scispace - formally typeset
Search or ask a question
Author

M. S. Sudhakar

Bio: M. S. Sudhakar is an academic researcher from Amal Jyothi College of Engineering, Kottayam. The author has contributed to research in topics: Neuromorphic engineering & Data compression. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

Papers
More filters
Journal ArticleDOI
TL;DR: A computationally simple and novel methodology Normalized Spatial Pseudo Codec (n-SPC) to compress MCEEG signals to detect sleep spindle was proposed and results indicate that the algorithm exhibits good storage efficiency and decompressed signal quality.
Abstract: Widespread use of Multichannel Electroencephalograph (MCEEG) in diversified fields ranging from clinical studies to Brain Computer Interface (BCI) application, has put in a lot of thrust in data pr...

11 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: Hardware efficient and dedicated human emotion classification processor for CND's and a look-up-table based logarithmic division unit (LDU) to represent the division features in machine learning (ML) applications.
Abstract: Chronic neurological disorders (CND's) are lifelong diseases and cannot be eradicated, but their severe effects can be alleviated by early preemptive measures. CND's, such as Alzheimer's, Autism Spectrum Disorder (ASD), and Amyotrophic Lateral Sclerosis (ALS), are the chronic ailment of the central nervous system that causes the degradation of emotional and cognitive abilities. Long term continuous monitoring with neuro-feedback of human emotions for patients with CND's is crucial in mitigating its harmful effect. This paper presents hardware efficient and dedicated human emotion classification processor for CND's. Scalp EEG is used for the emotion's classification using the valence and arousal scales. A linear support vector machine classifier is used with power spectral density, logarithmic interhemispheric power spectral ratio, and the interhemispheric power spectral difference of eight EEG channel locations suitable for a wearable non-invasive classification system. A look-up-table based logarithmic division unit (LDU) is to represent the division features in machine learning (ML) applications. The implemented LDU minimizes the cost of integer division by 34% for ML applications. The implemented emotion's classification processor achieved an accuracy of 72.96% and 73.14%, respectively, for the valence and arousal classification on multiple publicly available datasets. The 2 x 3mm2 processor is fabricated using a 0.18 μm 1P6M CMOS process with power and energy utilization of 2.04 mW and 16 μJ/classification, respectively, for 8-channel operation.

34 citations

Journal ArticleDOI
TL;DR: In this article, a survey was conducted between December 2020 and January 2021 among German epilepsy centers using well-established customer satisfaction (CS) and quality assurance metrics, and the greatest potential for improvement was identified for software and hardware stability as well as customer service.

3 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed an optimal tensor truncation method for performing compression of the data, which first reshapes the multi-channel EEG signal as a tensor and initially identifies the optimum size of the compressed tensor.

2 citations

Journal ArticleDOI
TL;DR: The principal goal of this study is to implement strategies for low power consumption rates during the neurostimulation device’s smooth and uninterrupted operation as well as during data transmission.
Abstract: Neurostimulation devices applied for the treatment of epilepsy that collect, encode, temporarily store, and transfer electroencephalographic (EEG) signals recorded intracranially from epileptic patients, suffer from short battery life spans. The principal goal of this study is to implement strategies for low power consumption rates during the device’s smooth and uninterrupted operation as well as during data transmission. Our approach is organised in three basic levels. The first level regards the initial modelling and creation of the template for the following two stages. The second level regards the development of code for programming integrated circuits and simulation. The third and final stage regards the transmitter’s implementation at the evaluation level. In particular, more than one software and device are involved in this phase, in order to achieve realistic performance. Our research aims to evolve such technologies so that they can transmit wireless data with simultaneous energy efficiency.

1 citations

Proceedings ArticleDOI
24 Sep 2021
TL;DR: In this article, the authors studied the effect of delta encoding on power dissipation for wireless transmission from implantable devices and showed that up to 23% power savings are possible for a negligible power penalty due to the delta encoding process.
Abstract: This paper studies the Delta encoding scheme and its effect on power dissipation, for wireless transmission from implantable devices. The study was performed on data from electroencephalographic signals. For the implementation of the proposed system, the design approach followed three phases. The first design phase is related to the initial modelling. The second phase included the development of the hardware description code for the proposed system. The third and last phase is related to the evaluation of the transmitted signal and the measurement of the power dissipation. The results showed that up to 23% power savings are possible for a negligible power penalty due to the delta encoding process.

1 citations