# Dr Saam Iranmanesh

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Visiting Researcher

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### Location

907Electrical EngineeringSouth Kensington Campus

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## Publications

Publication Type
Year
to

7 results found

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

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

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

Rodriguez Villegas E, Iranmanesh S, 2017, A 950 nW analog-based data reduction chip for wearable EEG systems in epilepsy, IEEE Journal of Solid State Circuits, Vol: 52, Pages: 2362-2373, ISSN: 0018-9200

Long-term electroencephalogram (EEG) monitoring is an important tool used for the diagnosis of epilepsy. Truly Wearable EEG can be considered as the future of ambulatory EEG units, which are the current standard for long-term EEG monitoring. Replacing these short lifetime, bulky units with long-lasting miniature and wearable devices which can be easily worn by patients will result in more EEG data being acquired for longer monitoring periods. This paper presents an analog-based data reduction integrated circuit that would reduce the amount of power required to transmit EEG data by identifying the sections of data that are interesting for diagnostic purposes while discarding the background activity. Using the data reduction system as part of a miniature wireless, EEG monitoring unit would yield significant reductions in power consumption since the transmitter will only be switched ON based on the data reduction system output. A system prototype chip has been fabricated in a 0.35 μm CMOS process. The system consumes 760 nA from a 1.25 V supply and is able to achieve a sensitivity of 87%, while transmitting 45% of the overall EEG data.

Journal article

Iranmanesh S, Rodriguez Villegas E, 2017, An ultra-low power sleep spindle detection system on chip, IEEE Transactions on Biomedical Circuits and Systems, Vol: 11, Pages: 858-866, ISSN: 1940-9990

This paper describes a full system-on-chip to automatically detect sleep spindle events from scalp EEG signals. These events, which are known to play an important role on memory consolidation during sleep, are also characteristic of a number of neurological diseases. The operation of the system is based on a previously reported algorithm, which used the Teager energy operator, together with the Spectral Edge Frequency (SEF50) achieving more than 70% sensitivity and 98% specificity. The algorithm is now converted into a hardware analog based customized implementation in order to achieve extremely low levels of power. Experimental results prove that the system, which is fabricated in a 0.18 μm CMOS technology, is able to operate from a 1.25 V power supply consuming only 515 nW, with an accuracy that is comparable to its software counterpart.

Journal article

Iranmanesh S, Eid M, Rodriguez-Villegas E, 2016, Optimizing simulation times in biomedical systems containing Quasi-Infinite Resistors, 2nd IEEE Nordic Circuits and Systems Conference (NORCAS), Publisher: IEEE

Conference paper

Iranmanesh S, Raikos G, Jiang Z, Rodriguez-Villegas Eet al., 2016, CMOS implementation of a low power absolute value comparator circuit, 14th IEEE International New Circuits and Systems Conference (NEWCAS), Publisher: IEEE, ISSN: 2472-467X

Conference paper

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