in signal information can have an impact on the algorithms used to extract the key sleep parameters. This paper studies the impact of six different state-of-the-art compression methods, including wavelet, SPIHT, filter and predictor-based methods, analysing their effects on the reconstructed signal quality particularly for automatic sleep staging applications. It looks at how the overall sleep staging accuracy as well as the detection accuracy of different sleep stages is reduced as a result of different EEG compression methods. It shows that the SPIHT and predictor-based compression methods outperform wavelet and filter-based methods in preserving the relevant signal features. It also shows that compression ratios of up to 65 can be achieved using the QSPIHT method with less than 10% loss in overall sleep staging accuracy.

Journal article

Sharma P, Anas Imtiaz S, Rodriguez-Villegas E, 2019, Acoustic sensing as a novel wearable approach for cardiac monitoring at the wrist, Scientific Reports - Nature, Vol: 9, ISSN: 2045-2322

This paper introduces the concept of using acoustic sensing over the radial artery to extract cardiac parameters for continuous vital sign monitoring. It proposes a novel measurement principle that allows detection of the heart sounds together with the pulse wave, an attribute not possible with existing photoplethysmography (PPG)-based methods for monitoring at wrist. The validity of the proposed principle is demonstrated using a new miniature, battery-operated wearable device to sense the acoustic signals and a novel algorithm to extract the heart rate from these signals. The algorithm utilizes the power spectral analysis of the acoustic pulse signal to detect the S1 sounds and additionally, the K-means method to remove motion artifacts for an accurate heartbeat detection. It has been validated on a dataset consisting of 12 subjects with a data length of 6 hours. The results demonstrate an accuracy of 98.78\%, mean absolute error of 0.28 bpm, limits of agreement between -1.68 and 1.69 bpm, and a correlation coefficient of 0.998 with reference to a state-of-the-art PPG-based commercial device. The results in this proof of concept study demonstrate the potential of this new sensing modality to be used as an alternative, or to complement existing methods, for continuous monitoring of heart rate at wrist.

Journal article

Iranmanesh S, Raikos G, Imtiaz S, Rodriguez Villegas Eet al., 2019, A seizure based power reduction SoC for wearable EEG In epilepsy, IEEE Access, Vol: 7, Pages: 151682-151691, ISSN: 2169-3536

Epilepsy is one of the most common serious braindisorders affecting 1% of the world population. Epileptic seizureevents are caused by abnormal excessive neuronal activity in thebrain, which may be associated with behavioural changes thatseverely affect the patients’ quality of life. These events are manifested as abnormal activity in electroencephalography (EEG)recordings of individuals with epilepsy. This paper presents theon-chip implementation of an algorithm that, operating on theprinciple of data selection applied to seizures, would be able toreduce the power consumption of EEG devices, and consequentlytheir size, thereby significantly increasing their usability. In orderto reduce the power consumed by the on-chip implementation ofthe algorithm, mathematical approximations have been carriedout to allow for an analog implementation, resulting in the powerconsumed by the system to be negligible in comparison to otherblocks in an EEG device. The system has been fabricated in a0.18 µm CMOS process, consumes 1.14 µW from a 1.25 V supplyand achieves a sensitivity of 98.5% while only selecting 52.5%of the EEG data for transmission.

Journal article

Kok XH, Anas Imtiaz S, Rodriguez-Villegas E, 2019, A novel method for automatic identification of respiratory disease from acoustic recordings., 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE, Pages: 2589-2592, ISSN: 1557-170X

This paper evaluates the use of breath sound recordings to automatically determine the respiratory health status of a subject. A number of features were investigated and Wilcoxon Rank Sum statistical test was used to determine the significance of the extracted features. The significant features were then passed to a feature selection algorithm based on mutual information, to determine the combination of features that provided minimal redundancy and maximum relevance. The algorithm was tested on a publicly accessible respiratory sounds database. With the testing dataset, the trained classifier achieved accuracy of 87.1%, sensitivity of 86.8% and specificity of 93.6%. These are promising results showing the possibility of determining the presence or absence of respiratory disease using breath sounds recordings.

