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Instrumentation Amplifier

Instrumentation Amplifier

An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...

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Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

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ECG Authentication Hardware Design With Low-Power Signal Processing and Neural Network Optimization With Low

Sai Kiran Cherupally, Shihui Yin, Deepak Kadetotad

IEEE Transactions on Biomedical Circuits and Systems

|February 21, 2020

Summary

This study introduces a novel, low-power device authentication system using electrocardiogram (ECG) signals. The system achieves high accuracy with minimal power consumption, offering a secure biometric solution for wearable devices.

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A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

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Published on: July 7, 2023

Area of Science:

Biometric Authentication
Wearable Device Security
Low-Power Hardware Design

Background:

Biometric authentication is increasingly integrated into portable electronics and wearable devices for secure access.
Existing biometric methods face challenges in power consumption and integration with medical data collection.

Purpose of the Study:

To develop an accurate, low-power device authentication system utilizing electrocardiogram (ECG) signals.
To implement efficient hardware for ECG-based biometrics using neural networks.

Main Methods:

Developed an ECG processor with front-end signal processing and back-end neural networks (NNs).
Trained NNs using a cost function minimizing intra-individual and maximizing inter-individual distances.
Implemented efficient low-power hardware using fixed coefficients and joint optimization of low-precision and structured sparsity for NNs.

Main Results:

Achieved low power consumption of 62.37 μW and 75.41 μW for two hardware instances.
Attained low equal error rates of 1.36% and 1.21% on a 741-subject ECG database.
Hardware evaluated at 10 kHz clock frequency and 1.2 V supply.

Conclusions:

The proposed ECG authentication system offers a secure and energy-efficient biometric solution.
The implemented hardware demonstrates the feasibility of real-time, low-power ECG-based authentication.
This technology has potential applications in securing personal medical data and device access.