Machine Learning based CMOS Readout Circuit for Advance Detection of Parkinson’s Disease
Jyoti M Roogi
VTU Scholar CMR Institute of Technology Bengaluru,India, CMR Institute of Technology, AECS Layout Bengaluru
Corresponding Author: firstname.lastname@example.org
The Oxford College of Engineering Bommanahalli Bengaluru, The Oxford College of Engineering Bengaluru, India
An Organic chemical called Dopamine belongs to catecholamine and phenethylamine chemical families. A neurotransmitter chemical released by neurons and is one of prime function of Dopamine in the brain. They are essential in communicating messages for all parts of the brain and between the brain and body organs. Body movement is controlled by dopamine. A lack of or an insufficient dopamine generation in part of the brain can lead to Parkinson ’s Disease (PD). It is a one of neurological disorder that affects body movement. It may cause stiffness, trembling in body parts. Detection of low level dopamine is challenging and complex as the low level of dopamine is related to Parkinson’s disease. In this chapter we present an approach towards early detection of this neurological disorder PD by employing CMOS readout circuit which measures and amplifies low level dopamine in the form of electrical signal from brain with help of electrodes.ADC is used to convert amplified analog signal to digital information. Machine learning algorithms are used to predict the disease based on the data received from the readout circuit. Dopamine level is measured in current which ranges from pA to nA. CMOS Amplifiers are used to strengthen the acquired signal in the range of millivolts (mV) with the help of bio amplifiers. For conversion of acquired current in the range of pA to voltage with amplitude of micro voltage (µV) CMOS front called Transimpedance amplifier (TIA) is employed. This chapter provides complete design and analysis of low noise, low power CMOS machine learning based readout circuit for detection and prediction of Parkinson’s disease.
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