Level-crossing ADC modeling for wireless electrocardiogram signal acquisition system

Tlili, Mariam; Maalej, Asma; Ben-Romdhane, Manel; Bali, Mohamed Chaker; Rivet, François; Dallet, Dominique; Rebai, Chiheb

doi:10.1109/i2mtc.2016.7520544

Cited by 5 publications

(7 citation statements)

References 14 publications

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“…Then, the M-bit digital-toanalog converter (DAC) produces the updated analog reference voltages. Moreover, a control logic is used to set the digital value of the crossed level, ECG out ; as the output sample [24]. In fact, the LC-ADC signal-dependency is due to this behavior.…”

Section: Lc-adc Architecturementioning

confidence: 99%

“…Firstly, the signals are up-sampled at 1-MHz frequency. This step is done to obtain a pseudo analog form of the signal comparing to the sampling frequency applied in Nyquist [24,35]. Secondly, since the LC-ADC resolution, M; has an impact on the quantization step, the converter's model is simulated with M varying from 8 to 10 bits [24].…”

Section: Lc-adc Compression Performancesmentioning

confidence: 99%

“…This step is done to obtain a pseudo analog form of the signal comparing to the sampling frequency applied in Nyquist [24,35]. Secondly, since the LC-ADC resolution, M; has an impact on the quantization step, the converter's model is simulated with M varying from 8 to 10 bits [24]. These values are typical for ECG amplitude quantization and also ensure good quality reconstructed signals which require at least 8-bit resolution [11,24,32,36].…”

Section: Lc-adc Compression Performancesmentioning

confidence: 99%

“…Thus, in order to relax compression requirements and data volumes, a signal-dependent ADC architecture is interesting [23][24][25]. In the last decade, sampling with level-crossing ADC (LC-ADC) has caught researchers' attention.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, it only acquires relevant information by comparing the signal to a specific set of levels. Thus, sampling frequency changes according to signal variation and the circuit activity is reduced [23][24][25]. Thanks to ECG low innovation rate feature, less data is processed after sampling compared to the conventional ADC.…”

Section: Introductionmentioning

confidence: 99%

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On the wavelet-based compressibility of continuous-time sampled ECG signal for e-health applications

et al. 2020

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This paper presents a compression study of electrocardiogram (ECG) signals for e-Health cardiac online diagnostic systems. The study uses 75 real electrocardiogram records sampled with continuous-time level-crossing (LC) analog-to-digital converter (ADC). This signal-dependent LC-ADC compresses signals compared to conventional ADC but further compression is needed especially for long-time monitoring applications. The orthogonal matching pursuit algorithm is simulated to evaluate ECG compression with 54 orthogonal and biorthogonal wavelets. For LC-ADC amplitude output compression, Biorthogonal3.1 (bior3.1) wavelet achieves optimal performances in terms of compression ratio (CR) while ensuring 2-% percentage root-mean-square difference (PRD). The PRD must be limited to this value to ensure a very good quality signals after decompression. For circuit implementation purposes, bior3.1 wavelet is proposed as a multiplier-free decomposition step and a noncomplex global and hard thresholding process is achieved. The average CR is 63% and PRD varies between 0.1 and 2.1% leading to a very good diagnostic quality.

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Section: Lc-adc Architecturementioning

confidence: 99%

Section: Lc-adc Compression Performancesmentioning

confidence: 99%

Section: Lc-adc Compression Performancesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the wavelet-based compressibility of continuous-time sampled ECG signal for e-health applications

et al. 2020

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Level-crossing ADC design and evaluation methodology for normal and pathological electrocardiogram signals measurement

et al. 2018

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ECG Classification With Event-Driven Sampling

Saeed,

Märtens,

Larras

et al. 2024

IEEE Access

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Electrocardiogram (ECG) data's high dimensionality challenges real-time arrhythmia classification. Our approach employs functional approximation to condense ECG recordings into a compact feature set for simpler classification using Chebyshev polynomials. These polynomials, with 200 time points and 80 coefficients, accurately represent arrhythmias in an 81 × 1 feature vector. We prove Chebyshev polynomials act as implicit low-pass filters on input signals. Using MIT-BIH Arrhythmia and MIT-BIH Supraventricular Arrhythmia datasets, we introduce classifiers that achieve significant accuracy. A threelayered Artificial Neural Network yields high F1-scores (0.99, 0.90, 0.93, and 0.76 for classes N, S, V, and F) with minimal parameters (20,964), surpassing existing models. Furthermore, our proposed ECG classification system exhibits minimal computational demands, requiring only 0.1 MIPS per beat. We also propose efficient signal reconstruction methods, with the iterative approach showcasing accurate reconstruction with negligible error. This approach accommodates various data sampling types and determines optimal Chebyshev coefficients for capturing signal bandwidth.INDEX TERMS level-crossing ADC, electrocardiograms, functional approximation, Chebyshev polynomials, artificial neural networks, arrhythmia, support vector machines, bandwidth analysis I. INTRODUCTION A RRHYTHMIA, an abnormal heart rhythm, is a significant medical condition. Atrial fibrillation, the most common form of arrhythmia, is projected to affect millions of people in the United States and Europe in the coming decades [1]. Automated arrhythmia classification is a widely researched area that can help with early diagnosis and improve patient care by providing long-term remote cardiac monitoring. Several hardware-friendly designs have been proposed for real-time arrhythmia classification. Notably, the literature includes real-time patient-specific ECG classifiers as demonstrated in prior works such as [2]-[4]. Abubakar and colleagues introduced a wearable long-term ECG processor for arrhythmia classification, employing a reduced feature set [5]. Additionally, a low-complexity antidictionary-based ECG classifier was proposed by Duforest et al. [6]. Tang et al. presented a patient-specific arrhythmia classifier with low complexity, utilizing support vector machines [7]. Another promising approach for real-time wearable arrhythmia classification involves utilising a novel sampling technique at the analogue front end, employing level-crossing ADCs.Recent research has demonstrated a growing interest in level-crossing ADCs (LC-ADCs) due to their potential to reduce data streams and battery consumption. Li et al. [8] introduced an ECG front-end featuring LC-ADC, showcasing its potential for low-power, high-performance applications. In a different approach, Marisa et al. presented a pseudo-asynchronous LC-ADC with dynamic comparators for implantable biomedical sensing. This design offers energy efficiency, a smaller chip area, and robust performance in noisy co...

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Level-crossing ADC modeling for wireless electrocardiogram signal acquisition system

Cited by 5 publications

References 14 publications

On the wavelet-based compressibility of continuous-time sampled ECG signal for e-health applications

On the wavelet-based compressibility of continuous-time sampled ECG signal for e-health applications

Level-crossing ADC design and evaluation methodology for normal and pathological electrocardiogram signals measurement

ECG Classification With Event-Driven Sampling

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