Conference paper

Pramono RXA, Imtiaz SA, Rodriguez-Villegas E, 2019, Evaluation of mel-frequency cepstrum for wheeze analysis, Engineering in Medicine and Biology Society (EMBC), Annual International Conference of the IEEE, Publisher: IEEE, Pages: 4686-4689, ISSN: 1557-170X

Monitoring of wheezes is an integral part of managing Chronic Respiratory Diseases such as asthma and Chronic Obstructive Pulmonary Disease (COPD). Recently, there is a growing interest in automatic detection of wheezes and the use of Mel-Frequency Cepstral Coefficients (MFCC) have been shown to achieve encouraging detection performance. While the successful use of MFCC for identifying wheezes has been demonstrated, it is not clear which MFCC coefficients are actually useful for detecting wheezes. The objective of this paper is to characterize and study the effectiveness of individual coefficients in discriminating between wheezes and normal respiratory sounds. The coefficients have been evaluated in terms of histogram dissimilarity and linear separability. Further, a comparison between the use of single coefficient against other combinations of coefficients is also presented. The results demonstrate MFCC-2 coefficient to be significantly more effective than all the other coefficients in discriminating between wheezes and normal respiratory sounds sampled at 8000 Hz.

Conference paper

Pramono RXA, Imtiaz SA, Rodriguez-Villegas E, 2019, Automatic identification of cough events from acoustic signals., 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Pages: 217-220

Cough is a common symptom of numerous respiratory diseases. In certain cases, such as asthma and COPD, early identification of coughs is useful for the management of these diseases. This paper presents an algorithm for automatic identification of cough events from acoustic signals. The algorithm is based on only four features of the acoustic signals including LPC coefficient, tonality index, spectral flatness and spectral centroid with a logistic regression model to label sound segments into cough and non-cough events. The algorithm achieves sensitivity of of 86.78%, specificity of 99.42%, and F1-score of 88.74%. Its high performance despite its small size of feature-space demonstrate its potential for use in remote patient monitoring systems for automatic cough detection using acoustic signals.

Conference paper

Adhi Pramono RX, Anas Imtiaz S, Rodriguez-Villegas E, 2019, Automatic cough detection in acoustic signal using spectral features., 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE, Pages: 7153-7156, ISSN: 1557-170X

Cough is a common symptom that manifests in numerous respiratory diseases. In chronic respiratory diseases, such as asthma and COPD, monitoring of cough is an integral part in managing the disease. This paper presents an algorithm for automatic detection of cough events from acoustic signals. The algorithm uses only three spectral features with a logistic regression model to separate sound segments into cough and non-cough events. The spectral features were derived using simple calculation from two frequency bands of the sound spectrum. The frequency bands of interest were chosen based on its characteristics in the spectrum. The algorithm achieved high sensitivity of 90.31%, specificity of 98.14%, and F1-score of 88.70%. Its low-complexity and high detection performance demonstrate its potential for use in remote patient monitoring systems for real-time, automatic cough detection.

Conference paper

Imtiaz S, Iranmanesh S, Rodriguez Villegas E, 2019, A low power system with EEG data reduction for long-term epileptic seizures monitoring, IEEE Access, Vol: 7, Pages: 71195-71208, ISSN: 2169-3536

Long-term monitoring of epilepsy patients requires low-power systems that can record and transmit electroencephalogram data over extended periods of time. Since seizure events are rare, long-term monitoring inherently results in large amounts of data that are recorded and hence need to be reduced. This paper presents an ultra-low power integrated circuit implementation of a data reduction algorithm for epilepsy monitoring, specific to seizure events. The algorithm uses line length of the electroencephalogram signals as the key discriminating feature to classify epochs of data as seizure or non-seizure events. It is implemented in AMS 0.18- $\mu \text{m}$ CMOS technology and its output is connected to a Bluetooth low energy transceiver to wirelessly transmit potential seizure events. All the modules of the algorithm have been implemented on chip to use a small number of clock cycles and remain mostly in an idle mode. The algorithm, on the chip, achieves 50% of data reduction with a sensitivity of 80% for capturing seizure events. The overall power consumption of the chip is measured to be 23 $\mu \text{W}$ , while the full system with wireless transmission consumes 743 $\mu \text{W}$ . The results in this paper demonstrate the feasibility of a long-term seizure monitoring system capable of running autonomously for over two weeks.

