Design and Implementation of Low Noise Amplifier and Variable Gain Amplifier for ECG Systems

Panchal, Shreelekha; T, Shruthi I.; Uniyal, Sarita; Tantry, Shashidhar

doi:10.1109/i2ct54291.2022.9824947

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…The ECG signal from the pre-amplifier is about 20mV and is too low to be transmitted to the post amplifier. After processed by the voltage amplifier which has high common-mode gain and difference-mode gain, the voltage of the ECG signal can be enhanced [5]. As the recorder requires a signal with sufficient current and power, after passing through the voltage amplifier circuit, the signal is sent to the power amplifier circuit to obtain sufficient current and power.…”

Section: The Amplification Process Of the Signalmentioning

confidence: 99%

A Double- electrodes Amplifier use for Portable ECG Equipment

Zhu¹

2023

HSET

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Wearable electrocardiogram (ECG) sensors is a simple test that can be used to check people’s heart's rhythm and electrical activity. With individuals’ increasing attention to their own health, developing a timely and portable bio-sensing recorder has become an urgent task. This paper presents the working principle of a common ECG signal recorder. A double-electrode voltage amplifier that can be used for portable ECG sensors has been set out to design. The amplifier is mainly composed of a differential amplifier circuit, which can lower power supply and power consumption. CoolSpice environment was utilized to simulate the circuit and analyze the result. The simulation presented that the common-mode gain is under 30dB up to 5KHz and the differential-mode gain is larger than 40 dB up to 5KHz. And the total current consumption has been under 20uA. The result demonstrated that the amplifier can perform well. The noise from human body has been eliminated by the amplifier. The amplifier has the ability to extract and amplify clean ECG signals and its structure has the characteristics of low cost and portability. It may be used in portable wearable ECG sensor technology.

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Section: The Amplification Process Of the Signalmentioning

confidence: 99%

A Double- electrodes Amplifier use for Portable ECG Equipment

Zhu¹

2023

HSET

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EEG-based driver states discrimination by noise fraction analysis and novel clustering algorithm

Wang³

et al. 2023

Biomedical Engineering / Biomedizinische Technik

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Driver states are reported as one of the principal factors in driving safety. Distinguishing the driving driver state based on the artifact-free electroencephalogram (EEG) signal is an effective means, but redundant information and noise will inevitably reduce the signal-to-noise ratio of the EEG signal. This study proposes a method to automatically remove electrooculography (EOG) artifacts by noise fraction analysis. Specifically, multi-channel EEG recordings are collected after the driver experiences a long time driving and after a certain period of rest respectively. Noise fraction analysis is then applied to remove EOG artifacts by separating the multichannel EEG into components by optimizing the signal-to-noise quotient. The representation of data characteristics of the EEG after denoising is found in the Fisher ratio space. Additionally, a novel clustering algorithm is designed to identify denoising EEG by combining cluster ensemble and probability mixture model (CEPM). The EEG mapping plot is used to illustrate the effectiveness and efficiency of noise fraction analysis on the denoising of EEG signals. Adjusted rand index (ARI) and accuracy (ACC) are used to demonstrate clustering performance and precision. The results showed that the noise artifacts in the EEG were removed and the clustering accuracy of all participants was above 90%, resulting in a high driver fatigue recognition rate.

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Design and Implementation of Low Noise Amplifier and Variable Gain Amplifier for ECG Systems

Cited by 2 publications

References 10 publications

A Double- electrodes Amplifier use for Portable ECG Equipment

A Double- electrodes Amplifier use for Portable ECG Equipment

EEG-based driver states discrimination by noise fraction analysis and novel clustering algorithm

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