Sigma delta modulator in wearable brain wave recording system for personalized healthcare

An electroencephalogram (EEG) is a critical brain wave signal which contains information that is of great use corresponding to various states of a brain. Signal analysis of EEG is an effective tool for early diagnosis of mental and neurological disorders, including autism, Attention-Deficit/Hyperact...

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Main Author: Qi, Mingchen
Other Authors: Goh Wang Ling
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2023
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Online Access:https://hdl.handle.net/10356/166476
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1664762023-07-04T16:32:54Z Sigma delta modulator in wearable brain wave recording system for personalized healthcare Qi, Mingchen Goh Wang Ling School of Electrical and Electronic Engineering EWLGOH@ntu.edu.sg Engineering::Electrical and electronic engineering An electroencephalogram (EEG) is a critical brain wave signal which contains information that is of great use corresponding to various states of a brain. Signal analysis of EEG is an effective tool for early diagnosis of mental and neurological disorders, including autism, Attention-Deficit/Hyperactivity Disorder (ADHD), dementia, Alzheimer’s disease, etc. Commercially available EEG sensing devices of this time are usually very large, expensive, power-consuming, and is cumbersome if the user/patient needs to keep the device is close proximity, having the demand to monitor their health daily. A miniatured wearable EEG monitoring system is certainly much preferred for remote monitoring and diagnosis and diagnosis for personalized daily healthcare come true. The core of a miniaturized wearable EEG monitoring system are sensors and signal processors. A data converter is needed to build a bridge bonding the analogue output of sensors and digital input signal for the signal processor. This implies that an ADC is crucial to connecting the two cores of the wearable EEG system. Sigma-delta ADCs are a type of high-resolution, low-speed, and low-power ADC that operates by oversampling a signal, followed by noise-shaping, and a decimation filter. They are typically used where a high-resolution conversion of a low-frequency signal is required, such as audio, EEG signal and industrial control applications. Sigma-delta ADCs have several advantages, including low power consumption, low cost, and high dynamic range. Futhermore, they are less susceptible or sensitive to interference and noise because of their noiseshaping capabilities. This dissertation serves to examine the principles and operating characteristics of first-order and second-order sigma-delta ADCs, and discusses the advantages and drawbacks of this type of converter. Master of Science (Electronics) 2023-04-28T01:34:10Z 2023-04-28T01:34:10Z 2023 Thesis-Master by Coursework Qi, M. (2023). Sigma delta modulator in wearable brain wave recording system for personalized healthcare. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166476 https://hdl.handle.net/10356/166476 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Qi, Mingchen
Sigma delta modulator in wearable brain wave recording system for personalized healthcare
description An electroencephalogram (EEG) is a critical brain wave signal which contains information that is of great use corresponding to various states of a brain. Signal analysis of EEG is an effective tool for early diagnosis of mental and neurological disorders, including autism, Attention-Deficit/Hyperactivity Disorder (ADHD), dementia, Alzheimer’s disease, etc. Commercially available EEG sensing devices of this time are usually very large, expensive, power-consuming, and is cumbersome if the user/patient needs to keep the device is close proximity, having the demand to monitor their health daily. A miniatured wearable EEG monitoring system is certainly much preferred for remote monitoring and diagnosis and diagnosis for personalized daily healthcare come true. The core of a miniaturized wearable EEG monitoring system are sensors and signal processors. A data converter is needed to build a bridge bonding the analogue output of sensors and digital input signal for the signal processor. This implies that an ADC is crucial to connecting the two cores of the wearable EEG system. Sigma-delta ADCs are a type of high-resolution, low-speed, and low-power ADC that operates by oversampling a signal, followed by noise-shaping, and a decimation filter. They are typically used where a high-resolution conversion of a low-frequency signal is required, such as audio, EEG signal and industrial control applications. Sigma-delta ADCs have several advantages, including low power consumption, low cost, and high dynamic range. Futhermore, they are less susceptible or sensitive to interference and noise because of their noiseshaping capabilities. This dissertation serves to examine the principles and operating characteristics of first-order and second-order sigma-delta ADCs, and discusses the advantages and drawbacks of this type of converter.
author2 Goh Wang Ling
author_facet Goh Wang Ling
Qi, Mingchen
format Thesis-Master by Coursework
author Qi, Mingchen
author_sort Qi, Mingchen
title Sigma delta modulator in wearable brain wave recording system for personalized healthcare
title_short Sigma delta modulator in wearable brain wave recording system for personalized healthcare
title_full Sigma delta modulator in wearable brain wave recording system for personalized healthcare
title_fullStr Sigma delta modulator in wearable brain wave recording system for personalized healthcare
title_full_unstemmed Sigma delta modulator in wearable brain wave recording system for personalized healthcare
title_sort sigma delta modulator in wearable brain wave recording system for personalized healthcare
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/166476
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