Journal article

Pramono RXA, Imtiaz SA, Rodriguez-Villegas E, 2019, Evaluation of features for classification of wheezes and normal respiratory sounds, PLoS ONE, Vol: 14, Pages: e0213659-e0213659, ISSN: 1932-6203

Chronic Respiratory Diseases (CRDs), such as Asthma and Chronic Obstructive Pulmonary Disease (COPD), are leading causes of deaths worldwide. Although both Asthma and COPD are not curable, they can be managed by close monitoring of symptoms to prevent worsening of the condition. One key symptom that needs to be monitored is the occurrence of wheezing sounds during breathing since its early identification could prevent serious exacerbations. Since wheezing can happen randomly without warning, a long-term monitoring system with automatic wheeze detection could be extremely helpful to manage these respiratory diseases. This study evaluates the discriminatory ability of different types of feature used in previous related studies, with a total size of 105 individual features, for automatic identification of wheezing sound during breathing. A linear classifier is used to determine the best features for classification by evaluating several performance metrics, including ranksum statistical test, area under the sensitivity--specificity curve (AUC), F1 score, Matthews Correlation Coefficient (MCC), and relative computation time. Tonality index attained the highest effect size, at 87.95%, and was found to be the feature with the lowest p-value when ranksum significance test was performed. Third MFCC coefficient achieved the highest AUC and average optimum F1 score at 0.8919 and 82.67% respectively, while the highest average optimum MCC was obtained by the first coefficient of a 6th order LPC. The best possible combination of two and three features for wheeze detection is also studied. The study concludes with an analysis of the different trade-offs between accuracy, reliability, and computation requirements of the different features since these will be highly useful for researchers when designing algorithms for automatic wheeze identification.

Journal article

Sharma P, Imtiaz SA, Rodriguez Villegas E, 2019, An algorithm for heart rate extraction from acoustic recordings at the neck, IEEE Transactions on Biomedical Engineering, Vol: 66, Pages: 246-256, ISSN: 0018-9294

Heart rate is an important physiological parameter to assess the cardiac condition of an individual and is traditionally determined by attaching multiple electrodes on the chest of a subject to record the electrical activity of the heart. The installation and handling complexities of such systems does not prove feasible for a user to undergo a long-term monitoring in the home settings. A small-sized, battery-operated wearable monitoring device is placed on the suprasternal notch at neck to record acoustic signals containing information about breathing and cardiac sounds. The heart sounds obtained are heavily corrupted by the respiratory cycles and other external artifacts. This paper presents a novel algorithm for reliably extracting the heart rate from such acoustic recordings, keeping in mind the constraints posed by the wearable technology. The methodology constructs the Hilbert energy envelope of the signal by calculating its instantaneous characteristics to segment and classify a cardiac cycle into S1 and S2 sounds using their timing characteristics. The algorithm is tested on a dataset consisting of 13 subjects with an approximate data length of 75 hours and achieves an accuracy of 94.34%, an RMS error of 3.96 bpm and a correlation coefficient of 0.93 with reference to a commercial device in use.

Journal article

Dwivedi AK, Imtiaz SA, Rodriguez Villegas E, 2018, Algorithms for automatic analysis and classification of heart sounds – a systematic review, IEEE Access, Vol: 7, Pages: 8316-8345, ISSN: 2169-3536

