Ultra‐low power digital front‐end for single lead ECG acquisition integrated with a time‐to‐digital converter

Mishra, Biswajit; Thakkar, Sanket; Jain, Nupur

doi:10.1049/iet-cdt.2019.0027

Cited by 3 publications

(3 citation statements)

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“…Additionally, digital systems have good immunity to electrical noise and environmental fluctuations, which make them a reliable choice for long-term applications. Recent applications of digital signal processing include a low-power feature extractor for speech recognition [12] and a front-end for an electrocardiogram acquisition system [13]. DSP energy-efficiency has historically followed Gene's Law, doubling every 18 months due to the power savings afforded by rapid technology scaling [14].…”

Section: Proposal Novelty and Overviewmentioning

confidence: 99%

An Acoustic Vehicle Detector and Classifier Using a Reconfigurable Analog/Mixed-Signal Platform

Bhattacharyya

Andryzcik

Graham

2020

JLPEA

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The wireless sensor nodes used in a growing number of remote sensing applications are deployed in inaccessible locations or are subjected to severe energy constraints. Audio-based sensing offers flexibility in node placement and is popular in low-power schemes. Thus, in this paper, a node architecture with low power consumption and in-the-field reconfigurability is evaluated in the context of an acoustic vehicle detection and classification (hereafter “AVDC”) scenario. The proposed architecture utilizes an always-on field-programmable analog array (FPAA) as a low-power event detector to selectively wake a microcontroller unit (MCU) when a significant event is detected. When awoken, the MCU verifies the vehicle class asserted by the FPAA and transmits the relevant information. The AVDC system is trained by solving a classification problem using a lexicographic, nonlinear programming algorithm. On a testing dataset comprising of data from ten cars, ten trucks, and 40 s of wind noise, the AVDC system has a detection accuracy of 100%, a classification accuracy of 95%, and no false alarms. The mean power draw of the FPAA is 43 μ W and the mean power consumption of the MCU and radio during its validation and wireless transmission process is 40.9 mW. Overall, this paper demonstrates that the utilization of an FPAA-based signal preprocessor can greatly improve the flexibility and power consumption of wireless sensor nodes.

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Section: Proposal Novelty and Overviewmentioning

confidence: 99%

An Acoustic Vehicle Detector and Classifier Using a Reconfigurable Analog/Mixed-Signal Platform

Bhattacharyya

Andryzcik

Graham

2020

JLPEA

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“…Cardiovascular ECG wave amplitudes cause variations in their levels. Accurate recognition of the wave parameters is required for diagnosis 19 . Digital filters like finite impulse response (FIR) and infinite impulse response (IIR) are utilized in signal processing.…”

Section: Introductionmentioning

confidence: 99%

“…Accurate recognition of the wave parameters is required for diagnosis. 19 Digital filters like finite impulse response (FIR) and infinite impulse response (IIR) are utilized in signal processing. If noise is the problem, the filters like low pass, IIR or FIR moderately reduce the noise.…”

Section: Introductionmentioning

confidence: 99%

Field programmable gate array implementation of an adaptive filtering based noise reduction and enhanced compression technique for healthcare applications

Aruna

Ekambaram

Padmaja

2022

Trans Emerging Tel Tech

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Nowadays, the development of e-health monitoring devices is getting more demand for remote health applications. In addition, cardiovascular diseases is considered the most chronic disease in recent years. So, this requires a fast diagnosis and therapy to clear this issue and requires an information transformation from the patient to the distant hospital. But, it faces many challenges like more energy consumption, data security, storage, and transmission. In addition, the electrocardiogram (ECG) data must be processed in real-time, and the information must have high predictability and lossless compression. So, in this article, an enhanced set partitioning in hierarchical tree decoder with deep belief neural network is developed for ECG compression. Due to an inappropriate channel in telemedicine applications, the ECG signals transmitted through a wireless network are affected by noise. So, fast normalized least mean square (FNLMS) algorithm based adaptive filter is deliberated to eliminate the unwanted noise from the ECG signal. Also, the structure of the FNLMs based adaptive filter is modified by developing a systolic structure to increase the speed of filtering process. Moreover, the parallel and pipelined architecture is provided for the proposed compression unit to reduce the processing time and the area overhead as compared to the existing methods. A publicly available database called the MIT-BIH database and real-time ECG data records are used for simulation purposes. The proposed design was implemented on the Xilinx platform and the MATLAB tool. The performance of the proposed design is estimated by the parameters like compression ratio, signal to noise ratio, root mean square error, and percentage root mean square difference (PRD), PRD normalized, and quality-score. It is compared with existing designs to show the efficiency of our proposed design.

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Low Area Digital Front-End for Single Lead ECG Acquisition

Neelamsetty

Balivada

2021

2021 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques

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Ultra‐low power digital front‐end for single lead ECG acquisition integrated with a time‐to‐digital converter

Cited by 3 publications

References 20 publications

An Acoustic Vehicle Detector and Classifier Using a Reconfigurable Analog/Mixed-Signal Platform

An Acoustic Vehicle Detector and Classifier Using a Reconfigurable Analog/Mixed-Signal Platform

Field programmable gate array implementation of an adaptive filtering based noise reduction and enhanced compression technique for healthcare applications

Low Area Digital Front-End for Single Lead ECG Acquisition

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