Hardware/Software Co-design of an ECG- PPG Preprocessor: A Qualitative &amp; Quantitative Analysis

Chowdhury, Aditta; Das, Diba; Cheung, Ray C. C.; Chowdhury, Mehdi Hasan

doi:10.1109/ecce57851.2023.10101536

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…Photoplethsmogram (PPG) is an optically obtained biosignal that is used to detect volumetric changes in blood volume [1]. It is a low-cost and non-invasive technique that uses a light and a photodetector to measure the volumetric blood flow [2], [3]. So, it can be used for detecting abnormalities related to blood and heart.…”

Section: Introductionmentioning

confidence: 99%

Hardware and software co-design for detecting hypertension from photoplethysmogram

Chowdhury,

Das

et al. 2024

IJECE

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Hypertension is one of the leading causes of cardiovascular disease morbidity in the world. If remains untreated, it may cause severe damage like heart attack or even death. Early detection is required to prevent the development of other cardiac abnormalities. Photoplethysmogram (PPG) is a bio signal that can be obtained optically by a sensor. It is studied to monitor the change of volume of blood and detect heart conditions. Previous studies have already applied PPG to detect hypertension at the software level. In this article, along with software-based detection, we have implemented a digital hardware-based system for detecting hypertension from signals recorded using PPG sensor. Xilinx ZedBoard Zynq-7000 field programmable gate array (FPGA) board is utilized for designing the embedded system. The hypertension detection accuracy is 98.02% at the software level while for the digital system, it is 96.05% consuming 0.374 W power. The study can be analyzed for other cardiac disease detection and medical equipment development.

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Section: Introductionmentioning

confidence: 99%

Hardware and software co-design for detecting hypertension from photoplethysmogram

Chowdhury,

Das

et al. 2024

IJECE

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“…These designs provide cost-effectiveness, quick monitoring, remote healthcare facilities, etc. [24]. POC systems based on EOG can be utilized in detecting eye diseases and various eye conditions.…”

Section: Introductionmentioning

confidence: 99%

Application Specific Reconfigurable Processor for Eyeblink Detection from Dual-Channel EOG Signal

Das,

Chowdhury,

Chowdhury

et al. 2023

JLPEA

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The electrooculogram (EOG) is one of the most significant signals carrying eye movement information, such as blinks and saccades. There are many human–computer interface (HCI) applications based on eye blinks. For example, the detection of eye blinks can be useful for paralyzed people in controlling wheelchairs. Eye blink features from EOG signals can be useful in drowsiness detection. In some applications of electroencephalograms (EEGs), eye blinks are considered noise. The accurate detection of eye blinks can help achieve denoised EEG signals. In this paper, we aimed to design an application-specific reconfigurable binary EOG signal processor to classify blinks and saccades. This work used dual-channel EOG signals containing horizontal and vertical EOG signals. At first, the EOG signals were preprocessed, and then, by extracting only two features, the root mean square (RMS) and standard deviation (STD), blink and saccades were classified. In the classification stage, 97.5% accuracy was obtained using a support vector machine (SVM) at the simulation level. Further, we implemented the system on Xilinx Zynq-7000 FPGAs by hardware/software co-design. The processing was entirely carried out using a hybrid serial–parallel technique for low-power hardware optimization. The overall hardware accuracy for detecting blinks was 95%. The on-chip power consumption for this design was 0.8 watts, whereas the dynamic power was 0.684 watts (86%), and the static power was 0.116 watts (14%).

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FPGA Implementation of PPG-Based Cardiovascular Diseases and Diabetes Classification Algorithm

Chowdhury,

Das

et al. 2024

Arab J Sci Eng

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Hardware/Software Co-design of an ECG- PPG Preprocessor: A Qualitative & Quantitative Analysis

Cited by 7 publications

References 25 publications

Hardware and software co-design for detecting hypertension from photoplethysmogram

Hardware and software co-design for detecting hypertension from photoplethysmogram

Application Specific Reconfigurable Processor for Eyeblink Detection from Dual-Channel EOG Signal

FPGA Implementation of PPG-Based Cardiovascular Diseases and Diabetes Classification Algorithm

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