Cardiovascular diseases currently pose the highest threat to human health around the world. Proper investigation of the abnormalities in heart sounds is known to provide vital clinical information that can assist in the diagnosis and management of cardiac conditions. However, despite significant advances in the development of algorithms for automated classification and analysis of heart sounds, the validity of different approaches has not been systematically reviewed. This paper provides an in-depth systematic review and critical analysis of all the existing approaches for automatic identification and classification of the heart sounds. All statements on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2009 Checklist were followed and addressed thoroughly to maintain the quality of the accounted systematic review. Out of 1347 research articles available in the academic databases from 1963 to 2018, 117 peer-reviewed articles were found to fall under the search and selection criteria of this paper. Amongst them: 53 articles are focused on segmentation, 72 of the studies are related to the feature extraction approaches and 88 to classification, and 56 reported on the databases and heart sounds acquisition. From this review, it is clear that, although a lot of research has been done in the field of automated analysis, there is still some work to be done to develop robust methods for identification and classification of various events in the cardiac cycle so that this could be effectively used to improve the diagnosis and management of cardiovascular diseases in combination with the wearable mobile technologies.

Journal article

Rodriguez Villegas E, Iranmanesh S, Imtiaz SA, 2018, Wearable medical devices: high level system design considerations and trade-offs, IEEE Solid State Circuits Magazine, Vol: 10, Pages: 43-52, ISSN: 1943-0582

Wearable devices have seen tremendous growth over the last 10 years. This has been madepossible with the ever-shrinking electronics, reduction in costs as well as the rise in mobilecomputing making it possible to share significant computational workload. Recent estimatesshow an annual growth of 17% in wearable devices in the year 2017 with over 300 milliondevices being sold. It is also projected that over 500 million devices will be sold by the year2021 [1]. While these figures show some staggering growth and potential for wearable devices,a detailed look at the numbers reveal that the application areas where wearable devices havebeen a success are quite limited. Most of these devices whichare consideredwearable, takethe form of smartwatches, fitness trackers, body worn cameras and headphones. It should beemphasized that the mentioned numbers are for devices that are made for consumers and usedmostly for entertainment, wellness and general health purposes. The benefits provided by mostof these health-related wearable devices are insufficient for medical usage mainly because oflow quality data and insufficient accuracy in classificationtasks.While wearables for consumer use will continue to grow, it is important to keep in mindthe distinction between consumer and medical-grade devices. In the sphere of medical devices,wearables for monitoring, diagnosing and real-time management of illnesses is still at a veryearly stage. One of the main reasons for this slow growth, as well as adoption, is the designof such devices, which is inherently very challenging. In this paper, we will first look at theneed for wearable devices to improve healthcare in order to understand and define a set of requirements for the design of such devices. Subsequently,based on these requirements, we willlook at the challenges that exist in the development of wearable medical devices particularlyfrom the perspective of their system and circuit level implementations.

Journal article

Garcia-Lopez I, Imtiaz SA, Rodriguez-Villegas E, 2018, Characterization study of neck photoplethysmography, EMBC 2018, Publisher: IEEE, Pages: 4355-4358

This paper presents a comparison between finger and neck photoplethysmography (PPG) in order to assess the potential and limitations of this, non-conventionally used, body site for application in pulse oximetry. PPG signals were recorded at both sites from healthy subjects to inspect the differences in average waveforms, as well as in oxygen saturation (SpO2) and heart rate (HR) estimation. The results show significant differences in the average PPG pulse waveforms for different contour features such as diastolic or dicrotic notch amplitude, among others. The results show that the HR estimated from signals obtained with the neck sensor are strongly correlated to the output of the reference finger (R=0.862, MAE=1.27 BPM), whereas SpO2 measurements are not that accurately predicted (R=0.129, MAE=11.7%). Spectrograms under different breathing conditions revealed that the respiratory frequency is more predominant in neck PPG than in finger, which has a great potential for respiratory rate (RR) extraction. These are very promising results for the suitability of the neck as an alternative location for monitoring of respiratory diseases, and specifically for sleep apnea.

