ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Smartphone-Based Point-of-Care System Using Continuous-Wave Portable Doppler

Jana, B and Biswas, R and Nath, PK and Saha, G and Banerjee, S (2020) Smartphone-Based Point-of-Care System Using Continuous-Wave Portable Doppler. In: IEEE Transactions on Instrumentation and Measurement, 69 (10). pp. 8352-8361.

[img] PDF
ieee_tra_ins_mea_69_10_2020_8352-8361.pdf - Published Version
Restricted to Registered users only

Download (3MB) | Request a copy
Official URL: https://dx.doi.org/10.1109/TIM.2020.2987654


Point-of-care Ultrasound (PoCUS) is a safe, repeatable, and inexpensive bedside diagnostic tool. Over the years, PoCUS services are adopted in resource-limited settings for faster and useful outcomes. For a cost-effective and power-efficient solution, a smartphone-based portable continuous-wave Doppler ultrasound (US) system has been developed for diagnosing peripheral arterial diseases based on the hemodynamic feature values. The proposed system includes the analog front end (AFE), signal processing and display unit (SPDU), and smartphone application. The AFE acquires blood flow signal from the brachial artery using an 8-MHz pencil probe, extracts the Doppler shift frequency, and transfers to the SPDU through 12-bit analog-to-digital converter. To provide an area and power-efficient solution, SPDU is embedded in a field-programmable gate array (FPGA)-based single chip. A COordinate Rotation DIgital Computer (CORDIC)-based custom-designed 512-point fast Fourier transform is implemented in that FPGA for displaying the blood flow spectrogram in real time. For back-end processing, the smartphone application receives a spectrogram through Bluetooth, removes noise, extracts hemodynamic features, and diagnoses using a machine learning framework. The device has been examined on 18 volunteers (normal: 17 and abnormal: 1), while the accuracy is found to be 94 in the pretrained support vector machine classifier. For validation, the spectrogram of the normal and abnormal subjects and parameter values are compared with the commercial device. Overall, the handheld device is minimally trained operator-dependent and consumes < 4 W of power for real-time processing. Such smartphone-based feature extraction and automated diagnosis can facilitate the point-of-care system and provide a baseline for early assessment. © 1963-2012 IEEE.

Item Type: Journal Article
Publication: IEEE Transactions on Instrumentation and Measurement
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: Copyright to this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords: Analog to digital conversion; Blood; Cost effectiveness; Diagnosis; Digital computers; Fast Fourier transforms; Field programmable gate arrays (FPGA); Hemodynamics; Signal processing; Signal receivers; Spectrographs; Support vector machines; Turing machines; Ultrasonics, Analog to digital converters; Back-end processing; Co-ordinate rotation digital computers; Peripheral arterial disease; Point-of-care systems; Power-efficient solutions; Smart-phone applications; Support vector machine classifiers, Smartphones
Department/Centre: Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited: 27 Nov 2020 11:23
Last Modified: 27 Nov 2020 11:23
URI: http://eprints.iisc.ac.in/id/eprint/66814

Actions (login required)

View Item View Item