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

• Published in: IEEE transactions on instrumentation and measurement Vol. 69; no. 10; pp. 8352 - 8361
• Main Authors: Jana, Biswabandhu, Biswas, Rakesh, Nath, Pallab Kumar, Saha, Goutam, Banerjee, Swapna
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analog to digital conversion; Analog to digital converters; Android; Blood; Blood flow; Bluetooth; Continuous radiation; Diagnostic software; Diagnostic systems; Digital computers; Displays; Doppler effect; Fast Fourier transformations; Feature extraction; Field programmable gate arrays; field-programmable gate arrays (FPGAs); Fourier transforms; Hemodynamics; Machine learning; Point of care testing; point-of-care (POC); Probes; Process control; Signal processing; Smartphones; Spectrogram; support vector machine (SVM); Support vector machines; Ultrasonic imaging; Ultrasound
• ISSN: 0018-9456; 1557-9662
• DOI: 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.