Conference paper

Peng M, Imtiaz SA, Rodriguez-Villegas E, 2017, Pulse oximetry in the neck - a proof of concept, Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE, Pages: 877-880, ISSN: 1558-4615

Oxygen saturation levels are routinely monitoredin clinical settings. Pulse oximetry, in transmittance operationmode, is the most common method of estimating oxygen satu-ration (SpO2). This is inexpensive and non-invasive and thusallows for long-term monitoring. However, it suffers from issuessuch as signal integrity, reliability and patient comfortability.As a result, there is an interest in exploring other locations onthe body where oxygen saturation can be measured reliably. Inthis paper, a wearable device has been designed to study thefeasibility of extracting photoplethysmogram (PPG) signals atthe neck in reflectance pulse oximetry mode. It explores thesignal integrity and strength compared to other locations aswell as the presence of motion artefacts in that location. Theresults demonstrate that the PPG signals acquired at the neckshow a very strong correlation (r=0.82) with the SpO2valuesobtained using a commercial device. Further, the SpO2valuesare calculated with an accuracy of 98.6%.

Conference paper

Torres I, Echebarria U, Imtiaz SA, Peng M, Rodriguez-Villegas Eet al., 2017, Monitoring smoking behaviour using a wearable acoustic sensor, Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE, Pages: 4459-4462, ISSN: 1558-4615

Smoking is a cause of multiple health problemsresulting in diseases which can also be fatal. It is well knownthat smoking has long-term impact on the health of anindividual as well. While a number of studies have looked atthe impact of smoking on health and its economic impacts,most of these rely on input from smokers in the form ofquestionnaires and surveys. Long-term monitoring of smokinghabits and behaviour is thus not possible because of the lackof means to do so. This paper proposes the use of a wearabledevice to monitor breathing signals of subjects. It is shown thatthe acoustic properties of a smoking breath are different froma non-smoking breath. To encapsulate these differences, severalfeatures from a breath segment are extracted and used with asimple classifier to automatically identify smoking breaths. Theproposed algorithm detected smoking and non-smoking breathswith average accuracy of 66% and 99% respectively.

Conference paper

Jiang Z, Huxter JR, Bowyer S, Blockeel AJ, Butler J, imtiaz, Wafford KA, Phillips KG, Tricklebank MD, Marston HM, Rodriguez Eet al., 2017, TaiNi: maximizing research output whilst improving animals' welfare in neurophysiology experiments, Scientific Reports, Vol: 7, ISSN: 2045-2322

Understanding brain function at the cell and circuit level requires representation of neuronal activity through multiple recording sites and at high sampling rates. Traditional tethered recording systems restrict movement and limit the environments suitable for testing, while existing wireless technology is still too heavy for extended recording in mice. Here we tested TaiNi, a novel ultra-lightweight (<2 g) low power wireless system allowing 72-hours of recording from 16 channels sampled at ~19.5 KHz (9.7 KHz bandwidth). We captured local field potentials and action-potentials while mice engaged in unrestricted behaviour in a variety of environments and while performing tasks. Data was synchronized to behaviour with sub-second precision. Comparisons with a state-of-the-art wireless system demonstrated a significant improvement in behaviour owing to reduced weight. Parallel recordings with a tethered system revealed similar spike detection and clustering. TaiNi represents a significant advance in both animal welfare in electrophysiological experiments, and the scope for continuously recording large amounts of data from small animals.

Journal article

Imtiaz SA, Jiang Z, Rodriguez Villegas E, 2017, An ultra-low power system-on-chip for automatic sleep staging, IEEE Journal of Solid State Circuits, Vol: 52, Pages: 822-833, ISSN: 1558-173X

This paper presents an ultra-low power SoC for automatic sleep staging using a single electroen-cephalography (EEG) channel. The system integrates an analog front-end for EEG data acquisition and adigital processor to extract spectral features from this data and classify them into one of the sleep stages.The digital processor consists of multiple blocks implementing an automatic sleep staging algorithmthat uses a set of contextual decision trees controlled by a state machine. The processor is designedto stay in idle mode at most times waking up only when computations are required. In addition, themathematical operations are implemented in a way such that the number of datapath components neededis very small. The SoC is implemented in AMS 0.18μm CMOS technology and is powered using asingle 1.25V supply. Its power consumption is measured to be575μW while its classification accuracyusing real EEG data is 98.7%.

