A DSP Practical Application: Working on ECG Signal

Vidal-Silva, Cristian; Philominraj, Andrew; Rio, Carolina

doi:10.5772/25499

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…ECG signal mostly varies from 10µV − 5mV in amplitude and mostly concentrated between 0.05Hz -35 Hz frequency [43]. According to [44][45][46], 0.2mV -5mV voltage range is considered for the design. Frequency ranging from 0.05Hz -250 Hz is chosen for design as per [44,47].…”

Section: Ecg Acquisition System Designmentioning

confidence: 99%

Design and Analysis of a High Gain, Low-Noise and Low-Power Analog Front End for ECG Acquisition in 45nm Technology Using Gm/Id Method

Emon,

Salim,

Chowdhury

2024

Preprint

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In this work, an analog front-end (AFE) circuit for Electrocardiogram (ECG) detection system has been designed, implemented, and investigated in an industry-standard Cadence simulation framework using advanced technology node of 45 nm. The AFE consists of an instrumentation amplifier, a Butterworth band-pass filter (with fifth-order low-pass and second-order high-pass sections), and a second-order notch filter- all are based on two-stage, Miller-compensated operational transconductance amplifiers (OTA). The OTAs have been designed employing the $g_m/I_D$ methodology. Both the pre-layout and post-layout simulation were carried out. The layout consumes an area of 0.0058 mm$^2$ without the resistors and capacitors. Analysis of various simulation results were carried out for the proposed AFE. The circuit demonstrates a post-layout bandwidth of 239 Hz with a variable gain between 44 to 58 dB, a notch depth of -56.4 dB at 50.1 Hz, a total harmonic distortion (THD) of -59.65 dB (less than 1\%), an input referred noise spectral density of $

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Section: Ecg Acquisition System Designmentioning

confidence: 99%

Design and Analysis of a High Gain, Low-Noise and Low-Power Analog Front End for ECG Acquisition in 45nm Technology Using Gm/Id Method

Emon,

Salim,

Chowdhury

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…ECG signals mostly vary from 10 µV to 5 mV in amplitude and are mostly concentrated between 0.05 Hz and 35 Hz frequency [46]. According to [47][48][49], a 0.2 mV-5 mV voltage range is considered for the design. Frequency ranging from 0.05 Hz to 250 Hz is chosen for the design, as per [4,47,50,51].…”

Section: Ecg Acquisition System Designmentioning

confidence: 99%

Design and Analysis of a High-Gain, Low-Noise, and Low-Power Analog Front End for Electrocardiogram Acquisition in 45 nm Technology Using gm/ID Method

Emon,

Salim,

Chowdhury

2024

Electronics

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In this work, an analog front-end (AFE) circuit for an electrocardiogram (ECG) detection system has been designed, implemented, and investigated in an industry-standard Cadence simulation framework using an advanced technology node of 45 nm. The AFE consists of an instrumentation amplifier, a Butterworth band-pass filter (with fifth-order low-pass and second-order high-pass sections), and a second-order notch filter—all are based on two-stage, Miller-compensated operational transconductance amplifiers (OTA). The OTAs have been designed employing the gm/ID methodology. Both the pre-layout and post-layout simulation are carried out. The layout consumes an area of 0.00628 mm2 without the resistors and capacitors. Analysis of various simulation results are carried out for the proposed AFE. The circuit demonstrates a post-layout bandwidth of 239 Hz, with a variable gain between 44 and 58 dB, a notch depth of −56.4 dB at 50.1 Hz, a total harmonic distortion (THD) of −59.65 dB (less than 1%), an input-referred noise spectral density of <34 μVrms/Hz at the pass-band, a dynamic range of 52.71 dB, and a total power consumption of 10.88 μW with a supply of ±0.6 V. Hence, the AFE exhibits the promise of high-quality signal acquisition capability required for portable ECG detection systems in modern healthcare.

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“…An ECG is a recording of the heart's electrical activity over a timespan. The ECG signal consists of several beats comprising the P wave, QRS complex, and T wave, depending on the individual's heart condition [78,79]. The properties of the ECG signal can change during both NREM and REM sleep stages.…”

Section: Electrocardiogram (Ecg) and Respiratorymentioning

confidence: 99%

Machine-Learning-Based-Approaches for Sleep Stage Classification Utilising a Combination of Physiological Signals: A Systematic Review

Almutairi,

Hassan,

Datta

2023

Applied Sciences

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Increasingly prevalent sleep disorders worldwide significantly affect the well-being of individuals. Sleep disorder can be detected by dividing sleep into different stages. Hence, the accurate classification of sleep stages is crucial for detecting sleep disorders. The use of machine learning techniques on physiological signals has shown promising results in the automatic classification of sleep stages. The integration of information from multichannel physiological signals has shown to further enhance the accuracy of such classification. Existing literature reviews focus on studies utilising a single channel of EEG signals for sleep stage classification. However, other review studies focus on models developed for sleep stage classification, utilising either a single channel of physiological signals or a combination of various physiological signals. This review focuses on the classification of sleep stages through the integration of combined multichannel physiological signals and machine learning methods. We conducted a comprehensive review spanning from the year 2000 to 2023, aiming to provide a thorough and up-to-date resource for researchers in the field. We analysed approximately 38 papers investigating sleep stage classification employing various machine learning techniques integrated with combined signals. In this study, we describe the models proposed in the existing literature for sleep stage classification, discuss their limitations, and identify potential areas for future research.

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A DSP Practical Application: Working on ECG Signal

Cited by 5 publications

References 11 publications

Design and Analysis of a High Gain, Low-Noise and Low-Power Analog Front End for ECG Acquisition in 45nm Technology Using Gm/Id Method

Design and Analysis of a High Gain, Low-Noise and Low-Power Analog Front End for ECG Acquisition in 45nm Technology Using Gm/Id Method

Design and Analysis of a High-Gain, Low-Noise, and Low-Power Analog Front End for Electrocardiogram Acquisition in 45 nm Technology Using gm/ID Method

Machine-Learning-Based-Approaches for Sleep Stage Classification Utilising a Combination of Physiological Signals: A Systematic Review

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