Journal article

Patrick KCA, Imtiaz SA, Bowyer S, Rodriguez Villegas Eet al., 2016, An Algorithm for Automatic Detection of Drowsiness for Use inWearable EEG Systems, IEEE EMBC 2016, Publisher: IEEE, ISSN: 1557-170X

Lack of proper restorative sleep can induce sleepinessat odd hours making a person drowsy. This onset of drowsinesscan be detrimental for the individual in a number of waysif it happens at an unwanted time. For example, drowsinesswhile driving a vehicle or operating heavy machinery poses athreat to the safety and wellbeing of individuals as well as thosearound them. Timely detection of drowsiness can prevent theoccurrence of unfortunate accidents thereby improving roadand work environment safety. In this paper, by analyzing theelectroencephalographic (EEG) signals of human subjects inthe frequency domain, several features across different EEGchannels are explored. Of these, three features are identified tohave a strong correlation with drowsiness. A weighted sum ofthese defining features, extracted from a single EEG channel,is then used with a simple classifier to automatically separatethe state of wakefulness from drowsiness. The proposed algorithmresulted in drowsiness detection sensitivity of 85% andspecificity of 93%.

Conference paper

Imtiaz SA, Mardell JAMES, Saremi-Yarahmadi SIAVASH, Rodriguez Villegas ESTHERet al., 2016, ECG Artefact Identification and Removal in mHealth Systems for Continuous Patient Monitoring, Healthcare Technology Letters, Vol: 3, Pages: 171-176, ISSN: 2053-3713

Continuous patient monitoring systems acquire enormous amounts of data that is either manually analysed by doctors or automaticallyprocessed using intelligent algorithms. Sections of data acquired over long period of time can be corrupted with artefacts due to patientmovement, sensor placement and interference from other sources. Because of the large volume of data these artefacts need to be automaticallyidentified so that the analysis systems and doctors are aware of them while making medical diagnosis. This paper explores three importantfactors that must be considered and quantified for the design and evaluation of automatic artefact identification algorithms: signal quality,interpretation quality and computational complexity. The first two are useful to determine the effectiveness of an algorithm while the third isparticularly vital in mHealth systems where computational resources are heavily constrained. A series of artefact identification and filteringalgorithms are then presented focusing on the electrocardiography data. These algorithms are quantified using the three metrics to demonstratehow different algorithms can be evaluated and compared to select the best ones for a given wireless sensor network.

Journal article

Pramono R, Imtiaz SA, Rodriguez Villegas ESTHER, 2016, A Cough-Based Algorithm for Automatic Diagnosis of Pertussis, PLOS One, Vol: 11, ISSN: 1932-6203

Pertussis is a contagious respiratory disease which mainly affects young children and can be fatal if left untreated. The World Health Organization estimates 16 million pertussis cases annually worldwide resulting in over 200,000 deaths. It is prevalent mainly in developing countries where it is difficult to diagnose due to the lack of healthcare facilities and medical professionals. Hence, a low-cost, quick and easily accessible solution is needed to provide pertussis diagnosis in such areas to contain an outbreak. In this paper we present an algorithm for automated diagnosis of pertussis using audio signals by analyzing cough and whoop sounds. The algorithm consists of three main blocks to perform automatic cough detection, cough classification and whooping sound detection. Each of these extract relevant features from the audio signal and subsequently classify them using a logistic regression model. The output from these blocks is collated to provide a pertussis likelihood diagnosis. The performance of the proposed algorithm is evaluated using audio recordings from 38 patients. The algorithm is able to diagnose all pertussis successfully from all audio recordings without any false diagnosis. It can also automatically detect individual cough sounds with 92% accuracy and PPV of 97%. The low complexity of the proposed algorithm coupled with its high accuracy demonstrates that it can be readily deployed using smartphones and can be extremely useful for quick identification or early screening of pertussis and for infection outbreaks control.

Journal article

Imtiaz SA, Rodriguez-Villegas E, 2015, Automatic sleep staging using state machine-controlled decision trees., 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE, Pages: 378-381, ISSN: 1557-170X

Automatic sleep staging from a reduced number of channels is desirable to save time, reduce costs and make sleep monitoring more accessible by providing home-based polysomnography. This paper introduces a novel algorithm for automatic scoring of sleep stages using a combination of small decision trees driven by a state machine. The algorithm uses two channels of EEG for feature extraction and has a state machine that selects a suitable decision tree for classification based on the prevailing sleep stage. Its performance has been evaluated using the complete dataset of 61 recordings from PhysioNet Sleep EDF Expanded database achieving an overall accuracy of 82% and 79% on training and test sets respectively. The algorithm has been developed with a very small number of decision tree nodes that are active at any given time making it suitable for use in resource-constrained wearable systems.

Conference paper

Imtiaz SA, Rodriguez-Villegas E, 2015, An open-source toolbox for standardized use of PhysioNet Sleep EDF Expanded Database., 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC, Publisher: IEEE, Pages: 6014-6017, ISSN: 1557-170X

PhysioNet Sleep EDF database has been the most popular source of data used for developing and testing many automatic sleep staging algorithms. However, the recordings from this database has been used in an inconsistent fashion. For example, arbitrary selection of start and end times from long term recordings, data-hypnogram mismatches, different performance metrics and hypnogram conversion from R&K to AASM. All these differences result in different data sections and performance metrics being used by researchers thereby making any direct comparison between algorithms very difficult. Recently, a superset of this database has been made available on PhysioNet, known as the Sleep EDF Expanded Database which includes 61 recordings. This provides an opportunity to standardize the way in which signals from this database should be used. With this goal in mind, we present in this paper a toolbox for automatically downloading and extracting recordings from the Sleep EDF Expanded database and converting them to a suitable format for use in MATLAB. This toolbox contains functions for selecting appropriate data for sleep analysis (based on our previous recommendations for sleep staging), hypnogram conversion and computation of performance metrics. Its use makes it simpler to start using the new sleep database and also provides a foundation for much-needed standardization in this research field.

Conference paper

Imtiaz SA, Logesparan L, Rodriguez-Villegas E, 2015, Performance-Power Consumption Tradeoff in Wearable Epilepsy Monitoring Systems, IEEE Journal of Biomedical and Health Informatics, Vol: 19, Pages: 1019-1028, ISSN: 2168-2208

Automated seizure detection methods can be used to reduce time and costs associated with analyzing large volumes of ambulatory EEG recordings. These methods however have to rely on very complex, power hungry algorithms, implemented on the system backend, in order to achieve acceptable levels of accuracy. In size, and therefore power-constrained EEG systems, an alternative approach to the problem of data reduction is online data selection, in which simpler algorithms select potential epileptiform activity for discontinuous recording but accurate analysis is still left to a medical practitioner. Such a diagnostic decision support system would still provide doctors with information relevant for diagnosis while reducing the time taken to analyze the EEG. For wearable systems with limited power budgets, data selection algorithm must be of sufficiently low complexity in order to reduce the amount of data transmitted and the overall power consumption. In this paper, we present a low-power hardware implementation of an online epileptic seizure data selection algorithm with encryption and data transmission and demonstrate the tradeoffs between its accuracy and the overall system power consumption. We demonstrate that overall power savings by data selection can be achieved by transmitting less than 40% of the data. We also show a 29% power reduction when selecting and transmitting 94% of all seizure events and only 10% of background EEG.

Journal article

Chen G, Imtiaz SA, Aguilar-Pelaez E, Rodriguez-Villegas Eet al., 2015, Algorithm for heart rate extraction in a novel wearable acoustic sensor., Healthcare Technology Letters, Vol: 2, Pages: 28-33, ISSN: 2053-3713

Phonocardiography is a widely used method of listening to the heart sounds and indicating the presence of cardiac abnormalities. Each heart cycle consists of two major sounds - S1 and S2 - that can be used to determine the heart rate. The conventional method of acoustic signal acquisition involves placing the sound sensor at the chest where this sound is most audible. Presented is a novel algorithm for the detection of S1 and S2 heart sounds and the use of them to extract the heart rate from signals acquired by a small sensor placed at the neck. This algorithm achieves an accuracy of 90.73 and 90.69%, with respect to heart rate value provided by two commercial devices, evaluated on more than 38 h of data acquired from ten different subjects during sleep in a pilot clinical study. This is the largest dataset for acoustic heart sound classification and heart rate extraction in the literature to date. The algorithm in this study used signals from a sensor designed to monitor breathing. This shows that the same sensor and signal can be used to monitor both breathing and heart rate, making it highly useful for long-term wearable vital signs monitoring.

Journal article

Rodriguez Villegas E, Imtiaz SA, 2014, A low computational cost algorithm for REM sleep detection using single channel EEG, Annals of Biomedical Engineering, Vol: 42, Pages: 2344-2359, ISSN: 0090-6964

The push towards low-power and wearable sleep systems requires using minimum number of recording channels to enhance battery life, keep processing load small and be more comfortable for the user. Since most sleep stages can be identified using EEG traces, enormous power savings could be achieved by using a single channel of EEG. However, detection of REM sleep from one channel EEG is challenging due to its electroencephalographic similarities with N1 and Wake stages. In this paper we investigate a novel feature in sleep EEG that demonstrates high discriminatory ability for detecting REM phases. We then use this feature, that is based on spectral edge frequency (SEF) in the 8–16 Hz frequency band, together with the absolute power and the relative power of the signal, to develop a simple REM detection algorithm. We evaluate the performance of this proposed algorithm with overnight single channel EEG recordings of 5 training and 15 independent test subjects. Our algorithm achieved sensitivity of 83%, specificity of 89% and selectivity of 61% on a test database consisting of 2221 REM epochs. It also achieved sensitivity and selectivity of 81 and 75% on PhysioNet Sleep-EDF database consisting of 8 subjects. These results demonstrate that SEF can be a useful feature for automatic detection of REM stages of sleep from a single channel EEG.

Journal article

Imtiaz SA, Rodriguez-Villegas E, 2014, Recommendations for Performance Assessment of Automatic Sleep Staging Algorithms, 36th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 5044-5047, ISSN: 1557-170X

A number of automatic sleep scoring algorithms have been published in the last few years. These can potentially help save time and reduce costs in sleep monitoring. However, the use of both R&K and AASM classification, different databases and varying performance metrics makes it extremely difficult to compare these algorithms. In this paper, we describe some readily available polysomnography databases and propose a set of recommendations and performance metrics to promote uniform testing and direct comparison of different algorithms. We use two different polysomnography databases with a simple sleep staging algorithm to demonstrate the usage of all recommendations and presentation of performance results. We also illustrate how seemingly similar results using two different databases can have contrasting accuracies in different sleep stages. Finally, we show how selection of different training and test subjects from the same database can alter the final performance results.

Conference paper

Imtiaz SA, Casson AJ, Rodriguez-Villegas E, 2014, Compression in Wearable Sensor Nodes: Impacts of Node Topology, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 61, Pages: 1080-1090, ISSN: 0018-9294

Journal article

Imtiaz SA, Rodriguez-Villegas E, 2014, Evaluating the use of line length for automatic sleep spindle detection, 36th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 5024-5027, ISSN: 1557-170X

Conference paper

Imtiaz SA, Saremi-Yarahmadi S, Rodriguez-Villegas E, 2013, Automatic detection of sleep spindles using Teager energy and spectral edge frequency, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 262-265

Sleep spindles are the hallmark of N2 stage of sleep. They are transient waveforms observed on sleep electroencephalogram and their identification is required for sleep staging. Due to the large number of sleep spindles appearing on an overnight sleep EEG, automating the detection of sleep spindles would be desirable, not only to save specialist time but also for fully automated sleep staging systems. A simple algorithm for automatic sleep spindle detection is presented in this paper using only one channel of EEG input. This algorithm uses Teager energy and spectral edge frequency to mark sleep spindles and results in a sensitivity of 80% and specificity of about 98%. It is also shown that more than 91% of spindles detected by the algorithm were in N2 and N3 stages combined.

Conference paper

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